WorldWideScience

Sample records for gene interaction

  1. Gene-environment interaction.

    Science.gov (United States)

    Manuck, Stephen B; McCaffery, Jeanne M

    2014-01-01

    With the advent of increasingly accessible technologies for typing genetic variation, studies of gene-environment (G×E) interactions have proliferated in psychological research. Among the aims of such studies are testing developmental hypotheses and models of the etiology of behavioral disorders, defining boundaries of genetic and environmental influences, and identifying individuals most susceptible to risk exposures or most amenable to preventive and therapeutic interventions. This research also coincides with the emergence of unanticipated difficulties in detecting genetic variants of direct association with behavioral traits and disorders, which may be obscured if genetic effects are expressed only in predisposing environments. In this essay we consider these and other rationales for positing G×E interactions, review conceptual models meant to inform G×E interpretations from a psychological perspective, discuss points of common critique to which G×E research is vulnerable, and address the role of the environment in G×E interactions.

  2. Duplicability of self-interacting human genes.

    LENUS (Irish Health Repository)

    Pérez-Bercoff, Asa

    2010-01-01

    BACKGROUND: There is increasing interest in the evolution of protein-protein interactions because this should ultimately be informative of the patterns of evolution of new protein functions within the cell. One model proposes that the evolution of new protein-protein interactions and protein complexes proceeds through the duplication of self-interacting genes. This model is supported by data from yeast. We examined the relationship between gene duplication and self-interaction in the human genome. RESULTS: We investigated the patterns of self-interaction and duplication among 34808 interactions encoded by 8881 human genes, and show that self-interacting proteins are encoded by genes with higher duplicability than genes whose proteins lack this type of interaction. We show that this result is robust against the system used to define duplicate genes. Finally we compared the presence of self-interactions amongst proteins whose genes have duplicated either through whole-genome duplication (WGD) or small-scale duplication (SSD), and show that the former tend to have more interactions in general. After controlling for age differences between the two sets of duplicates this result can be explained by the time since the gene duplication. CONCLUSIONS: Genes encoding self-interacting proteins tend to have higher duplicability than proteins lacking self-interactions. Moreover these duplicate genes have more often arisen through whole-genome rather than small-scale duplication. Finally, self-interacting WGD genes tend to have more interaction partners in general in the PIN, which can be explained by their overall greater age. This work adds to our growing knowledge of the importance of contextual factors in gene duplicability.

  3. Testing for gene-gene interaction with AMMI models.

    Science.gov (United States)

    Barhdadi, Amina; Dubé, Marie-Pierre

    2010-01-01

    Studies have shown that many common diseases are influenced by multiple genes and their interactions. There is currently a strong interest in testing for association between combinations of these genes and disease, in particular because genes that affect the risk of disease only in the presence of another genetic variant may not be detected in marginal analysis. In this paper we propose the use of additive main effect and multiplicative interaction (AMMI) models to detect and to quantify gene-gene interaction effects for a quantitative trait. The objective of the present research is to demonstrate the practical advantages of these models to describe complex interaction between two unlinked loci. Although gene-gene interactions have often been defined as a deviance from additive genetic effects, the residual term has generally not been appropriately treated. The AMMI models allow for the analysis of a two way factorial data structure and combine the analysis of variance of the two main genotype effects with a principal component analysis of the residual multiplicative interaction. The AMMI models for gene-gene interaction presented here allow for the testing of non additivity between the two loci, and also describe how their interaction structure fits the existing non-additivity. Moreover, these models can be used to identify the specific two genotypes combinations that contribute to the significant gene-gene interaction. We describe the use of the biplot to display the structure of the interaction and evaluate the performance of the AMMI and the special cases of the AMMI previously described by Tukey and Mandel with simulated data sets. Our simulated study showed that the AMMI model is as powerful as general linear models when the interaction is not modeled in the presence of marginal effects. However, in the presence of pure epitasis, i.e. in the absence of marginal effects, the AMMI method was not found to be superior to other tested regression methods.

  4. Gene-gene Interaction Analyses for Atrial Fibrillation

    NARCIS (Netherlands)

    Lin, Honghuang; Mueller-Nurasyid, Martina; Smith, Albert V; Arking, Dan E; Barnard, John; Bartz, Traci M; Lunetta, Kathryn L; Lohman, Kurt; Kleber, Marcus E; Lubitz, Steven A; Geelhoed, Bastiaan; Trompet, Stella; Niemeijer, Maartje N; Kacprowski, Tim; Chasman, Daniel I; Klarin, Derek; Sinner, Moritz F; Waldenberger, Melanie; Meitinger, Thomas; Harris, Tamara B; Launer, Lenore J; Soliman, Elsayed Z; Chen, Lin Y; Smith, Jonathan D; Van Wagoner, David R; Rotter, Jerome I; Psaty, Bruce M; Xie, Zhijun; Hendricks, Audrey E; Ding, Jingzhong; Delgado, Graciela E; Verweij, Niek; van der Harst, Pim; Macfarlane, Peter W; Ford, Ian; Hofman, Albert; Uitterlinden, André; Heeringa, Jan; Franco, Oscar H; Kors, Jan A; Weiss, Stefan; Völzke, Henry; Rose, Lynda M; Natarajan, Pradeep; Kathiresan, Sekar; Kääb, Stefan; Gudnason, Vilmundur; Alonso, Alvaro; Chung, Mina K; Heckbert, Susan R; Benjamin, Emelia J; Liu, Yongmei; März, Winfried; Rienstra, Michiel; Jukema, J Wouter; Stricker, Bruno H; Dörr, Marcus; Albert, Christine M; Ellinor, Patrick T

    2016-01-01

    Atrial fibrillation (AF) is a heritable disease that affects more than thirty million individuals worldwide. Extensive efforts have been devoted to the study of genetic determinants of AF. The objective of our study is to examine the effect of gene-gene interaction on AF susceptibility. We performed

  5. Gene-gene Interaction Analyses for Atrial Fibrillation

    NARCIS (Netherlands)

    H. Lin (Honghuang); M. Mueller-Nurasyid; A.V. Smith (Albert Vernon); D.E. Arking (Dan); J. Barnard (John); T.M. Bartz (Traci M.); K.L. Lunetta (Kathryn); K. Lohman (Kurt); M.E. Kleber (Marcus); S.A. Lubitz (Steven); Geelhoed, B. (Bastiaan); S. Trompet (Stella); M.N. Niemeijer (Maartje); T. Kacprowski (Tim); D.I. Chasman (Daniel); Klarin, D. (Derek); M.F. Sinner (Moritz); M. Waldenberger (Melanie); T. Meitinger (Thomas); T.B. Harris (Tamara); Launer, L.J. (Lenore J.); E.Z. Soliman (Elsayed Z.); L. Chen (Lin); J.D. Smith (Jonathan); D.R. van Wagoner (David); Rotter, J.I. (Jerome I.); B.M. Psaty (Bruce); Xie, Z. (Zhijun); A.E. Hendricks (Audrey E.); Ding, J. (Jingzhong); G.E. Delgado (Graciela E.); N. Verweij (Niek); P. van der Harst (Pim); P.W. MacFarlane (Peter); I. Ford (Ian); A. Hofman (Albert); A.G. Uitterlinden (André); J. Heeringa (Jan); O.H. Franco (Oscar); J.A. Kors (Jan); Weiss, S. (Stefan); H. Völzke (Henry); L.M. Rose (Lynda); Natarajan, P. (Pradeep); S. Kathiresan (Sekar); S. Kääb (Stefan); V. Gudnason (Vilmundur); A. Alonso (Alvaro); M.K. Chung (Mina); S.R. Heckbert (Susan); E.J. Benjamin (Emelia); Y. Liu (Yongmei); W. März (Winfried); S.A. Rienstra; J.W. Jukema (Jan Wouter); B.H.Ch. Stricker (Bruno); M. Dörr (Marcus); C.M. Albert (Christine); P.T. Ellinor (Patrick)

    2016-01-01

    textabstractAtrial fibrillation (AF) is a heritable disease that affects more than thirty million individuals worldwide. Extensive efforts have been devoted to the study of genetic determinants of AF. The objective of our study is to examine the effect of gene-gene interaction on AF susceptibility.

  6. Prediction of drug-drug interactions from chemogenomic and gene-gene interactions and analysis of drug-drug interactions

    OpenAIRE

    2013-01-01

    The interactions between multiple drugs administered to an organism concurrently, whether in the form of synergy or antagonism, are of clinical relevance. Moreover, un-derstanding the mechanisms and nature of drug-drug interactions is of great practical and theoretical interest. Work has previously been done on gene-gene and gene-drug interactions, but the prediction and rationalization of drug-drug interactions from this data is not straightforward. We present a strategy for attacking this p...

  7. Coevolution of gene expression among interacting proteins

    Energy Technology Data Exchange (ETDEWEB)

    Fraser, Hunter B.; Hirsh, Aaron E.; Wall, Dennis P.; Eisen,Michael B.

    2004-03-01

    Physically interacting proteins or parts of proteins are expected to evolve in a coordinated manner that preserves proper interactions. Such coevolution at the amino acid-sequence level is well documented and has been used to predict interacting proteins, domains, and amino acids. Interacting proteins are also often precisely coexpressed with one another, presumably to maintain proper stoichiometry among interacting components. Here, we show that the expression levels of physically interacting proteins coevolve. We estimate average expression levels of genes from four closely related fungi of the genus Saccharomyces using the codon adaptation index and show that expression levels of interacting proteins exhibit coordinated changes in these different species. We find that this coevolution of expression is a more powerful predictor of physical interaction than is coevolution of amino acid sequence. These results demonstrate previously uncharacterized coevolution of gene expression, adding a different dimension to the study of the coevolution of interacting proteins and underscoring the importance of maintaining coexpression of interacting proteins over evolutionary time. Our results also suggest that expression coevolution can be used for computational prediction of protein protein interactions.

  8. Gene-gene interaction between tuberculosis candidate genes in a South African population.

    Science.gov (United States)

    de Wit, Erika; van der Merwe, Lize; van Helden, Paul D; Hoal, Eileen G

    2011-02-01

    In a complex disease such as tuberculosis (TB) it is increasingly evident that gene-gene interactions play a far more important role in an individual's susceptibility to develop the disease than single polymorphisms on their own, as one gene can enhance or hinder the expression of another gene. Gene-gene interaction analysis is a new approach to elucidate susceptibility to TB. The possibility of gene-gene interactions was assessed, focusing on 11 polymorphisms in nine genes (DC-SIGN, IFN-γ, IFNGR1, IL-8, IL-1Ra, MBL, NRAMP1, RANTES, and SP-D) that have been associated with TB, some repeatedly. An optimal model, which best describes and predicts TB case-control status, was constructed. Significant interactions were detected between eight pairs of variants. The models fitted the observed data extremely well, with p activation is greatly enhanced by IFN-γ and IFN-γ response elements that are present in the human NRAMP1 promoter region, providing further evidence for their interaction. This study enabled us to test the theory that disease outcome may be due to interaction of several gene effects. With eight instances of statistically significant gene-gene interactions, the importance of epistasis is clearly identifiable in this study. Methods for studying gene-gene interactions are based on a multilocus and multigene approach, consistent with the nature of complex-trait diseases, and may provide the paradigm for future genetic studies of TB.

  9. Gene by environment interaction in asthma

    NARCIS (Netherlands)

    Koppelman, Gerard H.

    2006-01-01

    Asthma is a chronic inflammatory disease of the airways that is highly prevalent in the Western world. It is a genetically complex disease caused by multiple genetic and environmental factors, which may interact. Genetic research has recently incorporated environmental factors to investigate gene by

  10. Gene-Drug Interaction in Stroke

    Directory of Open Access Journals (Sweden)

    Serena Amici

    2011-01-01

    Several polymorphisms have been studied and some have been associated with positive drug-gene interaction on stroke, but the superiority of the genotype-guided approach over the clinical approach has not been proved yet; for this reason, it is not routinely recommended.

  11. Biological Implications of Gene-Environment Interaction

    Science.gov (United States)

    Rutter, Michael

    2008-01-01

    Gene-environment interaction (G x E) has been treated as both a statistical phenomenon and a biological reality. It is argued that, although there are important statistical issues that need to be considered, the focus has to be on the biological implications of G x E. Four reports of G x E deriving from the Dunedin longitudinal study are used as…

  12. Gene-gene and gene-environmental interactions of childhood asthma: a multifactor dimension reduction approach.

    Directory of Open Access Journals (Sweden)

    Ming-Wei Su

    Full Text Available BACKGROUND: The importance of gene-gene and gene-environment interactions on asthma is well documented in literature, but a systematic analysis on the interaction between various genetic and environmental factors is still lacking. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a population-based, case-control study comprised of seventh-grade children from 14 Taiwanese communities. A total of 235 asthmatic cases and 1,310 non-asthmatic controls were selected for DNA collection and genotyping. We examined the gene-gene and gene-environment interactions between 17 single-nucleotide polymorphisms in antioxidative, inflammatory and obesity-related genes, and childhood asthma. Environmental exposures and disease status were obtained from parental questionnaires. The model-free and non-parametrical multifactor dimensionality reduction (MDR method was used for the analysis. A three-way gene-gene interaction was elucidated between the gene coding glutathione S-transferase P (GSTP1, the gene coding interleukin-4 receptor alpha chain (IL4Ra and the gene coding insulin induced gene 2 (INSIG2 on the risk of lifetime asthma. The testing-balanced accuracy on asthma was 57.83% with a cross-validation consistency of 10 out of 10. The interaction of preterm birth and indoor dampness had the highest training-balanced accuracy at 59.09%. Indoor dampness also interacted with many genes, including IL13, beta-2 adrenergic receptor (ADRB2, signal transducer and activator of transcription 6 (STAT6. We also used likelihood ratio tests for interaction and chi-square tests to validate our results and all tests showed statistical significance. CONCLUSIONS/SIGNIFICANCE: The results of this study suggest that GSTP1, INSIG2 and IL4Ra may influence the lifetime asthma susceptibility through gene-gene interactions in schoolchildren. Home dampness combined with each one of the genes STAT6, IL13 and ADRB2 could raise the asthma risk.

  13. Finding gene-environment interactions for Phobias

    OpenAIRE

    Gregory, Alice M.; Lau, Jennifer Y. F.; Eley, Thalia C

    2008-01-01

    Phobias are common disorders causing a great deal of suffering. Studies of gene-environment interaction (G × E) have revealed much about the complex processes underlying the development of various psychiatric disorders but have told us little about phobias. This article describes what is already known about genetic and environmental influences upon phobias and suggests how this information can be used to optimise the chances of discovering G × Es for phobias. In addition to the careful concep...

  14. Dominance from the perspective of gene-gene and gene-chemical interactions.

    Science.gov (United States)

    Gladki, Arkadiusz; Zielenkiewicz, Piotr; Kaczanowski, Szymon

    2016-02-01

    In this study, we used genetic interaction (GI) and gene-chemical interaction (GCI) data to compare mutations with different dominance phenotypes. Our analysis focused primarily on Saccharomyces cerevisiae, where haploinsufficient genes (HI; genes with dominant loss-of-function mutations) were found to be participating in gene expression processes, namely, the translation and regulation of gene transcription. Non-ribosomal HI genes (mainly regulators of gene transcription) were found to have more GIs and GCIs than haplosufficient (HS) genes. Several properties seem to lead to the enrichment of interactions, most notably, the following: importance, pleiotropy, gene expression level and gene expression variation. Importantly, after these properties were appropriately considered in the analysis, the correlation between dominance and GI/GCI degrees was still observed. Strikingly, for the GCIs of heterozygous strains, haploinsufficiency was the only property significantly correlated with the number of GCIs. We found ribosomal HI genes to be depleted in GIs/GCIs. This finding can be explained by their high variation in gene expression under different genetic backgrounds and environmental conditions. We observed the same distributions of GIs among non-ribosomal HI, ribosomal HI and HS genes in three other species: Schizosaccharomyces pombe, Drosophila melanogaster and Homo sapiens. One potentially interesting exception was the lack of significant differences in the degree of GIs between non-ribosomal HI and HS genes in Schizosaccharomyces pombe.

  15. Choline metabolites: gene by diet interactions.

    Science.gov (United States)

    Smallwood, Tangi; Allayee, Hooman; Bennett, Brian J

    2016-02-01

    The review highlights recent advances in our understanding of the interactions between genetic polymorphisms in genes that metabolize choline and the dietary requirements of choline and how these interactions relate to human health and disease. The importance of choline as an essential nutrient has been well established, but our appreciation of the interaction between our underlying genetic architecture and dietary choline requirements is only beginning. It has been shown in both human and animal studies that choline deficiencies contribute to diseases such as nonalcoholic fatty liver disease and various neurodegenerative diseases. An adequate supply of dietary choline is important for optimum development, highlighted by the increased maternal requirements during fetal development and in breast-fed infants. We discuss recent studies investigating variants in PEMT and MTHFR1 that are associated with a variety of birth defects. In addition to genetic interactions, we discuss several recent studies that uncover changes in fetal global methylation patterns in response to maternal dietary choline intake that result in changes in gene expression in the offspring. In contrast to the developmental role of adequate choline, there is now an appreciation of the role choline has in cardiovascular disease through the gut microbiota-mediated metabolite trimethylamine N-oxide. This pathway highlights some of our understanding of how the microbiome affects nutrient processing and bioavailability. Finally, to better characterize the genetic architecture regulating choline requirements, we discuss recent results focused on identifying polymorphisms that regulate choline and its derivative products. Here we discuss recent studies that have advanced our understanding of how specific alleles in key choline metabolism genes are related to dietary choline requirements and human disease.

  16. Gene-Environment Interaction in Parkinson's Disease

    DEFF Research Database (Denmark)

    Chuang, Yu-Hsuan; Lill, Christina M; Lee, Pei-Chen

    2016-01-01

    ) metabolizes caffeine; thus, gene polymorphisms in ADORA2A and CYP1A2 may influence the effect coffee consumption has on PD risk. METHODS: In a population-based case-control study (PASIDA) in Denmark (1,556 PD patients and 1,606 birth year- and gender-matched controls), we assessed interactions between...... interactions for ADORA2A rs5760423 and heavy vs. light coffee consumption in incident (OR interaction = 0.66 [95% CI 0.46-0.94], p = 0.02) but not prevalent PD. We did not observe interactions for CYP1A2 rs762551 and rs2472304 in incident or prevalent PD. In meta-analyses, PD associations with daily coffee...... consumption were strongest among carriers of variant alleles in both ADORA2A and CYP1A2. CONCLUSION: We corroborated results from a previous report that described interactions between ADORA2A and CYP1A2 polymorphisms and coffee consumption. Our results also suggest that survivor bias may affect results...

  17. Finding gene-environment interactions for phobias.

    Science.gov (United States)

    Gregory, Alice M; Lau, Jennifer Y F; Eley, Thalia C

    2008-03-01

    Phobias are common disorders causing a great deal of suffering. Studies of gene-environment interaction (G x E) have revealed much about the complex processes underlying the development of various psychiatric disorders but have told us little about phobias. This article describes what is already known about genetic and environmental influences upon phobias and suggests how this information can be used to optimise the chances of discovering G x Es for phobias. In addition to the careful conceptualisation of new studies, it is suggested that data already collected should be re-analysed in light of increased understanding of processes influencing phobias.

  18. Polymorphism Interaction Analysis (PIA: a method for investigating complex gene-gene interactions

    Directory of Open Access Journals (Sweden)

    Chanock Stephen J

    2008-03-01

    Full Text Available Abstract Background The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs. Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives. Results We developed the Polymorphism Interaction Analysis tool (PIA version 2.0 to include different approaches for ranking and scoring SNP combinations, to account for imbalances between case and control ratios, stratify on particular factors, and examine associations of user-defined pathways (based on SNP or gene with case status. PIA v. 2.0 detected 2-SNP interactions as the highest ranking model 77% of the time, using simulated data sets of genetic models of interaction (minor allele frequency = 0.2; heritability = 0.01; N = 1600 generated previously [Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007, 31:306–315.]. Interacting SNPs were detected in both balanced (20 SNPs and imbalanced data (case:control 1:2 and 1:4, 10 SNPs in the context of non-interacting SNPs. Conclusion PIA v. 2.0 is a useful tool for exploring gene*gene or gene*environment interactions and identifying a small number of putative associations which may be investigated further using other

  19. Random matrix analysis for gene interaction networks in cancer cells

    CERN Document Server

    Kikkawa, Ayumi

    2016-01-01

    Motivation: The investigation of topological modifications of the gene interaction networks in cancer cells is essential for understanding the desease. We study gene interaction networks in various human cancer cells with the random matrix theory. This study is based on the Cancer Network Galaxy (TCNG) database which is the repository of huge gene interactions inferred by Bayesian network algorithms from 256 microarray experimental data downloaded from NCBI GEO. The original GEO data are provided by the high-throughput microarray expression experiments on various human cancer cells. We apply the random matrix theory to the computationally inferred gene interaction networks in TCNG in order to detect the universality in the topology of the gene interaction networks in cancer cells. Results: We found the universal behavior in almost one half of the 256 gene interaction networks in TCNG. The distribution of nearest neighbor level spacing of the gene interaction matrix becomes the Wigner distribution when the net...

  20. Detection of gene x gene interactions in genome-wide association studies of human population data

    National Research Council Canada - National Science Library

    Musani, Solomon K; Shriner, Daniel; Liu, Nianjun; Feng, Rui; Coffey, Christopher S; Yi, Nengjun; Tiwari, Hemant K; Allison, David B

    2007-01-01

    Empirical evidence supporting the commonality of gene x gene interactions, coupled with frequent failure to replicate results from previous association studies, has prompted statisticians to develop...

  1. Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity.

    Science.gov (United States)

    De, Rishika; Hu, Ting; Moore, Jason H; Gilbert-Diamond, Diane

    2015-01-01

    Recent findings have reemphasized the importance of epistasis, or gene-gene interactions, as a contributing factor to the unexplained heritability of obesity. Network-based methods such as statistical epistasis networks (SEN), present an intuitive framework to address the computational challenge of studying pairwise interactions between thousands of genetic variants. In this study, we aimed to analyze pairwise interactions that are associated with Body Mass Index (BMI) between SNPs from twelve genes robustly associated with obesity (BDNF, ETV5, FAIM2, FTO, GNPDA2, KCTD15, MC4R, MTCH2, NEGR1, SEC16B, SH2B1, and TMEM18). We used information gain measures to identify all SNP-SNP interactions among and between these genes that were related to obesity (BMI > 30 kg/m(2)) within the Framingham Heart Study Cohort; interactions exceeding a certain threshold were used to build an SEN. We also quantified whether interactions tend to occur more between SNPs from the same gene (dyadicity) or between SNPs from different genes (heterophilicity). We identified a highly connected SEN of 709 SNPs and 1241 SNP-SNP interactions. Combining the SEN framework with dyadicity and heterophilicity analyses, we found 1 dyadic gene (TMEM18, P-value = 0.047) and 3 heterophilic genes (KCTD15, P-value = 0.045; SH2B1, P-value = 0.003; and TMEM18, P-value = 0.001). We also identified a lncRNA SNP (rs4358154) as a key node within the SEN using multiple network measures. This study presents an analytical framework to characterize the global landscape of genetic interactions from genome-wide arrays and also to discover nodes of potential biological significance within the identified network.

  2. A global test for gene-gene interactions based on random matrix theory.

    Science.gov (United States)

    Frost, H Robert; Amos, Christopher I; Moore, Jason H

    2016-12-01

    Statistical interactions between markers of genetic variation, or gene-gene interactions, are believed to play an important role in the etiology of many multifactorial diseases and other complex phenotypes. Unfortunately, detecting gene-gene interactions is extremely challenging due to the large number of potential interactions and ambiguity regarding marker coding and interaction scale. For many data sets, there is insufficient statistical power to evaluate all candidate gene-gene interactions. In these cases, a global test for gene-gene interactions may be the best option. Global tests have much greater power relative to multiple individual interaction tests and can be used on subsets of the markers as an initial filter prior to testing for specific interactions. In this paper, we describe a novel global test for gene-gene interactions, the global epistasis test (GET), that is based on results from random matrix theory. As we show via simulation studies based on previously proposed models for common diseases including rheumatoid arthritis, type 2 diabetes, and breast cancer, our proposed GET method has superior performance characteristics relative to existing global gene-gene interaction tests. A glaucoma GWAS data set is used to demonstrate the practical utility of the GET method.

  3. Literature Mining and Ontology based Analysis of Host-Brucella Gene-Gene Interaction Network.

    Science.gov (United States)

    Karadeniz, İlknur; Hur, Junguk; He, Yongqun; Özgür, Arzucan

    2015-01-01

    Brucella is an intracellular bacterium that causes chronic brucellosis in humans and various mammals. The identification of host-Brucella interaction is crucial to understand host immunity against Brucella infection and Brucella pathogenesis against host immune responses. Most of the information about the inter-species interactions between host and Brucella genes is only available in the text of the scientific publications. Many text-mining systems for extracting gene and protein interactions have been proposed. However, only a few of them have been designed by considering the peculiarities of host-pathogen interactions. In this paper, we used a text mining approach for extracting host-Brucella gene-gene interactions from the abstracts of articles in PubMed. The gene-gene interactions here represent the interactions between genes and/or gene products (e.g., proteins). The SciMiner tool, originally designed for detecting mammalian gene/protein names in text, was extended to identify host and Brucella gene/protein names in the abstracts. Next, sentence-level and abstract-level co-occurrence based approaches, as well as sentence-level machine learning based methods, originally designed for extracting intra-species gene interactions, were utilized to extract the interactions among the identified host and Brucella genes. The extracted interactions were manually evaluated. A total of 46 host-Brucella gene interactions were identified and represented as an interaction network. Twenty four of these interactions were identified from sentence-level processing. Twenty two additional interactions were identified when abstract-level processing was performed. The Interaction Network Ontology (INO) was used to represent the identified interaction types at a hierarchical ontology structure. Ontological modeling of specific gene-gene interactions demonstrates that host-pathogen gene-gene interactions occur at experimental conditions which can be ontologically represented. Our

  4. Coevolution of gene expression among interacting proteins

    OpenAIRE

    2004-01-01

    Physically interacting proteins or parts of proteins are expected to evolve in a coordinated manner that preserves proper interactions. Such coevolution at the amino acid-sequence level is well documented and has been used to predict interacting proteins, domains, and amino acids. Interacting proteins are also often precisely coexpressed with one another, presumably to maintain proper stoichiometry among interacting components. Here, we show that the expression levels of physically inter...

  5. Predictability of Genetic Interactions from Functional Gene Modules

    Directory of Open Access Journals (Sweden)

    Jonathan H. Young

    2017-02-01

    Full Text Available Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

  6. Phytosterols and phytosterolemia: gene-diet interactions.

    Science.gov (United States)

    Izar, Maria C; Tegani, Daniela M; Kasmas, Soraia H; Fonseca, Francisco A

    2011-02-01

    Phytosterol intake is recommended as an adjunctive therapy for hypercholesterolemia, and plant sterols/stanols can reduce cholesterol absorption at the intestinal lumen through the Niemann-Pick C1 Like 1 (NPC1L1) transporter pathway by competitive solubilization in mixed micelles. Phytosterol absorption is of less magnitude than cholesterol and is preferably secreted in the intestinal lumen by ABCG5/G8 transporters. Therefore, plasma levels of plant sterols/stanols are negligible compared with cholesterol, under an ordinary diet. The mechanisms of cholesterol and plant sterols absorption and the whole-body pool of sterols are discussed in this chapter. There is controversy about treatment with statins inducing further increase in plasma non-cholesterol sterols raising concerns about the safety of supplementation of plant sterols to such drugs. In addition, increase in plant sterols has also been reported upon consumption of plant sterol-enriched foods, regardless of other treatments. Rare mutations on ABCG5/G8 transporters affecting cholesterol/non-cholesterol extrusion, causing sitosterolemia with xanthomas and premature atheroslerotic disease are now known, and cholesterol/plant sterols absorption inhibitor, ezetimibe, emerges as the drug that reduces phytosterolemia and promotes xanthoma regression. On the other hand, common polymorphisms affecting the NPC1L1 transporter can interfere with the action of ezetimibe. Gene-diet interactions participate in this intricate network modulating the expression of genetic variants on specific phenotypes and can also affect the individual response to the hypolipidemic treatment. These very interesting aspects promoted a great deal of research in the field.

  7. Environmental confounding in gene-environment interaction studies.

    Science.gov (United States)

    Vanderweele, Tyler J; Ko, Yi-An; Mukherjee, Bhramar

    2013-07-01

    We show that, in the presence of uncontrolled environmental confounding, joint tests for the presence of a main genetic effect and gene-environment interaction will be biased if the genetic and environmental factors are correlated, even if there is no effect of either the genetic factor or the environmental factor on the disease. When environmental confounding is ignored, such tests will in fact reject the joint null of no genetic effect with a probability that tends to 1 as the sample size increases. This problem with the joint test vanishes under gene-environment independence, but it still persists if estimating the gene-environment interaction parameter itself is of interest. Uncontrolled environmental confounding will bias estimates of gene-environment interaction parameters even under gene-environment independence, but it will not do so if the unmeasured confounding variable itself does not interact with the genetic factor. Under gene-environment independence, if the interaction parameter without controlling for the environmental confounder is nonzero, then there is gene-environment interaction either between the genetic factor and the environmental factor of interest or between the genetic factor and the unmeasured environmental confounder. We evaluate several recently proposed joint tests in a simulation study and discuss the implications of these results for the conduct of gene-environment interaction studies.

  8. Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

    Science.gov (United States)

    Hur, Junguk; Özgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

    2015-01-01

    Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher's exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with 'INO_' prefix. A new annotation property, 'has literature mining keywords', was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher's exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these

  9. Molecular Basis of Gene-Gene Interaction: Cyclic Cross-Regulation of Gene Expression and Post-GWAS Gene-Gene Interaction Involved in Atrial Fibrillation.

    Directory of Open Access Journals (Sweden)

    Yufeng Huang

    2015-08-01

    Full Text Available Atrial fibrillation (AF is the most common cardiac arrhythmia at the clinic. Recent GWAS identified several variants associated with AF, but they account for <10% of heritability. Gene-gene interaction is assumed to account for a significant portion of missing heritability. Among GWAS loci for AF, only three were replicated in the Chinese Han population, including SNP rs2106261 (G/A substitution in ZFHX3, rs2200733 (C/T substitution near PITX2c, and rs3807989 (A/G substitution in CAV1. Thus, we analyzed the interaction among these three AF loci. We demonstrated significant interaction between rs2106261 and rs2200733 in three independent populations and combined population with 2,020 cases/5,315 controls. Compared to non-risk genotype GGCC, two-locus risk genotype AATT showed the highest odds ratio in three independent populations and the combined population (OR=5.36 (95% CI 3.87-7.43, P=8.00×10-24. The OR of 5.36 for AATT was significantly higher than the combined OR of 3.31 for both GGTT and AACC, suggesting a synergistic interaction between rs2106261 and rs2200733. Relative excess risk due to interaction (RERI analysis also revealed significant interaction between rs2106261 and rs2200733 when exposed two copies of risk alleles (RERI=2.87, P<1.00×10-4 or exposed to one additional copy of risk allele (RERI=1.29, P<1.00×10-4. The INTERSNP program identified significant genotypic interaction between rs2106261 and rs2200733 under an additive by additive model (OR=0.85, 95% CI: 0.74-0.97, P=0.02. Mechanistically, PITX2c negatively regulates expression of miR-1, which negatively regulates expression of ZFHX3, resulting in a positive regulation of ZFHX3 by PITX2c; ZFHX3 positively regulates expression of PITX2C, resulting in a cyclic loop of cross-regulation between ZFHX3 and PITX2c. Both ZFHX3 and PITX2c regulate expression of NPPA, TBX5 and NKX2.5. These results suggest that cyclic cross-regulation of gene expression is a molecular basis for gene-gene

  10. Effects related to gene-gene interactions of peroxisome proliferator-activated receptor on essential hypertension

    Institute of Scientific and Technical Information of China (English)

    俞浩

    2013-01-01

    Objective To explore the impact of the gene-gene interaction among the single nucleotide polymorphisms(SNPs) of peroxisome proliferator-activated receptorα/δ/γ on essential hypertension(EH).Methods

  11. Reconstructability analysis as a tool for identifying gene-gene interactions in studies of human diseases.

    Science.gov (United States)

    Shervais, Stephen; Kramer, Patricia L; Westaway, Shawn K; Cox, Nancy J; Zwick, Martin

    2010-01-01

    There are a number of common human diseases for which the genetic component may include an epistatic interaction of multiple genes. Detecting these interactions with standard statistical tools is difficult because there may be an interaction effect, but minimal or no main effect. Reconstructability analysis (RA) uses Shannon's information theory to detect relationships between variables in categorical datasets. We applied RA to simulated data for five different models of gene-gene interaction, and find that even with heritability levels as low as 0.008, and with the inclusion of 50 non-associated genes in the dataset, we can identify the interacting gene pairs with an accuracy of > or =80%. We applied RA to a real dataset of type 2 non-insulin-dependent diabetes (NIDDM) cases and controls, and closely approximated the results of more conventional single SNP disease association studies. In addition, we replicated prior evidence for epistatic interactions between SNPs on chromosomes 2 and 15.

  12. Gene x environment interactions as dynamical systems: clinical implications

    National Research Council Canada - National Science Library

    Sarah S. Knox

    2015-01-01

    The etiology and progression of the chronic diseases that account for the highest rates of mortality in the US, namely, cardiovascular diseases and cancers, involve complex gene x environment interactions...

  13. Torsion-mediated interaction between adjacent genes.

    Directory of Open Access Journals (Sweden)

    Sam Meyer

    2014-09-01

    Full Text Available DNA torsional stress is generated by virtually all biomolecular processes involving the double helix, in particular transcription where a significant level of stress propagates over several kilobases. If another promoter is located in this range, this stress may strongly modify its opening properties, and hence facilitate or hinder its transcription. This mechanism implies that transcribed genes distant of a few kilobases are not independent, but coupled by torsional stress, an effect for which we propose the first quantitative and systematic model. In contrast to previously proposed mechanisms of transcriptional interference, the suggested coupling is not mediated by the transcription machineries, but results from the universal mechanical features of the double-helix. The model shows that the effect likely affects prokaryotes as well as eukaryotes, but with different consequences owing to their different basal levels of torsion. It also depends crucially on the relative orientation of the genes, enhancing the expression of eukaryotic divergent pairs while reducing that of prokaryotic convergent ones. To test the in vivo influence of the torsional coupling, we analyze the expression of isolated gene pairs in the Drosophila melanogaster genome. Their orientation and distance dependence is fully consistent with the model, suggesting that torsional gene coupling may constitute a widespread mechanism of (coregulation in eukaryotes.

  14. Visualizing Gene - Interactions within the Rice and Maize Network

    Science.gov (United States)

    Sampong, A.; Feltus, A.; Smith, M.

    2014-12-01

    The purpose of this research was to design a simpler visualization tool for comparing or viewing gene interaction graphs in systems biology. This visualization tool makes it possible and easier for a researcher to visualize the biological metadata of a plant and interact with the graph on a webpage. Currently available visualization software like Cytoscape and Walrus are difficult to interact with and do not scale effectively for large data sets, limiting the ability to visualize interactions within a biological system. The visualization tool developed is useful for viewing and interpreting the dataset of a gene interaction network. The graph layout drawn by this visualization tool is an improvement from the previous method of comparing lines of genes in two separate data files to, now having the ability to visually see the layout of the gene networks and how the two systems are related. The graph layout presented by the visualization tool draws a graph of the sample rice and maize gene networks, linking the common genes found in both plants and highlighting the functions served by common genes from each plant. The success of this visualization tool will enable Dr. Feltus to continue his investigations and draw conclusions on the biological evolution of the sorghum plant as well. REU Funded by NSF ACI Award 1359223 Vetria L. Byrd, PI

  15. Gene × Smoking Interactions on Human Brain Gene Expression: Finding Common Mechanisms in Adolescents and Adults

    Science.gov (United States)

    Wolock, Samuel L.; Yates, Andrew; Petrill, Stephen A.; Bohland, Jason W.; Blair, Clancy; Li, Ning; Machiraju, Raghu; Huang, Kun; Bartlett, Christopher W.

    2013-01-01

    Background: Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets…

  16. Gene × Smoking Interactions on Human Brain Gene Expression: Finding Common Mechanisms in Adolescents and Adults

    Science.gov (United States)

    Wolock, Samuel L.; Yates, Andrew; Petrill, Stephen A.; Bohland, Jason W.; Blair, Clancy; Li, Ning; Machiraju, Raghu; Huang, Kun; Bartlett, Christopher W.

    2013-01-01

    Background: Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets…

  17. Neural correlates of gene-environment interactions in ADHD

    NARCIS (Netherlands)

    van der Meer, Dennis

    2016-01-01

    The way we respond to our environment partly depends on our genes. So-called gene-environment interactions (GxE) may explain why some children develop attention-deficit/hyperactivity disorder (ADHD) when exposed to a stressful environment, whereas others do not. Knowledge of GxE may therefore not on

  18. Gene environment interactions in bipolar disorder.

    Science.gov (United States)

    Pregelj, Peter

    2011-09-01

    It has been estimated that the heritable component of bipolar disorder ranges between 80 and 90%. However, even genome-wide association studies explain only a fraction of phenotypic variability not resolving the problem of "lost heritability". Although direct evidence for epigenetic dysfunction in bipolar disorder is still limited, methodological technologies in epigenomic profiling have advanced, offering even single cell analysing and resolving the problem of cell heterogeneity in epigenetics research. Gene overlapping with other mental disorders represents another problem in identifying potential susceptibility genes in bipolar disorder. Better understanding of the interplay between multiple environmental and genetic factors involved in the patogenesis of bipolar disorder could provide relevant information for treatment of patients with this complex disorder. Future studies on the role of these factors in psychopathological conditions, subphenotypes and endophenotypes may greatly benefit by using more precise clinical data and a combined approach with multiple research tools incorporated into a single study.

  19. Approximation scheme based on effective interactions for stochastic gene regulation

    CERN Document Server

    Ohkubo, Jun

    2010-01-01

    Since gene regulatory systems contain sometimes only a small number of molecules, these systems are not described well by macroscopic rate equations; a master equation approach is needed for such cases. We develop an approximation scheme for dealing with the stochasticity of the gene regulatory systems. Using an effective interaction concept, original master equations can be reduced to simpler master equations, which can be solved analytically. We apply the approximation scheme to self-regulating systems with monomer or dimer interactions, and a two-gene system with an exclusive switch. The approximation scheme can recover bistability of the exclusive switch adequately.

  20. Gene-Environment Interactions in Asthma: Genetic and Epigenetic Effects.

    Science.gov (United States)

    Lee, Jong-Uk; Kim, Jeong Dong; Park, Choon-Sik

    2015-07-01

    Over the past three decades, a large number of genetic studies have been aimed at finding genetic variants associated with the risk of asthma, applying various genetic and genomic approaches including linkage analysis, candidate gene polymorphism studies, and genome-wide association studies (GWAS). However, contrary to general expectation, even single nucleotide polymorphisms (SNPs) discovered by GWAS failed to fully explain the heritability of asthma. Thus, application of rare allele polymorphisms in well defined phenotypes and clarification of environmental factors have been suggested to overcome the problem of 'missing' heritability. Such factors include allergens, cigarette smoke, air pollutants, and infectious agents during pre- and post-natal periods. The first and simplest interaction between a gene and the environment is a candidate interaction of both a well known gene and environmental factor in a direct physical or chemical interaction such as between CD14 and endotoxin or between HLA and allergens. Several GWAS have found environmental interactions with occupational asthma, aspirin exacerbated respiratory disease, tobacco smoke-related airway dysfunction, and farm-related atopic diseases. As one of the mechanisms behind gene-environment interaction is epigenetics, a few studies on DNA CpG methylation have been reported on subphenotypes of asthma, pitching the exciting idea that it may be possible to intervene at the junction between the genome and the environment. Epigenetic studies are starting to include data from clinical samples, which will make them another powerful tool for re-search on gene-environment interactions in asthma.

  1. Artificial neural networks modeling gene-environment interaction

    Directory of Open Access Journals (Sweden)

    Günther Frauke

    2012-05-01

    Full Text Available Abstract Background Gene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only. Results In our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor. Conclusion Our study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.

  2. Gene-physical activity interactions and their impact on diabetes

    DEFF Research Database (Denmark)

    Oskari Kilpeläinen, Tuomas; Franks, Paul W

    2014-01-01

    mechanisms of how type 2 diabetes develops, which could open up new avenues for the development of novel treatments. It has also been postulated that knowledge of interactions could improve the prevention and treatment of type 2 diabetes by enabling targeted interventions. The present chapter will introduce...... to an equal bout of physical activity. Individuals with specific genetic profiles are also expected to be more responsive to the beneficial effects of physical activity in the prevention of type 2 diabetes. Identification of such gene-physical activity interactions could give new insights into the biological...... the reader to the recent advances in the genetics of type 2 diabetes, summarize the current evidence on gene-physical activity interactions in relation to type 2 diabetes, and outline how information on gene-physical activity interactions might help improve the prevention and treatment of type 2 diabetes...

  3. A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

    Directory of Open Access Journals (Sweden)

    Ching Lee Koo

    2013-01-01

    Full Text Available Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs, support vector machine (SVM, and random forests (RFs in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

  4. Gene × physical activity interactions in obesity

    DEFF Research Database (Denmark)

    Ahmad, Shafqat; Rukh, Gull; Varga, Tibor V

    2013-01-01

    -administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age(2), sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were......Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished...... in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self...

  5. Gene-Environment Interactions in Severe Mental Illness

    Directory of Open Access Journals (Sweden)

    Rudolf eUher

    2014-05-01

    Full Text Available Severe mental illness is a broad category that includes schizophrenia, bipolar disorder and severe depression. Both genetic disposition and environmental exposures play important roles in the development of severe mental illness. Multiple lines of evidence suggest that the roles of genetic and environmental depend on each other. Gene-environment interactions may underlie the paradox of strong environmental factors for highly heritable disorders, the low estimates of shared environmental influences in twin studies of severe mental illness and the heritability gap between twin and molecular heritability estimates. Sons and daughters of parents with severe mental illness are more vulnerable to the effects of prenatal and postnatal environmental exposures, suggesting that the expression of genetic liability depends on environment. In the last decade, gene-environment interactions involving specific molecular variants in candidate genes have been identified. Replicated findings include an interaction between a polymorphism in the AKT1 gene and cannabis use in the development of psychosis and an interaction between the length polymorphism of the serotonin transporter gene and childhood maltreatment in the development of persistent depressive disorder. Bipolar disorder has been underinvestigated, with only a single study showing an interaction between a functional polymorphism in BDNF and stressful life events triggering bipolar depressive episodes. The first systematic search for gene-environment interactions has found that a polymorphism in CTNNA3 may sensitise the developing brain to the pathogenic effect of cytomegalovirus in utero, leading to schizophrenia in adulthood. Strategies for genome-wide investigations will likely include coordination between epidemiological and genetic research efforts, systematic assessment of multiple environmental factors in large samples, and prioritization of genetic variants.

  6. Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

    Science.gov (United States)

    Tian, Xue W; Lim, Joon S

    2015-01-01

    Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

  7. Polyamine-DNA interactions and development of gene delivery vehicles.

    Science.gov (United States)

    Thomas, T J; Tajmir-Riahi, H A; Thomas, Thresia

    2016-10-01

    Polyamines are positively charged organic cations under physiologic ionic and pH conditions and hence they interact with negatively charged macromolecules such as DNA and RNA. Although electrostatic interaction is the predominant mode of polyamine-nucleic acid interactions, site- and structure-specific binding has also been recognized. A major consequence of polyamine-DNA interaction is the collapse of DNA to nanoparticles of approximately 100 nm diameter. Electron and atomic force microscopic studies have shown that these nanoparticles are spheroids, toroids and rods. DNA transport to cells for gene therapy applications requires the condensation of DNA to nanoparticles and hence the study of polyamines and related compounds with nucleic acids has received technological importance. In addition to natural and synthetic polyamines, several amine-terminated or polyamine-substituted agents are under intense investigation for non-viral gene delivery vehicles.

  8. Genome-wide gene-gene interaction analysis for next-generation sequencing.

    Science.gov (United States)

    Zhao, Jinying; Zhu, Yun; Xiong, Momiao

    2016-03-01

    The critical barrier in interaction analysis for next-generation sequencing (NGS) data is that the traditional pairwise interaction analysis that is suitable for common variants is difficult to apply to rare variants because of their prohibitive computational time, large number of tests and low power. The great challenges for successful detection of interactions with NGS data are (1) the demands in the paradigm of changes in interaction analysis; (2) severe multiple testing; and (3) heavy computations. To meet these challenges, we shift the paradigm of interaction analysis between two SNPs to interaction analysis between two genomic regions. In other words, we take a gene as a unit of analysis and use functional data analysis techniques as dimensional reduction tools to develop a novel statistic to collectively test interaction between all possible pairs of SNPs within two genome regions. By intensive simulations, we demonstrate that the functional logistic regression for interaction analysis has the correct type 1 error rates and higher power to detect interaction than the currently used methods. The proposed method was applied to a coronary artery disease dataset from the Wellcome Trust Case Control Consortium (WTCCC) study and the Framingham Heart Study (FHS) dataset, and the early-onset myocardial infarction (EOMI) exome sequence datasets with European origin from the NHLBI's Exome Sequencing Project. We discovered that 6 of 27 pairs of significantly interacted genes in the FHS were replicated in the independent WTCCC study and 24 pairs of significantly interacted genes after applying Bonferroni correction in the EOMI study.

  9. Gene-air pollution interaction and cardiovascular disease: a review

    OpenAIRE

    Zanobetti, Antonella; Baccarelli, Andrea; Schwartz, Joel

    2011-01-01

    Genetic susceptibility is likely to play a role in response to air pollution. Hence, gene-environment interactions studies can be a tool for exploring the mechanisms and the importance of the pathway in the association between air pollution and a cardiovascular outcome. In this article we present a systematic review of the studies which have examined gene–environment interactions in relation to the cardiovascular health effects of air pollutants. We identified 16 papers meeting our search cri...

  10. A gene-based information gain method for detecting gene-gene interactions in case-control studies.

    Science.gov (United States)

    Li, Jin; Huang, Dongli; Guo, Maozu; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Zhang, Ruijie; Jiang, Yongshuai; Lv, Hongchao; Wang, Limei

    2015-11-01

    Currently, most methods for detecting gene-gene interactions (GGIs) in genome-wide association studies are divided into SNP-based methods and gene-based methods. Generally, the gene-based methods can be more powerful than SNP-based methods. Some gene-based entropy methods can only capture the linear relationship between genes. We therefore proposed a nonparametric gene-based information gain method (GBIGM) that can capture both linear relationship and nonlinear correlation between genes. Through simulation with different odds ratio, sample size and prevalence rate, GBIGM was shown to be valid and more powerful than classic KCCU method and SNP-based entropy method. In the analysis of data from 17 genes on rheumatoid arthritis, GBIGM was more effective than the other two methods as it obtains fewer significant results, which was important for biological verification. Therefore, GBIGM is a suitable and powerful tool for detecting GGIs in case-control studies.

  11. GSTP1 is a hub gene for gene-air pollution interactions on childhood asthma.

    Science.gov (United States)

    Su, M W; Tsai, C H; Tung, K Y; Hwang, B F; Liang, P H; Chiang, B L; Yang, Y H; Lee, Y L

    2013-12-01

    There is growing evidence that multiple genes and air pollutants are associated with asthma. By identifying the effect of air pollution on the general population, the effects of air pollution on childhood asthma can be better understood. We conducted the Taiwan Children Health Study (TCHS) to investigate the influence of gene-air pollution interactions on childhood asthma. Complete monitoring data for the ambient air pollutants were collected from Taiwan Environmental Protection Agency air monitoring stations. Our results show a significant two-way gene-air pollution interaction between glutathione S-transferase P (GSTP1) and PM10 on the risk of childhood asthma. Interactions between GSTP1 and different types of air pollutants have a higher information gain than other gene-air pollutant combinations. Our study suggests that interaction between GSTP1 and PM10 is the most influential gene-air pollution interaction model on childhood asthma. The different types of air pollution combined with the GSTP1 gene may alter the susceptibility to childhood asthma. It implies that GSTP1 is an important hub gene in the anti-oxidative pathway that buffers the harmful effects of air pollution. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  12. Gene-gene and gene-environment interactions in prostate, breast and colorectal cancer

    DEFF Research Database (Denmark)

    Kopp, Tine Iskov

    transporters and IL-10 in relation to CRC. Paper V illustrated that genetic variations in CYP19A1 predicts circulating sex-hormone levels in postmenopausal women, and that alcohol intake affects female sex-hormone concentrations in the blood. However, it was not possible to put PPARγ and the aromatase...... single-gene mutations due to their low frequency in the general population. Overall, the contribution from hereditary factors to the causation of BC is only 27%, whereas genetics contributes to 35% and 42% for CRC and PC, respectively. Additionally, immigrations studies point to environmental factors......, such as alcohol consumption, smoking, obesity, inflammation and high meat intake; whereas other factors protect against cancer, such as high intake of dietary fibre, fruits and vegetables, and physical activity. Investigating the interactions between genetic variations and environmental factors, such as dietary...

  13. Perinatal Gene-Gene and Gene-Environment Interactions on IgE Production and Asthma Development

    Directory of Open Access Journals (Sweden)

    Jen-Chieh Chang

    2012-01-01

    Full Text Available Atopic asthma is a complex disease associated with IgE-mediated immune reactions. Numerous genome-wide studies identified more than 100 genes in 22 chromosomes associated with atopic asthma, and different genetic backgrounds in different environments could modulate susceptibility to atopic asthma. Current knowledge emphasizes the effect of tobacco smoke on the development of childhood asthma. This suggests that asthma, although heritable, is significantly affected by gene-gene and gene-environment interactions. Evidence has recently shown that molecular mechanism of a complex disease may be limited to not only DNA sequence differences, but also gene-environmental interactions for epigenetic difference. This paper reviews and summarizes how gene-gene and gene-environment interactions affect IgE production and the development of atopic asthma in prenatal and childhood stages. Based on the mechanisms responsible for perinatal gene-environment interactions on IgE production and development of asthma, we formulate several potential strategies to prevent the development of asthma in the perinatal stage.

  14. Semiparametric bayesian analysis of gene-environment interactions

    OpenAIRE

    Lobach, I.

    2010-01-01

    A key component to prevention and control of complex diseases, such as cancer, diabetes, hypertension, is to analyze the genetic and environmental factors that lead to the development of these complex diseases. We propose a Bayesian approach for analysis of gene-environment interactions that efficiently models information available in the observed data and a priori biomedical knowledge.

  15. The importance of gene-environment interactions in human obesity.

    Science.gov (United States)

    Reddon, Hudson; Guéant, Jean-Louis; Meyre, David

    2016-09-01

    The worldwide obesity epidemic has been mainly attributed to lifestyle changes. However, who becomes obese in an obesity-prone environment is largely determined by genetic factors. In the last 20 years, important progress has been made in the elucidation of the genetic architecture of obesity. In parallel with successful gene identifications, the number of gene-environment interaction (GEI) studies has grown rapidly. This paper reviews the growing body of evidence supporting gene-environment interactions in the field of obesity. Heritability, monogenic and polygenic obesity studies provide converging evidence that obesity-predisposing genes interact with a variety of environmental, lifestyle and treatment exposures. However, some skepticism remains regarding the validity of these studies based on several issues, which include statistical modelling, confounding, low replication rate, underpowered analyses, biological assumptions and measurement precision. What follows in this review includes (1) an introduction to the study of GEI, (2) the evidence of GEI in the field of obesity, (3) an outline of the biological mechanisms that may explain these interaction effects, (4) methodological challenges associated with GEI studies and potential solutions, and (5) future directions of GEI research. Thus far, this growing body of evidence has provided a deeper understanding of GEI influencing obesity and may have tremendous applications in the emerging field of personalized medicine and individualized lifestyle recommendations. © 2016 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.

  16. Rare disease relations through common genes and protein interactions.

    Science.gov (United States)

    Fernandez-Novo, Sara; Pazos, Florencio; Chagoyen, Monica

    2016-06-01

    ODCs (Orphan Disease Connections), available at http://csbg.cnb.csic.es/odcs, is a novel resource to explore potential molecular relations between rare diseases. These molecular relations have been established through the integration of disease susceptibility genes and human protein-protein interactions. The database currently contains 54,941 relations between 3032 diseases.

  17. MiRTargetLink--miRNAs, Genes and Interaction Networks.

    Science.gov (United States)

    Hamberg, Maarten; Backes, Christina; Fehlmann, Tobias; Hart, Martin; Meder, Benjamin; Meese, Eckart; Keller, Andreas

    2016-04-14

    Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented. Users can select whether predicted targets, experimentally validated targets with strong or weak evidence, or combinations of those are considered. Central genes or miRNAs are highlighted and users can navigate through the network interactively. To discover the most relevant biochemical processes influenced by the target network, gene set analysis and miRNA set analysis are integrated. As a showcase for miRTargetLink, we analyze targets of five cardiac miRNAs. miRTargetLink is freely available without restrictions at www.ccb.uni-saarland.de/mirtargetlink.

  18. Risk score modeling of multiple gene to gene interactions using aggregated-multifactor dimensionality reduction

    Directory of Open Access Journals (Sweden)

    Dai Hongying

    2013-01-01

    Full Text Available Abstract Background Multifactor Dimensionality Reduction (MDR has been widely applied to detect gene-gene (GxG interactions associated with complex diseases. Existing MDR methods summarize disease risk by a dichotomous predisposing model (high-risk/low-risk from one optimal GxG interaction, which does not take the accumulated effects from multiple GxG interactions into account. Results We propose an Aggregated-Multifactor Dimensionality Reduction (A-MDR method that exhaustively searches for and detects significant GxG interactions to generate an epistasis enriched gene network. An aggregated epistasis enriched risk score, which takes into account multiple GxG interactions simultaneously, replaces the dichotomous predisposing risk variable and provides higher resolution in the quantification of disease susceptibility. We evaluate this new A-MDR approach in a broad range of simulations. Also, we present the results of an application of the A-MDR method to a data set derived from Juvenile Idiopathic Arthritis patients treated with methotrexate (MTX that revealed several GxG interactions in the folate pathway that were associated with treatment response. The epistasis enriched risk score that pooled information from 82 significant GxG interactions distinguished MTX responders from non-responders with 82% accuracy. Conclusions The proposed A-MDR is innovative in the MDR framework to investigate aggregated effects among GxG interactions. New measures (pOR, pRR and pChi are proposed to detect multiple GxG interactions.

  19. Comparative genomics of free-living Gammaproteobacteria: pathogenesis-related genes or interaction-related genes?

    Science.gov (United States)

    Vázquez-Rosas-Landa, Mirna; Ponce-Soto, Gabriel Yaxal; Eguiarte, Luis E; Souza, V

    2017-07-31

    Bacteria have numerous strategies to interact with themselves and with their environment, but genes associated with these interactions are usually cataloged as pathogenic. To understand the role that these genes have not only in pathogenesis but also in bacterial interactions, we compared the genomes of eight bacteria from human-impacted environments with those of free-living bacteria from the Cuatro Ciénegas Basin (CCB), a relatively pristine oligotrophic site. Fifty-one genomes from CCB bacteria, including Pseudomonas, Vibrio, Photobacterium and Aeromonas, were analyzed. We found that the CCB strains had several virulence-related genes, 15 of which were common to all strains and were related to flagella and chemotaxis. We also identified the presence of Type III and VI secretion systems, which leads us to propose that these systems play an important role in interactions among bacterial communities beyond pathogenesis. None of the CCB strains had pathogenicity islands, despite having genes associated with antibiotics. Integrons were rare, while CRISPR elements were common. The idea that pathogenicity-related genes in many cases form part of a wider strategy used by bacteria to interact with other organisms could help us to understand the role of pathogenicity-related elements in an ecological and evolutionary framework leading toward a more inclusive One Health concept. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  20. Gene-environment interactions in sporadic Parkinson's disease.

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    Benmoyal-Segal, Liat; Soreq, Hermona

    2006-06-01

    Much has been learned in recent years about the genetics of familial Parkinson's disease. However, far less is known about those malfunctioning genes which contribute to the emergence and/or progression of the vast majority of cases, the 'sporadic Parkinson's disease', which is the focus of our current review. Drastic differences in the reported prevalence of Parkinson's disease in different continents and countries suggest ethnic and/or environmental-associated multigenic contributions to this disease. Numerous association studies showing variable involvement of multiple tested genes in these distinct locations support this notion. Also, variable increases in the risk of Parkinson's disease due to exposure to agricultural insecticides indicate complex gene-environment interactions, especially when genes involved in protection from oxidative stress are explored. Further consideration of the brain regions damaged in Parkinson's disease points at the age-vulnerable cholinergic-dopaminergic balance as being involved in the emergence of sporadic Parkinson's disease in general and in the exposure-induced risks in particular. More specifically, the chromosome 7 ACHE/PON1 locus emerges as a key region controlling this sensitive balance, and animal model experiments are compatible with this concept. Future progress in the understanding of the genetics of sporadic Parkinson's disease depends on globally coordinated, multileveled studies of gene-environment interactions.

  1. The use of gene interaction networks to improve the identification of cancer driver genes

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    Emilie Ramsahai

    2017-01-01

    Full Text Available Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.

  2. Gene interactions in the evolution of genomic imprinting.

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    Wolf, J B; Brandvain, Y

    2014-08-01

    Numerous evolutionary theories have been developed to explain the epigenetic phenomenon of genomic imprinting. Here, we explore a subset of theories wherein non-additive genetic interactions can favour imprinting. In the simplest genic interaction--the case of underdominance--imprinting can be favoured to hide effectively low-fitness heterozygous genotypes; however, as there is no asymmetry between maternally and paternally inherited alleles in this model, other means of enforcing monoallelic expression may be more plausible evolutionary outcomes than genomic imprinting. By contrast, more successful interaction models of imprinting rely on an asymmetry between the maternally and paternally inherited alleles at a locus that favours the silencing of one allele as a means of coordinating the expression of high-fitness allelic combinations. For example, with interactions between autosomal loci, imprinting functionally preserves high-fitness genotypes that were favoured by selection in the previous generation. In this scenario, once a focal locus becomes imprinted, selection at interacting loci favours a matching imprint. Uniparental transmission generates similar asymmetries for sex chromosomes and cytoplasmic factors interacting with autosomal loci, with selection favouring the expression of either maternal or paternally derived autosomal alleles depending on the pattern of transmission of the uniparentally inherited factor. In a final class of models, asymmetries arise when genes expressed in offspring interact with genes expressed in one of its parents. Under such a scenario, a locus evolves to have imprinted expression in offspring to coordinate the interaction with its parent's genome. We illustrate these models and explore key links and differences using a unified framework.

  3. Simultaneous clustering of multiple gene expression and physical interaction datasets.

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    Manikandan Narayanan

    2010-04-01

    Full Text Available Many genome-wide datasets are routinely generated to study different aspects of biological systems, but integrating them to obtain a coherent view of the underlying biology remains a challenge. We propose simultaneous clustering of multiple networks as a framework to integrate large-scale datasets on the interactions among and activities of cellular components. Specifically, we develop an algorithm JointCluster that finds sets of genes that cluster well in multiple networks of interest, such as coexpression networks summarizing correlations among the expression profiles of genes and physical networks describing protein-protein and protein-DNA interactions among genes or gene-products. Our algorithm provides an efficient solution to a well-defined problem of jointly clustering networks, using techniques that permit certain theoretical guarantees on the quality of the detected clustering relative to the optimal clustering. These guarantees coupled with an effective scaling heuristic and the flexibility to handle multiple heterogeneous networks make our method JointCluster an advance over earlier approaches. Simulation results showed JointCluster to be more robust than alternate methods in recovering clusters implanted in networks with high false positive rates. In systematic evaluation of JointCluster and some earlier approaches for combined analysis of the yeast physical network and two gene expression datasets under glucose and ethanol growth conditions, JointCluster discovers clusters that are more consistently enriched for various reference classes capturing different aspects of yeast biology or yield better coverage of the analysed genes. These robust clusters, which are supported across multiple genomic datasets and diverse reference classes, agree with known biology of yeast under these growth conditions, elucidate the genetic control of coordinated transcription, and enable functional predictions for a number of uncharacterized genes.

  4. Differences in Gene-Gene Interactions in Graves' Disease Patients Stratified by Age of Onset.

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    Beata Jurecka-Lubieniecka

    Full Text Available Graves' disease (GD is a complex disease in which genetic predisposition is modified by environmental factors. Each gene exerts limited effects on the development of autoimmune disease (OR = 1.2-1.5. An epidemiological study revealed that nearly 70% of the risk of developing inherited autoimmunological thyroid diseases (AITD is the result of gene interactions. In the present study, we analyzed the effects of the interactions of multiple loci on the genetic predisposition to GD. The aim of our analyses was to identify pairs of genes that exhibit a multiplicative interaction effect.A total of 709 patients with GD were included in the study. The patients were stratified into more homogeneous groups depending on the age at time of GD onset: younger patients less than 30 years of age and older patients greater than 30 years of age. Association analyses were performed for genes that influence the development of GD: HLADRB1, PTPN22, CTLA4 and TSHR. The interactions among polymorphisms were analyzed using the multiple logistic regression and multifactor dimensionality reduction (MDR methods.GD patients stratified by the age of onset differed in the allele frequencies of the HLADRB1*03 and 1858T polymorphisms of the PTPN22 gene (OR = 1.7, p = 0.003; OR = 1.49, p = 0.01, respectively. We evaluated the genetic interactions of four SNPs in a pairwise fashion with regard to disease risk. The coexistence of HLADRB1 with CTLA4 or HLADRB1 with PTPN22 exhibited interactions on more than additive levels (OR = 3.64, p = 0.002; OR = 4.20, p < 0.001, respectively. These results suggest that interactions between these pairs of genes contribute to the development of GD. MDR analysis confirmed these interactions.In contrast to a single gene effect, we observed that interactions between the HLADRB1/PTPN22 and HLADRB1/CTLA4 genes more closely predicted the risk of GD onset in young patients.

  5. Gene-Diet Interaction and Precision Nutrition in Obesity.

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    Heianza, Yoriko; Qi, Lu

    2017-04-07

    The rapid rise of obesity during the past decades has coincided with a profound shift of our living environment, including unhealthy dietary patterns, a sedentary lifestyle, and physical inactivity. Genetic predisposition to obesity may have interacted with such an obesogenic environment in determining the obesity epidemic. Growing studies have found that changes in adiposity and metabolic response to low-calorie weight loss diets might be modified by genetic variants related to obesity, metabolic status and preference to nutrients. This review summarized data from recent studies of gene-diet interactions, and discussed integration of research of metabolomics and gut microbiome, as well as potential application of the findings in precision nutrition.

  6. GSNO Reductase and β2 Adrenergic Receptor Gene-gene Interaction: Bronchodilator Responsiveness to Albuterol

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    Choudhry, Shweta; Que, Loretta G.; Yang, Zhonghui; Liu, Limin; Eng, Celeste; Kim, Sung O.; Kumar, Gunjan; Thyne, Shannon; Chapela, Rocio; Rodriguez-Santana, Jose R.; Rodriguez-Cintron, William; Avila, Pedro C.; Stamler, Jonathan S.; Burchard, Esteban G.

    2010-01-01

    Background Short-acting inhaled β2-agonists such as albuterol are used for bronchodilation and are the mainstay of asthma treatment worldwide. There is significant variation in bronchodilator responsiveness to albuterol not only between individuals but also across racial/ethnic groups. The β2-adrenergic receptor (β2AR) is the target for β2-agonist drugs. The enzyme S-nitrosoglutathione reductase (GSNOR), which regulates levels of the endogenous bronchodilator S-nitrosoglutathione, has been shown to modulate the response to β2-agonists. Objective We hypothesized that there are pharmacogenetic interactions between GSNOR and β2AR gene variants which are associated with variable response to albuterol. Methods We performed family-based analyses to test for association between GSNOR gene variants and asthma and related phenotypes in 609 Puerto Rican and Mexican families with asthma. In addition, we tested these subjects for pharmacogenetic interaction between GSNOR and β2AR gene variants and responsiveness to albuterol using linear regression. Cell transfection experiments were performed to test the potential effect of the GSNOR gene variants. Results Among Puerto Ricans, several GSNOR SNPs and a haplotype in the 3′UTR were significantly associated with increased risk for asthma and lower bronchodilator responsiveness (p = 0.04 to 0.007). The GSNOR risk haplotype affects expression of GSNOR mRNA and protein, suggesting a gain of function. Furthermore, gene-gene interaction analysis provided evidence of pharmacogenetic interaction between GSNOR and β2AR gene variants and the response to albuterol in Puerto Rican (p = 0.03), Mexican (p = 0.15) and combined Puerto Rican and Mexican asthmatics (p = 0.003). Specifically, GSNOR+17059*β2AR+46 genotype combinations (TG+GG*AG and TG+GG*GG) were associated with lower bronchodilator response. Conclusion Genotyping of GSNOR and β2AR genes may be a useful in identifying Latino subjects, who might benefit from adjuvant

  7. Association and gene-gene interactions study of reelin signaling pathway related genes with autism in the Han Chinese population.

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    Shen, Yidong; Xun, Guanglei; Guo, Hui; He, Yiqun; Ou, Jianjun; Dong, Huixi; Xia, Kun; Zhao, Jingping

    2016-04-01

    Autism is a neurodevelopmental disorder with unclear etiology. Reelin had been proposed to participate in the etiology of autism due to its important role in brain development. The goal of this study was to explore the association and gene-gene interactions of reelin signaling pathway related genes (RELN, VLDLR, LRP8, DAB1, FYN, and CDK5) with autism in Han Chinese population. Genotyping data of the six genes were obtained from a recent genome-wide association study performed in 430 autistic children who fulfilled the DSM-IV-TR criteria for autistic disorder, and 1,074 healthy controls. Single marker case-control association analysis and haplotype case-control association analysis were conducted after the data was screened. Multifactor dimensionality reduction (MDR) was applied to further test gene-gene interactions. Neither the single marker nor the haplotype association tests found any significant difference between the autistic group and the control group after permutation test of 1,000 rounds. The 4-locus MDR model (comprising rs6143734, rs1858782, rs634500, and rs1924267 which belong to RELN and DAB1) was determined to be the model with the highest cross-validation consistency (CVC) and testing balanced accuracy. The results indicate that an interaction between RELN and DAB1 may increase the risk of autism in the Han Chinese population. Furthermore, it can also be inferred that the involvement of RELN in the etiology of autism would occur through interaction with DAB1.

  8. Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

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    Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

    2015-01-01

    In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

  9. Association testing to detect gene-gene interactions on sex chromosomes in trio data

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    Yeonok eLee

    2013-11-01

    Full Text Available Autism Spectrum Disorder (ASD occurs more often among males than females in a 4:1 ratio. Among theories used to explain the causes of ASD, the X chromosome and the Y chromosome theories attribute ASD to X-linked mutation and the male-limited gene expressions on the Y chromosome, respectively. Despite the rationale of the theory, studies have failed to attribute the sex-biased ratio to the significant linkage or association on the regions of interest on X chromosome. We further study the gender biased ratio by examining the possible interaction effects between two genes in the sex chromosomes. We propose a logistic regression model with mixed effects to detect gene-gene interactions on sex chromosomes. We investigated the power and type I error rates of the approach for a range of minor allele frequencies and varying linkage disequilibrium between markers and QTLs. We also evaluated the robustness of the model to population stratification. We applied the model to a trio-family data set with an ASD affected male child to study gene-gene interactions on sex chromosomes.

  10. Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis

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    Ueki Masao

    2012-05-01

    Full Text Available Abstract Background Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. Results We adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium data. Conclusions Based on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.

  11. The DAO gene is associated with schizophrenia and interacts with other genes in the Taiwan Han Chinese population.

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    Hsin-Chou Yang

    Full Text Available BACKGROUND: Schizophrenia is a highly heritable disease with a polygenic mode of inheritance. Many studies have contributed to our understanding of the genetic underpinnings of schizophrenia, but little is known about how interactions among genes affect the risk of schizophrenia. This study aimed to assess the associations and interactions among genes that confer vulnerability to schizophrenia and to examine the moderating effect of neuropsychological impairment. METHODS: We analyzed 99 SNPs from 10 candidate genes in 1,512 subject samples. The permutation-based single-locus, multi-locus association tests, and a gene-based multifactorial dimension reduction procedure were used to examine genetic associations and interactions to schizophrenia. RESULTS: We found that no single SNP was significantly associated with schizophrenia. However, a risk haplotype, namely A-T-C of the SNP triplet rsDAO7-rsDAO8-rsDAO13 of the DAO gene, was strongly associated with schizophrenia. Interaction analyses identified multiple between-gene and within-gene interactions. Between-gene interactions including DAO*DISC1 , DAO*NRG1 and DAO*RASD2 and a within-gene interaction for CACNG2 were found among schizophrenia subjects with severe sustained attention deficits, suggesting a modifying effect of impaired neuropsychological functioning. Other interactions such as the within-gene interaction of DAO and the between-gene interaction of DAO and PTK2B were consistently identified regardless of stratification by neuropsychological dysfunction. Importantly, except for the within-gene interaction of CACNG2, all of the identified risk haplotypes and interactions involved SNPs from DAO. CONCLUSIONS: These results suggest that DAO, which is involved in the N-methyl-d-aspartate receptor regulation, signaling and glutamate metabolism, is the master gene of the genetic associations and interactions underlying schizophrenia. Besides, the interaction between DAO and RASD2 has provided

  12. Gene-gene interactions among genetic variants from obesity candidate genes for nonobese and obese populations in type 2 diabetes.

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    Lin, Eugene; Pei, Dee; Huang, Yi-Jen; Hsieh, Chang-Hsun; Wu, Lawrence Shih-Hsin

    2009-08-01

    Recent studies indicate that obesity may play a key role in modulating genetic predispositions to type 2 diabetes (T2D). This study examines the main effects of both single-locus and multilocus interactions among genetic variants in Taiwanese obese and nonobese individuals to test the hypothesis that obesity-related genes may contribute to the etiology of T2D independently and/or through such complex interactions. We genotyped 11 single nucleotide polymorphisms for 10 obesity candidate genes including adrenergic beta-2-receptor surface, adrenergic beta-3-receptor surface, angiotensinogen, fat mass and obesity associated gene, guanine nucleotide binding protein beta polypeptide 3 (GNB3), interleukin 6 receptor, proprotein convertase subtilisin/kexin type 1 (PCSK1), uncoupling protein 1, uncoupling protein 2, and uncoupling protein 3. There were 389 patients diagnosed with T2D and 186 age- and sex-matched controls. Single-locus analyses showed significant main effects of the GNB3 and PCSK1 genes on the risk of T2D among the nonobese group (p = 0.002 and 0.047, respectively). Further, interactions involving GNB3 and PCSK1 were suggested among the nonobese population using the generalized multifactor dimensionality reduction method (p = 0.001). In addition, interactions among angiotensinogen, fat mass and obesity associated gene, GNB3, and uncoupling protein 3 genes were found in a significant four-locus generalized multifactor dimensionality reduction model among the obese population (p = 0.001). The results suggest that the single nucleotide polymorphisms from the obesity candidate genes may contribute to the risk of T2D independently and/or in an interactive manner according to the presence or absence of obesity.

  13. Three Approaches to Modeling Gene-Environment Interactions in Longitudinal Family Data: Gene-Smoking Interactions in Blood Pressure.

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    Basson, Jacob; Sung, Yun Ju; de Las Fuentes, Lisa; Schwander, Karen L; Vazquez, Ana; Rao, Dabeeru C

    2016-01-01

    Blood pressure (BP) has been shown to be substantially heritable, yet identified genetic variants explain only a small fraction of the heritability. Gene-smoking interactions have detected novel BP loci in cross-sectional family data. Longitudinal family data are available and have additional promise to identify BP loci. However, this type of data presents unique analysis challenges. Although several methods for analyzing longitudinal family data are available, which method is the most appropriate and under what conditions has not been fully studied. Using data from three clinic visits from the Framingham Heart Study, we performed association analysis accounting for gene-smoking interactions in BP at 31,203 markers on chromosome 22. We evaluated three different modeling frameworks: generalized estimating equations (GEE), hierarchical linear modeling, and pedigree-based mixed modeling. The three models performed somewhat comparably, with multiple overlaps in the most strongly associated loci from each model. Loci with the greatest significance were more strongly supported in the longitudinal analyses than in any of the component single-visit analyses. The pedigree-based mixed model was more conservative, with less inflation in the variant main effect and greater deflation in the gene-smoking interactions. The GEE, but not the other two models, resulted in substantial inflation in the tail of the distribution when variants with minor allele frequency familial and longitudinal data. © 2015 WILEY PERIODICALS, INC.

  14. Gene-Gene Interactions in the Folate Metabolic Pathway and the Risk of Conotruncal Heart Defects

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    Philip J. Lupo

    2010-01-01

    Full Text Available Conotruncal and related heart defects (CTRD are common, complex malformations. Although there are few established risk factors, there is evidence that genetic variation in the folate metabolic pathway influences CTRD risk. This study was undertaken to assess the association between inherited (i.e., case and maternal gene-gene interactions in this pathway and the risk of CTRD. Case-parent triads (n=727, ascertained from the Children's Hospital of Philadelphia, were genotyped for ten functional variants of nine folate metabolic genes. Analyses of inherited genotypes were consistent with the previously reported association between MTHFR A1298C and CTRD (adjusted P=.02, but provided no evidence that CTRD was associated with inherited gene-gene interactions. Analyses of the maternal genotypes provided evidence of a MTHFR C677T/CBS 844ins68 interaction and CTRD risk (unadjusted P=.02. This association is consistent with the effects of this genotype combination on folate-homocysteine biochemistry but remains to be confirmed in independent study populations.

  15. Inverse gene-for-gene interactions contribute additively to tan spot susceptibility in wheat

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    Tan spot of wheat, caused by Pyrenophora tritici-repentis, is an important disease in almost all wheat-growing areas of the world. The disease system is known to involve at least three fungal-produced necrotrophic effectors (NEs) that interact with corresponding host sensitivity (S) genes in an inv...

  16. Association study of polymorphisms in the ABO gene and their gene-gene interactions with ischemic stroke in Chinese population.

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    Li, Hao; Cai, Yong; Xu, An-Ding

    2017-10-06

    To investigate the impact of 4 single nucleotide polymorphisms (SNPs) within ABO gene and their gene-gene interactions on ischemic stroke (IS) susceptibility in Chinese Han population. A total of 1993 participants (1375 males, 618 females) were selected, including 991 IS patients and 1002 normal controls. The SNPstats (http://bioinfo.iconcologia.net/SNPstats) was used for Hardy-Weinberg equilibrium (HWE) test. Generalized multifactor dimensionality reduction (GMDR) was used to screen the best interaction combination among 4 SNPs within ABO gene. Logistic regression was performed to calculate the ORs (95%CI) for interaction between SNPs. Both rs579459 and rs505922 within ABO gene were associated with IS risk in additive and dominant models. IS risks were higher in those with minor alleles of rs579459 and rs505922 than those with wild-type homozygotes, OR (95%CI) were 1.62 (1.19-2.10) and 1.69 (1.23-2.18), respectively. We did not find any relation of rs651007 and rs529565 with IS risk in both additive and dominant models. GMDR model indicated a significant two-locus model (P = .0010) involving rs505922 and rs579459, indicating a potential interaction between rs505922 and rs579459, the cross-validation consistency of the two-locus models was 9/10, and the testing accuracy was 60.72%. We also found that participants with rs505922- TC/CC and rs579459- TC/CC genotype have the highest IS risk, compared to participants with rs505922- TT and rs579459- TT genotype, OR (95%CI) was 2.94 (1.28-4.66). We found that rs579459 and rs505922 within ABO gene and their interaction were both associated with increased IS risk in Chinese population. © 2017 Wiley Periodicals, Inc.

  17. Linking Genes to Cardiovascular Diseases: Gene Action and Gene-Environment Interactions.

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    Pasipoularides, Ares

    2015-12-01

    A unique myocardial characteristic is its ability to grow/remodel in order to adapt; this is determined partly by genes and partly by the environment and the milieu intérieur. In the "post-genomic" era, a need is emerging to elucidate the physiologic functions of myocardial genes, as well as potential adaptive and maladaptive modulations induced by environmental/epigenetic factors. Genome sequencing and analysis advances have become exponential lately, with escalation of our knowledge concerning sometimes controversial genetic underpinnings of cardiovascular diseases. Current technologies can identify candidate genes variously involved in diverse normal/abnormal morphomechanical phenotypes, and offer insights into multiple genetic factors implicated in complex cardiovascular syndromes. The expression profiles of thousands of genes are regularly ascertained under diverse conditions. Global analyses of gene expression levels are useful for cataloging genes and correlated phenotypes, and for elucidating the role of genes in maladies. Comparative expression of gene networks coupled to complex disorders can contribute insights as to how "modifier genes" influence the expressed phenotypes. Increasingly, a more comprehensive and detailed systematic understanding of genetic abnormalities underlying, for example, various genetic cardiomyopathies is emerging. Implementing genomic findings in cardiology practice may well lead directly to better diagnosing and therapeutics. There is currently evolving a strong appreciation for the value of studying gene anomalies, and doing so in a non-disjointed, cohesive manner. However, it is challenging for many-practitioners and investigators-to comprehend, interpret, and utilize the clinically increasingly accessible and affordable cardiovascular genomics studies. This survey addresses the need for fundamental understanding in this vital area.

  18. The interactions of genes, age, and environment in glaucoma pathogenesis.

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    Doucette, Lance P; Rasnitsyn, Alexandra; Seifi, Morteza; Walter, Michael A

    2015-01-01

    Glaucoma, a progressive degenerative condition that results in the death of retinal ganglion cells, is one of the leading causes of blindness, affecting millions worldwide. The mechanisms underlying glaucoma are not well understood, although years of studies have shown that the largest risk factors are elevated intraocular pressure, age, and genetics. Eleven genes and multiple loci have been identified as contributing factors. These genes act by a number of mechanisms, including mechanical stress, ischemic/oxidative stress, and neurodegeneration. We summarize the recent advances in the understanding of glaucoma and propose a unified hypothesis for glaucoma pathogenesis. Glaucoma does not result from a single pathological mechanism, but rather a combination of pathways that are influenced by genes, age, and environment. In particular, we hypothesize that, in the presence of genetic risk factors, exposure to environment stresses results in an earlier age of onset for glaucoma. This hypothesis is based upon the overlap of the molecular pathways in which glaucoma genes are involved. Because of the interactions between these processes, it is likely that there are common therapies that may be effective for different subtypes of glaucoma.

  19. Chemical-gene interaction networks and causal reasoning for ...

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    Evaluating the potential human health and ecological risks associated with exposures to complex chemical mixtures in the environment is one of the main challenges of chemical safety assessment and environmental protection. There is a need for approaches that can help to integrate chemical monitoring and biological effects data to evaluate risks associated with chemicals present in the environment. Here, we used prior knowledge about chemical-gene interactions to develop a knowledge assembly model for detected chemicals at five locations near the North Branch and Chisago wastewater treatment plants (WWTP) in the St. Croix River Basin, MN and WI. The assembly model was used to generate hypotheses about the biological impacts of the chemicals at each location. The hypotheses were tested using empirical hepatic gene expression data from fathead minnows exposed for 12 d at each location. Empirical gene expression data were also mapped to the assembly models to evaluate the likelihood of a chemical contributing to the observed biological responses using richness and concordance statistics. The prior knowledge approach was able predict the observed biological pathways impacted at one site but not the other. Atrazine was identified as a potential contributor to the observed gene expression responses at a location upstream of the North Branch WTTP. Four chemicals were identified as contributors to the observed biological responses at the effluent and downstream o

  20. Spatially Uniform ReliefF (SURF for computationally-efficient filtering of gene-gene interactions

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    Greene Casey S

    2009-09-01

    Full Text Available Abstract Background Genome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single points of failure but instead likely arise from the joint failure of two or more interacting components. The hope in genome-wide screens is that these points of failure can be linked to single nucleotide polymorphisms (SNPs which confer disease susceptibility. Detecting interacting variants that lead to disease in the absence of single-gene effects is difficult however, and methods to exhaustively analyze sets of these variants for interactions are combinatorial in nature thus making them computationally infeasible. Efficient algorithms which can detect interacting SNPs are needed. ReliefF is one such promising algorithm, although it has low success rate for noisy datasets when the interaction effect is small. ReliefF has been paired with an iterative approach, Tuned ReliefF (TuRF, which improves the estimation of weights in noisy data but does not fundamentally change the underlying ReliefF algorithm. To improve the sensitivity of studies using these methods to detect small effects we introduce Spatially Uniform ReliefF (SURF. Results SURF's ability to detect interactions in this domain is significantly greater than that of ReliefF. Similarly SURF, in combination with the TuRF strategy significantly outperforms TuRF alone for SNP selection under an epistasis model. It is important to note that this success rate increase does not require an increase in algorithmic complexity and allows for increased success rate, even with the removal of a nuisance parameter from the algorithm. Conclusion Researchers performing genetic association studies and aiming to discover gene-gene interactions associated with increased disease susceptibility should use SURF in place of ReliefF. For instance, SURF should be

  1. Genotype-based association models of complex diseases to detect gene-gene and gene-environment interactions.

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    Lobach, Iryna; Fan, Ruzong; Manga, Prashiela

    A central problem in genetic epidemiology is to identify and rank genetic markers involved in a disease. Complex diseases, such as cancer, hypertension, diabetes, are thought to be caused by an interaction of a panel of genetic factors, that can be identified by markers, which modulate environmental factors. Moreover, the effect of each genetic marker may be small. Hence, the association signal may be missed unless a large sample is considered, or a priori biomedical data are used. Recent advances generated a vast variety of a priori information, including linkage maps and information about gene regulatory dependence assembled into curated pathway databases. We propose a genotype-based approach that takes into account linkage disequilibrium (LD) information between genetic markers that are in moderate LD while modeling gene-gene and gene-environment interactions. A major advantage of our method is that the observed genetic information enters a model directly thus eliminating the need to estimate haplotype-phase. Our approach results in an algorithm that is inexpensive computationally and does not suffer from bias induced by haplotype-phase ambiguity. We investigated our model in a series of simulation experiments and demonstrated that the proposed approach results in estimates that are nearly unbiased and have small variability. We applied our method to the analysis of data from a melanoma case-control study and investigated interaction between a set of pigmentation genes and environmental factors defined by age and gender. Furthermore, an application of our method is demonstrated using a study of Alcohol Dependence.

  2. Gene-Diet Interaction and Precision Nutrition in Obesity

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    Yoriko Heianza

    2017-04-01

    Full Text Available The rapid rise of obesity during the past decades has coincided with a profound shift of our living environment, including unhealthy dietary patterns, a sedentary lifestyle, and physical inactivity. Genetic predisposition to obesity may have interacted with such an obesogenic environment in determining the obesity epidemic. Growing studies have found that changes in adiposity and metabolic response to low-calorie weight loss diets might be modified by genetic variants related to obesity, metabolic status and preference to nutrients. This review summarized data from recent studies of gene-diet interactions, and discussed integration of research of metabolomics and gut microbiome, as well as potential application of the findings in precision nutrition.

  3. Exploring Plant Co-Expression and Gene-Gene Interactions with CORNET 3.0.

    Science.gov (United States)

    Van Bel, Michiel; Coppens, Frederik

    2017-01-01

    Selecting and filtering a reference expression and interaction dataset when studying specific pathways and regulatory interactions can be a very time-consuming and error-prone task. In order to reduce the duplicated efforts required to amass such datasets, we have created the CORNET (CORrelation NETworks) platform which allows for easy access to a wide variety of data types: coexpression data, protein-protein interactions, regulatory interactions, and functional annotations. The CORNET platform outputs its results in either text format or through the Cytoscape framework, which is automatically launched by the CORNET website.CORNET 3.0 is the third iteration of the web platform designed for the user exploration of the coexpression space of plant genomes, with a focus on the model species Arabidopsis thaliana. Here we describe the platform: the tools, data, and best practices when using the platform. We indicate how the platform can be used to infer networks from a set of input genes, such as upregulated genes from an expression experiment. By exploring the network, new target and regulator genes can be discovered, allowing for follow-up experiments and more in-depth study. We also indicate how to avoid common pitfalls when evaluating the networks and how to avoid over interpretation of the results.All CORNET versions are available at http://bioinformatics.psb.ugent.be/cornet/ .

  4. Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions.

    Science.gov (United States)

    Kuppuswamy, Usha; Ananthasubramanian, Seshan; Wang, Yanli; Balakrishnan, Narayanaswamy; Ganapathiraju, Madhavi K

    2014-04-03

    The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown. We carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of ~58% and ~ 40% for localization and functions respectively of proteins were determined at a threshold of ~30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k-nearest neighbor classifier confirmed that our results compared favorably. This approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in

  5. Double-bottom chaotic map particle swarm optimization based on chi-square test to determine gene-gene interactions.

    Science.gov (United States)

    Yang, Cheng-Hong; Lin, Yu-Da; Chuang, Li-Yeh; Chang, Hsueh-Wei

    2014-01-01

    Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer.

  6. Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions

    Directory of Open Access Journals (Sweden)

    Cheng-Hong Yang

    2014-01-01

    Full Text Available Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05. Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer.

  7. Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

    Science.gov (United States)

    Hur, Junguk; Özgür, Arzucan; He, Yongqun

    2017-03-14

    Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of

  8. Sleep Duration and Depressive Symptoms: A Gene-Environment Interaction

    Science.gov (United States)

    Watson, Nathaniel F.; Harden, Kathryn Paige; Buchwald, Dedra; Vitiello, Michael V.; Pack, Allan I.; Strachan, Eric; Goldberg, Jack

    2014-01-01

    Objective: We used quantitative genetic models to assess whether sleep duration modifies genetic and environmental influences on depressive symptoms. Method: Participants were 1,788 adult twins from 894 same-sex twin pairs (192 male and 412 female monozygotic [MZ] pairs, and 81 male and 209 female dizygotic [DZ] pairs] from the University of Washington Twin Registry. Participants self-reported habitual sleep duration and depressive symptoms. Data were analyzed using quantitative genetic interaction models, which allowed the magnitude of additive genetic, shared environmental, and non-shared environmental influences on depressive symptoms to vary with sleep duration. Results: Within MZ twin pairs, the twin who reported longer sleep duration reported fewer depressive symptoms (ec = -0.17, SE = 0.06, P sleep duration interaction effect on depressive symptoms (a'c = 0.23, SE = 0.08, P sleep duration and depressive symptoms. Among individuals with sleep duration within the normal range (7-8.9 h/night), the total heritability (h2) of depressive symptoms was approximately 27%. However, among individuals with sleep duration within the low (sleep duration extremes (5 h/night: h2 = 53%; 10 h/night: h2 = 49%). Conclusion: Genetic contributions to depressive symptoms increase at both short and long sleep durations. Citation: Watson NF; Harden KP; Buchwald D; Vitiello MV; Pack AI; Stachan E; Goldberg J. Sleep duration and depressive symptoms: a gene-environment interaction. SLEEP 2014;37(2):351-358. PMID:24497663

  9. Genome-wide search for gene-gene interactions in colorectal cancer.

    Directory of Open Access Journals (Sweden)

    Shuo Jiao

    Full Text Available Genome-wide association studies (GWAS have successfully identified a number of single-nucleotide polymorphisms (SNPs associated with colorectal cancer (CRC risk. However, these susceptibility loci known today explain only a small fraction of the genetic risk. Gene-gene interaction (GxG is considered to be one source of the missing heritability. To address this, we performed a genome-wide search for pair-wise GxG associated with CRC risk using 8,380 cases and 10,558 controls in the discovery phase and 2,527 cases and 2,658 controls in the replication phase. We developed a simple, but powerful method for testing interaction, which we term the Average Risk Due to Interaction (ARDI. With this method, we conducted a genome-wide search to identify SNPs showing evidence for GxG with previously identified CRC susceptibility loci from 14 independent regions. We also conducted a genome-wide search for GxG using the marginal association screening and examining interaction among SNPs that pass the screening threshold (p<10(-4. For the known locus rs10795668 (10p14, we found an interacting SNP rs367615 (5q21 with replication p = 0.01 and combined p = 4.19×10(-8. Among the top marginal SNPs after LD pruning (n = 163, we identified an interaction between rs1571218 (20p12.3 and rs10879357 (12q21.1 (nominal combined p = 2.51×10(-6; Bonferroni adjusted p = 0.03. Our study represents the first comprehensive search for GxG in CRC, and our results may provide new insight into the genetic etiology of CRC.

  10. A novel fuzzy set based multifactor dimensionality reduction method for detecting gene-gene interaction.

    Science.gov (United States)

    Jung, Hye-Young; Leem, Sangseob; Lee, Sungyoung; Park, Taesung

    2016-12-01

    Gene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification. We regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure. Two simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD. We propose a novel Fuzzy MDR method to detect gene-gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. NEXT-GENERATION ANALYSIS OF CATARACTS: DETERMINING KNOWLEDGE DRIVEN GENE-GENE INTERACTIONS USING BIOFILTER, AND GENE-ENVIRONMENT INTERACTIONS USING THE PHENX TOOLKIT*

    Science.gov (United States)

    Pendergrass, Sarah A.; Verma, Shefali S.; Holzinger, Emily R.; Moore, Carrie B.; Wallace, John; Dudek, Scott M.; Huggins, Wayne; Kitchner, Terrie; Waudby, Carol; Berg, Richard; McCarty, Catherine A.; Ritchie, Marylyn D.

    2013-01-01

    Investigating the association between biobank derived genomic data and the information of linked electronic health records (EHRs) is an emerging area of research for dissecting the architecture of complex human traits, where cases and controls for study are defined through the use of electronic phenotyping algorithms deployed in large EHR systems. For our study, 2580 cataract cases and 1367 controls were identified within the Marshfield Personalized Medicine Research Project (PMRP) Biobank and linked EHR, which is a member of the NHGRI-funded electronic Medical Records and Genomics (eMERGE) Network. Our goal was to explore potential gene-gene and gene-environment interactions within these data for 529,431 single nucleotide polymorphisms (SNPs) with minor allele frequency > 1%, in order to explore higher level associations with cataract risk beyond investigations of single SNP-phenotype associations. To build our SNP-SNP interaction models we utilized a prior-knowledge driven filtering method called Biofilter to minimize the multiple testing burden of exploring the vast array of interaction models possible from our extensive number of SNPs. Using the Biofilter, we developed 57,376 prior-knowledge directed SNP-SNP models to test for association with cataract status. We selected models that required 6 sources of external domain knowledge. We identified 5 statistically significant models with an interaction term with p-value < 0.05, as well as an overall model with p-value < 0.05 associated with cataract status. We also conducted gene-environment interaction analyses for all GWAS SNPs and a set of environmental factors from the PhenX Toolkit: smoking, UV exposure, and alcohol use; these environmental factors have been previously associated with the formation of cataracts. We found a total of 288 models that exhibit an interaction term with a p-value ≤ 1×10−4 associated with cataract status. Our results show these approaches enable advanced searches for epistasis

  12. Association of HS6ST3 gene polymorphisms with obesity and triglycerides: gene × gender interaction

    Indian Academy of Sciences (India)

    Ke-Sheng Wang; Liang Wang; Xuefeng Liu; Min Zeng

    2013-12-01

    The heparan sulfate 6-O-sulfotransferase 3 (HS6ST3) gene is involved in heparan sulphate and heparin metabolism, and has been reported to be associated with diabetic retinopathy in type 2 diabetes. We hypothesized that HS6ST3 gene polymorphisms might play an important role in obesity and related phenotypes (such as triglycerides). We examined genetic associations of 117 single-nucleotide polymorphisms (SNPs) within the HS6ST3 gene with obesity and triglycerides using two Caucasian samples: the Marshfield sample (1442 obesity cases and 2122 controls), and the Health aging and body composition (Health ABC) sample (305 cases and 1336 controls). Logistic regression analysis of obesity as a binary trait and linear regression analysis of triglycerides as a continuous trait, adjusted for age and sex, were performed using PLINK. Single marker analysis showed that six SNPs in the Marshfield sample and one SNP in the Health ABC sample were associated with obesity $(P \\lt 0.05)$. SNP rs535812 revealed a stronger association with obesity in meta-analysis of these two samples $(P = 0.0105)$. The T–A haplotype from rs878950 and rs9525149 revealed significant association with obesity in the Marshfield sample $(P = 0.012)$. Moreover, nine SNPs showed associations with triglycerides in the Marshfield sample $(P \\lt 0.05)$ and the best signal was rs1927796 $(P = 0.00858)$. In addition, rs7331762 showed a strong gene × gender interaction $(P = 0.00956)$ for obesity while rs1927796 showed a strong gene × gender interaction $(P = 0.000625)$ for triglycerides in the Marshfield sample. These findings contribute new insights into the pathogenesis of obesity and triglycerides and demonstrate the importance of gender differences in the aetiology.

  13. Gene-environment interactions and alcohol use and dependence: current status and future challenges

    NARCIS (Netherlands)

    Zwaluw, C.S. van der; Engels, R.C.M.E.

    2009-01-01

    To discuss the current status of gene-environment interaction research with regard to alcohol use and dependence. Further, we highlight the difficulties concerning gene-environment studies. Overview of the current evidence for gene-environment interactions in alcohol outcomes, and of the associated

  14. Gene-gene interaction and RNA splicing profiles of MAP2K4 gene in rheumatoid arthritis.

    Science.gov (United States)

    Shchetynsky, Klementy; Protsyuk, Darya; Ronninger, Marcus; Diaz-Gallo, Lina-Marcela; Klareskog, Lars; Padyukov, Leonid

    2015-05-01

    We performed gene-gene interaction analysis, with HLA-DRB1 shared epitope (SE) alleles for 195 SNPs within immunologically important MAP2K, MAP3K and MAP4K gene families, in 2010 rheumatoid arthritis (RA) patients and 2280 healthy controls. We found a significant statistical interaction for rs10468473 with SE alleles in autoantibody-positive RA. Individuals heterozygous for rs10468473 demonstrated higher expression of total MAP2K4 mRNA in blood, compared to A-allele homozygous. We discovered a novel, putatively translated, "cassette exon" RNA splice form of MAP2K4, differentially expressed in peripheral blood mononuclear cells from 88 RA cases and controls. Within the group of RA patients, we observed a correlation of MAP2K4 isoform expression with carried SE alleles, autoantibody, and rheumatoid factor profiles. TNF-dependent modulation of isoform expression pattern was detected in the Jurkat cell line. Our data suggest a genetic interaction between MAP2K4 and HLA-DRB1, and the importance of rs10468473 and MAP2K4 splice variants in the development of autoantibody-positive RA.

  15. Subtle gene-environment interactions driving paranoia in daily life.

    Science.gov (United States)

    Simons, C J P; Wichers, M; Derom, C; Thiery, E; Myin-Germeys, I; Krabbendam, L; van Os, J

    2009-02-01

    It has been suggested that genes impact on the degree to which minor daily stressors cause variation in the intensity of subtle paranoid experiences. The objective of the present study was to test the hypothesis that catechol-O-methyltransferase (COMT) Val(158)Met and brain-derived neurotrophic factor (BDNF) Val(66)Met in part mediate genetic effects on paranoid reactivity to minor stressors. In a general population sample of 579 young adult female twins, on the one hand, appraisals of (1) event-related stress and (2) social stress and, on the other hand, feelings of paranoia in the flow of daily life were assessed using momentary assessment technology for five consecutive days. Multilevel regression analyses were used to examine moderation of daily life stress-induced paranoia by COMT Val(158)Met and BDNF Val(66)Met genotypes. Catechol-O-methyltransferase Val carriers displayed more feelings of paranoia in response to event stress compared with Met carriers. Brain-derived neurotrophic factor Met carriers showed more social-stress-induced paranoia than individuals with the Val/Val genotype. Thus, paranoia in the flow of daily life may be the result of gene-environment interactions that can be traced to different types of stress being moderated by different types of genetic variation.

  16. Fatty acid-gene interactions, adipokines and obesity.

    Science.gov (United States)

    Stryjecki, C; Mutch, D M

    2011-03-01

    It is now recognized that the low-grade inflammation observed with obesity is associated with the development of a wide range of downstream complications. As such, there is considerable interest in elucidating the regulatory mechanisms underlying the production of inflammatory molecules to improve the prevention and treatment of obesity and its co-morbidities. White adipose tissue is no longer considered a passive reservoir for storing lipids, but rather an important organ influencing energy metabolism, insulin sensitivity and inflammation by the secretion of proteins, commonly referred to as adipokines. Dysregulation of several adipokines, such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and adiponectin, contributes to the low-grade inflammation that is a hallmark of obesity. Evidence now suggests that fatty acids represent a class of molecules that can modulate adipokine production, thereby influencing inflammatory status. Although the precise molecular mechanisms by which dietary fats regulate adipokine production remain unclear, recent findings indicate that diet-gene interactions may have an important role in the transcriptional and secretory regulation of adipokines. Single-nucleotide polymorphisms in the genes encoding TNF-α, IL-6 and adiponectin can modify circulating levels of these adipokines and, subsequently, obesity-related phenotypes. This genetic variation can also alter the influence of dietary fatty acids on adipokine production. Therefore, the current review will show that it is paramount to consider both genetic information and dietary fat intake to unravel the inter-individual variability in inflammatory response observed in intervention protocols targeting obesity.

  17. Leveraging gene-environment interactions and endotypes for asthma gene discovery.

    Science.gov (United States)

    Bønnelykke, Klaus; Ober, Carole

    2016-03-01

    Asthma is a heterogeneous clinical syndrome that includes subtypes of disease with different underlying causes and disease mechanisms. Asthma is caused by a complex interaction between genes and environmental exposures; early-life exposures in particular play an important role. Asthma is also heritable, and a number of susceptibility variants have been discovered in genome-wide association studies, although the known risk alleles explain only a small proportion of the heritability. In this review, we present evidence supporting the hypothesis that focusing on more specific asthma phenotypes, such as childhood asthma with severe exacerbations, and on relevant exposures that are involved in gene-environment interactions (GEIs), such as rhinovirus infections, will improve detection of asthma genes and our understanding of the underlying mechanisms. We will discuss the challenges of considering GEIs and the advantages of studying responses to asthma-associated exposures in clinical birth cohorts, as well as in cell models of GEIs, to dissect the context-specific nature of genotypic risks, to prioritize variants in genome-wide association studies, and to identify pathways involved in pathogenesis in subgroups of patients. We propose that such approaches, in spite of their many challenges, present great opportunities for better understanding of asthma pathogenesis and heterogeneity and, ultimately, for improving prevention and treatment of disease.

  18. Gene-Environment Interactions in Stress Response Contribute Additively to a Genotype-Environment Interaction.

    Directory of Open Access Journals (Sweden)

    Takeshi Matsui

    2016-07-01

    Full Text Available How combinations of gene-environment interactions collectively give rise to genotype-environment interactions is not fully understood. To shed light on this problem, we genetically dissected an environment-specific poor growth phenotype in a cross of two budding yeast strains. This phenotype is detectable when certain segregants are grown on ethanol at 37°C ('E37', a condition that differs from the standard culturing environment in both its carbon source (ethanol as opposed to glucose and temperature (37°C as opposed to 30°C. Using recurrent backcrossing with phenotypic selection, we identified 16 contributing loci. To examine how these loci interact with each other and the environment, we focused on a subset of four loci that together can lead to poor growth in E37. We measured the growth of all 16 haploid combinations of alleles at these loci in all four possible combinations of carbon source (ethanol or glucose and temperature (30 or 37°C in a nearly isogenic population. This revealed that the four loci act in an almost entirely additive manner in E37. However, we also found that these loci have weaker effects when only carbon source or temperature is altered, suggesting that their effect magnitudes depend on the severity of environmental perturbation. Consistent with such a possibility, cloning of three causal genes identified factors that have unrelated functions in stress response. Thus, our results indicate that polymorphisms in stress response can show effects that are intensified by environmental stress, thereby resulting in major genotype-environment interactions when multiple of these variants co-occur.

  19. Protein-Protein Interaction Network and Gene Ontology

    Science.gov (United States)

    Choi, Yunkyu; Kim, Seok; Yi, Gwan-Su; Park, Jinah

    Evolution of computer technologies makes it possible to access a large amount and various kinds of biological data via internet such as DNA sequences, proteomics data and information discovered about them. It is expected that the combination of various data could help researchers find further knowledge about them. Roles of a visualization system are to invoke human abilities to integrate information and to recognize certain patterns in the data. Thus, when the various kinds of data are examined and analyzed manually, an effective visualization system is an essential part. One instance of these integrated visualizations can be combination of protein-protein interaction (PPI) data and Gene Ontology (GO) which could help enhance the analysis of PPI network. We introduce a simple but comprehensive visualization system that integrates GO and PPI data where GO and PPI graphs are visualized side-by-side and supports quick reference functions between them. Furthermore, the proposed system provides several interactive visualization methods for efficiently analyzing the PPI network and GO directedacyclic- graph such as context-based browsing and common ancestors finding.

  20. Study of oral clefts: Indication of gene-environment interaction

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, S.J.; Beaty, T.H.; Panny, S. [Johns Hopkins Univ., Baltimore, MD (United States)] [and others

    1994-09-01

    In this study of infants with isolated birth defects, 69 cleft palate-only (CPO) cases, 114 cleft lip with or without palate (CL/P), and 284 controls with non-cleft birth defects (all born in Maryland during 1984-1992) were examined to test for associations among genetic markers and different oral clefts. Modest associations were found between transforming growth factor {alpha} (TGF{alpha}) marker and CPO, as well as that between D17S579 (Mfd188) and CL/P in this study. The association between TGF{alpha} marker and CPO reflects a statistical interaction between mother`s smoking and child`s TGF{alpha} genotype. A significantly higher risk of CPO was found among those reporting maternal smoking during pregnancy and carrying less common TGF{alpha} TaqI allele (odds ratio=7.02 with 95% confidence interval 1.8-27.6). This gene-environment interaction was also found among those who reported no family history of any type of birth defect (odds ratio=5.60 with 95% confidence interval 1.4-22.9). Similar associations were seen for CL/P, but these were not statistically significant.

  1. Gene-environment Interactions in the Etiology of Dental Caries.

    Science.gov (United States)

    Yildiz, G; Ermis, R B; Calapoglu, N S; Celik, E U; Türel, G Y

    2016-01-01

    Dental caries is a multifactorial disease that can be conceptualized as an interaction between genetic and environmental risk factors. The aim of this study is to examine the effects of AMELX, CA6, DEFB1, and TAS2R38 gene polymorphism and gene-environment interactions on caries etiology and susceptibility in adults. Genomic DNA was extracted from the buccal mucosa, and adults aged 20 to 60 y were placed into 1 of 2 groups: low caries risk (DMFT ≤ 5; n = 77) and high caries risk (DMFT ≥ 14; n = 77). The frequency of AMELX (+522), CA6 (T55M), DEFB1 (G-20A), and TAS2R38 (A49P) single-nucleotide polymorphisms was genotyped with the polymerase chain reaction-restriction fragment length polymorphism method. Environmental risk factors examined in the study included plaque amount, toothbrushing frequency, dietary intake between meals, saliva secretion rate, saliva buffer capacity, mutans streptococci counts, and lactobacilli counts. There was no difference between the caries risk groups in relation to AMELX (+522) polymorphism (χ(2) test, P > 0.05). The distribution of CA6 genotype and allele frequencies in the low caries risk group did not differ from the high caries risk group (χ(2) test, P > 0.05). Polymorphism of DEFB1 (G-20A) was positively associated, and TAS2R38 (A49P) negatively associated, with caries risk (χ(2) test, P = 0.000). There were significant differences between caries susceptibility and each environmental risk factor, except for the saliva secretion rate (Mann-Whitney U test, P = 0.000). Based on stepwise multiple linear regression analyses, dental plaque amount, lactobacilli count, age, and saliva buffer capacity, as well as DEFB1 (G-20A), TAS2R38 (A49P), and CA6 (T55M) gene polymorphism, explained a total of 87.8% of the variations in DMFT scores. It can be concluded that variation in CA6 (T55M), DEFB1 (G-20A), and TAS2R38 (A49P) may be associated with caries experience in Turkish adults with a high level of dental plaque, lactobacilli count

  2. Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network

    Directory of Open Access Journals (Sweden)

    Chen Xin

    2012-10-01

    Full Text Available Abstract Background The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN. Methods In this study, we proposed a method to identify CRGs based on Gene Ontology (GO and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs’ GO and network characteristics. Lastly, we evaluated the performance of the proposed method. Results We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC for our method is 65.2%, whereas that for the traditional method is 55.2%. Conclusions Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable

  3. The gene-gene interaction of INSIG-SCAP-SREBP pathway on the risk of obesity in Chinese children.

    Science.gov (United States)

    Liu, Fang-Hong; Song, Jie-Yun; Shang, Xiao-Rui; Meng, Xiang-Rui; Ma, Jun; Wang, Hai-Jun

    2014-01-01

    Childhood obesity has become a global public health problem in recent years. This study aimed to explore the association of genetic variants in INSIG-SCAP-SREBP pathway with obesity in Chinese children. A case-control study was conducted, including 705 obese cases and 1,325 nonobese controls. We genotyped 15 single nucleotide polymorphisms (SNPs) of five genes in INSIG-SCAP-SREBP pathway, including insulin induced gene 1 (INSIG1), insulin induced gene 2 (INSIG2), SREBP cleavage-activating protein gene (SCAP), sterol regulatory element binding protein gene 1 (SREBP1), and sterol regulatory element binding protein gene 2 (SREBP2). We used generalized multifactor dimensionality reduction (GMDR) and logistic regression to investigate gene-gene interactions. Single polymorphism analyses showed that SCAP rs12487736 and rs12490383 were nominally associated with obesity. We identified a 3-locus interaction on obesity in GMDR analyses (P = 0.001), involving 3 genetic variants of INSIG2, SCAP, and SREBP2. The individuals in high-risk group of the 3-locus combinations had a 79.9% increased risk of obesity compared with those in low-risk group (OR = 1.799, 95% CI: 1.475-2.193, P = 6.61 × 10(-9)). We identified interaction of three genes in INSIG-SCAP-SREBP pathway on risk of obesity, revealing that these genes affect obesity more likely through a complex interaction pattern than single gene effect.

  4. Identifying the Interaction between Genes and Gene Products Based on Frequently Seen Verbs in Medline Abstracts.

    Science.gov (United States)

    Sekimizu; Park; Tsujii

    1998-01-01

    We have selected the most frequently seen verbs from raw texts made up of 1-million-words of Medline abstracts, and we were able to identify (or bracket) noun phrases contained in the corpus, with a precision rate of 90%. Then, based on the noun-phrase-bracketted corpus, we tried to find the subject and object terms for some frequently seen verbs in the domain. The precision rate of finding the right subject and object for each verb was about 73%. This task was only made possible because we were able to linguistically analyze (or parse) a large quantity of a raw corpus. Our approach will be useful for classifying genes and gene products and for identifying the interaction between them. It is the first step of our effort in building a genome-related thesaurus and hierarchies in a fully automatic way.

  5. Topology association analysis in weighted protein interaction network for gene prioritization

    Science.gov (United States)

    Wu, Shunyao; Shao, Fengjing; Zhang, Qi; Ji, Jun; Xu, Shaojie; Sun, Rencheng; Sun, Gengxin; Du, Xiangjun; Sui, Yi

    2016-11-01

    Although lots of algorithms for disease gene prediction have been proposed, the weights of edges are rarely taken into account. In this paper, the strengths of topology associations between disease and essential genes are analyzed in weighted protein interaction network. Empirical analysis demonstrates that compared to other genes, disease genes are weakly connected with essential genes in protein interaction network. Based on this finding, a novel global distance measurement for gene prioritization with weighted protein interaction network is proposed in this paper. Positive and negative flow is allocated to disease and essential genes, respectively. Additionally network propagation model is extended for weighted network. Experimental results on 110 diseases verify the effectiveness and potential of the proposed measurement. Moreover, weak links play more important role than strong links for gene prioritization, which is meaningful to deeply understand protein interaction network.

  6. Identification of genes involved in radioresistance of nasopharyngeal carcinoma by integrating gene ontology and protein-protein interaction networks.

    Science.gov (United States)

    Guo, Ya; Zhu, Xiao-Dong; Qu, Song; Li, Ling; Su, Fang; Li, Ye; Huang, Shi-Ting; Li, Dan-Rong

    2012-01-01

    Radioresistance remains one of the important factors in relapse and metastasis of nasopharyngeal carcinoma. Thus, it is imperative to identify genes involved in radioresistance and explore the underlying biological processes in the development of radioresistance. In this study, we used cDNA microarrays to select differential genes between radioresistant CNE-2R and parental CNE-2 cell lines. One hundred and eighty-three significantly differentially expressed genes (pgenes were upregulated and 45 genes were downregulated in CNE-2R. We further employed publicly available bioinformatics related software, such as GOEAST and STRING to examine the relationship among differentially expressed genes. The results show that these genes were involved in type I interferon-mediated signaling pathway biological processes; the nodes tended to have high connectivity with the EGFR pathway, IFN-related pathways, NF-κB. The node STAT1 has high connectivity with other nodes in the protein-protein interaction (PPI) networks. Finally, the reliability of microarray data was validated for selected genes by semi-quantitative RT-PCR and Western blotting. The results were consistent with the microarray data. Our study suggests that microarrays combined with gene ontology and protein interaction networks have great value in the identification of genes of radioresistance in nasopharyngeal carcinoma; genes involved in several biological processes and protein interaction networks may be relevant to NPC radioresistance; in particular, the verified genes CCL5, STAT1-α, STAT2 and GSTP1 may become potential biomarkers for predicting NPC response to radiotherapy.

  7. Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining.

    Science.gov (United States)

    Hur, Junguk; Ozgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

    2012-12-20

    Fever is one of the most common adverse events of vaccines. The detailed mechanisms of fever and vaccine-associated gene interaction networks are not fully understood. In the present study, we employed a genome-wide, Centrality and Ontology-based Network Discovery using Literature data (CONDL) approach to analyse the genes and gene interaction networks associated with fever or vaccine-related fever responses. Over 170,000 fever-related articles from PubMed abstracts and titles were retrieved and analysed at the sentence level using natural language processing techniques to identify genes and vaccines (including 186 Vaccine Ontology terms) as well as their interactions. This resulted in a generic fever network consisting of 403 genes and 577 gene interactions. A vaccine-specific fever sub-network consisting of 29 genes and 28 gene interactions was extracted from articles that are related to both fever and vaccines. In addition, gene-vaccine interactions were identified. Vaccines (including 4 specific vaccine names) were found to directly interact with 26 genes. Gene set enrichment analysis was performed using the genes in the generated interaction networks. Moreover, the genes in these networks were prioritized using network centrality metrics. Making scientific discoveries and generating new hypotheses were possible by using network centrality and gene set enrichment analyses. For example, our study found that the genes in the generic fever network were more enriched in cell death and responses to wounding, and the vaccine sub-network had more gene enrichment in leukocyte activation and phosphorylation regulation. The most central genes in the vaccine-specific fever network are predicted to be highly relevant to vaccine-induced fever, whereas genes that are central only in the generic fever network are likely to be highly relevant to generic fever responses. Interestingly, no Toll-like receptors (TLRs) were found in the gene-vaccine interaction network. Since

  8. Music training and speech perception: a gene-environment interaction.

    Science.gov (United States)

    Schellenberg, E Glenn

    2015-03-01

    Claims of beneficial side effects of music training are made for many different abilities, including verbal and visuospatial abilities, executive functions, working memory, IQ, and speech perception in particular. Such claims assume that music training causes the associations even though children who take music lessons are likely to differ from other children in music aptitude, which is associated with many aspects of speech perception. Music training in childhood is also associated with cognitive, personality, and demographic variables, and it is well established that IQ and personality are determined largely by genetics. Recent evidence also indicates that the role of genetics in music aptitude and music achievement is much larger than previously thought. In short, music training is an ideal model for the study of gene-environment interactions but far less appropriate as a model for the study of plasticity. Children seek out environments, including those with music lessons, that are consistent with their predispositions; such environments exaggerate preexisting individual differences. © 2015 New York Academy of Sciences.

  9. Two-Way Gene Interaction From Microarray Data Based on Correlation Methods

    OpenAIRE

    Alavi Majd, Hamid; Talebi, Atefeh; Gilany, Kambiz; Khayyer, Nasibeh

    2016-01-01

    Background Gene networks have generated a massive explosion in the development of high-throughput techniques for monitoring various aspects of gene activity. Networks offer a natural way to model interactions between genes, and extracting gene network information from high-throughput genomic data is an important and difficult task. Objectives The purpose of this study is to construct a two-way gene network based on parametric and nonparametric correlation coefficients. The first step in const...

  10. A global test for gene‐gene interactions based on random matrix theory

    Science.gov (United States)

    Amos, Christopher I.; Moore, Jason H.

    2016-01-01

    ABSTRACT Statistical interactions between markers of genetic variation, or gene‐gene interactions, are believed to play an important role in the etiology of many multifactorial diseases and other complex phenotypes. Unfortunately, detecting gene‐gene interactions is extremely challenging due to the large number of potential interactions and ambiguity regarding marker coding and interaction scale. For many data sets, there is insufficient statistical power to evaluate all candidate gene‐gene interactions. In these cases, a global test for gene‐gene interactions may be the best option. Global tests have much greater power relative to multiple individual interaction tests and can be used on subsets of the markers as an initial filter prior to testing for specific interactions. In this paper, we describe a novel global test for gene‐gene interactions, the global epistasis test (GET), that is based on results from random matrix theory. As we show via simulation studies based on previously proposed models for common diseases including rheumatoid arthritis, type 2 diabetes, and breast cancer, our proposed GET method has superior performance characteristics relative to existing global gene‐gene interaction tests. A glaucoma GWAS data set is used to demonstrate the practical utility of the GET method. PMID:27386793

  11. Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry.

    Science.gov (United States)

    Kumar, Ravindra; Samal, Sabindra K; Routray, Samapika; Dash, Rupesh; Dixit, Anshuman

    2017-05-30

    In the recent years, bioinformatics methods have been reported with a high degree of success for candidate gene identification. In this milieu, we have used an integrated bioinformatics approach assimilating information from gene ontologies (GO), protein-protein interaction (PPI) and network analysis to predict candidate genes related to oral squamous cell carcinoma (OSCC). A total of 40973 PPIs were considered for 4704 cancer-related genes to construct human cancer gene network (HCGN). The importance of each node was measured in HCGN by ten different centrality measures. We have shown that the top ranking genes are related to a significantly higher number of diseases as compared to other genes in HCGN. A total of 39 candidate oral cancer target genes were predicted by combining top ranked genes and the genes corresponding to significantly enriched oral cancer related GO terms. Initial verification using literature and available experimental data indicated that 29 genes were related with OSCC. A detailed pathway analysis led us to propose a role for the selected candidate genes in the invasion and metastasis in OSCC. We further validated our predictions using immunohistochemistry (IHC) and found that the gene FLNA was upregulated while the genes ARRB1 and HTT were downregulated in the OSCC tissue samples.

  12. Gene-Gene-Environment Interactions of Serotonin Transporter, Monoamine Oxidase A and Childhood Maltreatment Predict Aggressive Behavior in Chinese Adolescents

    Science.gov (United States)

    Zhang, Yun; Ming, Qing-sen; Yi, Jin-yao; Wang, Xiang; Chai, Qiao-lian; Yao, Shu-qiao

    2017-01-01

    Gene-environment interactions that moderate aggressive behavior have been identified independently in the serotonin transporter (5-HTT) gene and monoamine oxidase A gene (MAOA). The aim of the present study was to investigate epistasis interactions between MAOA-variable number tandem repeat (VNTR), 5-HTTlinked polymorphism (LPR) and child abuse and the effects of these on aggressive tendencies in a group of otherwise healthy adolescents. A group of 546 Chinese male adolescents completed the Child Trauma Questionnaire and Youth self-report of the Child Behavior Checklist. Buccal cells were collected for DNA analysis. The effects of childhood abuse, MAOA-VNTR, 5-HTTLPR genotypes and their interactive gene-gene-environmental effects on aggressive behavior were analyzed using a linear regression model. The effect of child maltreatment was significant, and a three-way interaction among MAOA-VNTR, 5-HTTLPR and sexual abuse (SA) relating to aggressive behaviors was identified. Chinese male adolescents with high expression of the MAOA-VNTR allele and 5-HTTLPR “SS” genotype exhibited the highest aggression tendencies with an increase in SA during childhood. The findings reported support aggression being a complex behavior involving the synergistic effects of gene-gene-environment interactions. PMID:28203149

  13. Diet-gene interactions between dietary fat intake and common polymorphisms in determining lipid metabolism

    Energy Technology Data Exchange (ETDEWEB)

    Corella, D.

    2009-07-01

    Current dietary guidelines for fat intake have not taken into consideration the possible genetic differences underlying the individual variability in responsiveness to dietary components. Genetic variability has been identified in humans for all the known lipid metabolism-related genes resulting in a plethora of candidate genes and genetic variants to examine in diet-gene interaction studies focused on fat consumption. Some examples of fat-gene interaction are reviewed. These include: the interaction between total intake and the 14C/T in the hepatic lipase gene promoter in determining high-density lipoprotein cholesterol (HDL-C) metabolism; the interaction between polyunsaturated fatty acids (PUFA) and the 5G/A polymorphism in the APOA1 gene plasma HDL-C concentrations; the interaction between PUFA and the L162V polymorphism in the PPARA gene in determining triglycerides and APOC3 concentrations; and the interaction between PUFA intake and the -1131T>C in the APOA5 gene in determining triglyceride metabolism. Although hundreds of diet-gene interaction studies in lipid metabolism have been published, the level of evidence to make specific nutritional recommendations to the population is still low and more research in nutrigenetics has to be undertaken. (Author) 31 refs.

  14. Correlations between genetic variance and adiposity measures, and gene × gene interactions for obesity in postmenopausal Vietnamese women

    Indian Academy of Sciences (India)

    Tran Quang Binh; Yutaka Nakahori; Vu Thi Thu Hien; Nguyen Cong Khan; Nguyen Thi Lam; Le Bach Mai; Shigeru Yamamoto

    2011-04-01

    Although environmental factors are important, there is considerable evidence that genes also have a significant role in the pathogenesis of obesity. We conducted a population-based study to investigate the relationship between candidate genes for obesity (UCP1, UCP2, ADRA2B, ADRB3, LEPR, VDR and ESR1) and adiposity measures (body mass index, body fat percentage, weight, waist circumference and waist–hip ratio) in terms of individual gene and gene × gene interaction in models unadjusted and adjusted for covariates (age, years since menopause, educational level and total energy intake). Postmenopausal women with TC genotype of ESR1 gene had higher body fat percentage than those with TT genotype in the models unadjusted and adjusted for the covariates ($P = 0.006$ in adjusted model). In multiple logistic regression analysis, BsmI and ApaI SNPs of VDR genes were significantly associated with overweight and obesity. The UCP2–VDR ApaI interaction to susceptibility of overweight and obesity was first observed from logistic regression analysis, and then confirmed in the multifactor dimensionality reduction method unadjusted and adjusted for the covariates. This interaction had 69.09% prediction accuracy for overweight and obesity ($P = 0.001$, sign test). In conclusion, the study suggests the significant association of ESR1 and VDR genes with adiposity measures and the UCP2–VDR ApaI interaction to susceptibility to being overweight and obesity in postmenopausal Vietnamese women.

  15. Measured Gene-by-Environment Interaction in Relation to Attention-Deficit/Hyperactivity Disorder

    Science.gov (United States)

    Nigg, Joel; Nikolas, Molly; Burt, S. Alexandra

    2010-01-01

    Objective: To summarize and evaluate the state of knowledge regarding the role of measured gene-by-environment interactions in relation to attention-deficit/hyperactivity disorder. Method: A selective review of methodologic issues was followed by a systematic search for relevant articles on measured gene-by-environment interactions; the search…

  16. Gene regulatory network interactions in sea urchin endomesoderm induction.

    Directory of Open Access Journals (Sweden)

    Aditya J Sethi

    2009-02-01

    Full Text Available A major goal of contemporary studies of embryonic development is to understand large sets of regulatory changes that accompany the phenomenon of embryonic induction. The highly resolved sea urchin pregastrular endomesoderm-gene regulatory network (EM-GRN provides a unique framework to study the global regulatory interactions underlying endomesoderm induction. Vegetal micromeres of the sea urchin embryo constitute a classic endomesoderm signaling center, whose potential to induce archenteron formation from presumptive ectoderm was demonstrated almost a century ago. In this work, we ectopically activate the primary mesenchyme cell-GRN (PMC-GRN that operates in micromere progeny by misexpressing the micromere determinant Pmar1 and identify the responding EM-GRN that is induced in animal blastomeres. Using localized loss-of -function analyses in conjunction with expression of endo16, the molecular definition of micromere-dependent endomesoderm specification, we show that the TGFbeta cytokine, ActivinB, is an essential component of this induction in blastomeres that emit this signal, as well as in cells that respond to it. We report that normal pregastrular endomesoderm specification requires activation of the Pmar1-inducible subset of the EM-GRN by the same cytokine, strongly suggesting that early micromere-mediated endomesoderm specification, which regulates timely gastrulation in the sea urchin embryo, is also ActivinB dependent. This study unexpectedly uncovers the existence of an additional uncharacterized micromere signal to endomesoderm progenitors, significantly revising existing models. In one of the first network-level characterizations of an intercellular inductive phenomenon, we describe an important in vivo model of the requirement of ActivinB signaling in the earliest steps of embryonic endomesoderm progenitor specification.

  17. Gene-environment and gene-gene interactions of specific MTHFR, MTR and CBS gene variants in relation to homocysteine in black South Africans.

    Science.gov (United States)

    Nienaber-Rousseau, Cornelie; Ellis, Suria M; Moss, Sarah J; Melse-Boonstra, Alida; Towers, G Wayne

    2013-11-01

    The methylenetetrahydrofolate reductase (MTHFR), cystathione-β-synthase (CBS) and methionine synthase (MTR) genes interact with each other and the environment. These interactions could influence homocysteine (Hcy) and diseases contingent thereon. We determined single nucleotide polymorphisms (SNPs) within these genes, their relationships and interactions with total Hcy concentrations within black South Africans to address the increased prevalence of diseases associated with Hcy. The MTHFR 677 TT and MTR 2756 AA genotypes were associated with higher Hcy concentrations (16.6 and 10.1 μmol/L; pCBS genotypes did not influence Hcy. We demonstrated interactions between the area of residence and the CBS T833C/844ins68 genotypes (p=0.005) so that when harboring the wildtype allele, rural subjects had significantly higher Hcy than their urban counterparts, but when hosting the variant allele the environment made no difference to Hcy. Between the CBS T833C/844ins68 or G9276A and MTHFR C677T genotypes, there were two-way interactions (p=0.003 and=0.004, respectively), with regard to Hcy. Subjects harboring the MTHFR 677 TT genotype in combination with the CBS 833 TT/homozygous 844 non-insert or the MTHFR 677 TT genotype in combination with the CBS 9276 GA/GG displayed higher Hcy concentrations. Therefore, some of the investigated genotypes affected Hcy; residential area changed the way in which the CBS T833C/844ins68 SNPs influenced Hcy concentrations highlighting the importance of environmental factors; and gene-gene interactions allude to epistatic effects. © 2013 Elsevier B.V. All rights reserved.

  18. Locus heterogeneity disease genes encode proteins with high interconnectivity in the human protein interaction network.

    Science.gov (United States)

    Keith, Benjamin P; Robertson, David L; Hentges, Kathryn E

    2014-01-01

    Mutations in genes potentially lead to a number of genetic diseases with differing severity. These disease genes have been the focus of research in recent years showing that the disease gene population as a whole is not homogeneous, and can be categorized according to their interactions. Locus heterogeneity describes a single disorder caused by mutations in different genes each acting individually to cause the same disease. Using datasets of experimentally derived human disease genes and protein interactions, we created a protein interaction network to investigate the relationships between the products of genes associated with a disease displaying locus heterogeneity, and use network parameters to suggest properties that distinguish these disease genes from the overall disease gene population. Through the manual curation of known causative genes of 100 diseases displaying locus heterogeneity and 397 single-gene Mendelian disorders, we use network parameters to show that our locus heterogeneity network displays distinct properties from the global disease network and a Mendelian network. Using the global human proteome, through random simulation of the network we show that heterogeneous genes display significant interconnectivity. Further topological analysis of this network revealed clustering of locus heterogeneity genes that cause identical disorders, indicating that these disease genes are involved in similar biological processes. We then use this information to suggest additional genes that may contribute to diseases with locus heterogeneity.

  19. A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.

    Directory of Open Access Journals (Sweden)

    Jason Ernst

    2008-03-01

    Full Text Available While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer, a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic-anaerobic shift interface.

  20. Contributions of renin-angiotensin system-related gene interactions to obesity in a Chinese population.

    Directory of Open Access Journals (Sweden)

    Jian-Bo Zhou

    Full Text Available BACKGROUND: Gene-gene interactions may be partly responsible for complex traits such as obesity. Increasing evidence suggests that the renin-angiotensin system (RAS contributes to the etiology of obesity. How the epistasis of genes in the RAS contributes to obesity is still under research. We aim to evaluate the contribution of RAS-related gene interactions to a predisposition of obesity in a Chinese population. METHODOLOGY AND PRINCIPAL FINDINGS: We selected six single nucleotide polymorphisms (SNPs located in angiotensin (AGT, angiotensin converting enzyme (ACE, angiotensin type 1 receptor (AGTR1, MAS1, nitric oxide synthase 3 (NOS3 and the bradykinin B2 receptor gene (BDKRB2, and genotyped them in 324 unrelated individuals with obesity (BMI ≥ 28 kg/m(2 and 373 non-obese controls (BMI 18.5 to <24 kg/m(2 from a large scale population-based cohort. We analyzed gene-gene interactions among 6 polymorphic loci using the Generalized Multifactor Dimensionality Reduction (GMDR method, which has been shown to be effective for detecting gene-gene interactions in case-control studies with relatively small samples. Then we used logistic regression models to confirm the best combination of loci identified in the GMDR. It showed a significant gene-gene interaction between the rs220721 polymorphism in the MAS1 gene and the rs1799722 polymorphism in the gene BDKB2R. The best two-locus combination scored 9 for cross-validation consistency and 9 for sign test (p = 0.0107. This interaction showed the maximum consistency and minimum prediction error among all gene-gene interaction models evaluated. Moreover, the combination of the MAS1 rs220721 and the BDKRB2 rs1799722 was associated with a significantly increased risk of obesity (OR 1.82, CI 95%: 1.15-2.88, p = 0.0103. CONCLUSIONS AND SIGNIFICANCE: These results suggest that the SNPs from the RAS-related genes may contribute to the risk of obesity in an interactive manner in a Chinese population. The gene-gene

  1. Informative Gene Selection and Direct Classification of Tumor Based on Chi-Square Test of Pairwise Gene Interactions

    Directory of Open Access Journals (Sweden)

    Hongyan Zhang

    2014-01-01

    Full Text Available In efforts to discover disease mechanisms and improve clinical diagnosis of tumors, it is useful to mine profiles for informative genes with definite biological meanings and to build robust classifiers with high precision. In this study, we developed a new method for tumor-gene selection, the Chi-square test-based integrated rank gene and direct classifier (χ2-IRG-DC. First, we obtained the weighted integrated rank of gene importance from chi-square tests of single and pairwise gene interactions. Then, we sequentially introduced the ranked genes and removed redundant genes by using leave-one-out cross-validation of the chi-square test-based Direct Classifier (χ2-DC within the training set to obtain informative genes. Finally, we determined the accuracy of independent test data by utilizing the genes obtained above with χ2-DC. Furthermore, we analyzed the robustness of χ2-IRG-DC by comparing the generalization performance of different models, the efficiency of different feature-selection methods, and the accuracy of different classifiers. An independent test of ten multiclass tumor gene-expression datasets showed that χ2-IRG-DC could efficiently control overfitting and had higher generalization performance. The informative genes selected by χ2-IRG-DC could dramatically improve the independent test precision of other classifiers; meanwhile, the informative genes selected by other feature selection methods also had good performance in χ2-DC.

  2. Literature-Based Discovery of IFN-γ and Vaccine-Mediated Gene Interaction Networks

    Directory of Open Access Journals (Sweden)

    Arzucan Özgür

    2010-01-01

    Full Text Available Interferon-gamma (IFN-γ regulates various immune responses that are often critical for vaccine-induced protection. In order to annotate the IFN-γ-related gene interaction network from a large amount of IFN-γ research reported in the literature, a literature-based discovery approach was applied with a combination of natural language processing (NLP and network centrality analysis. The interaction network of human IFN-γ (Gene symbol: IFNG and its vaccine-specific subnetwork were automatically extracted using abstracts from all articles in PubMed. Four network centrality metrics were further calculated to rank the genes in the constructed networks. The resulting generic IFNG network contains 1060 genes and 26313 interactions among these genes. The vaccine-specific subnetwork contains 102 genes and 154 interactions. Fifty six genes such as TNF, NFKB1, IL2, IL6, and MAPK8 were ranked among the top 25 by at least one of the centrality methods in one or both networks. Gene enrichment analysis indicated that these genes were classified in various immune mechanisms such as response to extracellular stimulus, lymphocyte activation, and regulation of apoptosis. Literature evidence was manually curated for the IFN-γ relatedness of 56 genes and vaccine development relatedness for 52 genes. This study also generated many new hypotheses worth further experimental studies.

  3. Oh, Behave! Behavior as an Interaction between Genes & the Environment

    Science.gov (United States)

    Weigel, Emily G.; DeNieu, Michael; Gall, Andrew J.

    2014-01-01

    This lesson is designed to teach students that behavior is a trait shaped by both genes and the environment. Students will read a scientific paper, discuss and generate predictions based on the ideas and data therein, and model the relationships between genes, the environment, and behavior. The lesson is targeted to meet the educational goals of…

  4. Oh, Behave! Behavior as an Interaction between Genes & the Environment

    Science.gov (United States)

    Weigel, Emily G.; DeNieu, Michael; Gall, Andrew J.

    2014-01-01

    This lesson is designed to teach students that behavior is a trait shaped by both genes and the environment. Students will read a scientific paper, discuss and generate predictions based on the ideas and data therein, and model the relationships between genes, the environment, and behavior. The lesson is targeted to meet the educational goals of…

  5. Modular reorganization of the global network of gene regulatory interactions during perinatal human brain development

    OpenAIRE

    Monzón-Sandoval, Jimena; Castillo-Morales, Atahualpa; Urrutia, Araxi O.; Gutierrez, Humberto

    2016-01-01

    Background During early development of the nervous system, gene expression patterns are known to vary widely depending on the specific developmental trajectories of different structures. Observable changes in gene expression profiles throughout development are determined by an underlying network of precise regulatory interactions between individual genes. Elucidating the organizing principles that shape this gene regulatory network is one of the central goals of developmental biology. Whether...

  6. Gene x Environment Interactions in Reading Disability and Attention-Deficit/Hyperactivity Disorder

    Science.gov (United States)

    Pennington, Bruce F.; McGrath, Lauren M.; Rosenberg, Jenni; Barnard, Holly; Smith, Shelley D.; Willcutt, Erik G.; Friend, Angela; DeFries, John C.; Olson, Richard K.

    2009-01-01

    This article examines Gene x Environment (G x E) interactions in two comorbid developmental disorders--reading disability (RD) and attention-deficit/hyperactivity disorder (ADHD)--as a window on broader issues on G x E interactions in developmental psychology. The authors first briefly review types of G x E interactions, methods for detecting…

  7. Gene x Environment Interactions in Reading Disability and Attention-Deficit/Hyperactivity Disorder

    Science.gov (United States)

    Pennington, Bruce F.; McGrath, Lauren M.; Rosenberg, Jenni; Barnard, Holly; Smith, Shelley D.; Willcutt, Erik G.; Friend, Angela; DeFries, John C.; Olson, Richard K.

    2009-01-01

    This article examines Gene x Environment (G x E) interactions in two comorbid developmental disorders--reading disability (RD) and attention-deficit/hyperactivity disorder (ADHD)--as a window on broader issues on G x E interactions in developmental psychology. The authors first briefly review types of G x E interactions, methods for detecting…

  8. Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors.

    Science.gov (United States)

    Usset, Joseph L; Raghavan, Rama; Tyrer, Jonathan P; McGuire, Valerie; Sieh, Weiva; Webb, Penelope; Chang-Claude, Jenny; Rudolph, Anja; Anton-Culver, Hoda; Berchuck, Andrew; Brinton, Louise; Cunningham, Julie M; DeFazio, Anna; Doherty, Jennifer A; Edwards, Robert P; Gayther, Simon A; Gentry-Maharaj, Aleksandra; Goodman, Marc T; Høgdall, Estrid; Jensen, Allan; Johnatty, Sharon E; Kiemeney, Lambertus A; Kjaer, Susanne K; Larson, Melissa C; Lurie, Galina; Massuger, Leon; Menon, Usha; Modugno, Francesmary; Moysich, Kirsten B; Ness, Roberta B; Pike, Malcolm C; Ramus, Susan J; Rossing, Mary Anne; Rothstein, Joseph; Song, Honglin; Thompson, Pamela J; van den Berg, David J; Vierkant, Robert A; Wang-Gohrke, Shan; Wentzensen, Nicolas; Whittemore, Alice S; Wilkens, Lynne R; Wu, Anna H; Yang, Hannah; Pearce, Celeste Leigh; Schildkraut, Joellen M; Pharoah, Paul; Goode, Ellen L; Fridley, Brooke L

    2016-05-01

    Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene-environment interactions related to hormone-related risk factors could differ between obese and non-obese women. We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case-control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10(-6)) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10(-5)). The most notable obesity-gene-hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10(-6)). We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2 Future work is needed to develop powerful statistical methods able to detect these complex interactions. Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780-90. ©2016 AACR. ©2016 American Association for Cancer Research.

  9. Gene-gene and gene-sex epistatic interactions of DNMT1, DNMT3A and DNMT3B in autoimmune thyroid disease.

    Science.gov (United States)

    Cai, Tian-Tian; Zhang, Jian; Wang, Xuan; Song, Rong-Hua; Qin, Qiu; Muhali, Fatuma-Said; Zhou, Jiao-Zhen; Xu, Jian; Zhang, Jin-An

    2016-07-30

    The aim of this study was to investigate the associations of DNA methyltransferases (DNMTs) polymorphisms with susceptibility to autoimmune thyroid diseases (AITDs) and to test gene-gene/gene-sex epistasis interactions. Eight single-nucleotide polymorphisms (SNPs) in DNMT1, DNMT3A and DNMT3B were selected and genotyped by multiplex polymerase chain reaction combined with ligase detection reaction method (PCR-LDR). A total of 685 Graves' disease (GD) patients, 353 Hashimoto's thyroiditis (HT) patients and 909 healthy controls were included in the final analysis. Epistasis was tested by additive model, multiplicative model and general multifactor dimensionality reduction (general MDR). Rs2424913 (DNMT3B) and rs2228611 (DNMT1) were associated with susceptibility to AITD and GD in the dominant and overdominant model, respectively (rs2424913: P=0.009 for AITD, P=0.0041 for GD; rs2228611: P=0.035 for AITD, P=0.043 for GD). Multiplicative and multiple high dimensional gene-gene or gene-sex interactions were also observed in this study. We have found evidence for a potential role of rs2424913 (DNMT3B) and rs2228611 (DNMT1) in AITD susceptibility and identified novel gene-gene/gene-sex interactions in AITD. Our study may highlight sex and genes of DNMTs family as contributors to the pathogenesis of AITD.

  10. Common sources of bias in gene-lifestyle interaction studies of cardiometabolic disease

    DEFF Research Database (Denmark)

    Oskari Kilpeläinen, Tuomas

    2013-01-01

    The role of gene x lifestyle interactions in the development of cardiometabolic diseases is often highlighted, but very few robustly replicated examples of interactions exist in the literature. The slow pace of discoveries may largely be due to interaction effects being generally small in magnitude...... and/or more complex than initially thought. However, the progress may also be hindered by the poor accuracy in large-scale epidemiological studies to estimate the true interaction effect sizes. Often, this bias tends to underestimate the interaction effect, leading to inadequate statistical power...... to detect the interaction. In this review, I will discuss the most common sources of bias in the estimation of gene x lifestyle interactions, and will discuss how such factors could be addressed in the future to enhance our potential to identify and replicate interactions for cardiometabolic diseases....

  11. Mapping Interactive Cancer Susceptibility Genes in Prostate Cancer

    Science.gov (United States)

    2007-04-01

    further analysis around this FHIT marker. Under the assumption of a recessive model, we attempted to narrow the disease interval by examining key meiotic ...examining key meiotic recombinants. A and B, physical map illustrating marker and FHIT exon locations. Solid bar, FHIT gene boundary; vertical bars, exons 5...gene, spanning the chromosome 3p14.2 fragile site and renal carcinoma-associated t(3;8) breakpoint, is abnormal in digestive tract cancers. Cell 1996;84

  12. Assessment of gene-by-sex interaction effect on bone mineral density

    DEFF Research Database (Denmark)

    Liu, Ching-Ti; Estrada, Karol; Yerges-Armstrong, Laura M;

    2012-01-01

    Sexual dimorphism in various bone phenotypes, including bone mineral density (BMD), is widely observed; however, the extent to which genes explain these sex differences is unclear. To identify variants with different effects by sex, we examined gene-by-sex autosomal interactions genome-wide, and ......Sexual dimorphism in various bone phenotypes, including bone mineral density (BMD), is widely observed; however, the extent to which genes explain these sex differences is unclear. To identify variants with different effects by sex, we examined gene-by-sex autosomal interactions genome...

  13. The interaction between smoking and HLA genes in multiple sclerosis

    DEFF Research Database (Denmark)

    Hedström, Anna Karin; Katsoulis, Michail; Hössjer, Ola

    2017-01-01

    Interactions between environment and genetics may contribute to multiple sclerosis (MS) development. We investigated whether the previously observed interaction between smoking and HLA genotype in the Swedish population could be replicated, refined and extended to include other populations. We used...... populations from the Nordic studies (6265 cases, 8401 controls). In both the pooled analyses and in the combined Nordic material, interactions were observed between HLA-DRB*15 and absence of HLA-A*02 and between smoking and each of the genetic risk factors. Two way interactions were observed between each...... with never smokers without these genetic risk factors (OR 12.7, 95% CI 10.8-14.9). The risk of MS associated with HLA genotypes is strongly influenced by smoking status and vice versa. Since the function of HLA molecules is to present peptide antigens to T cells, the demonstrated interactions strongly...

  14. Using Linkage Analysis to Detect Gene-Gene Interactions. 2. Improved Reliability and Extension to More-Complex Models.

    Directory of Open Access Journals (Sweden)

    Susan E Hodge

    Full Text Available Detecting gene-gene interaction in complex diseases has become an important priority for common disease genetics, but most current approaches to detecting interaction start with disease-marker associations. These approaches are based on population allele frequency correlations, not genetic inheritance, and therefore cannot exploit the rich information about inheritance contained within families. They are also hampered by issues of rigorous phenotype definition, multiple test correction, and allelic and locus heterogeneity. We recently developed, tested, and published a powerful gene-gene interaction detection strategy based on conditioning family data on a known disease-causing allele or a disease-associated marker allele4. We successfully applied the method to disease data and used computer simulation to exhaustively test the method for some epistatic models. We knew that the statistic we developed to indicate interaction was less reliable when applied to more-complex interaction models. Here, we improve the statistic and expand the testing procedure. We computer-simulated multipoint linkage data for a disease caused by two interacting loci. We examined epistatic as well as additive models and compared them with heterogeneity models. In all our models, the at-risk genotypes are "major" in the sense that among affected individuals, a substantial proportion has a disease-related genotype. One of the loci (A has a known disease-related allele (as would have been determined from a previous analysis. We removed (pruned family members who did not carry this allele; the resultant dataset is referred to as "stratified." This elimination step has the effect of raising the "penetrance" and detectability at the second locus (B. We used the lod scores for the stratified and unstratified data sets to calculate a statistic that either indicated the presence of interaction or indicated that no interaction was detectable. We show that the new method is robust

  15. A gene-gene interaction between polymorphisms in the OCT2 and MATE1 genes influences the renal clearance of metformin

    DEFF Research Database (Denmark)

    Hougaard Christensen, Mette Marie; Pedersen, Rasmus Steen; Stage, Tore Bjerregaard;

    2013-01-01

    The aim of this study was to determine the association between the renal clearance (CL(renal)) of metformin in healthy Caucasian volunteers and the single-nucleotide polymorphism (SNP) c.808G>T (rs316019) in OCT2 as well as the relevance of the gene-gene interactions between this SNP and (a...

  16. High Order Gene-Gene Interactions in Eight Single Nucleotide Polymorphisms of Renin-Angiotensin System Genes for Hypertension Association Study

    Directory of Open Access Journals (Sweden)

    Cheng-Hong Yang

    2015-01-01

    Full Text Available Several single nucleotide polymorphisms (SNPs of renin-angiotensin system (RAS genes are associated with hypertension (HT but most of them are focusing on single locus effects. Here, we introduce an unbalanced function based on multifactor dimensionality reduction (MDR for multiloci genotypes to detect high order gene-gene (SNP-SNP interaction in unbalanced cases and controls of HT data. Eight SNPs of three RAS genes (angiotensinogen, AGT; angiotensin-converting enzyme, ACE; angiotensin II type 1 receptor, AT1R in HT and non-HT subjects were included that showed no significant genotype differences. In 2- to 6-locus models of the SNP-SNP interaction, the SNPs of AGT and ACE genes were associated with hypertension (bootstrapping odds ratio [Boot-OR] = 1.972~3.785; 95%, confidence interval (CI 1.26~6.21; P<0.005. In 7- and 8-locus model, SNP A1166C of AT1R gene is joined to improve the maximum Boot-OR values of 4.050 to 4.483; CI = 2.49 to 7.29; P<1.63E−08. In conclusion, the epistasis networks are identified by eight SNP-SNP interaction models. AGT, ACE, and AT1R genes have overall effects with susceptibility to hypertension, where the SNPs of ACE have a mainly hypertension-associated effect and show an interacting effect to SNPs of AGT and AT1R genes.

  17. DGIdb 2.0: mining clinically relevant drug-gene interactions.

    Science.gov (United States)

    Wagner, Alex H; Coffman, Adam C; Ainscough, Benjamin J; Spies, Nicholas C; Skidmore, Zachary L; Campbell, Katie M; Krysiak, Kilannin; Pan, Deng; McMichael, Joshua F; Eldred, James M; Walker, Jason R; Wilson, Richard K; Mardis, Elaine R; Griffith, Malachi; Griffith, Obi L

    2016-01-01

    The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that consolidates disparate data sources describing drug-gene interactions and gene druggability. It provides an intuitive graphical user interface and a documented application programming interface (API) for querying these data. DGIdb was assembled through an extensive manual curation effort, reflecting the combined information of twenty-seven sources. For DGIdb 2.0, substantial updates have been made to increase content and improve its usefulness as a resource for mining clinically actionable drug targets. Specifically, nine new sources of drug-gene interactions have been added, including seven resources specifically focused on interactions linked to clinical trials. These additions have more than doubled the overall count of drug-gene interactions. The total number of druggable gene claims has also increased by 30%. Importantly, a majority of the unrestricted, publicly-accessible sources used in DGIdb are now automatically updated on a weekly basis, providing the most current information for these sources. Finally, a new web view and API have been developed to allow searching for interactions by drug identifiers to complement existing gene-based search functionality. With these updates, DGIdb represents a comprehensive and user friendly tool for mining the druggable genome for precision medicine hypothesis generation.

  18. Gene, environment, and brain-gut interactions in irritable bowel syndrome.

    Science.gov (United States)

    Fukudo, Shin; Kanazawa, Motoyori

    2011-04-01

    The genetic predisposition and influence of environment may underlie in the pathogenesis and/or pathophysiology of irritable bowel syndrome (IBS). This phenomenon, gene x environment interaction together with brain-gut interactions is emerging area to be clarified in IBS research. Earlier studies focused on candidate genes of neurotransmitters, cytokines, and growth factors. Among them, some studies but not all studies revealed association between phenotypes of IBS and 5-hydroxytryptamine (5-HT)-related genes, noradrenaline-related genes, and cytokine genes. Recent prospective cohort study showed that genes encoding immune and adhesion molecules were associated with post-infectious etiology of IBS. Psychosocial stressors and intraluminal factors especially microbiota are keys to develop IBS. IBS patients may have abnormal gut microbiota as well as increased organic acids. IBS is disorder that relates to brain-gut interactions, emotional dysregulation, and illness behaviors. Brain imaging with or without combination of visceral stimulation enables us to depict the detailed information of brain-gut interactions. In IBS patients, thalamus, insula, anterior cingulate cortex, amygdala, and brainstem were more activated in response to visceral stimulation than controls. Corticotropin-releasing hormone and 5-HT are the candidate substances which regulate exaggerated brain-gut response. In conclusion, gene x environment interaction together with brain-gut interactions may play crucial roles in IBS development. Further fundamental research on this issue is warranted.

  19. A systematic gene-gene and gene-environment interaction analysis of DNA repair genes XRCC1, XRCC2, XRCC3, XRCC4, and oral cancer risk.

    Science.gov (United States)

    Yang, Cheng-Hong; Lin, Yu-Da; Yen, Ching-Yui; Chuang, Li-Yeh; Chang, Hsueh-Wei

    2015-04-01

    Oral cancer is the sixth most common cancer worldwide with a high mortality rate. Biomarkers that anticipate susceptibility, prognosis, or response to treatments are much needed. Oral cancer is a polygenic disease involving complex interactions among genetic and environmental factors, which require multifaceted analyses. Here, we examined in a dataset of 103 oral cancer cases and 98 controls from Taiwan the association between oral cancer risk and the DNA repair genes X-ray repair cross-complementing group (XRCCs) 1-4, and the environmental factors of smoking, alcohol drinking, and betel quid (BQ) chewing. We employed logistic regression, multifactor dimensionality reduction (MDR), and hierarchical interaction graphs for analyzing gene-gene (G×G) and gene-environment (G×E) interactions. We identified a significantly elevated risk of the XRCC2 rs2040639 heterozygous variant among smokers [adjusted odds ratio (OR) 3.7, 95% confidence interval (CI)=1.1-12.1] and alcohol drinkers [adjusted OR=5.7, 95% CI=1.4-23.2]. The best two-factor based G×G interaction of oral cancer included the XRCC1 rs1799782 and XRCC2 rs2040639 [OR=3.13, 95% CI=1.66-6.13]. For the G×E interaction, the estimated OR of oral cancer for two (drinking-BQ chewing), three (XRCC1-XRCC2-BQ chewing), four (XRCC1-XRCC2-age-BQ chewing), and five factors (XRCC1-XRCC2-age-drinking-BQ chewing) were 32.9 [95% CI=14.1-76.9], 31.0 [95% CI=14.0-64.7], 49.8 [95% CI=21.0-117.7] and 82.9 [95% CI=31.0-221.5], respectively. Taken together, the genotypes of XRCC1 rs1799782 and XRCC2 rs2040639 DNA repair genes appear to be significantly associated with oral cancer. These were enhanced by exposure to certain environmental factors. The observations presented here warrant further research in larger study samples to examine their relevance for routine clinical care in oncology.

  20. Gene Prospector: An evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases

    Directory of Open Access Journals (Sweden)

    Khoury Muin J

    2008-12-01

    Full Text Available Abstract Background Millions of single nucleotide polymorphisms have been identified as a result of the human genome project and the rapid advance of high throughput genotyping technology. Genetic association studies, such as recent genome-wide association studies (GWAS, have provided a springboard for exploring the contribution of inherited genetic variation and gene/environment interactions in relation to disease. Given the capacity of such studies to produce a plethora of information that may then be described in a number of publications, selecting possible disease susceptibility genes and identifying related modifiable risk factors is a major challenge. A Web-based application for finding evidence of such relationships is key to the development of follow-up studies and evidence for translational research. We developed a Web-based application that selects and prioritizes potential disease-related genes by using a highly curated and updated literature database of genetic association studies. The application, called Gene Prospector, also provides a comprehensive set of links to additional data sources. Results We compared Gene Prospector results for the query "Parkinson" with a list of 13 leading candidate genes (Top Results from a curated, specialty database for genetic associations with Parkinson disease (PDGene. Nine of the thirteen leading candidate genes from PDGene were in the top 10th percentile of the ranked list from Gene Prospector. In fact, Gene Prospector included more published genetic association studies for the 13 leading candidate genes than PDGene did. Conclusion Gene Prospector provides an online gateway for searching for evidence about human genes in relation to diseases, other phenotypes, and risk factors, and provides links to published literature and other online data sources. Gene Prospector can be accessed via http://www.hugenavigator.net/HuGENavigator/geneProspectorStartPage.do.

  1. An assessment of gene-by-gene interactions as a tool to unfold missing heritability in dyslexia.

    Science.gov (United States)

    Mascheretti, S; Bureau, A; Trezzi, V; Giorda, R; Marino, C

    2015-07-01

    Even if substantial heritability has been reported and candidate genes have been identified extensively, all known marker associations explain only a small proportion of the phenotypic variance of developmental dyslexia (DD) and related quantitative phenotypes. Gene-by-gene interaction (also known as "epistasis"--G × G) triggers a non-additive effect of genes at different loci and should be taken into account in explaining part of the missing heritability of this complex trait. We assessed potential G × G interactions among five DD candidate genes, i.e., DYX1C1, DCDC2, KIAA0319, ROBO1, and GRIN2B, upon DD-related neuropsychological phenotypes in 493 nuclear families with DD, by implementing two complementary regression-based approaches: (1) a general linear model equation whereby the trait is predicted by the main effect of the number of rare alleles of the two genes and by the effect of the interaction between them, and (2) a family-based association test to detect G × G interactions between two unlinked markers by splitting up the association effect into a between- and a within-family genetic orthogonal components. After applying 500,000 permutations and correcting for multiple testing, both methods show that G × G effects between markers within the DYX1C1, KIAA0319/TTRAP, and GRIN2B genes lower the memory letters composite z-score of on average 0.55 standard deviation. We provided initial evidence that the effects of familial transmission of synergistic interactions between genetic risk variants can be exploited in the study of the etiology of DD, explain part of its missing heritability, and assist in designing customized charts of individualized neurocognitive impairments in complex disorders, such as DD.

  2. Use of Information Measures and Their Approximations to Detect Predictive Gene-Gene Interaction

    Directory of Open Access Journals (Sweden)

    Jan Mielniczuk

    2017-01-01

    Full Text Available We reconsider the properties and relationships of the interaction information and its modified versions in the context of detecting the interaction of two SNPs for the prediction of a binary outcome when interaction information is positive. This property is called predictive interaction, and we state some new sufficient conditions for it to hold true. We also study chi square approximations to these measures. It is argued that interaction information is a different and sometimes more natural measure of interaction than the logistic interaction parameter especially when SNPs are dependent. We introduce a novel measure of predictive interaction based on interaction information and its modified version. In numerical experiments, which use copulas to model dependence, we study examples when the logistic interaction parameter is zero or close to zero for which predictive interaction is detected by the new measure, while it remains undetected by the likelihood ratio test.

  3. MDM2 SNP309, gene-gene interaction, and tumor susceptibility: an updated meta-analysis

    Directory of Open Access Journals (Sweden)

    Wu Wei

    2011-05-01

    Full Text Available Abstract Background The tumor suppressor gene p53 is involved in multiple cellular pathways including apoptosis, transcriptional control, and cell cycle regulation. In the last decade it has been demonstrated that the single nucleotide polymorphism (SNP at codon 72 of the p53 gene is associated with the risk for development of various neoplasms. MDM2 SNP309 is a single nucleotide T to G polymorphism located in the MDM2 gene promoter. From the time that this well-characterized functional polymorphism was identified, a variety of case-control studies have been published that investigate the possible association between MDM2 SNP309 and cancer risk. However, the results of the published studies, as well as the subsequent meta-analyses, remain contradictory. Methods To investigate whether currently published epidemiological studies can clarify the potential interaction between MDM2 SNP309 and the functional genetic variant in p53 codon72 (Arg72Pro and p53 mutation status, we performed a meta-analysis of the risk estimate on 27,813 cases with various tumor types and 30,295 controls. Results The data we reviewed indicated that variant homozygote 309GG and heterozygote 309TG were associated with a significant increased risk of all tumor types (homozygote comparison: odds ratio (OR = 1.25, 95% confidence interval (CI = 1.13-1.37; heterozygote comparison: OR = 1.10, 95% CI = 1.03-1.17. We also found that the combination of GG and Pro/Pro, TG and Pro/Pro, GG and Arg/Arg significantly increased the risk of cancer (OR = 3.38, 95% CI = 1.77-6.47; OR = 1.88, 95% CI = 1.26-2.81; OR = 1.96, 95% CI = 1.01-3.78, respectively. In a stratified analysis by tumor location, we also found a significant increased risk in brain, liver, stomach and uterus cancer (OR = 1.47, 95% CI = 1.06-2.03; OR = 2.24, 95%CI = 1.57-3.18; OR = 1.54, 95%CI = 1.04-2.29; OR = 1.34, 95%CI = 1.07-1.29, respectively. However, no association was seen between MDM2 SNP309 and tumor susceptibility

  4. Obesity and the metabolic syndrome: Impact of gene-diet interaction ...

    African Journals Online (AJOL)

    Obesity and the metabolic syndrome: Impact of gene-diet interaction. ... Individuals exposed to the same environmental risk factors or treatment strategies ... It is therefore important to know how certain genomic and lifestyle factors combine in ...

  5. Gene expression changes during Giardia-host cell interactions in serum-free medium.

    Science.gov (United States)

    Ferella, Marcela; Davids, Barbara J; Cipriano, Michael J; Birkeland, Shanda R; Palm, Daniel; Gillin, Frances D; McArthur, Andrew G; Svärd, Staffan

    2014-10-01

    Serial Analysis of Gene Expression (SAGE) was used to quantify transcriptional changes in Giardia intestinalis during its interaction with human intestinal epithelial cells (IECs, HT-29) in serum free M199 medium. Transcriptional changes were compared to those in trophozoites alone in M199 and in TYI-S-33 Giardia growth medium. In total, 90 genes were differentially expressed, mainly those involved in cellular redox homeostasis, metabolism and small molecule transport but also cysteine proteases and structural proteins of the giardin family. Only 29 genes changed their expression due to IEC interaction and the rest were due to M199 medium. Although our findings generated a small dataset, it was consistent with our earlier microarray studies performed under different interaction conditions. This study has confined the number of genes in Giardia to a small subset that specifically change their expression due to interaction with IECs.

  6. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

    National Research Council Canada - National Science Library

    Chen, Edward Y; Tan, Christopher M; Kou, Yan; Duan, Qiaonan; Wang, Zichen; Meirelles, Gabriela Vaz; Clark, Neil R; Ma'ayan, Avi

    2013-01-01

    .... Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive...

  7. Gene by Environment Interaction and Resilience: Effects of Child Maltreatment and Serotonin, Corticotropin Releasing Hormone, Dopamine, and Oxytocin Genes

    Science.gov (United States)

    Cicchetti, Dante; Rogosch, Fred A.

    2013-01-01

    In this investigation, gene-environment interaction effects in predicting resilience in adaptive functioning among maltreated and nonmaltreated low-income children (N = 595) were examined. A multi-component index of resilient functioning was derived and levels of resilient functioning were identified. Variants in four genes, 5-HTTLPR, CRHR1, DRD4 -521C/T, and OXTR, were investigated. In a series of ANCOVAs, child maltreatment demonstrated a strong negative main effect on children’s resilient functioning, whereas no main effects for any of the genotypes of the respective genes were found. However, gene-environment interactions involving genotypes of each of the respective genes and maltreatment status were obtained. For each respective gene, among children with a specific genotype, the relative advantage in resilient functioning of nonmaltreated compared to maltreated children was stronger than was the case for nonmaltreated and maltreated children with other genotypes of the respective gene. Across the four genes, a composite of the genotypes that more strongly differentiated resilient functioning between nonmaltreated and maltreated children provided further evidence of genetic variations influencing resilient functioning in nonmaltreated children, whereas genetic variation had a negligible effect on promoting resilience among maltreated children. Additional effects were observed for children based on the number of subtypes of maltreatment children experienced, as well as for abuse and neglect subgroups. Finally, maltreated and nonmaltreated children with high levels of resilience differed in their average number of differentiating genotypes. These results suggest that differential resilient outcomes are based on the interaction between genes and developmental experiences. PMID:22559122

  8. Gene × Environment interaction and resilience: effects of child maltreatment and serotonin, corticotropin releasing hormone, dopamine, and oxytocin genes.

    Science.gov (United States)

    Cicchetti, Dante; Rogosch, Fred A

    2012-05-01

    In this investigation, gene-environment interaction effects in predicting resilience in adaptive functioning among maltreated and nonmaltreated low-income children (N = 595) were examined. A multicomponent index of resilient functioning was derived and levels of resilient functioning were identified. Variants in four genes (serotonin transporter linked polymorphic region, corticotropin releasing hormone receptor 1, dopamine receptor D4-521C/T, and oxytocin receptor) were investigated. In a series of analyses of covariance, child maltreatment demonstrated a strong negative main effect on children's resilient functioning, whereas no main effects for any of the genotypes of the respective genes were found. However, gene-environment interactions involving genotypes of each of the respective genes and maltreatment status were obtained. For each respective gene, among children with a specific genotype, the relative advantage in resilient functioning of nonmaltreated compared to maltreated children was stronger than was the case for nonmaltreated and maltreated children with other genotypes of the respective gene. Across the four genes, a composite of the genotypes that more strongly differentiated resilient functioning between nonmaltreated and maltreated children provided further evidence of genetic variations influencing resilient functioning in nonmaltreated children, whereas genetic variation had a negligible effect on promoting resilience among maltreated children. Additional effects were observed for children based on the number of subtypes of maltreatment children experienced, as well as for abuse and neglect subgroups. Finally, maltreated and nonmaltreated children with high levels of resilience differed in their average number of differentiating genotypes. These results suggest that differential resilient outcomes are based on the interaction between genes and developmental experiences.

  9. CYP1A1, GCLC, AGT, AGTR1 gene-gene interactions in community-acquired pneumonia pulmonary complications.

    Science.gov (United States)

    Salnikova, Lyubov E; Smelaya, Tamara V; Golubev, Arkadiy M; Rubanovich, Alexander V; Moroz, Viktor V

    2013-11-01

    This study was conducted to establish the possible contribution of functional gene polymorphisms in detoxification/oxidative stress and vascular remodeling pathways to community-acquired pneumonia (CAP) susceptibility in the case-control study (350 CAP patients, 432 control subjects) and to predisposition to the development of CAP complications in the prospective study. All subjects were genotyped for 16 polymorphic variants in the 14 genes of xenobiotics detoxification CYP1A1, AhR, GSTM1, GSTT1, ABCB1, redox-status SOD2, CAT, GCLC, and vascular homeostasis ACE, AGT, AGTR1, NOS3, MTHFR, VEGFα. Risk of pulmonary complications (PC) in the single locus analysis was associated with CYP1A1, GCLC and AGTR1 genes. Extra PC (toxic shock syndrome and myocarditis) were not associated with these genes. We evaluated gene-gene interactions using multi-factor dimensionality reduction, and cumulative gene risk score approaches. The final model which included >5 risk alleles in the CYP1A1 (rs2606345, rs4646903, rs1048943), GCLC, AGT, and AGTR1 genes was associated with pleuritis, empyema, acute respiratory distress syndrome, all PC and acute respiratory failure (ARF). We considered CYP1A1, GCLC, AGT, AGTR1 gene set using Set Distiller mode implemented in GeneDecks for discovering gene-set relations via the degree of sharing descriptors within a given gene set. N-acetylcysteine and oxygen were defined by Set Distiller as the best descriptors for the gene set associated in the present study with PC and ARF. Results of the study are in line with literature data and suggest that genetically determined oxidative stress exacerbation may contribute to the progression of lung inflammation.

  10. Gene-gene interaction and functional impact of polymorphisms on innate immune genes in controlling Plasmodium falciparum blood infection level.

    Directory of Open Access Journals (Sweden)

    Madhumita Basu

    Full Text Available Genetic variations in toll-like receptors and cytokine genes of the innate immune pathways have been implicated in controlling parasite growth and the pathogenesis of Plasmodium falciparum mediated malaria. We previously published genetic association of TLR4 non-synonymous and TNF-α promoter polymorphisms with P.falciparum blood infection level and here we extend the study considerably by (i investigating genetic dependence of parasite-load on interleukin-12B polymorphisms, (ii reconstructing gene-gene interactions among candidate TLRs and cytokine loci, (iii exploring genetic and functional impact of epistatic models and (iv providing mechanistic insights into functionality of disease-associated regulatory polymorphisms. Our data revealed that carriage of AA (P = 0.0001 and AC (P = 0.01 genotypes of IL12B 3'UTR polymorphism was associated with a significant increase of mean log-parasitemia relative to rare homozygous genotype CC. Presence of IL12B+1188 polymorphism in five of six multifactor models reinforced its strong genetic impact on malaria phenotype. Elevation of genetic risk in two-component models compared to the corresponding single locus and reduction of IL12B (2.2 fold and lymphotoxin-α (1.7 fold expressions in patients'peripheral-blood-mononuclear-cells under TLR4Thr399Ile risk genotype background substantiated the role of Multifactor Dimensionality Reduction derived models. Marked reduction of promoter activity of TNF-α risk haplotype (C-C-G-G compared to wild-type haplotype (T-C-G-G with (84% and without (78% LPS stimulation and the loss of binding of transcription factors detected in-silico supported a causal role of TNF-1031. Significantly lower expression of IL12B+1188 AA (5 fold and AC (9 fold genotypes compared to CC and under-representation (P = 0.0048 of allele A in transcripts of patients' PBMCs suggested an Allele-Expression-Imbalance. Allele (A+1188C dependent differential stability (2 fold of IL12B-transcripts upon

  11. Gene expression analysis uncovers novel hedgehog interacting protein (HHIP) effects in human bronchial epithelial cells.

    Science.gov (United States)

    Zhou, Xiaobo; Qiu, Weiliang; Sathirapongsasuti, J Fah; Cho, Michael H; Mancini, John D; Lao, Taotao; Thibault, Derek M; Litonjua, Augusto A; Bakke, Per S; Gulsvik, Amund; Lomas, David A; Beaty, Terri H; Hersh, Craig P; Anderson, Christopher; Geigenmuller, Ute; Raby, Benjamin A; Rennard, Stephen I; Perrella, Mark A; Choi, Augustine M K; Quackenbush, John; Silverman, Edwin K

    2013-05-01

    Hedgehog interacting protein (HHIP) was implicated in chronic obstructive pulmonary disease (COPD) by genome-wide association studies (GWAS). However, it remains unclear how HHIP contributes to COPD pathogenesis. To identify genes regulated by HHIP, we performed gene expression microarray analysis in a human bronchial epithelial cell line (Beas-2B) stably infected with HHIP shRNAs. HHIP silencing led to differential expression of 296 genes; enrichment for variants nominally associated with COPD was found. Eighteen of the differentially expressed genes were validated by real-time PCR in Beas-2B cells. Seven of 11 validated genes tested in human COPD and control lung tissues demonstrated significant gene expression differences. Functional annotation indicated enrichment for extracellular matrix and cell growth genes. Network modeling demonstrated that the extracellular matrix and cell proliferation genes influenced by HHIP tended to be interconnected. Thus, we identified potential HHIP targets in human bronchial epithelial cells that may contribute to COPD pathogenesis.

  12. Gene transcription analysis during interaction between potato and Ralstonia solanacearum

    NARCIS (Netherlands)

    Li, G.C.; Jin, L.P.; Wang, X.W.; Xie, K.Y.; Yang, Y.; Vossen, van der E.A.G.; Huang, S.W.; Qu, D.Y.

    2010-01-01

    Bacterial wilt (BW) caused by Ralstonia solanacearum (Rs) is an important quarantine disease that spreads worldwide and infects hundreds of plant species. The BW defense response of potato is a complicated continuous process, which involves transcription of a battery of genes. The molecular mechanis

  13. An empirical fuzzy multifactor dimensionality reduction method for detecting gene-gene interactions.

    Science.gov (United States)

    Leem, Sangseob; Park, Taesung

    2017-03-14

    Detection of gene-gene interaction (GGI) is a key challenge towards solving the problem of missing heritability in genetics. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. MDR reduces the dimensionality of multi-factor by means of binary classification into high-risk (H) or low-risk (L) groups. Unfortunately, this simple binary classification does not reflect the uncertainty of H/L classification. Thus, we proposed Fuzzy MDR to overcome limitations of binary classification by introducing the degree of membership of two fuzzy sets H/L. While Fuzzy MDR demonstrated higher power than that of MDR, its performance is highly dependent on the several tuning parameters. In real applications, it is not easy to choose appropriate tuning parameter values. In this work, we propose an empirical fuzzy MDR (EF-MDR) which does not require specifying tuning parameters values. Here, we propose an empirical approach to estimating the membership degree that can be directly estimated from the data. In EF-MDR, the membership degree is estimated by the maximum likelihood estimator of the proportion of cases(controls) in each genotype combination. We also show that the balanced accuracy measure derived from this new membership function is a linear function of the standard chi-square statistics. This relationship allows us to perform the standard significance test using p-values in the MDR framework without permutation. Through two simulation studies, the power of the proposed EF-MDR is shown to be higher than those of MDR and Fuzzy MDR. We illustrate the proposed EF-MDR by analyzing Crohn's disease (CD) and bipolar disorder (BD) in the Wellcome Trust Case Control Consortium (WTCCC) dataset. We propose an empirical Fuzzy MDR for detecting GGI using the maximum likelihood of the proportion of cases(controls) as the membership degree of the genotype combination. The program written in R for EF-MDR is available at http://statgen.snu.ac.kr/software/EF-MDR .

  14. Methylobacterium-plant interaction genes regulated by plant exudate and quorum sensing molecules

    Directory of Open Access Journals (Sweden)

    Manuella Nóbrega Dourado

    2013-12-01

    Full Text Available Bacteria from the genus Methylobacterium interact symbiotically (endophytically and epiphytically with different plant species. These interactions can promote plant growth or induce systemic resistance, increasing plant fitness. The plant colonization is guided by molecular communication between bacteria-bacteria and bacteria-plants, where the bacteria recognize specific exuded compounds by other bacteria (e.g. homoserine molecules and/or by the plant roots (e.g. flavonoids, ethanol and methanol, respectively. In this context, the aim of this study was to evaluate the effect of quorum sensing molecules (N-acyl-homoserine lactones and plant exudates (including ethanol in the expression of a series of bacterial genes involved in Methylobacterium-plant interaction. The selected genes are related to bacterial metabolism (mxaF, adaptation to stressful environment (crtI, phoU and sss, to interactions with plant metabolism compounds (acdS and pathogenicity (patatin and phoU. Under in vitro conditions, our results showed the differential expression of some important genes related to metabolism, stress and pathogenesis, thereby AHL molecules up-regulate all tested genes, except phoU, while plant exudates induce only mxaF gene expression. In the presence of plant exudates there is a lower bacterial density (due the endophytic and epiphytic colonization, which produce less AHL, leading to down regulation of genes when compared to the control. Therefore, bacterial density, more than plant exudate, influences the expression of genes related to plant-bacteria interaction.

  15. Population genetics of fungal and oomycete effectors involved in gene-for-gene interactions.

    Science.gov (United States)

    Stukenbrock, Eva H; McDonald, Bruce A

    2009-04-01

    Antagonistic coevolution between plants and pathogens has generated a broad array of attack and defense mechanisms. In the classical avirulence (Avr) gene-for-gene model, the pathogen gene evolves to escape host recognition while the host resistance (R) gene evolves to track the evolving pathogen elicitor. In the case of host-specific toxins (HST), the evolutionary arms race may be inverted, with the gene encoding the pathogen toxin evolving to maintain recognition of the host sensitivity target while the host sensitivity gene evolves to escape binding with the toxin. Pathogen effector genes, including those encoding Avr elicitors and HST, often show elevated levels of polymorphism reflecting the coevolutionary arms race between host and pathogen. However, selection can also eliminate variation in the coevolved gene and its neighboring regions when advantageous alleles are swept to fixation. The distribution and diversity of corresponding host genes will have a major impact on the distribution and diversity of effectors in the pathogen population. Population genetic analyses including both hosts and their pathogens provide an essential tool to understand the diversity and dynamics of effector genes. Here, we summarize current knowledge about the population genetics of fungal and oomycete effector genes, focusing on recent studies that have used both spatial and temporal collections to assess the diversity and distribution of alleles and to monitor changes in allele frequencies over time. These studies illustrate that effector genes exhibit a significant degree of diversity at both small and large sampling scales, suggesting that local selection plays an important role in their evolution. They also illustrate that Avr elicitors and HST may be recognizing the same R genes in plants, leading to evolutionary outcomes that differ for necrotrophs and biotrophs while affecting the evolution of the corresponding R genes. Under this scenario, the optimal number of R genes

  16. Heritability of insulin sensitivity and lipid profile depend on BMI : evidence for gene-obesity interaction

    NARCIS (Netherlands)

    Wang, X.; Ding, X.; Su, S.; Spector, T. D.; Mangino, M.; Iliadou, A.; Snieder, H.

    2009-01-01

    Evidence from candidate gene studies suggests that obesity may modify genetic susceptibility to type 2 diabetes and dyslipidaemia. On an aggregate level, gene-obesity interactions are expected to result in different heritability estimates at different obesity levels. However, this hypothesis has nev

  17. Heritability of insulin sensitivity and lipid profile depend on BMI : evidence for gene-obesity interaction

    NARCIS (Netherlands)

    Wang, X.; Ding, X.; Su, S.; Spector, T. D.; Mangino, M.; Iliadou, A.; Snieder, H.

    2009-01-01

    Evidence from candidate gene studies suggests that obesity may modify genetic susceptibility to type 2 diabetes and dyslipidaemia. On an aggregate level, gene-obesity interactions are expected to result in different heritability estimates at different obesity levels. However, this hypothesis has

  18. Gene-Environment Interactions in Genome-Wide Association Studies: Current Approaches and New Directions

    Science.gov (United States)

    Winham, Stacey J.; Biernacka, Joanna M.

    2013-01-01

    Background: Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized…

  19. Identification of new genetic susceptibility loci for breast cancer through consideration of gene-environment interactions

    DEFF Research Database (Denmark)

    Schoeps, Anja; Rudolph, Anja; Seibold, Petra

    2014-01-01

    Genes that alter disease risk only in combination with certain environmental exposures may not be detected in genetic association analysis. By using methods accounting for gene-environment (G × E) interaction, we aimed to identify novel genetic loci associated with breast cancer risk. Up to 34,47...

  20. Interacting genes in the pine-fusiform rust forest pathosystem

    Science.gov (United States)

    H.V. Amerson; T.L. Kubisiak; S.A. Garcia; G.C. Kuhlman; C.D. Nelson; S.E. McKeand; T.J. Mullin; B. Li

    2005-01-01

    Fusiform rust (FR) disease of pines, caused by Cronartium quercuum f.sp. fusiforme (Cqf), is the most destructive disease in pine plantations of the southern U. S. The NCSU fusiform rust program, in conjunction with the USDA-Forest Service in Saucier, MS and Athens, GA, has research underway to elucidate some of the genetic interactions in this...

  1. Plant interactions with multiple insect herbivores: from community to genes

    NARCIS (Netherlands)

    Stam, J.M.; Kroes, A.; Li, Y.; Gols, R.; Loon, van J.J.A.; Poelman, E.H.; Dicke, M.

    2014-01-01

    Every plant is a member of a complex insect community that consists of tens to hundreds of species that belong to different trophic levels. The dynamics of this community are critically influenced by the plant, which mediates interactions between community members that can occur on the plant simulta

  2. Plant interactions with multiple insect herbivores: from community to genes

    NARCIS (Netherlands)

    Stam, J.M.; Kroes, A.; Li, Y.; Gols, R.; Loon, van J.J.A.; Poelman, E.H.; Dicke, M.

    2014-01-01

    Every plant is a member of a complex insect community that consists of tens to hundreds of species that belong to different trophic levels. The dynamics of this community are critically influenced by the plant, which mediates interactions between community members that can occur on the plant

  3. Gene-Lifestyle Interaction and Type 2 Diabetes

    DEFF Research Database (Denmark)

    Langenberg, Claudia; Sharp, Stephen J; Franks, Paul W

    2014-01-01

    BACKGROUND: Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the...

  4. The Genome-Wide Interaction Network of Nutrient Stress Genes in Escherichia coli

    Directory of Open Access Journals (Sweden)

    Jean-Philippe Côtôé

    2016-11-01

    Full Text Available Conventional efforts to describe essential genes in bacteria have typically emphasized nutrient-rich growth conditions. Of note, however, are the set of genes that become essential when bacteria are grown under nutrient stress. For example, more than 100 genes become indispensable when the model bacterium Escherichia coli is grown on nutrient-limited media, and many of these nutrient stress genes have also been shown to be important for the growth of various bacterial pathogens in vivo. To better understand the genetic network that underpins nutrient stress in E. coli, we performed a genome-scale cross of strains harboring deletions in some 82 nutrient stress genes with the entire E. coli gene deletion collection (Keio to create 315,400 double deletion mutants. An analysis of the growth of the resulting strains on rich microbiological media revealed an average of 23 synthetic sick or lethal genetic interactions for each nutrient stress gene, suggesting that the network defining nutrient stress is surprisingly complex. A vast majority of these interactions involved genes of unknown function or genes of unrelated pathways. The most profound synthetic lethal interactions were between nutrient acquisition and biosynthesis. Further, the interaction map reveals remarkable metabolic robustness in E. coli through pathway redundancies. In all, the genetic interaction network provides a powerful tool to mine and identify missing links in nutrient synthesis and to further characterize genes of unknown function in E. coli. Moreover, understanding of bacterial growth under nutrient stress could aid in the development of novel antibiotic discovery platforms.

  5. GeneWiz browser: An Interactive Tool for Visualizing Sequenced Chromosomes

    DEFF Research Database (Denmark)

    Hallin, Peter Fischer; Stærfeldt, Hans Henrik; Rotenberg, Eva;

    2009-01-01

    We present an interactive web application for visualizing genomic data of prokaryotic chromosomes. The tool (GeneWiz browser) allows users to carry out various analyses such as mapping alignments of homologous genes to other genomes, mapping of short sequencing reads to a reference chromosome......, and calculating DNA properties such as curvature or stacking energy along the chromosome. The GeneWiz browser produces an interactive graphic that enables zooming from a global scale down to single nucleotides, without changing the size of the plot. Its ability to disproportionally zoom provides optimal...

  6. Rice–arsenate interactions in hydroponics: a three-gene model for tolerance

    Science.gov (United States)

    Norton, Gareth J.; Nigar, Meher; Dasgupta, Tapash; Meharg, Andrew A.; Price, Adam H.

    2008-01-01

    In this study, the genetic mapping of the tolerance of root growth to 13.3 μM arsenate [As(V)] using the Bala×Azucena population is improved, and candidate genes for further study are identified. A remarkable three-gene model of tolerance is advanced, which appears to involve epistatic interaction between three major genes, two on chromosome 6 and one on chromosome 10. Any combination of two of these genes inherited from the tolerant parent leads to the plant having tolerance. Lists of potential positional candidate genes are presented. These are then refined using whole genome transcriptomics data and bioinformatics. Physiological evidence is also provided that genes related to phosphate transport are unlikely to be behind the genetic loci conferring tolerance. These results offer testable hypotheses for genes related to As(V) tolerance that might offer strategies for mitigating arsenic (As) accumulation in consumed rice. PMID:18453529

  7. Rice-arsenate interactions in hydroponics: a three-gene model for tolerance.

    Science.gov (United States)

    Norton, Gareth J; Nigar, Meher; Williams, Paul N; Dasgupta, Tapash; Meharg, Andrew A; Price, Adam H

    2008-01-01

    In this study, the genetic mapping of the tolerance of root growth to 13.3 muM arsenate [As(V)] using the BalaxAzucena population is improved, and candidate genes for further study are identified. A remarkable three-gene model of tolerance is advanced, which appears to involve epistatic interaction between three major genes, two on chromosome 6 and one on chromosome 10. Any combination of two of these genes inherited from the tolerant parent leads to the plant having tolerance. Lists of potential positional candidate genes are presented. These are then refined using whole genome transcriptomics data and bioinformatics. Physiological evidence is also provided that genes related to phosphate transport are unlikely to be behind the genetic loci conferring tolerance. These results offer testable hypotheses for genes related to As(V) tolerance that might offer strategies for mitigating arsenic (As) accumulation in consumed rice.

  8. Pleiohomeotic interacts with the core transcription elongation factor Spt5 to regulate gene expression in Drosophila.

    Directory of Open Access Journals (Sweden)

    Robert Harvey

    Full Text Available The early elongation checkpoint regulated by Positive Transcription Elongation Factor b (P-TEFb is a critical control point for the expression of many genes. Spt5 interacts directly with RNA polymerase II and has an essential role in establishing this checkpoint, and also for further transcript elongation. Here we demonstrate that Drosophila Spt5 interacts both physically and genetically with the Polycomb Group (PcG protein Pleiohomeotic (Pho, and the majority of Pho binding sites overlap with Spt5 binding sites across the genome in S2 cells. Our results indicate that Pho can interact with Spt5 to regulate transcription elongation in a gene specific manner.

  9. Antioxidant Defense Enzyme Genes and Asthma Susceptibility: Gender-Specific Effects and Heterogeneity in Gene-Gene Interactions between Pathogenetic Variants of the Disease

    Directory of Open Access Journals (Sweden)

    Alexey V. Polonikov

    2014-01-01

    Full Text Available Oxidative stress resulting from an increased amount of reactive oxygen species and an imbalance between oxidants and antioxidants plays an important role in the pathogenesis of asthma. The present study tested the hypothesis that genetic susceptibility to allergic and nonallergic variants of asthma is determined by complex interactions between genes encoding antioxidant defense enzymes (ADE. We carried out a comprehensive analysis of the associations between adult asthma and 46 single nucleotide polymorphisms of 34 ADE genes and 12 other candidate genes of asthma in Russian population using set association analysis and multifactor dimensionality reduction approaches. We found for the first time epistatic interactions between ADE genes underlying asthma susceptibility and the genetic heterogeneity between allergic and nonallergic variants of the disease. We identified GSR (glutathione reductase and PON2 (paraoxonase 2 as novel candidate genes for asthma susceptibility. We observed gender-specific effects of ADE genes on the risk of asthma. The results of the study demonstrate complexity and diversity of interactions between genes involved in oxidative stress underlying susceptibility to allergic and nonallergic asthma.

  10. Power of multifactor dimensionality reduction and penalized logistic regression for detecting gene-gene Interaction in a case-control study

    Directory of Open Access Journals (Sweden)

    Brott Marcia J

    2009-12-01

    Full Text Available Abstract Background There is a growing awareness that interaction between multiple genes play an important role in the risk of common, complex multi-factorial diseases. Many common diseases are affected by certain genotype combinations (associated with some genes and their interactions. The identification and characterization of these susceptibility genes and gene-gene interaction have been limited by small sample size and large number of potential interactions between genes. Several methods have been proposed to detect gene-gene interaction in a case control study. The penalized logistic regression (PLR, a variant of logistic regression with L2 regularization, is a parametric approach to detect gene-gene interaction. On the other hand, the Multifactor Dimensionality Reduction (MDR is a nonparametric and genetic model-free approach to detect genotype combinations associated with disease risk. Methods We compared the power of MDR and PLR for detecting two-way and three-way interactions in a case-control study through extensive simulations. We generated several interaction models with different magnitudes of interaction effect. For each model, we simulated 100 datasets, each with 200 cases and 200 controls and 20 SNPs. We considered a wide variety of models such as models with just main effects, models with only interaction effects or models with both main and interaction effects. We also compared the performance of MDR and PLR to detect gene-gene interaction associated with acute rejection(AR in kidney transplant patients. Results In this paper, we have studied the power of MDR and PLR for detecting gene-gene interaction in a case-control study through extensive simulation. We have compared their performances for different two-way and three-way interaction models. We have studied the effect of different allele frequencies on these methods. We have also implemented their performance on a real dataset. As expected, none of these methods were

  11. GEGEINTOOL: A Computer-Based Tool for Automated Analysis of Gene-Gene Interactions in Large Epidemiological Studies in Cardiovascular Genomics

    Directory of Open Access Journals (Sweden)

    Oscar Coltell

    2013-06-01

    Full Text Available Current methods of data analysis of gene-gene interactions in complex diseases, after taking into account environmental factors using traditional approaches, are inefficient. High-throughput methods of analysis in large scale studies including thousands of subjects and hundreds of SNPs should be implemented. We developed an integrative computer tool, GEGEINTOOL (GEne- GEne INTeraction tOOL, for large-scale analysis of gene-gene interactions, in human studies of complex diseases including a large number of subjects, SNPs, as well as environmental factors. That resource uses standard statistical packages (SPSS, etc. to build and fit the gene-gene interaction models by means of syntax scripts in predicting one or more continuous or dichotomic phenotypes. Codominant, dominant and recessive genetic interaction models including control for covariates are automatically created for each SNP in order to test the best model. From the standard outputs, GEGEINTOOL extracts a selected set of parameters (regression coefficients, p-values, adjusted means, etc., and groups them in a single MS Excel Spreadsheet. The tool allows editing the set of filter parameters, filtering the selected results depending on p-values, as well as plotting the selected gene-gene interactions to check consistency. In conclusion, GEGEINTOOL is a useful and friendly tool for exploring and identifying gene-gene interactions in complex diseases.

  12. [Site of fidget gene action and its interaction with the ocular retardation gene in cultured mouse embryo retinas].

    Science.gov (United States)

    Koniukhov, B V; Ugol'kova, T S

    1978-01-01

    The eye rudiments of 10 and 11 days old mouse embryos of the genotypes +/+ +/+, fi/fi +/+, +/+ or/or, fi/fi or/or and 11 days old embryos of the genotype fi/+ or/+ were cultivated in vitro during 24, 48 and 72 hrs. The expression of the fi gene was shown in the cells of the cultivated fi/fi +/+ retina: its proliferative activity was inhibited. The fi gene was not active in the cells of the developing lens and the anomalies of the latter in homozygotes arose secondarily, due to the inhibition of growth of the retinal rudiment. The fi and or genes interacted synergically in the cultivated fi/fi or/or retina, thus resulting in the marked inhibition of its mitotic activity. This suggests that both the genes act in the retinal cells and, apparently, affect different links of the biochemical chain of events in the preparation of DNA replication.

  13. PREFACE: Physics approaches to protein interactions and gene regulation Physics approaches to protein interactions and gene regulation

    Science.gov (United States)

    Nussinov, Ruth; Panchenko, Anna R.; Przytycka, Teresa

    2011-06-01

    Physics approaches focus on uncovering, modeling and quantitating the general principles governing the micro and macro universe. This has always been an important component of biological research, however recent advances in experimental techniques and the accumulation of unprecedented genome-scale experimental data produced by these novel technologies now allow for addressing fundamental questions on a large scale. These relate to molecular interactions, principles of bimolecular recognition, and mechanisms of signal propagation. The functioning of a cell requires a variety of intermolecular interactions including protein-protein, protein-DNA, protein-RNA, hormones, peptides, small molecules, lipids and more. Biomolecules work together to provide specific functions and perturbations in intermolecular communication channels often lead to cellular malfunction and disease. A full understanding of the interactome requires an in-depth grasp of the biophysical principles underlying individual interactions as well as their organization in cellular networks. Phenomena can be described at different levels of abstraction. Computational and systems biology strive to model cellular processes by integrating and analyzing complex data from multiple experimental sources using interdisciplinary tools. As a result, both the causal relationships between the variables and the general features of the system can be discovered, which even without knowing the details of the underlying mechanisms allow for putting forth hypotheses and predicting the behavior of the systems in response to perturbation. And here lies the strength of in silico models which provide control and predictive power. At the same time, the complexity of individual elements and molecules can be addressed by the fields of molecular biophysics, physical biology and structural biology, which focus on the underlying physico-chemical principles and may explain the molecular mechanisms of cellular function. In this issue

  14. Sequential pattern mining for discovering gene interactions and their contextual information from biomedical texts.

    Science.gov (United States)

    Cellier, Peggy; Charnois, Thierry; Plantevit, Marc; Rigotti, Christophe; Crémilleux, Bruno; Gandrillon, Olivier; Kléma, Jiří; Manguin, Jean-Luc

    2015-01-01

    Discovering gene interactions and their characterizations from biological text collections is a crucial issue in bioinformatics. Indeed, text collections are large and it is very difficult for biologists to fully take benefit from this amount of knowledge. Natural Language Processing (NLP) methods have been applied to extract background knowledge from biomedical texts. Some of existing NLP approaches are based on handcrafted rules and thus are time consuming and often devoted to a specific corpus. Machine learning based NLP methods, give good results but generate outcomes that are not really understandable by a user. We take advantage of an hybridization of data mining and natural language processing to propose an original symbolic method to automatically produce patterns conveying gene interactions and their characterizations. Therefore, our method not only allows gene interactions but also semantics information on the extracted interactions (e.g., modalities, biological contexts, interaction types) to be detected. Only limited resource is required: the text collection that is used as a training corpus. Our approach gives results comparable to the results given by state-of-the-art methods and is even better for the gene interaction detection in AIMed. Experiments show how our approach enables to discover interactions and their characterizations. To the best of our knowledge, there is few methods that automatically extract the interactions and also associated semantics information. The extracted gene interactions from PubMed are available through a simple web interface at https://bingotexte.greyc.fr/. The software is available at https://bingo2.greyc.fr/?q=node/22.

  15. Gene-environment interaction and male reproductive function

    DEFF Research Database (Denmark)

    Axelsson, Jonatan; Bonde, Jens Peter; Giwercman, Yvonne L;

    2010-01-01

    As genetic factors can hardly explain the changes taking place during short time spans, environmental and lifestyle-related factors have been suggested as the causes of time-related deterioration of male reproductive function. However, considering the strong heterogeneity of male fecundity between...... that specific genotypes may confer a larger risk of male reproductive disorders following certain exposures. This paper presents a critical review of animal and human evidence on how genes may modify environmental effects on male reproductive function. Some examples have been found that support this mechanism...... of reproduction, namely environmental and lifestyle factors as the cause of sperm DNA damage. It remains to be investigated to what extent such genetic changes, by natural conception or through the use of assisted reproductive techniques, are transmitted to the next generation, thereby causing increased morbidity...

  16. Consortium analysis of gene and gene-folate interactions in purine and pyrimidine metabolism pathways with ovarian carcinoma risk

    Science.gov (United States)

    Kelemen, Linda E.; Terry, Kathryn L.; Goodman, Marc T.; Webb, Penelope M.; Bandera, Elisa V.; McGuire, Valerie; Rossing, Mary Anne; Wang, Qinggang; Dicks, Ed; Tyrer, Jonathan P.; Song, Honglin; Kupryjanczyk, Jolanta; Dansonka-Mieszkowska, Agnieszka; Plisiecka-Halasa, Joanna; Timorek, Agnieszka; Menon, Usha; Gentry-Maharaj, Aleksandra; Gayther, Simon A.; Ramus, Susan J.; Narod, Steven A.; Risch, Harvey A.; McLaughlin, John R.; Siddiqui, Nadeem; Glasspool, Rosalind; Paul, James; Carty, Karen; Gronwald, Jacek; Lubiński, Jan; Jakubowska, Anna; Cybulski, Cezary; Kiemeney, Lambertus A.; Massuger, Leon F. A. G.; van Altena, Anne M.; Aben, Katja K. H.; Olson, Sara H.; Orlow, Irene; Cramer, Daniel W.; Levine, Douglas A.; Bisogna, Maria; Giles, Graham G.; Southey, Melissa C.; Bruinsma, Fiona; Kjær, Susanne Krüger; Høgdall, Estrid; Jensen, Allan; Høgdall, Claus K.; Lundvall, Lene; Engelholm, Svend-Aage; Heitz, Florian; du Bois, Andreas; Harter, Philipp; Schwaab, Ira; Butzow, Ralf; Nevanlinna, Heli; Pelttari, Liisa M.; Leminen, Arto; Thompson, Pamela J.; Lurie, Galina; Wilkens, Lynne R.; Lambrechts, Diether; Van Nieuwenhuysen, Els; Lambrechts, Sandrina; Vergote, Ignace; Beesley, Jonathan; Fasching, Peter A.; Beckmann, Matthias W.; Hein, Alexander; Ekici, Arif B.; Doherty, Jennifer A.; Wu, Anna H.; Pearce, Celeste L.; Pike, Malcolm C.; Stram, Daniel; Chang-Claude, Jenny; Rudolph, Anja; Dörk, Thilo; Dürst, Matthias; Hillemanns, Peter; Runnebaum, Ingo B.; Bogdanova, Natalia; Antonenkova, Natalia; Odunsi, Kunle; Edwards, Robert P.; Kelley, Joseph L.; Modugno, Francesmary; Ness, Roberta B.; Karlan, Beth Y.; Walsh, Christine; Lester, Jenny; Orsulic, Sandra; Fridley, Brooke L.; Vierkant, Robert A.; Cunningham, Julie M.; Wu, Xifeng; Lu, Karen; Liang, Dong; Hildebrandt, Michelle A.T.; Weber, Rachel Palmieri; Iversen, Edwin S.; Tworoger, Shelley S.; Poole, Elizabeth M.; Salvesen, Helga B.; Krakstad, Camilla; Bjorge, Line; Tangen, Ingvild L.; Pejovic, Tanja; Bean, Yukie; Kellar, Melissa; Wentzensen, Nicolas; Brinton, Louise A.; Lissowska, Jolanta; Garcia-Closas, Montserrat; Campbell, Ian G.; Eccles, Diana; Whittemore, Alice S.; Sieh, Weiva; Rothstein, Joseph H.; Anton-Culver, Hoda; Ziogas, Argyrios; Phelan, Catherine M.; Moysich, Kirsten B.; Goode, Ellen L.; Schildkraut, Joellen M.; Berchuck, Andrew; Pharoah, Paul D.P.; Sellers, Thomas A.; Brooks-Wilson, Angela; Cook, Linda S.; Le, Nhu D.

    2014-01-01

    Scope We re-evaluated previously reported associations between variants in pathways of one-carbon (folate) transfer genes and ovarian carcinoma (OC) risk, and in related pathways of purine and pyrimidine metabolism, and assessed interactions with folate intake. Methods and Results Odds ratios (OR) for 446 genetic variants were estimated among 13,410 OC cases and 22,635 controls and among 2,281 cases and 3,444 controls with folate information. Following multiple testing correction, the most significant main effect associations were for DPYD variants rs11587873 (OR=0.92, P=6x10−5) and rs828054 (OR=1.06, P=1x10−4). Thirteen variants in the pyrimidine metabolism genes, DPYD, DPYS, PPAT and TYMS, also interacted significantly with folate in a multi-variant analysis (corrected P=9.9x10−6) but collectively explained only 0.2% of OC risk. Although no other associations were significant after multiple testing correction, variants in SHMT1 in one-carbon transfer, previously reported with OC, suggested lower risk at higher folate (Pinteraction=0.03-0.006). Conclusions Variation in pyrimidine metabolism genes, particularly DPYD, which was previously reported to be associated with OC, may influence risk; however, stratification by folate intake is unlikely to modify disease risk appreciably in these women. SHMT1 SNP-byfolate interactions are plausible but require further validation. Polymorphisms in selected genes in purine metabolism were not associated with OC. PMID:25066213

  17. Differential gene expression and Hog1 interaction with osmoresponsive genes in the extremely halotolerant black yeast Hortaea werneckii

    Directory of Open Access Journals (Sweden)

    Plemenitaš Ana

    2007-08-01

    Full Text Available Abstract Background Fluctuations in external salinity force eukaryotic cells to respond by changes in the gene expression of proteins acting in protective biochemical processes, thus counteracting the changing osmotic pressure. The high-osmolarity glycerol (HOG signaling pathway is essential for the efficient up-regulation of the osmoresponsive genes. In this study, the differential gene expression of the extremely halotolerant black yeast Hortaea werneckii was explored. Furthermore, the interaction of mitogen-activated protein kinase HwHog1 and RNA polymerase II with the chromatin in cells adapted to an extremely hypersaline environment was analyzed. Results A cDNA subtraction library was constructed for H. werneckii, adapted to moderate salinity or an extremely hypersaline environment of 4.5 M NaCl. An uncommon osmoresponsive set of 95 differentially expressed genes was identified. The majority of these had not previously been connected with the adaptation of salt-sensitive S. cerevisiae to hypersaline conditions. The transcriptional response in hypersaline-adapted and hypersaline-stressed cells showed that only a subset of the identified genes responded to acute salt-stress, whereas all were differentially expressed in adapted cells. Interaction with HwHog1 was shown for 36 of the 95 differentially expressed genes. The majority of the identified osmoresponsive and HwHog1-dependent genes in H. werneckii have not been previously reported as Hog1-dependent genes in the salt-sensitive S. cerevisiae. The study further demonstrated the co-occupancy of HwHog1 and RNA polymerase II on the chromatin of 17 up-regulated and 2 down-regulated genes in 4.5 M NaCl-adapted H. werneckii cells. Conclusion Extremely halotolerant H. werneckii represents a suitable and highly relevant organism to study cellular responses to environmental salinity. In comparison with the salt-sensitive S. cerevisiae, this yeast shows a different set of genes being expressed at

  18. Contrasting Patterns in the Evolution of Vertebrate MLX Interacting Protein (MLXIP and MLX Interacting Protein-Like (MLXIPL Genes.

    Directory of Open Access Journals (Sweden)

    Parmveer Singh

    Full Text Available ChREBP and MondoA are glucose-sensitive transcription factors that regulate aspects of energy metabolism. Here we performed a phylogenomic analysis of Mlxip (encoding MondoA and Mlxipl (encoding ChREBP genes across vertebrates. Analysis of extant Mlxip and Mlxipl genes suggests that the most recent common ancestor of these genes was composed of 17 coding exons. Single copy genes encoding both ChREBP and MondoA, along with their interacting partner Mlx, were found in diverse vertebrate genomes, including fish that have experienced a genome duplication. This observation suggests that a single Mlx gene has been retained to maintain coordinate regulation of ChREBP and MondoA. The ChREBP-β isoform, the more potent and constitutively active isoform, appeared with the evolution of tetrapods and is absent from the Mlxipl genes of fish. Evaluation of the conservation of ChREBP and MondoA sequences demonstrate that MondoA is better conserved and potentially mediates more ancient function in glucose metabolism.

  19. Comparison of 2 models for gene-environment interactions: an example of simulated gene-medication interactions on systolic blood pressure in family-based data.

    Science.gov (United States)

    Fernández-Rhodes, Lindsay; Hodonsky, Chani J; Graff, Mariaelisa; Love, Shelly-Ann M; Howard, Annie Green; Seyerle, Amanda A; Avery, Christy L; Chittoor, Geetha; Franceschini, Nora; Voruganti, V Saroja; Young, Kristin; O'Connell, Jeffrey R; North, Kari E; Justice, Anne E

    2016-01-01

    Nearly half of adults in the United States who are diagnosed with hypertension use blood-pressure-lowering medications. Yet there is a large interindividual variability in the response to these medications. Two complementary gene-environment interaction methods have been published and incorporated into publicly available software packages to examine interaction effects, including whether genetic variants modify the association between medication use and blood pressure. The first approach uses a gene-environment interaction term to measure the change in outcome when both the genetic marker and medication are present (the "interaction model"). The second approach tests for effect-size differences between strata of an environmental exposure (the "med-diff" approach). However, no studies have quantitatively compared how these methods perform with respect to 1 or 2 degree of freedom (DF) tests or in family-based data sets. We evaluated these 2 approaches using simulated genotype-medication response interactions at 3 single nucleotide polymorphisms (SNPs) across a range of minor allele frequencies (MAFs 0.1-5.4 %) using the Genetic Analysis Workshop 19 family sample. The estimated interaction effect sizes were on average larger in the interaction model approach compared to the med-diff approach. The true positive proportion was higher for the med-diff approach for SNPs less than 1 % MAF, but higher for the interaction model when common variants were evaluated (MAF >5 %). The interaction model produced lower false-positive proportions than expected (5 %) across a range of MAFs for both the 1DF and 2DF tests. In contrast, the med-diff approach produced higher but stable false-positive proportions around 5 % across MAFs for both tests. Although the 1DF tests both performed similarly for common variants, the interaction model estimated true interaction effects with less bias and higher true positive proportions than the med-diff approach. However, if rare variation (MAF

  20. Case-control studies of gene-environment interaction: Bayesian design and analysis.

    Science.gov (United States)

    Mukherjee, Bhramar; Ahn, Jaeil; Gruber, Stephen B; Ghosh, Malay; Chatterjee, Nilanjan

    2010-09-01

    With increasing frequency, epidemiologic studies are addressing hypotheses regarding gene-environment interaction. In many well-studied candidate genes and for standard dietary and behavioral epidemiologic exposures, there is often substantial prior information available that may be used to analyze current data as well as for designing a new study. In this article, first, we propose a proper full Bayesian approach for analyzing studies of gene-environment interaction. The Bayesian approach provides a natural way to incorporate uncertainties around the assumption of gene-environment independence, often used in such an analysis. We then consider Bayesian sample size determination criteria for both estimation and hypothesis testing regarding the multiplicative gene-environment interaction parameter. We illustrate our proposed methods using data from a large ongoing case-control study of colorectal cancer investigating the interaction of N-acetyl transferase type 2 (NAT2) with smoking and red meat consumption. We use the existing data to elicit a design prior and show how to use this information in allocating cases and controls in planning a future study that investigates the same interaction parameters. The Bayesian design and analysis strategies are compared with their corresponding frequentist counterparts.

  1. Genes-environment interactions in obesity- and diabetes-associated pancreatic cancer: A GWAS data analysis

    Science.gov (United States)

    Tang, Hongwei; Wei, Peng; Duell, Eric J.; Risch, Harvey A.; Olson, Sara H.; Bueno-de-Mesquita, H. Bas; Gallinger, Steven; Holly, Elizabeth A.; Petersen, Gloria M.; Bracci, Paige M.; McWilliams, Robert R.; Jenab, Mazda; Riboli, Elio; Tjønneland, Anne; Boutron-Ruault, Marie Christine; Kaaks, Rudolf; Trichopoulos, Dimitrios; Panico, Salvatore; Sund, Malin; Peeters, Petra H.M; Khaw, Kay-Tee; Amos, Christopher I; Li, Donghui

    2013-01-01

    Background Obesity and diabetes are potentially alterable risk factors for pancreatic cancer. Genetic factors that modify the associations of obesity and diabetes with pancreatic cancer have previously not been examined at the genome-wide level. Methods Using GWAS genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study of 2,028 cases and 2,109 controls to examine gene-obesity and gene-diabetes interactions in relation to pancreatic cancer risk by employing the likelihood ratio test (LRT) nested in logistic regression models and Ingenuity Pathway Analysis (IPA). Results After adjusting for multiple comparisons, a significant interaction of the chemokine signaling pathway with obesity (P = 3.29 × 10−6) and a near significant interaction of calcium signaling pathway with diabetes (P = 1.57 × 10−4) in modifying the risk of pancreatic cancer was observed. These findings were supported by results from IPA analysis of the top genes with nominal interactions. The major contributing genes to the two top pathways include GNGT2, RELA, TIAM1 and GNAS. None of the individual genes or SNPs except one SNP remained significant after adjusting for multiple testing. Notably, SNP rs10818684 of the PTGS1 gene showed an interaction with diabetes (P = 7.91 × 10−7) at a false discovery rate of 6%. Conclusions Genetic variations in inflammatory response and insulin resistance may affect the risk of obesity and diabetes-related pancreatic cancer. These observations should be replicated in additional large datasets. Impact Gene-environment interaction analysis may provide new insights into the genetic susceptibility and molecular mechanisms of obesity- and diabetes-related pancreatic cancer. PMID:24136929

  2. Genes-environment interactions in obesity- and diabetes-associated pancreatic cancer: a GWAS data analysis.

    Science.gov (United States)

    Tang, Hongwei; Wei, Peng; Duell, Eric J; Risch, Harvey A; Olson, Sara H; Bueno-de-Mesquita, H Bas; Gallinger, Steven; Holly, Elizabeth A; Petersen, Gloria M; Bracci, Paige M; McWilliams, Robert R; Jenab, Mazda; Riboli, Elio; Tjønneland, Anne; Boutron-Ruault, Marie Christine; Kaaks, Rudolf; Trichopoulos, Dimitrios; Panico, Salvatore; Sund, Malin; Peeters, Petra H M; Khaw, Kay-Tee; Amos, Christopher I; Li, Donghui

    2014-01-01

    Obesity and diabetes are potentially alterable risk factors for pancreatic cancer. Genetic factors that modify the associations of obesity and diabetes with pancreatic cancer have previously not been examined at the genome-wide level. Using genome-wide association studies (GWAS) genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study of 2,028 cases and 2,109 controls to examine gene-obesity and gene-diabetes interactions in relation to pancreatic cancer risk by using the likelihood-ratio test nested in logistic regression models and Ingenuity Pathway Analysis (IPA). After adjusting for multiple comparisons, a significant interaction of the chemokine signaling pathway with obesity (P = 3.29 × 10(-6)) and a near significant interaction of calcium signaling pathway with diabetes (P = 1.57 × 10(-4)) in modifying the risk of pancreatic cancer were observed. These findings were supported by results from IPA analysis of the top genes with nominal interactions. The major contributing genes to the two top pathways include GNGT2, RELA, TIAM1, and GNAS. None of the individual genes or single-nucleotide polymorphism (SNP) except one SNP remained significant after adjusting for multiple testing. Notably, SNP rs10818684 of the PTGS1 gene showed an interaction with diabetes (P = 7.91 × 10(-7)) at a false discovery rate of 6%. Genetic variations in inflammatory response and insulin resistance may affect the risk of obesity- and diabetes-related pancreatic cancer. These observations should be replicated in additional large datasets. A gene-environment interaction analysis may provide new insights into the genetic susceptibility and molecular mechanisms of obesity- and diabetes-related pancreatic cancer.

  3. Prioritization of gene regulatory interactions from large-scale modules in yeast

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    Bringas Ricardo

    2008-01-01

    Full Text Available Abstract Background The identification of groups of co-regulated genes and their transcription factors, called transcriptional modules, has been a focus of many studies about biological systems. While methods have been developed to derive numerous modules from genome-wide data, individual links between regulatory proteins and target genes still need experimental verification. In this work, we aim to prioritize regulator-target links within transcriptional modules based on three types of large-scale data sources. Results Starting with putative transcriptional modules from ChIP-chip data, we first derive modules in which target genes show both expression and function coherence. The most reliable regulatory links between transcription factors and target genes are established by identifying intersection of target genes in coherent modules for each enriched functional category. Using a combination of genome-wide yeast data in normal growth conditions and two different reference datasets, we show that our method predicts regulatory interactions with significantly higher predictive power than ChIP-chip binding data alone. A comparison with results from other studies highlights that our approach provides a reliable and complementary set of regulatory interactions. Based on our results, we can also identify functionally interacting target genes, for instance, a group of co-regulated proteins related to cell wall synthesis. Furthermore, we report novel conserved binding sites of a glycoprotein-encoding gene, CIS3, regulated by Swi6-Swi4 and Ndd1-Fkh2-Mcm1 complexes. Conclusion We provide a simple method to prioritize individual TF-gene interactions from large-scale transcriptional modules. In comparison with other published works, we predict a complementary set of regulatory interactions which yields a similar or higher prediction accuracy at the expense of sensitivity. Therefore, our method can serve as an alternative approach to prioritization for

  4. The interaction of BDNF and NTRK2 gene increases the susceptibility of paranoid schizophrenia.

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    Zheng Lin

    Full Text Available The association between BDNF gene functional Val66Met polymorphism rs6265 and the schizophrenia is far from being consistent. In addition to the heterogeneous in schizophrenia per se leading to the inconsistent results, the interaction among multi-genes is probably playing the main role in the pathogenesis of schizophrenia, but not a single gene. Neurotrophic tyrosine kinase receptor 2 (NTRK2 is the high-affinity receptor of BDNF, and was reported to be associated with mood disorders, though no literature reported the association with schizophrenia. Thus, in the present study, total 402 patients with paranoid schizophrenia (the most common subtype of schizophrenia and matched 406 healthy controls were recruited to investigate the role of rs6265 in BDNF, three polymorphisms in NTRK2 gene (rs1387923, rs2769605 and rs1565445 and their interaction in the susceptibility to paranoid schizophrenia in a Chinese Han population. We did not observe significant differences in allele and genotype frequencies between patients and healthy controls for all four polymorphisms separately. The haplotype analysis also showed no association between haplotype of NTRK2 genes (rs1387923, rs2769605, and rs1565445 and paranoid schizophrenia. However, we found the association between the interaction of BDNF and NTRK2 with paranoid schizophrenia by using the MDR method followed by conventional statistical analysis. The best gene-gene interaction model was a three-locus model (BDNF rs6265, NTRK2 rs1387923 and NTRK2 rs2769605, in which one low-risk and three high-risk four-locus genotype combinations were identified. Our findings implied that single polymorphism of rs6265 rs1387923, rs2769605, and rs1565445 in BDNF and NTRK2 were not associated with the development of paranoid schizophrenia in a Han population, however, the interaction of BDNF and NTRK2 genes polymorphisms (BDNF-rs6265, NTRK2-rs1387923 and NTRK2-rs2769605 may be involved in the susceptibility to paranoid

  5. The interaction of BDNF and NTRK2 gene increases the susceptibility of paranoid schizophrenia.

    Science.gov (United States)

    Lin, Zheng; Su, Yousong; Zhang, Chengfang; Xing, Mengjuan; Ding, Wenhua; Liao, Liwei; Guan, Yangtai; Li, Zezhi; Cui, Donghong

    2013-01-01

    The association between BDNF gene functional Val66Met polymorphism rs6265 and the schizophrenia is far from being consistent. In addition to the heterogeneous in schizophrenia per se leading to the inconsistent results, the interaction among multi-genes is probably playing the main role in the pathogenesis of schizophrenia, but not a single gene. Neurotrophic tyrosine kinase receptor 2 (NTRK2) is the high-affinity receptor of BDNF, and was reported to be associated with mood disorders, though no literature reported the association with schizophrenia. Thus, in the present study, total 402 patients with paranoid schizophrenia (the most common subtype of schizophrenia) and matched 406 healthy controls were recruited to investigate the role of rs6265 in BDNF, three polymorphisms in NTRK2 gene (rs1387923, rs2769605 and rs1565445) and their interaction in the susceptibility to paranoid schizophrenia in a Chinese Han population. We did not observe significant differences in allele and genotype frequencies between patients and healthy controls for all four polymorphisms separately. The haplotype analysis also showed no association between haplotype of NTRK2 genes (rs1387923, rs2769605, and rs1565445) and paranoid schizophrenia. However, we found the association between the interaction of BDNF and NTRK2 with paranoid schizophrenia by using the MDR method followed by conventional statistical analysis. The best gene-gene interaction model was a three-locus model (BDNF rs6265, NTRK2 rs1387923 and NTRK2 rs2769605), in which one low-risk and three high-risk four-locus genotype combinations were identified. Our findings implied that single polymorphism of rs6265 rs1387923, rs2769605, and rs1565445 in BDNF and NTRK2 were not associated with the development of paranoid schizophrenia in a Han population, however, the interaction of BDNF and NTRK2 genes polymorphisms (BDNF-rs6265, NTRK2-rs1387923 and NTRK2-rs2769605) may be involved in the susceptibility to paranoid schizophrenia.

  6. Nonparametric Estimates of Gene × Environment Interaction Using Local Structural Equation Modeling

    Science.gov (United States)

    Briley, Daniel A.; Harden, K. Paige; Bates, Timothy C.; Tucker-Drob, Elliot M.

    2017-01-01

    Gene × Environment (G×E) interaction studies test the hypothesis that the strength of genetic influence varies across environmental contexts. Existing latent variable methods for estimating G×E interactions in twin and family data specify parametric (typically linear) functions for the interaction effect. An improper functional form may obscure the underlying shape of the interaction effect and may lead to failures to detect a significant interaction. In this article, we introduce a novel approach to the behavior genetic toolkit, local structural equation modeling (LOSEM). LOSEM is a highly flexible nonparametric approach for estimating latent interaction effects across the range of a measured moderator. This approach opens up the ability to detect and visualize new forms of G×E interaction. We illustrate the approach by using LOSEM to estimate gene × socioeconomic status (SES) interactions for six cognitive phenotypes. Rather than continuously and monotonically varying effects as has been assumed in conventional parametric approaches, LOSEM indicated substantial nonlinear shifts in genetic variance for several phenotypes. The operating characteristics of LOSEM were interrogated through simulation studies where the functional form of the interaction effect was known. LOSEM provides a conservative estimate of G×E interaction with sufficient power to detect statistically significant G×E signal with moderate sample size. We offer recommendations for the application of LOSEM and provide scripts for implementing these biometric models in Mplus and in OpenMx under R. PMID:26318287

  7. NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology.

    Science.gov (United States)

    Wei, Qing; Khan, Ishita K; Ding, Ziyun; Yerneni, Satwica; Kihara, Daisuke

    2017-03-20

    The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion. NaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions. We developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo .

  8. Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

    Institute of Scientific and Technical Information of China (English)

    GAO Lei; LI Xia; GUO Zheng; ZHU MingZhu; LI YanHui; RAO ShaoQi

    2007-01-01

    GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interaction data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automatically selects the most appropriate functional classes as specific as possible during the learning process, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to "biology process" by three measures particularly designed for functional classes organized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

  9. For better and for worse: genes and parenting interact to predict future behavior in romantic relationships.

    Science.gov (United States)

    Masarik, April S; Conger, Rand D; Donnellan, M Brent; Stallings, Michael C; Martin, Monica J; Schofield, Thomas J; Neppl, Tricia K; Scaramella, Laura V; Smolen, Andrew; Widaman, Keith F

    2014-06-01

    We tested the differential susceptibility hypothesis with respect to connections between interactions in the family of origin and subsequent behaviors with romantic partners. Focal or target participants (G2) in an ongoing longitudinal study (N = 352) were observed interacting with their parents (G1) during adolescence and again with their romantic partners in adulthood. Independent observers rated positive engagement and hostility by G1 and G2 during structured interaction tasks. We created an index for hypothesized genetic plasticity by summing G2's allelic variation for polymorphisms in 5 genes (serotonin transporter gene [linked polymorphism], 5-HTT; ankyrin repeat and kinase domain containing 1 gene/dopamine receptor D2 gene, ANKK1/DRD2; dopamine receptor D4 gene, DRD4; dopamine active transporter gene, DAT; and catechol-O-methyltransferase gene, COMT). Consistent with the differential susceptibility hypothesis, G2s exposed to more hostile and positively engaged parenting behaviors during adolescence were more hostile or positively engaged toward a romantic partner if they had higher scores on the genetic plasticity index. In short, genetic factors moderated the connection between earlier experiences in the family of origin and future romantic relationship behaviors, for better and for worse.

  10. Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

  11. Gene by neuroticism interaction and cognitive function among older adults.

    Science.gov (United States)

    Dar-Nimrod, Ilan; Chapman, Benjamin P; Robbins, John A; Porsteinsson, Anton; Mapstone, Mark; Duberstein, Paul R

    2012-11-01

    Both apolipoprotein E (ApoE) ε-4 allele(s) and elevated trait neuroticism, the tendency to experience distress, are associated with cognitive function among older adults. We predicted that neuroticism moderates the association between ApoE and cognitive function and also explored whether other personality dimensions (openness to experience, agreeableness, extraversion, and conscientiousness) affect the association between ApoE status and cognitive function. Five-hundred and ninety-seven older adults (mean age of 78 years) enrolled in the Ginkgo Evaluation of Memory study completed the NEO five-factor inventory of personality. Cognitive function was assessed via the cognitive portion of the Alzheimer's Disease Assessment Scale, and a blood sample for ApoE genotyping was drawn. As hypothesized, regression analysis indicated that neuroticism moderated the relationship between the presence of ApoE ε-4 and cognitive function. Individuals with high neuroticism scores had significantly lower scores on the cognitive portion of the Alzheimer's Disease Assessment Scale compared with individuals with low neuroticism scores, but this was true only among carriers of ApoE ε-4 (interaction effect β = 0.124, p = 0.028). There was scant evidence that other personality dimensions moderate the association between ApoE ε-4 and cognitive function. Cognitive function may be affected by ApoE and neuroticism acting in tandem. Research on the underlying physiological mechanisms by which neuroticism amplifies the effect of ApoE ε-4 is warranted. The study of genotype by phenotype interactions provides an important and useful direction for the study of cognitive function among older adults and for the development of novel prevention programs. Copyright © 2012 John Wiley & Sons, Ltd.

  12. Distilling a Visual Network of Retinitis Pigmentosa Gene-Protein Interactions to Uncover New Disease Candidates.

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    Daniel Boloc

    Full Text Available Retinitis pigmentosa (RP is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA. The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies.We have built an RP-specific network (RPGeNet by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space.In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates.

  13. Interaction between γ-aminobutyric acid A receptor genes: new evidence in migraine susceptibility.

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    Marlene Quintas

    Full Text Available Migraine is a common neurological episodic disorder with a female-to-male prevalence 3- to 4-fold higher, suggesting a possible X-linked genetic component. Our aims were to assess the role of common variants of gamma-aminobutyric acid A receptor (GABAAR genes, located in the X-chromosome, in migraine susceptibility and the possible interaction between them. An association study with 188 unrelated cases and 286 migraine-free controls age- and ethnic matched was performed. Twenty-three tagging SNPs were selected in three genes (GABRE, GABRA3 and GABRQ. Allelic, genotypic and haplotypic frequencies were compared between cases and controls. We also focused on gene-gene interactions. The AT genotype of rs3810651 of GABRQ gene was associated with an increased risk for migraine (OR: 4.07; 95% CI: 1.71-9.73, p=0.002, while the CT genotype of rs3902802 (OR: 0.41; 95% CI: 0.21-0.78, p=0.006 and GA genotype of rs2131190 of GABRA3 gene (OR: 0.53; 95% CI: 0.32-0.88, p=0.013 seem to be protective factors. All associations were found in the female group and maintained significance after Bonferroni correction. We also found three nominal associations in the allelic analyses although there were no significant results in the haplotypic analyses. Strikingly, we found strong interactions between six SNPs encoding for different subunits of GABAAR, all significant after permutation correction. To our knowledge, we show for the first time, the putative involvement of polymorphisms in GABAAR genes in migraine susceptibility and more importantly we unraveled a role for novel gene-gene interactions opening new perspectives for the development of more effective treatments.

  14. Distilling a Visual Network of Retinitis Pigmentosa Gene-Protein Interactions to Uncover New Disease Candidates

    Science.gov (United States)

    Boloc, Daniel; Castillo-Lara, Sergio; Marfany, Gemma; Gonzàlez-Duarte, Roser; Abril, Josep F.

    2015-01-01

    Background Retinitis pigmentosa (RP) is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA). The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies. Methodology We have built an RP-specific network (RPGeNet) by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space. Conclusions In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates. PMID:26267445

  15. Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Baoman Wang

    2015-01-01

    Full Text Available Apoptosis is the process of programmed cell death (PCD that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

  16. DNA-water interactions distinguish messenger RNA genes from transfer RNA genes.

    Science.gov (United States)

    Khandelwal, Garima; Jayaram, B

    2012-05-30

    Physicochemical properties of DNA sequences as a guide to developing insights into genome organization has received little attention. Here, we utilize the energetics of DNA to further advance the knowledge on its language at a molecular level. Specifically, we ask the question whether physicochemical properties of different functional units on genomes differ. We extract intramolecular and solvation energies of different DNA base pair steps from a comprehensive set of molecular dynamics simulations. We then investigate the solvation behavior of DNA sequences coding for mRNAs and tRNAs. Distinguishing mRNA genes from tRNA genes is a tricky problem in genome annotation without assumptions on length of DNA and secondary structure of the product of transcription. We find that solvation energetics of DNA behaves as an extremely efficient property in discriminating 2,063,537 genes coding for mRNAs from 56,251 genes coding for tRNAs in all (~1500) completely sequenced prokaryotic genomes.

  17. JAK/STAT and Hox Dynamic Interactions in an Organogenetic Gene Cascade.

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    Pedro B Pinto

    2015-07-01

    Full Text Available Organogenesis is controlled by gene networks activated by upstream selector genes. During development the gene network is activated stepwise, with a sequential deployment of successive transcription factors and signalling molecules that modify the interaction of the elements of the network as the organ forms. Very little is known about the steps leading from the early specification of the cells that form the organ primordium to the moment when a robust gene network is in place. Here we study in detail how a Hox protein induces during early embryogenesis a simple organogenetic cascade that matures into a complex gene network through the activation of feedback and feed forward interaction loops. To address how the network organization changes during development and how the target genes integrate the genetic information it provides, we analyze in Drosophila the induction of posterior spiracle organogenesis by the Hox gene Abdominal-B (Abd-B. Initially, Abd-B activates in the spiracle primordium a cascade of transcription factors and signalling molecules including the JAK/STAT signalling pathway. We find that at later stages STAT activity feeds back directly into Abd-B, initiating the transformation of the Hox cascade into a gene-network. Focusing on crumbs, a spiracle downstream target gene of Abd-B, we analyze how a modular cis regulatory element integrates the dynamic network information set by Abd-B and the JAK/STAT signalling pathway during development. We describe how a Hox induced genetic cascade transforms into a robust gene network during organogenesis due to the repeated interaction of Abd-B and one of its targets, the JAK/STAT signalling cascade. Our results show that in this network STAT functions not just as a direct transcription factor, but also acts as a "counter-repressor", uncovering a novel mode for STAT directed transcriptional regulation.

  18. JAK/STAT and Hox Dynamic Interactions in an Organogenetic Gene Cascade

    Science.gov (United States)

    Pinto, Pedro B.; Espinosa-Vázquez, Jose Manuel; Rivas, María Luísa; Hombría, James Castelli-Gair

    2015-01-01

    Organogenesis is controlled by gene networks activated by upstream selector genes. During development the gene network is activated stepwise, with a sequential deployment of successive transcription factors and signalling molecules that modify the interaction of the elements of the network as the organ forms. Very little is known about the steps leading from the early specification of the cells that form the organ primordium to the moment when a robust gene network is in place. Here we study in detail how a Hox protein induces during early embryogenesis a simple organogenetic cascade that matures into a complex gene network through the activation of feedback and feed forward interaction loops. To address how the network organization changes during development and how the target genes integrate the genetic information it provides, we analyze in Drosophila the induction of posterior spiracle organogenesis by the Hox gene Abdominal-B (Abd-B). Initially, Abd-B activates in the spiracle primordium a cascade of transcription factors and signalling molecules including the JAK/STAT signalling pathway. We find that at later stages STAT activity feeds back directly into Abd-B, initiating the transformation of the Hox cascade into a gene-network. Focusing on crumbs, a spiracle downstream target gene of Abd-B, we analyze how a modular cis regulatory element integrates the dynamic network information set by Abd-B and the JAK/STAT signalling pathway during development. We describe how a Hox induced genetic cascade transforms into a robust gene network during organogenesis due to the repeated interaction of Abd-B and one of its targets, the JAK/STAT signalling cascade. Our results show that in this network STAT functions not just as a direct transcription factor, but also acts as a "counter-repressor", uncovering a novel mode for STAT directed transcriptional regulation. PMID:26230388

  19. Clock genes × stress × reward interactions in alcohol and substance use disorders.

    Science.gov (United States)

    Perreau-Lenz, Stéphanie; Spanagel, Rainer

    2015-06-01

    Adverse life events and highly stressful environments have deleterious consequences for mental health. Those environmental factors can potentiate alcohol and drug abuse in vulnerable individuals carrying specific genetic risk factors, hence producing the final risk for alcohol- and substance-use disorders development. The nature of these genes remains to be fully determined, but studies indicate their direct or indirect relation to the stress hypothalamo-pituitary-adrenal (HPA) axis and/or reward systems. Over the past decade, clock genes have been revealed to be key-players in influencing acute and chronic alcohol/drug effects. In parallel, the influence of chronic stress and stressful life events in promoting alcohol and substance use and abuse has been demonstrated. Furthermore, the reciprocal interaction of clock genes with various HPA-axis components, as well as the evidence for an implication of clock genes in stress-induced alcohol abuse, have led to the idea that clock genes, and Period genes in particular, may represent key genetic factors to consider when examining gene × environment interaction in the etiology of addiction. The aim of the present review is to summarize findings linking clock genes, stress, and alcohol and substance abuse, and to propose potential underlying neurobiological mechanisms.

  20. Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models

    Science.gov (United States)

    Marr, Julia; Bock, Gavin; Desbonnet, Lieve; Waddington, John

    2016-01-01

    The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia. PMID:27725886

  1. Genes and sex hormones interaction in neurodevelopmental disorders.

    Science.gov (United States)

    Romano, Emilia; Cosentino, Livia; Laviola, Giovanni; De Filippis, Bianca

    2016-08-01

    The prevalence, age of onset and symptomatology of many neurodevelopmental disorders strongly differ between genders. This review examines sex biases in human neurodevelopmental disorders and in validated animal models. A focus is made on disorders of well-established genetic origin, such as Rett syndrome, CDKL5-associated disorders, Fragile X and Down syndrome. Autism is also addressed, given its paradigmatic role as a sex-biased neurodevelopmental disorder. Reviewed literature confirms that a complex interaction between genetic factors and sex hormones may underlie the differential susceptibility of genders and may impact the severity of symptoms in most of the analyzed neurodevelopmental disorders. Even though further studies addressing the advantages and disadvantages conferred by biological sex in this class of disorders are needed to disentangle the underlying mechanisms, present findings suggest that modulation of sex steroid-related pathways may represent an innovative approach for these diseases. Much effort is now expected to unravel the potential therapeutic efficacy of drugs targeting sex hormones-related signaling pathways in neurodevelopmental disorders of well-established genetic origin. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. [Functional interactions between promoters of neighboring yellow and CG3777 genes in Drosophila melanogaster].

    Science.gov (United States)

    Leman, D V; Parshikov, A F; Georgiev, P G; Maksimenko, O G

    2012-12-01

    It was shown by us previously that the transcription of the yellow gene can be affected by the promoter of the neighboring gene CG3777, which has a similar expression profile. In the present work, we continued studying the functional interactions between the promoters of the yellow and CG3777 genes in transgenic Drosophila strains. In this work, we used the failure of the yeast activator GAL4 to stimulate transcription from the promoter of the yellow gene for the case when GAL4-binding sites are localized at the 3'-end of the gene. It has been found that, if the 983-bp CG3777 gene promoter is inserted in transgenic strains in the same orientation with the yellow gene promoter, downstream from the sites of the GAL4 activator, the CG3777 promoter provides a strong stimulation of the yellow gene by the GAL4 activator. When the promoters of the yellow and CG3777 genes are inserted in opposite orientations relative to one another, no stimulation of the yellow gene by GAL4 is observed. Additional results obtained in the work demonstrate that the functional interacton between the CG3777 and yellow promoters depends on their mutual orientation and position relative to the GAL4-binding sites.

  3. Biology-Driven Gene-Gene Interaction Analysis of Age-Related Cataract in the eMERGE Network

    Science.gov (United States)

    Hall, Molly A; Verma, Shefali S; Wallace, John; Lucas, Anastasia; Berg, Richard L; Connolly, John; Crawford, Dana C; Crosslin, David R; de Andrade, Mariza; Doheny, Kimberly F; Haines, Jonathan L; Harley, John B; Jarvik, Gail P; Kitchner, Terrie; Kuivaniemi, Helena; Larson, Eric B; Carrell, David S; Tromp, Gerard; Vrabec, Tamara R; Pendergrass, Sarah A; McCarty, Catherine A; Ritchie, Marylyn D

    2015-01-01

    Bioinformatics approaches to examine gene-gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge-driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty-three SNP-SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell-to-cell adhesion signaling, cell-cell junction organization, and cell-cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP-SNP models, which included signal transduction and PI3K-Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology-driven method, applicable for any genome-wide association study dataset. PMID:25982363

  4. Dopamine transporter DAT and receptor DRD2 variants affect risk of lethal cocaine abuse: a gene-gene-environment interaction.

    Science.gov (United States)

    Sullivan, D; Pinsonneault, J K; Papp, A C; Zhu, H; Lemeshow, S; Mash, D C; Sadee, W

    2013-01-22

    Epistatic gene-gene interactions could contribute to the heritability of complex multigenic disorders, but few examples have been reported. Here, we focus on the role of aberrant dopaminergic signaling, involving the dopamine transporter DAT, a cocaine target, and the dopamine D2 receptor, which physically interacts with DAT. Splicing polymorphism rs2283265 of DRD2, encoding D2 receptors, were shown to confer risk of cocaine overdose/death (odds ratio ∼3) in subjects and controls from the Miami Dade County Brain Bank.(1) Risk of cocaine-related death attributable to the minor allele of rs2283265 was significantly enhanced to OR=7.5 (P=0.0008) in homozygous carriers of the main 6-repeat allele of DAT rs3836790, a regulatory VNTR in intron8 lacking significant effect itself. In contrast, carriers of the minor 5-repeat DAT allele showed no significant risk (OR=1.1, P=0.84). DAT rs3836790 and DRD2 rs2283265 also interacted by modulating DAT protein activity in the ventral putamen of cocaine abusers. In high-linkage disequilibrium with the VNTR, DAT rs6347 in exon9 yielded similar results. Assessing the impact of DAT alone, a rare DAT haplotype formed by the minor alleles of rs3836790 and rs27072, a regulatory DAT variant in the 3'-UTR, occurred in nearly one-third of the cocaine abusers but was absent in African American controls, apparently conferring strong risk. These results demonstrate gene-gene-drug interaction affecting risk of fatal cocaine intoxication.

  5. A non-parametric approach for detecting gene-gene interactions associated with age-at-onset outcomes.

    Science.gov (United States)

    Li, Ming; Gardiner, Joseph C; Breslau, Naomi; Anthony, James C; Lu, Qing

    2014-07-01

    Cox-regression-based methods have been commonly used for the analyses of survival outcomes, such as age-at-disease-onset. These methods generally assume the hazard functions are proportional among various risk groups. However, such an assumption may not be valid in genetic association studies, especially when complex interactions are involved. In addition, genetic association studies commonly adopt case-control designs. Direct use of Cox regression to case-control data may yield biased estimators and incorrect statistical inference. We propose a non-parametric approach, the weighted Nelson-Aalen (WNA) approach, for detecting genetic variants that are associated with age-dependent outcomes. The proposed approach can be directly applied to prospective cohort studies, and can be easily extended for population-based case-control studies. Moreover, it does not rely on any assumptions of the disease inheritance models, and is able to capture high-order gene-gene interactions. Through simulations, we show the proposed approach outperforms Cox-regression-based methods in various scenarios. We also conduct an empirical study of progression of nicotine dependence by applying the WNA approach to three independent datasets from the Study of Addiction: Genetics and Environment. In the initial dataset, two SNPs, rs6570989 and rs2930357, located in genes GRIK2 and CSMD1, are found to be significantly associated with the progression of nicotine dependence (ND). The joint association is further replicated in two independent datasets. Further analysis suggests that these two genes may interact and be associated with the progression of ND. As demonstrated by the simulation studies and real data analysis, the proposed approach provides an efficient tool for detecting genetic interactions associated with age-at-onset outcomes.

  6. HSD3B and gene-gene interactions in a pathway-based analysis of genetic susceptibility to bladder cancer.

    Directory of Open Access Journals (Sweden)

    Angeline S Andrew

    Full Text Available Bladder cancer is the 4(th most common cancer among men in the U.S. We analyzed variant genotypes hypothesized to modify major biological processes involved in bladder carcinogenesis, including hormone regulation, apoptosis, DNA repair, immune surveillance, metabolism, proliferation, and telomere maintenance. Logistic regression was used to assess the relationship between genetic variation affecting these processes and susceptibility in 563 genotyped urothelial cell carcinoma cases and 863 controls enrolled in a case-control study of incident bladder cancer conducted in New Hampshire, U.S. We evaluated gene-gene interactions using Multifactor Dimensionality Reduction (MDR and Statistical Epistasis Network analysis. The 3'UTR flanking variant form of the hormone regulation gene HSD3B2 was associated with increased bladder cancer risk in the New Hampshire population (adjusted OR 1.85 95%CI 1.31-2.62. This finding was successfully replicated in the Texas Bladder Cancer Study with 957 controls, 497 cases (adjusted OR 3.66 95%CI 1.06-12.63. The effect of this prevalent SNP was stronger among males (OR 2.13 95%CI 1.40-3.25 than females (OR 1.56 95%CI 0.83-2.95, (SNP-gender interaction P = 0.048. We also identified a SNP-SNP interaction between T-cell activation related genes GATA3 and CD81 (interaction P = 0.0003. The fact that bladder cancer incidence is 3-4 times higher in males suggests the involvement of hormone levels. This biologic process-based analysis suggests candidate susceptibility markers and supports the theory that disrupted hormone regulation plays a role in bladder carcinogenesis.

  7. Interaction of DNA repair gene polymorphisms and aflatoxin B1 in the risk of hepatocellular carcinoma

    OpenAIRE

    2014-01-01

    Aflatoxin B1 (AFB1) is an important environmental carcinogen and can induce DNA damage and involve in the carcinogenesis of hepatocellular carcinoma (HCC). The deficiency of DNA repair capacity related to the polymorphisms of DNA repair genes might play a central role in the process of HCC tumorigenesis. However, the interaction of DNA repair gene polymorphisms and AFB1 in the risk of hepatocellular carcinoma has not been elucidated. In this study, we investigated whether six polymorphisms (i...

  8. Prioritization of candidate genes for cattle reproductive traits, based on protein-protein interactions, gene expression, and text-mining

    DEFF Research Database (Denmark)

    Hulsegge, Ina; Woelders, Henri; Smits, Mari

    2013-01-01

    and processes in brain areas and pituitary involved in reproductive traits in cattle using information derived from three different data sources: gene expression, protein-protein interactions, and literature. We identified 59, 89, 53, 23, and 71 genes in bovine amygdala, dorsal hypothalamus, hippocampus......, pituitary, and ventral hypothalamus, respectively, potentially involved in processes underlying estrus and estrous behavior. Functional annotation of the candidate genes points to a number of tissue-specific processes of which the "neurotransmitter/ion channel/synapse" process in the amygdala, "steroid...... hormone receptor activity/ion binding" in the pituitary, "extracellular region" in the ventral hypothalamus, and "positive regulation of transcription/metabolic process" in the dorsal hypothalamus are most prominent. The regulation of the functional processes in the various tissues operate at different...

  9. A realistic assessment of methods for extracting gene/protein interactions from free text

    Directory of Open Access Journals (Sweden)

    Shepherd Adrian J

    2009-07-01

    Full Text Available Abstract Background The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research. In this paper we present a realistic evaluation of gene/protein interaction mining relevant to potential non-specialist users. Hence we have specifically avoided methods that are complex to install or require reimplementation, and we coupled our chosen extraction methods with a state-of-the-art biomedical named entity tagger. Results Our results show: that performance across different evaluation corpora is extremely variable; that the use of tagged (as opposed to gold standard gene and protein names has a significant impact on performance, with a drop in F-score of over 20 percentage points being commonplace; and that a simple keyword-based benchmark algorithm when coupled with a named entity tagger outperforms two of the tools most widely used to extract gene/protein interactions. Conclusion In terms of availability, ease of use and performance, the potential non-specialist user community interested in automatically extracting gene and/or protein interactions from free text is poorly served by current tools and systems. The public release of extraction tools that are easy to install and use, and that achieve state-of-art levels of performance should be treated as a high priority by the biomedical text mining community.

  10. Evidence for gene-environment interaction in a genome wide study of nonsyndromic cleft palate

    DEFF Research Database (Denmark)

    Beaty, Terri H; Ruczinski, Ingo; Murray, Jeffrey C

    2011-01-01

    consortium. Family-based association tests of single nucleotide polymorphisms (SNP) and three common maternal exposures (maternal smoking, alcohol consumption, and multivitamin supplementation) were used in a combined 2 df test for gene (G) and gene-environment (G × E) interaction simultaneously, plus...... G × E interaction was included. Among these, MLLT3 and SMC2 on chromosome 9 showed multiple SNPs resulting in an increased risk if the mother consumed alcohol during the peri-conceptual period (3 months prior to conception through the first trimester). TBK1 on chr. 12 and ZNF236 on chr. 18 showed...

  11. Genetic association of LMAN2L gene in schizophrenia and bipolar disorder and its interaction with ANK3 gene polymorphism.

    Science.gov (United States)

    Lim, Chor Hong; Zain, Shamsul Mohd; Reynolds, Gavin P; Zain, Mohd Aizat; Roffeei, Siti Norsyuhada; Zainal, Nor Zuraida; Kanagasundram, Sharmilla; Mohamed, Zahurin

    2014-10-03

    Recent studies have shown that bipolar disorder (BPD) and schizophrenia (SZ) share some common genetic risk factors. This study aimed to examine the association between candidate single nucleotide polymorphisms (SNPs) identified from genome-wide association studies (GWAS) and risk of BPD and SZ. A total of 715 patients (244 BPD and 471 SZ) and 593 controls were genotyped using the Sequenom MassARRAY platform. We showed a positive association between LMAN2L (rs6746896) and risk of both BPD and SZ in a pooled population (P-value=0.001 and 0.009, respectively). Following stratification by ethnicity, variants of the ANK3 gene (rs1938516 and rs10994336) were found to be associated with BPD in Malays (P-value=0.001 and 0.006, respectively). Furthermore, an association exists between another variant of LMAN2L (rs2271893) and SZ in the Malay and Indian ethnic groups (P-value=0.003 and 0.002, respectively). Gene-gene interaction analysis revealed a significant interaction between the ANK3 and LMAN2L genes (empirical P=0.0107). Significant differences were shown between patients and controls for two haplotype frequencies of LMAN2L: GA (P=0.015 and P=0.010, for BPD and SZ, respectively) and GG (P=0.013 for BPD). Our study showed a significant association between LMAN2L and risk of both BPD and SZ.

  12. Systemic virus-induced gene silencing allows functional characterization of maize genes during biotrophic interaction with Ustilago maydis.

    Science.gov (United States)

    van der Linde, Karina; Kastner, Christine; Kumlehn, Jochen; Kahmann, Regine; Doehlemann, Gunther

    2011-01-01

    Infection of maize (Zea mays) plants with the corn smut fungus Ustilago maydis leads to the formation of large tumors on the stem, leaves and inflorescences. In this biotrophic interaction, plant defense responses are actively suppressed by the pathogen, and previous transcriptome analyses of infected maize plants showed massive and stage-specific changes in host gene expression during disease progression. To identify maize genes that are functionally involved in the interaction with U. maydis, we adapted a virus-induced gene silencing (VIGS) system based on the brome mosaic virus (BMV) for maize. Conditions were established that allowed successful U. maydis infection of BMV-preinfected maize plants. This set-up enabled quantification of VIGS and its impact on U. maydis infection using a quantitative real-time PCR (qRT-PCR)-based readout. In proof-of-principle experiments, an U. maydis-induced terpene synthase was shown to negatively regulate disease development while a protein involved in cell death inhibition was required for full virulence of U. maydis. The results suggest that this system is a versatile tool for the rapid identification of maize genes that determine compatibility with U. maydis.

  13. GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores.

    Science.gov (United States)

    Chikkagoudar, Satish; Wang, Kai; Li, Mingyao

    2011-05-26

    Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. GENIE reads an entire genetic association study dataset into memory and partitions the dataset into fragments with non-overlapping sets of SNPs. For each fragment, GENIE analyzes: 1) the interaction of SNPs within it in parallel, and 2) the interaction between the SNPs of the current fragment and other fragments in parallel. We tested GENIE on a large-scale candidate gene study on high-density lipoprotein cholesterol. Using an NVIDIA Tesla C1060 graphics card, the GPU mode of GENIE achieves a speedup of 27 times over its single-core CPU mode run. GENIE is open-source, economical, user-friendly, and scalable. Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster GPU cards. Documentation, source code, and precompiled binaries can be downloaded from http://www.cceb.upenn.edu/~mli/software/GENIE/.

  14. A genome-wide analysis of gene-caffeine consumption interaction on basal cell carcinoma.

    Science.gov (United States)

    Li, Xin; Cornelis, Marilyn C; Liang, Liming; Song, Fengju; De Vivo, Immaculata; Giovannucci, Edward; Tang, Jean Y; Han, Jiali

    2016-12-01

    Animal models have suggested that oral or topical administration of caffeine could inhibit ultraviolet-induced carcinogenesis via the ataxia telangiectasia and rad3 (ATR)-related apoptosis. Previous epidemiological studies have demonstrated that increased caffeine consumption is associated with reduced risk of basal cell carcinoma (BCC). To identify common genetic markers that may modify this association, we tested gene-caffeine intake interaction on BCC risk in a genome-wide analysis. We included 3383 BCC cases and 8528 controls of European ancestry from the Nurses' Health Study and Health Professionals Follow-up Study. Single nucleotide polymorphism (SNP) rs142310826 near the NEIL3 gene showed a genome-wide significant interaction with caffeine consumption (P = 1.78 × 10(-8) for interaction) on BCC risk. There was no gender difference for this interaction (P = 0.64 for heterogeneity). NEIL3, a gene belonging to the base excision DNA repair pathway, encodes a DNA glycosylase that recognizes and removes lesions produced by oxidative stress. In addition, we identified several loci with P value for interaction caffeine consumption-related SNPs reported by previous genome-wide association studies and risk of BCC, both individually and jointly, but found no significant association. In sum, we identified a DNA repair gene that could be involved in caffeine-mediated skin tumor inhibition. Further studies are warranted to confirm these findings. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  15. Improving functional modules discovery by enriching interaction networks with gene profiles

    KAUST Repository

    Salem, Saeed

    2013-05-01

    Recent advances in proteomic and transcriptomic technologies resulted in the accumulation of vast amount of high-throughput data that span multiple biological processes and characteristics in different organisms. Much of the data come in the form of interaction networks and mRNA expression arrays. An important task in systems biology is functional modules discovery where the goal is to uncover well-connected sub-networks (modules). These discovered modules help to unravel the underlying mechanisms of the observed biological processes. While most of the existing module discovery methods use only the interaction data, in this work we propose, CLARM, which discovers biological modules by incorporating gene profiles data with protein-protein interaction networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional module discovery methods.

  16. Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes.

    Science.gov (United States)

    Dawed, Adem Y; Ali, Ashfaq; Zhou, Kaixin; Pearson, Ewan R; Franks, Paul W

    2017-08-25

    There is an extensive body of literature suggesting the involvement of multiple loci in regulating the action of metformin; most findings lack replication, without which distinguishing true-positive from false-positive findings is difficult. To address this, we undertook evidence-based, multiple data integration to determine the validity of published evidence. We (1) built a database of published data on gene-metformin interactions using an automated text-mining approach (n = 5963 publications), (2) generated evidence scores for each reported locus, (3) from which a rank-ordered gene set was generated, and (4) determined the extent to which this gene set was enriched for glycaemic response through replication analyses in a well-powered independent genome-wide association study (GWAS) dataset from the Genetics of Diabetes and Audit Research Tayside Study (GoDARTS). From the literature search, seven genes were identified that are related to the clinical outcomes of metformin. Fifteen genes were linked with either metformin pharmacokinetics or pharmacodynamics, and the expression profiles of a further 51 genes were found to be responsive to metformin. Gene-set enrichment analysis consisting of the three sets and two more composite sets derived from the above three showed no significant enrichment in four of the gene sets. However, we detected significant enrichment of genes in the least prioritised category (a gene set in which their expression is affected by metformin) with glycaemic response to metformin (p = 0.03). This gene set includes novel candidate genes such as SLC2A4 (p = 3.24 × 10(-04)) and G6PC (p = 4.77 × 10(-04)). We have described a semi-automated text-mining and evidence-scoring algorithm that facilitates the organisation and extraction of useful information about gene-drug interactions. We further validated the output of this algorithm in a drug-response GWAS dataset, providing novel candidate loci for gene-metformin interactions.

  17. Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study.

    Directory of Open Access Journals (Sweden)

    Claudia Langenberg

    2014-05-01

    Full Text Available BACKGROUND: Understanding of the genetic basis of type 2 diabetes (T2D has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. METHODS AND FINDINGS: The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10-4. Relative genetic risk (per standard deviation [4.4 risk alleles] was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10-3 and waist circumference (p for interaction  = 7.49×10-9. Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score. CONCLUSIONS: The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal

  18. Gene-environment interaction between the oxytocin receptor (OXTR) gene and parenting behaviour on children's theory of mind.

    Science.gov (United States)

    Wade, Mark; Hoffmann, Thomas J; Jenkins, Jennifer M

    2015-12-01

    Theory of mind (ToM) is the ability to interpret and understand human behaviour by representing the mental states of others. Like many human capacities, ToM is thought to develop through both complex biological and socialization mechanisms. However, no study has examined the joint effect of genetic and environmental influences on ToM. This study examined how variability in the oxytocin receptor gene (OXTR) and parenting behavior--two widely studied factors in ToM development-interacted to predict ToM in pre-school-aged children. Participants were 301 children who were part of an ongoing longitudinal birth cohort study. ToM was assessed at age 4.5 using a previously validated scale. Parenting was assessed through observations of mothers' cognitively sensitive behaviours. Using a family-based association design, it was suggestive that a particular variant (rs11131149) interacted with maternal cognitive sensitivity on children's ToM (P = 0.019). More copies of the major allele were associated with higher ToM as a function of increasing cognitive sensitivity. A sizeable 26% of the variability in ToM was accounted for by this interaction. This study provides the first empirical evidence of gene-environment interactions on ToM, supporting the notion that genetic factors may be modulated by potent environmental influences early in development.

  19. Representing virus-host interactions and other multi-organism processes in the Gene Ontology.

    Science.gov (United States)

    Foulger, R E; Osumi-Sutherland, D; McIntosh, B K; Hulo, C; Masson, P; Poux, S; Le Mercier, P; Lomax, J

    2015-07-28

    The Gene Ontology project is a collaborative effort to provide descriptions of gene products in a consistent and computable language, and in a species-independent manner. The Gene Ontology is designed to be applicable to all organisms but up to now has been largely under-utilized for prokaryotes and viruses, in part because of a lack of appropriate ontology terms. To address this issue, we have developed a set of Gene Ontology classes that are applicable to microbes and their hosts, improving both coverage and quality in this area of the Gene Ontology. Describing microbial and viral gene products brings with it the additional challenge of capturing both the host and the microbe. Recognising this, we have worked closely with annotation groups to test and optimize the GO classes, and we describe here a set of annotation guidelines that allow the controlled description of two interacting organisms. Building on the microbial resources already in existence such as ViralZone, UniProtKB keywords and MeGO, this project provides an integrated ontology to describe interactions between microbial species and their hosts, with mappings to the external resources above. Housing this information within the freely-accessible Gene Ontology project allows the classes and annotation structure to be utilized by a large community of biologists and users.

  20. Long-Range Chromosome Interactions Mediated by Cohesin Shape Circadian Gene Expression.

    Directory of Open Access Journals (Sweden)

    Yichi Xu

    2016-05-01

    Full Text Available Mammalian circadian rhythm is established by the negative feedback loops consisting of a set of clock genes, which lead to the circadian expression of thousands of downstream genes in vivo. As genome-wide transcription is organized under the high-order chromosome structure, it is largely uncharted how circadian gene expression is influenced by chromosome architecture. We focus on the function of chromatin structure proteins cohesin as well as CTCF (CCCTC-binding factor in circadian rhythm. Using circular chromosome conformation capture sequencing, we systematically examined the interacting loci of a Bmal1-bound super-enhancer upstream of a clock gene Nr1d1 in mouse liver. These interactions are largely stable in the circadian cycle and cohesin binding sites are enriched in the interactome. Global analysis showed that cohesin-CTCF co-binding sites tend to insulate the phases of circadian oscillating genes while cohesin-non-CTCF sites are associated with high circadian rhythmicity of transcription. A model integrating the effects of cohesin and CTCF markedly improved the mechanistic understanding of circadian gene expression. Further experiments in cohesin knockout cells demonstrated that cohesin is required at least in part for driving the circadian gene expression by facilitating the enhancer-promoter looping. This study provided a novel insight into the relationship between circadian transcriptome and the high-order chromosome structure.

  1. ExAtlas: An interactive online tool for meta-analysis of gene expression data.

    Science.gov (United States)

    Sharov, Alexei A; Schlessinger, David; Ko, Minoru S H

    2015-12-01

    We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein-protein interaction) are pre-loaded and can be used for functional annotations.

  2. Analysis of genetic interaction networks shows that alternatively spliced genes are highly versatile.

    Science.gov (United States)

    Talavera, David; Sheoran, Ritika; Lovell, Simon C

    2013-01-01

    Alternative splicing has the potential to increase the diversity of the transcriptome and proteome. Where more than one transcript arises from a gene they are often so different that they are quite unlikely to have the same function. However, it remains unclear if alternative splicing generally leads to a gene being involved in multiple biological processes or whether it alters the function within a single process. Knowing that genetic interactions occur between functionally related genes, we have used them as a proxy for functional versatility, and have analysed the sets of genes of two well-characterised model organisms: Caenorhabditis elegans and Drosophila melanogaster. Using network analyses we find that few genes are functionally homogenous (only involved in a few functionally-related biological processes). Moreover, there are differences between alternatively spliced genes and genes with a single transcript; specifically, genes with alternatively splicing are, on average, involved in more biological processes. Finally, we suggest that factors other than specific functional classes determine whether a gene is alternatively spliced.

  3. Influence of SNPs in nutrient-sensitive candidate genes and gene-diet interactions on blood lipids

    DEFF Research Database (Denmark)

    Brahe, Lena Kirchner; Angquist, Lars; Larsen, Lesli Hingstrup

    2013-01-01

    -cholesterol, HDL-cholesterol and TAG after an 8-week low-energy diet (only main effect), and a 6-month ad libitum weight maintenance diet, with different contents of dietary protein or glycaemic index. After adjusting for multiple testing, a SNP-dietary protein interaction effect on TAG was identified for lipin 1...... (LPIN1) rs4315495, with a decrease in TAG of - 0·26 mmol/l per A-allele/protein unit (95 % CI - 0·38, - 0·14, P= 0·000043). In conclusion, we investigated SNP-diet interactions for blood lipid profiles for 240 SNPs in twenty-four candidate genes, selected for their involvement in lipid metabolism...... pathways, and identified one significant interaction between LPIN1 rs4315495 and dietary protein for TAG concentration....

  4. Genetic Association and Gene-gene interaction of HAS2, HABP1 and HYAL3 Implicate Hyaluronan Metabolic Genes in Glaucomatous Neurodegeneration

    Directory of Open Access Journals (Sweden)

    Kaustuv Basu

    2012-01-01

    Full Text Available Hyaluronan (HA plays a significant role in maintaining aqueous humor outflow in trabecular meshwork, the primary ocular tissue involved in glaucoma. We examined potential association of the single nucleotide polymorphisms (SNPs of the HA synthesizing gene – hyaluronan synthase 2 (HAS2, hyaluronan binding protein 1 (HABP1 and HA catabolic gene hyaluronidase 3 (HYAL3 in the primary open angle glaucoma (POAG patients in the Indian population. Thirteen tagged SNPs (6 for HAS2, 3 for HABP1 and 4 for HYAL3 were genotyped in 116 high tension (HTG, 321 non-high tension glaucoma (NHTG samples and 96 unrelated, age-matched, glaucoma-negative, control samples. Allelic and genotypic association were analyzed by PLINK v1.04; haplotypes were identified using PHASE v2.1 and gene-gene interaction was analyzed using multifactor dimensionality reduction (MDR v2.0. An allelic association (rs6651224; p = 0.03; OR: 0.49; 95% CI: 0.25–0.94 was observed at the second intron (C>G of HAS2 both for NHTG and HTG. rs1057308 revealed a genotypic association (p = 0.03 at the 5’ UTR of HAS2 with only HTG. TCT haplotype (rs1805429 – rs2472614 – rs8072363 in HABP1 and TTAG and TTGA (rs2285044 – rs3774753 – rs1310073 – rs1076872 in HYAL3 were found to be significantly high (p < 0.05 both for HTG and NHTG compared to controls. Gene-gene interaction revealed HABP1 predominantly interacts with HAS2 in HTG while it associates with both HYAL3 and HAS2 in NHTG. This is the first genetic evidence, albeit from a smaller study, that the natural polymorphisms in the genes involved in hyaluronan metabolism are potentially involved in glaucomatous neurodegeneration.

  5. Early life adversity and serotonin transporter gene variation interact to affect DNA methylation of the corticotropin-releasing factor gene promoter region in the adult rat brain

    NARCIS (Netherlands)

    Doelen, R.H. van der; Arnoldussen, I.A.C.; Ghareh, H.; Och, L. van; Homberg, J.R.; Kozicz, L.T.

    2015-01-01

    The interaction between childhood maltreatment and the serotonin transporter (5-HTT) gene linked polymorphic region has been associated with increased risk to develop major depression. This Gene x Environment interaction has furthermore been linked with increased levels of anxiety and glucocorticoid

  6. Interaction between smoking history and gene expression levels impacts survival of breast cancer patients.

    Science.gov (United States)

    Andres, Sarah A; Bickett, Katie E; Alatoum, Mohammad A; Kalbfleisch, Theodore S; Brock, Guy N; Wittliff, James L

    2015-08-01

    In contrast to studies focused on cigarette smoking and risk of breast cancer occurrence, this study explored the influence of smoking on breast cancer recurrence and progression. The goal was to evaluate the interaction between smoking history and gene expression levels on recurrence and overall survival of breast cancer patients. Multivariable Cox proportional hazards models were fitted for 48 cigarette smokers, 50 non-smokers, and the total population separately to determine which gene expressions and gene expression/cigarette usage interaction terms were significant in predicting overall and disease-free survival in breast cancer patients. Using methods similar to Andres et al. (BMC Cancer 13:326, 2013a; Horm Cancer 4:208-221, 2013b), multivariable analyses revealed CENPN, CETN1, CYP1A1, IRF2, LECT2, and NCOA1 to be important predictors for both breast carcinoma recurrence and mortality among smokers. Additionally, COMT was important for recurrence, and NAT1 and RIPK1 were important for mortality. In contrast, only IRF2, CETN1, and CYP1A1 were significant for disease recurrence and mortality among non-smokers, with NAT2 additionally significant for survival. Analysis of interaction between smoking status and gene expression values using the combined samples revealed significant interactions between smoking status and CYP1A1, LECT2, and CETN1. Signatures consisting of 7-8 genes were highly predictive for breast cancer recurrence and overall survival among smokers, with median C-index values of 0.8 and 0.73 for overall survival and recurrence, respectively. In contrast, median C-index values for non-smokers was only 0.59. Hence, significant interactions between gene expression and smoking status can play a key role in predicting breast cancer patient outcomes.

  7. A new clinical evidence-based gene-environment interaction model of depression.

    Science.gov (United States)

    Bagdy, Gyorgy; Juhasz, Gabriella; Gonda, Xenia

    2012-12-01

    In our current understanding of mood disorders, the role of genes is diverse including the mediation of the effects of provoking and protective factors. Different or partially overlapping gene sets play a major role in the development of personality traits including also affective temperaments, in the mediation of the effects of environmental factors, and in the interaction of these elements in the development of depression. Certain genes are associated with personality traits and temperaments including e.g., neuroticism, impulsivity, openness, rumination and extroversion. Environmental factors consist of external (early and provoking life events, seasonal changes, social support etc.) and internal factors (hormones, biological rhythm generators, comorbid disorders etc). Some of these environmental factors, such as early life events and some prenatal events directly influence the development of personality traits and temperaments. In the NEWMOOD cohort polymorphisms of the genes of the serotonin transporter, 5-HT1A, 5-HT1B and 5-HT2A and endocannabinoid CB1 receptors, tryptophan hydroxylase, CREB1, BDNF and GIRK provide evidence for the involvement of these genes in the development of depression. Based on their role in this process they could be assigned to different gene sets. The role of certain genes, such as promoter polymorphisms of the serotonin transporter (5-HTTLPR) and CB1 receptor has been shown in more than one of the above factors. Furthermore, gene-gene interactions of these promoters associated with anxiety suggest the application of these polymorphisms in personalized medicine. In this review we introduce a new model including environmental factors, genes, trait and temperament markers based on human genetic studies.

  8. Gene-Environment Interactions in the Development of Complex Disease Phenotypes

    Directory of Open Access Journals (Sweden)

    Kenneth Olden

    2008-03-01

    Full Text Available The lack of knowledge about the earliest events in disease development is due to the multi-factorial nature of disease risk. This information gap is the consequence of the lack of appreciation for the fact that most diseases arise from the complex interactions between genes and the environment as a function of the age or stage of development of the individual. Whether an environmental exposure causes illness or not is dependent on the efficiency of the so-called “environmental response machinery” (i.e., the complex of metabolic pathways that can modulate response to environmental perturbations that one has inherited. Thus, elucidating the causes of most chronic diseases will require an understanding of both the genetic and environmental contribution to their etiology. Unfortunately, the exploration of the relationship between genes and the environment has been hampered in the past by the limited knowledge of the human genome, and by the inclination of scientists to study disease development using experimental models that consider exposure to a single environmental agent. Rarely in the past were interactions between multiple genes or between genes and environmental agents considered in studies of human disease etiology. The most critical issue is how to relate exposure-disease association studies to pathways and mechanisms. To understand how genes and environmental factors interact to perturb biological pathways to cause injury or disease, scientists will need tools with the capacity to monitor the global expression of thousands of genes, proteins and metabolites simultaneously. The generation of such data in multiple species can be used to identify conserved and functionally significant genes and pathways involved in geneenvironment interactions. Ultimately, it is this knowledge that will be used to guide agencies such as the U.S. Department of Health and Human Services in decisions regarding biomedical research funding

  9. Multiple analytical approaches reveal distinct gene-environment interactions in smokers and non smokers in lung cancer

    National Research Council Canada - National Science Library

    Ihsan, Rakhshan; Chauhan, Pradeep Singh; Mishra, Ashwani Kumar; Yadav, Dhirendra Singh; Kaushal, Mishi; Sharma, Jagannath Dev; Zomawia, Eric; Verma, Yogesh; Kapur, Sujala; Saxena, Sunita

    2011-01-01

    ...) and multifactor dimensionality reduction (MDR), was applied in 188 lung cancer cases and 290 controls to explore high order interactions among xenobiotic metabolizing genes and environmental risk factors...

  10. Multiple Analytical Approaches Reveal Distinct Gene-Environment Interactions in Smokers and Non Smokers in Lung Cancer: e29431

    National Research Council Canada - National Science Library

    Rakhshan Ihsan; Pradeep Singh Chauhan; Ashwani Kumar Mishra; Dhirendra Singh Yadav; Mishi Kaushal; Jagannath Dev Sharma; Eric Zomawia; Yogesh Verma; Sujala Kapur; Sunita Saxena

    2011-01-01

    ...) and multifactor dimensionality reduction (MDR), was applied in 188 lung cancer cases and 290 controls to explore high order interactions among xenobiotic metabolizing genes and environmental risk factors...

  11. A novel approach to simulate gene-environment interactions in complex diseases

    Directory of Open Access Journals (Sweden)

    Nicodemi Mario

    2010-01-01

    Full Text Available Abstract Background Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.. Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS, a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte

  12. Gene-Gene Interactions Among PRKCA, NOS3 and BDKRB2 Polymorphisms Affect the Antihypertensive Effects of Enalapril.

    Science.gov (United States)

    Oliveira-Paula, Gustavo H; Luizon, Marcelo R; Lacchini, Riccardo; Fontana, Vanessa; Silva, Pamela S; Biagi, Celso; Tanus-Santos, Jose E

    2017-03-01

    Protein kinase C (PKC) signalling is critically involved in the control of blood pressure. Angiotensin-converting enzyme inhibitors (ACEi) affect PKC expression and activity, which are partially associated with the responses to ACEi. We examined whether PRKCA (protein kinase C, alpha) polymorphisms (rs887797 C>T, rs1010544 T>C and rs16960228 G>A), or haplotypes, and gene-gene interactions within the ACEi pathway affect the antihypertensive responses in 104 hypertensive patients treated with enalapril as monotherapy. Patients were classified as poor responders (PR) or good responders (GR) to enalapril if their changes in mean arterial pressure were lower or higher than the median value, respectively. Multi-factor dimensionality reduction was used to characterize interactions among PRKCA, NOS3 (nitric oxide synthase 3) and BDKRB2 (bradykinin receptor B2) polymorphisms. The TC+CC genotypes for the rs1010544 polymorphism were more frequent in GR than in PR (p = 0.037). Conversely, the GA+AA genotypes for the rs16960228 polymorphism, and the CTA haplotype, were more frequent in PR than in GR (p = 0.040 and p = 0.008, respectively). Moreover, the GG genotype for the PRKCA rs16960228 polymorphism was associated with PR or GR depending on the genotypes for the rs2070744 (NOS3) and rs1799722 (BDKRB2) polymorphisms (p = 0.012). Our results suggest that PRKCA polymorphisms and gene-gene interactions within the ACEi pathway affect the antihypertensive responses to enalapril. © 2016 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society).

  13. Strategy for the mapping of interactive genes using bulked segregant analysis method and Mapmaker/Exp software

    Institute of Scientific and Technical Information of China (English)

    WU Weiren; HUANG Biguang

    2006-01-01

    A qualitative trait is usually controlled by a single gene, but it may be sometimes controlled by two or even more genes. This phenomenon is called gene interaction. Rapidly searching for linked molecular markers via bulked segregant analysis (BSA)and then constructing regional linkage map with Mapmaker/Exp has become a common approach to mapping single major genes. However, methods and computer programs developed for mapping single major genes cannot be simply applied to interactive genes because the genetic patterns of gene interactions are quite different from that of single-gene inheritance. Up to now, experimental methods for quickly screening molecular markers linked to interactive genes and statistical methods and corresponding computer softwares for simultaneously analyzing the linkage relationships of multiple molecular markers to an interactive gene have not been available. To solve this problem, in this paper, we propose a strategy for mapping interactive genes using BSA and Mapmaker/Exp. We demonstrate that Mapmaker/Exp' strategy using F2 generation (in a few cases, F3 generation is also needed). As BSA and Mapmaker/Exp have been broadly used in gene mapping studies and are well known by many researchers, the strategies proposed in this paper will be useful for practical researches.

  14. Identifying candidate genes affecting developmental time in Drosophila melanogaster: pervasive pleiotropy and gene-by-environment interaction

    Directory of Open Access Journals (Sweden)

    Hasson Esteban

    2008-08-01

    Full Text Available Abstract Background Understanding the genetic architecture of ecologically relevant adaptive traits requires the contribution of developmental and evolutionary biology. The time to reach the age of reproduction is a complex life history trait commonly known as developmental time. In particular, in holometabolous insects that occupy ephemeral habitats, like fruit flies, the impact of developmental time on fitness is further exaggerated. The present work is one of the first systematic studies of the genetic basis of developmental time, in which we also evaluate the impact of environmental variation on the expression of the trait. Results We analyzed 179 co-isogenic single P[GT1]-element insertion lines of Drosophila melanogaster to identify novel genes affecting developmental time in flies reared at 25°C. Sixty percent of the lines showed a heterochronic phenotype, suggesting that a large number of genes affect this trait. Mutant lines for the genes Merlin and Karl showed the most extreme phenotypes exhibiting a developmental time reduction and increase, respectively, of over 2 days and 4 days relative to the control (a co-isogenic P-element insertion free line. In addition, a subset of 42 lines selected at random from the initial set of 179 lines was screened at 17°C. Interestingly, the gene-by-environment interaction accounted for 52% of total phenotypic variance. Plastic reaction norms were found for a large number of developmental time candidate genes. Conclusion We identified components of several integrated time-dependent pathways affecting egg-to-adult developmental time in Drosophila. At the same time, we also show that many heterochronic phenotypes may arise from changes in genes involved in several developmental mechanisms that do not explicitly control the timing of specific events. We also demonstrate that many developmental time genes have pleiotropic effects on several adult traits and that the action of most of them is sensitive

  15. Evidence of gene-gene interaction and age-at-diagnosis effects in type 1 diabetes

    DEFF Research Database (Denmark)

    Howson, Joanna M M; Cooper, Jason D; Smyth, Deborah J

    2012-01-01

    diabetes samples (6,750 British diabetic individuals and 8,116 affected family samples of European descent) were genotyped at 38 confirmed type 1 diabetes-associated non-HLA regions and used to test for interaction of association with age-at-diagnosis, sex, and HLA class II genotypes using regression...... of the familial clustering of the disease....

  16. APOE modulates the correlation between triglycerides, cholesterol, and CHD through pleiotropy, and gene-by-gene interactions.

    Science.gov (United States)

    Maxwell, Taylor J; Ballantyne, Christie M; Cheverud, James M; Guild, Cameron S; Ndumele, Chiadi E; Boerwinkle, Eric

    2013-12-01

    Relationship loci (rQTL) exist when the correlation between multiple traits varies by genotype. rQTL often occur due to gene-by-gene (G × G) or gene-by-environmental interactions, making them a powerful tool for detecting G × G. Here we present an empirical analysis of apolipoprotein E (APOE) with respect to lipid traits and incident CHD leading to the discovery of loci that interact with APOE to affect these traits. We found that the relationship between total cholesterol (TC) and triglycerides (ln TG) varies by APOE isoform genotype in African-American (AA) and European-American (EA) populations. The e2 allele is associated with strong correlation between ln TG and TC while the e4 allele leads to little or no correlation. This led to a priori hypotheses that APOE genotypes affect the relationship of TC and/or ln TG with incident CHD. We found that APOE*TC was significant (P = 0.016) for AA but not EA while APOE*ln TG was significant for EA (P = 0.027) but not AA. In both cases, e2e2 and e2e3 had strong relationships between TC and ln TG with CHD while e2e4 and e4e4 results in little or no relationship between TC and ln TG with CHD. Using ARIC GWAS data, scans for loci that significantly interact with APOE produced four loci for African Americans (one CHD, one TC, and two HDL). These interactions contribute to the rQTL pattern. rQTL are a powerful tool to identify loci that modify the relationship between risk factors and disease and substantially increase statistical power for detecting G × G.

  17. SNP-SNP interaction network in angiogenesis genes associated with prostate cancer aggressiveness.

    Directory of Open Access Journals (Sweden)

    Hui-Yi Lin

    Full Text Available Angiogenesis has been shown to be associated with prostate cancer development. The majority of prostate cancer studies focused on individual single nucleotide polymorphisms (SNPs while SNP-SNP interactions are suggested having a great impact on unveiling the underlying mechanism of complex disease. Using 1,151 prostate cancer patients in the Cancer Genetic Markers of Susceptibility (CGEMS dataset, 2,651 SNPs in the angiogenesis genes associated with prostate cancer aggressiveness were evaluated. SNP-SNP interactions were primarily assessed using the two-stage Random Forests plus Multivariate Adaptive Regression Splines (TRM approach in the CGEMS group, and were then re-evaluated in the Moffitt group with 1,040 patients. For the identified gene pairs, cross-evaluation was applied to evaluate SNP interactions in both study groups. Five SNP-SNP interactions in three gene pairs (MMP16+ ROBO1, MMP16+ CSF1, and MMP16+ EGFR were identified to be associated with aggressive prostate cancer in both groups. Three pairs of SNPs (rs1477908+ rs1387665, rs1467251+ rs7625555, and rs1824717+ rs7625555 were in MMP16 and ROBO1, one pair (rs2176771+ rs333970 in MMP16 and CSF1, and one pair (rs1401862+ rs6964705 in MMP16 and EGFR. The results suggest that MMP16 may play an important role in prostate cancer aggressiveness. By integrating our novel findings and available biomedical literature, a hypothetical gene interaction network was proposed. This network demonstrates that our identified SNP-SNP interactions are biologically relevant and shows that EGFR may be the hub for the interactions. The findings provide valuable information to identify genotype combinations at risk of developing aggressive prostate cancer and improve understanding on the genetic etiology of angiogenesis associated with prostate cancer aggressiveness.

  18. Epigenetic genes and emotional reactivity to daily life events: a multi-step gene-environment interaction study.

    Directory of Open Access Journals (Sweden)

    Ehsan Pishva

    Full Text Available Recent human and animal studies suggest that epigenetic mechanisms mediate the impact of environment on development of mental disorders. Therefore, we hypothesized that polymorphisms in epigenetic-regulatory genes impact stress-induced emotional changes. A multi-step, multi-sample gene-environment interaction analysis was conducted to test whether 31 single nucleotide polymorphisms (SNPs in epigenetic-regulatory genes, i.e. three DNA methyltransferase genes DNMT1, DNMT3A, DNMT3B, and methylenetetrahydrofolate reductase (MTHFR, moderate emotional responses to stressful and pleasant stimuli in daily life as measured by Experience Sampling Methodology (ESM. In the first step, main and interactive effects were tested in a sample of 112 healthy individuals. Significant associations in this discovery sample were then investigated in a population-based sample of 434 individuals for replication. SNPs showing significant effects in both the discovery and replication samples were subsequently tested in three other samples of: (i 85 unaffected siblings of patients with psychosis, (ii 110 patients with psychotic disorders, and iii 126 patients with a history of major depressive disorder. Multilevel linear regression analyses showed no significant association between SNPs and negative affect or positive affect. No SNPs moderated the effect of pleasant stimuli on positive affect. Three SNPs of DNMT3A (rs11683424, rs1465764, rs1465825 and 1 SNP of MTHFR (rs1801131 moderated the effect of stressful events on negative affect. Only rs11683424 of DNMT3A showed consistent directions of effect in the majority of the 5 samples. These data provide the first evidence that emotional responses to daily life stressors may be moderated by genetic variation in the genes involved in the epigenetic machinery.

  19. Association of HS6ST3 gene polymorphisms with obesity and triglycerides: gene x gender interaction.

    Science.gov (United States)

    Wang, Ke-Sheng; Wang, Liang; Liu, Xuefeng; Zeng, Min

    2013-12-01

    The heparan sulfate 6-O-sulfotransferase 3 (HS6ST3) gene is involved in heparan sulphate and heparin metabolism, and has been reported to be associated with diabetic retinopathy in type 2 diabetes.We hypothesized that HS6ST3 gene polymorphisms might play an important role in obesity and related phenotypes (such as triglycerides). We examined genetic associations of 117 single-nucleotide polymorphisms (SNPs) within the HS6ST3 gene with obesity and triglycerides using two Caucasian samples: the Marshfield sample (1442 obesity cases and 2122 controls), and the Health aging and body composition (Health ABC) sample (305 cases and 1336 controls). Logistic regression analysis of obesity as a binary trait and linear regression analysis of triglycerides as a continuous trait, adjusted for age and sex, were performed using PLINK. Single marker analysis showed that six SNPs in the Marshfield sample and one SNP in the Health ABC sample were associated with obesity (P triglycerides in the Marshfield sample (P triglycerides in the Marshfield sample. These findings contribute new insights into the pathogenesis of obesity and triglycerides and demonstrate the importance of gender differences in the aetiology.

  20. Gene-gene interactions of fatty acid synthase (FASN) using multifactor-dimensionality reduction method in Korean cattle.

    Science.gov (United States)

    Lee, Jeayoung; Jin, Mehyun; Lee, Yoonseok; Ha, Jaejung; Yeo, Jungsou; Oh, Dongyep

    2014-01-01

    We examined the gene-gene interactions of five exonic single nucleotide polymorphisms (SNPs) in the gene encoding fatty acid synthase using 513 Korean cattle and using the model free and the non-parametrical multifactor dimensionality reduction method for the analysis. The five SNPs of g.12870 T>C, g.13126 T>C, g.15532 C>A, g.16907 T>C and g.17924 G>A associated with a variety of fatty acid compositions and marbling score were used in this study. The two-factor interaction between g.13126 T>C and g.15532 C>A had the highest training-balanced among the five-factor models and a testing-balanced accuracy at 70.18 % on C18:1 with a cross-validation consistency of 10 out of 10. Also, the two-factor interaction between g.13126 T>C and g.15532 C>A had the highest testing-balanced accuracy at 68.59 % with a 10 out of 10 cross-validation consistency, than any other models on MUFA. In MS, a single SNP g.15532 C>A had the best accuracy at 58.85 % and the two-factor interaction model g.12870 T>C and g.15532 C>A had the highest testing-balanced accuracy at 64.00 %. The three-factor interaction model g.12870 T>C, g.13126 T>C and g.15532 C>A was recorded as having a high testing-balanced accuracy of 63.24 %, but it was lower than the two-factor interaction model. We used likelihood ratio tests for interaction, and Chi square tests to validate our results, with all tests showing statistical significance. We also compared this with mean scores between the high-risk trait group and low-risk trait group. The genotypes of TTCA, TTAA and TCAA at g.15532 and g.13126 on C18:1, genotypes TTCC, TTCA, TTAA, TCAA CCAA at g.15532 and g.13126 on MUFA and genotypes CCCC, TCCA, CCCA, TTAA, TCAA and CCAA at g.15532 and g.12870 on MS were recommended for the genetic improvement of beef quality.

  1. Medicago truncatula gene responses specific to arbuscular mycorrhiza interactions with different species and genera of Glomeromycota.

    Science.gov (United States)

    Massoumou, M; van Tuinen, D; Chatagnier, O; Arnould, C; Brechenmacher, L; Sanchez, L; Selim, S; Gianinazzi, S; Gianinazzi-Pearson, V

    2007-05-01

    Plant genes exhibiting common responses to different arbuscular mycorrhizal (AM) fungi and not induced under other biological conditions have been sought for to identify specific markers for monitoring the AM symbiosis. A subset of 14 candidate Medicago truncatula genes was identified as being potentially mycorrhiza responsive in previous cDNA microarray analyses and exclusive to cDNA libraries derived from mycorrhizal root tissues. Transcriptional activity of the selected plant genes was compared during root interactions with seven AM fungi belonging to different species of Glomus, Acaulospora, Gigaspora, or Scutellospora, and under widely different biological conditions (mycorrhiza, phosphate fertilization, pathogenic/beneficial microbe interactions, incompatible plant genotype). Ten of the M. truncatula genes were commonly induced by all the tested AM fungal species, and all were activated by at least two fungi. Most of the plant genes were transcribed uniquely in mycorrhizal roots, and several were already active at the appressorium stage of fungal development. Novel data provide evidence that common recognition responses to phylogenetically different Glomeromycota exist in plants during events that are unique to mycorrhiza interactions. They indicate that plants should possess a mycorrhiza-specific genetic program which is comodulated by a broad spectrum of AM fungi.

  2. Beyond the French Flag Model: Exploiting Spatial and Gene Regulatory Interactions for Positional Information

    Science.gov (United States)

    Hillenbrand, Patrick; Gerland, Ulrich; Tkačik, Gašper

    2016-01-01

    A crucial step in the early development of multicellular organisms involves the establishment of spatial patterns of gene expression which later direct proliferating cells to take on different cell fates. These patterns enable the cells to infer their global position within a tissue or an organism by reading out local gene expression levels. The patterning system is thus said to encode positional information, a concept that was formalized recently in the framework of information theory. Here we introduce a toy model of patterning in one spatial dimension, which can be seen as an extension of Wolpert’s paradigmatic “French Flag” model, to patterning by several interacting, spatially coupled genes subject to intrinsic and extrinsic noise. Our model, a variant of an Ising spin system, allows us to systematically explore expression patterns that optimally encode positional information. We find that optimal patterning systems use positional cues, as in the French Flag model, together with gene-gene interactions to generate combinatorial codes for position which we call “Counter” patterns. Counter patterns can also be stabilized against noise and variations in system size or morphogen dosage by longer-range spatial interactions of the type invoked in the Turing model. The simple setup proposed here qualitatively captures many of the experimentally observed properties of biological patterning systems and allows them to be studied in a single, theoretically consistent framework. PMID:27676252

  3. Defense gene induction in the compatible interaction of soybean with Cercospora kikuchii and Diaporthe phaseolorum

    Science.gov (United States)

    We examined the compatible interaction of soybean [Glycine max (L.) Merr.] cultivar Dare with fungal pathogens Cercospora kikuchii (CK), a hemibiotroph, and Diaporthe phaseolorum (DP), a biotroph, in order to gain insight into basal host defense. Expression of defense genes in pathogen-, mock-, or ...

  4. Gene-environment interactions in early life and adulthood : implications for cocaine intake

    NARCIS (Netherlands)

    Veen, Rixt van der

    2008-01-01

    The objective of the research described in this thesis was to demonstrate the role of gene-environment interactions in the emergence of individual differences in cocaine use. For this purpose we used two inbred mouse strains, the C57Bl/6 (C57) and DBA/2 (DBA), which are known to differ in drug-intak

  5. Gene Expression Analysis of In Vitro Cocultures to Study Interactions between Breast Epithelium and Stroma

    Directory of Open Access Journals (Sweden)

    Patricia Casbas-Hernandez

    2011-01-01

    Full Text Available The interactions between breast epithelium and stroma are fundamental to normal tissue homeostasis and for tumor initiation and progression. Gene expression studies of in vitro coculture models demonstrate that in vitro models have relevance for tumor progression in vivo. For example, stromal gene expression has been shown to vary in association with tumor subtype in vivo, and analogous in vitro cocultures recapitulate subtype-specific biological interactions. Cocultures can be used to study cancer cell interactions with specific stromal components (e.g., immune cells, fibroblasts, endothelium and different representative cell lines (e.g., cancer-associated versus normal-associated fibroblasts versus established, immortalized fibroblasts can help elucidate the role of stromal variation in tumor phenotypes. Gene expression data can also be combined with cell-based assays to identify cellular phenotypes associated with gene expression changes. Coculture systems are manipulable systems that can yield important insights about cell-cell interactions and the cellular phenotypes that occur as tumor and stroma co-evolve.

  6. A cancer derived mutation in the Retinoblastoma gene with a distinct defect for LXCXE dependent interactions

    Directory of Open Access Journals (Sweden)

    Demone Jordan

    2010-03-01

    Full Text Available Abstract Background The interaction between viral oncoproteins such as Simian virus 40 TAg, adenovirus E1A, and human papilloma virus E7, and the retinoblastoma protein (pRB occurs through a well characterized peptide sequence, LXCXE, on the viral protein and a well conserved groove in the pocket domain of pRB. Cellular proteins, such as histone deacetylases, also use this mechanism to interact with the retinoblastoma protein to repress transcription at cell cycle regulated genes. For these reasons this region of the pRB pocket domain is thought to play a critical role in growth suppression. Results In this study, we identify and characterize a tumor derived allele of the retinoblastoma gene (RB1 that possesses a discrete defect in its ability to interact with LXCXE motif containing proteins that compromises proliferative control. To assess the frequency of similar mutations in the RB1 gene in human cancer, we screened blood and tumor samples for similar alleles. We screened almost 700 samples and did not detect additional mutations, indicating that this class of mutation is rare. Conclusions Our work provides proof of principal that alleles encoding distinct, partial loss of function mutations in the retinoblastoma gene that specifically lose LXCXE dependent interactions, are found in human cancer.

  7. Learning Motivation Mediates Gene-by-Socioeconomic Status Interaction on Mathematics Achievement in Early Childhood

    Science.gov (United States)

    Tucker-Drob, Elliot M.; Harden, K. Paige

    2012-01-01

    There is accumulating evidence that genetic influences on achievement are more pronounced among children living in higher socioeconomic status homes, and that these gene-by-environment interactions occur prior to children's entry into formal schooling. We hypothesized that one pathway through which socioeconomic status promotes genetic influences…

  8. Intellectual Interest Mediates Gene x Socioeconomic Status Interaction on Adolescent Academic Achievement

    Science.gov (United States)

    Tucker-Drob, Elliot M.; Harden, K. Paige

    2012-01-01

    Recent studies have demonstrated that genetic influences on cognitive ability and academic achievement are larger for children raised in higher socioeconomic status (SES) homes. However, little work has been done to document the psychosocial processes that underlie this Gene x Environment interaction. One process may involve the conversion of…

  9. Intellectual Interest Mediates Gene x Socioeconomic Status Interaction on Adolescent Academic Achievement

    Science.gov (United States)

    Tucker-Drob, Elliot M.; Harden, K. Paige

    2012-01-01

    Recent studies have demonstrated that genetic influences on cognitive ability and academic achievement are larger for children raised in higher socioeconomic status (SES) homes. However, little work has been done to document the psychosocial processes that underlie this Gene x Environment interaction. One process may involve the conversion of…

  10. Multiple genetic interaction experiments provide complementary information useful for gene function prediction.

    Directory of Open Access Journals (Sweden)

    Magali Michaut

    Full Text Available Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.

  11. Gene expression correlations in human cancer cell lines define molecular interaction networks for epithelial phenotype.

    Directory of Open Access Journals (Sweden)

    Kurt W Kohn

    Full Text Available Using gene expression data to enhance our knowledge of control networks relevant to cancer biology and therapy is a challenging but urgent task. Based on the premise that genes that are expressed together in a variety of cell types are likely to functions together, we derived mutually correlated genes that function together in various processes in epithelial-like tumor cells. Expression-correlated genes were derived from data for the NCI-60 human tumor cell lines, as well as data from the Broad Institute's CCLE cell lines. NCI-60 cell lines that selectively expressed a mutually correlated subset of tight junction genes served as a signature for epithelial-like cancer cells. Those signature cell lines served as a seed to derive other correlated genes, many of which had various other epithelial-related functions. Literature survey yielded molecular interaction and function information about those genes, from which molecular interaction maps were assembled. Many of the genes had epithelial functions unrelated to tight junctions, demonstrating that new function categories were elicited. The most highly correlated genes were implicated in the following epithelial functions: interactions at tight junctions (CLDN7, CLDN4, CLDN3, MARVELD3, MARVELD2, TJP3, CGN, CRB3, LLGL2, EPCAM, LNX1; interactions at adherens junctions (CDH1, ADAP1, CAMSAP3; interactions at desmosomes (PPL, PKP3, JUP; transcription regulation of cell-cell junction complexes (GRHL1 and 2; epithelial RNA splicing regulators (ESRP1 and 2; epithelial vesicle traffic (RAB25, EPN3, GRHL2, EHF, ADAP1, MYO5B; epithelial Ca(+2 signaling (ATP2C2, S100A14, BSPRY; terminal differentiation of epithelial cells (OVOL1 and 2, ST14, PRSS8, SPINT1 and 2; maintenance of apico-basal polarity (RAB25, LLGL2, EPN3. The findings provide a foundation for future studies to elucidate the functions of regulatory networks specific to epithelial-like cancer cells and to probe for anti-cancer drug targets.

  12. Inference of gene-phenotype associations via protein-protein interaction and orthology.

    Directory of Open Access Journals (Sweden)

    Panwen Wang

    Full Text Available One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.

  13. Gene interactions and genetics of blast resistance and yield attributes in rice (Oryza sativa L.)

    Indian Academy of Sciences (India)

    B. Divya; A. Biswas; S. Robin; R. Rabindran; A. John Joel

    2014-08-01

    Blast disease caused by the pathogen Pyricularia oryzae is a serious threat to rice production. Six generations viz., P1, P2, F1, F2, B1 and B2 of a cross between blast susceptible high-yielding rice cultivar ADT 43 and resistant near isogenic line (NIL) CT13432-3R, carrying four blast resistance genes Pi1, Pi2, Pi33 and Pi54 in combination were used to study the nature and magnitude of gene action for disease resistance and yield attributes. The epistatic interaction model was found adequate to explain the gene action in most of the traits. The interaction was complementary for number of productive tillers, economic yield, lesion number, infected leaf area and potential disease incidence but duplicate epistasis was observed for the remaining traits. Among the genotypes tested under epiphytotic conditions, gene pyramided lines were highly resistant to blast compared to individuals with single genes indicating that the nonallelic genes have a complementary effect when present together. The information on genetics of various contributing traits of resistance will further aid plant breeders in choosing appropriate breeding strategy for blast resistance and yield enhancement in rice.

  14. Understanding Epistatic Interactions between Genes Targeted by Non-coding Regulatory Elements in Complex Diseases

    Directory of Open Access Journals (Sweden)

    Min Kyung Sung

    2014-12-01

    Full Text Available Genome-wide association studies have proven the highly polygenic architecture of complex diseases or traits; therefore, single-locus-based methods are usually unable to detect all involved loci, especially when individual loci exert small effects. Moreover, the majority of associated single-nucleotide polymorphisms resides in non-coding regions, making it difficult to understand their phenotypic contribution. In this work, we studied epistatic interactions associated with three common diseases using Korea Association Resource (KARE data: type 2 diabetes mellitus (DM, hypertension (HT, and coronary artery disease (CAD. We showed that epistatic single-nucleotide polymorphisms (SNPs were enriched in enhancers, as well as in DNase I footprints (the Encyclopedia of DNA Elements [ENCODE] Project Consortium 2012, which suggested that the disruption of the regulatory regions where transcription factors bind may be involved in the disease mechanism. Accordingly, to identify the genes affected by the SNPs, we employed whole-genome multiple-cell-type enhancer data which discovered using DNase I profiles and Cap Analysis Gene Expression (CAGE. Assigned genes were significantly enriched in known disease associated gene sets, which were explored based on the literature, suggesting that this approach is useful for detecting relevant affected genes. In our knowledge-based epistatic network, the three diseases share many associated genes and are also closely related with each other through many epistatic interactions. These findings elucidate the genetic basis of the close relationship between DM, HT, and CAD.

  15. Paramutation:A Heritable Change in Gene Expression by Allelic Interactions In Trans

    Institute of Scientific and Technical Information of China (English)

    Maike Stam

    2009-01-01

    Epigenetic gene regulation involves the stable propagation of gene activity states through mitotic,and sometimes even meiotic,cell divisions without changes in DNA sequence.Paramutation is an epigenetic phenomenon involving changes in gene expression that are stably transmitted through mitosis as well as meiosis.These heritable changes are mediated by in trans interactions between homologous DNA sequences on different chromosomes.During these in trans interactions,epigenetic information is transferred from one allele of a gene to another allele of the same gene,resulting in a change in gene expression.Although paramutation was initially discovered in plants,it has recently been observed in mammals as well,suggesting that the mechanisms underlying paramutation might be evolutionarily conserved.Recent findings point to a crucial role for small RNAs in the paramutation process.In mice,small RNAs appear sufficient to induce paramutation,whereas in maize,it seems not to be the only player in the process.In this review,potential mechanisms are discussed in relation to the various paramutation phenomena.

  16. Interactions between the FTO and GNB3 genes contribute to varied clinical phenotypes in hypertension.

    Directory of Open Access Journals (Sweden)

    Rahul Kumar

    Full Text Available BACKGROUND: The genes FTO and GNB3 are implicated in essential hypertension but their interaction remains to be explored. This study investigates the role of interaction between the two genes in the pathophysiology of essential hypertension. METHODS/PRINCIPAL FINDINGS: In a case-control study comprising 750 controls and 550 patients, interaction between the polymorphisms of FTO and GNB3 was examined using multifactor dimensionality reduction (MDR. The influence of interaction on clinical phenotypes like systolic and diastolic blood pressure, mean arterial pressure and body mass index was also investigated. The 3-locus MDR model comprising FTO rs8050136C/A and GNB3 rs1129649T/C and rs5443C/T emerged as the best disease conferring model. Moreover, the interacted-genotypes having either 1, 2, 3, 4 or 5 risk alleles correlated with linearly increasing odds ratios of 1.91 (P = 0.027; 3.93 (P = 2.08E-06; 4.51 (P = 7.63E-07; 7.44 (P = 3.66E-08 and 11.57 (P = 1.18E-05, respectively, when compared with interacted-genotypes devoid of risk alleles. Furthermore, interactions among haplotypes of FTO (H1-9 and GNB3 (Ha-d differed by >1.5-fold for protective-haplotypes, CTGGC+TC [H2+Ha] and CTGAC+TC [H4+Ha] (OR = 0.39, P = 0.003; OR = 0.22, P = 6.86E-05, respectively and risk-haplotypes, AAAGC+CT [H3+Hc] and AAAGC+TT [H3+Hd] (OR = 2.91, P = 9.98E-06; OR = 2.50, P = 0.004, respectively compared to individual haplotypes. Moreover, the effectiveness of gene-gene interaction was further corroborated with a 1.29-, 1.25- and 1.38-fold higher SBP, MAP and BMI, respectively, in patients having risk interacted-haplotype H3+Hc and 2.48-fold higher SBP having risk interacted-haplotype H3+Hd compared to individual haplotypes. CONCLUSION: Interactions between genetic variants of FTO and GNB3 influence clinical parameters to augment hypertension.

  17. Gene-Environment Interactions of Circadian-Related Genes for Cardiometabolic Traits

    DEFF Research Database (Denmark)

    Dashti, Hassan S; Follis, Jack L; Smith, Caren E;

    2015-01-01

    , MTNR1B-rs10830963, NR1D1-rs2314339) and cardiometabolic traits (fasting glucose [FG], HOMA-insulin resistance, BMI, waist circumference, and HDL-cholesterol) to facilitate personalized recommendations. RESEARCH DESIGN AND METHODS: We conducted inverse-variance weighted, fixed-effect meta......-analyses of results of adjusted associations and interactions between dietary intake/sleep duration and selected variants on cardiometabolic traits from 15 cohort studies including up to 28,190 participants of European descent from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium....... RESULTS: We observed significant associations between relative macronutrient intakes and glycemic traits and short sleep duration (traits. No interactions were evident after accounting for multiple comparisons. However, we observed...

  18. Gene-nutrient interactions: importance of folic acid and vitamin B12 during early embryogenesis.

    Science.gov (United States)

    Finnell, Richard H; Shaw, Gary M; Lammer, Edward J; Rosenquist, Thomas H

    2008-06-01

    The role that nutritional factors play in mammalian development has received renewed attention over the past two decades as the scientific literature has exploded with reports that folic acid supplementation in the periconceptional period can protect embryos from a number of highly significant malformations. As is often the case, the relationship between B vitamin supplementation and improved pregnancy outcomes is more complicated than initially perceived, as the interaction between nutritional factors and selected genes must be considered. In this review, we attempt to summarize the complex clinical and experimental literature on nutritional factors, their biological transport mechanisms, and interactions with genetic polymorphisms that impact early embryogenesis. While not exhaustive, our goal was to provide an overview of important gene-nutrient interactions, focusing on folic acid and vitamin B12, to serve as a framework for understanding the multiple roles they play in early embryogenesis.

  19. Comparisons of power of statistical methods for gene-environment interaction analyses.

    Science.gov (United States)

    Ege, Markus J; Strachan, David P

    2013-10-01

    Any genome-wide analysis is hampered by reduced statistical power due to multiple comparisons. This is particularly true for interaction analyses, which have lower statistical power than analyses of associations. To assess gene-environment interactions in population settings we have recently proposed a statistical method based on a modified two-step approach, where first genetic loci are selected by their associations with disease and environment, respectively, and subsequently tested for interactions. We have simulated various data sets resembling real world scenarios and compared single-step and two-step approaches with respect to true positive rate (TPR) in 486 scenarios and (study-wide) false positive rate (FPR) in 252 scenarios. Our simulations confirmed that in all two-step methods the two steps are not correlated. In terms of TPR, two-step approaches combining information on gene-disease association and gene-environment association in the first step were superior to all other methods, while preserving a low FPR in over 250 million simulations under the null hypothesis. Our weighted modification yielded the highest power across various degrees of gene-environment association in the controls. An optimal threshold for step 1 depended on the interacting allele frequency and the disease prevalence. In all scenarios, the least powerful method was to proceed directly to an unbiased full interaction model, applying conventional genome-wide significance thresholds. This simulation study confirms the practical advantage of two-step approaches to interaction testing over more conventional one-step designs, at least in the context of dichotomous disease outcomes and other parameters that might apply in real-world settings.

  20. DEIVA: a web application for interactive visual analysis of differential gene expression profiles.

    Science.gov (United States)

    Harshbarger, Jayson; Kratz, Anton; Carninci, Piero

    2017-01-07

    Differential gene expression (DGE) analysis is a technique to identify statistically significant differences in RNA abundance for genes or arbitrary features between different biological states. The result of a DGE test is typically further analyzed using statistical software, spreadsheets or custom ad hoc algorithms. We identified a need for a web-based system to share DGE statistical test results, and locate and identify genes in DGE statistical test results with a very low barrier of entry. We have developed DEIVA, a free and open source, browser-based single page application (SPA) with a strong emphasis on being user friendly that enables locating and identifying single or multiple genes in an immediate, interactive, and intuitive manner. By design, DEIVA scales with very large numbers of users and datasets. Compared to existing software, DEIVA offers a unique combination of design decisions that enable inspection and analysis of DGE statistical test results with an emphasis on ease of use.

  1. [Interaction of the mutant aphakia, fidget and ocular retardation genes in mice].

    Science.gov (United States)

    Koniukhov, B V; Nonchev, S G

    1982-07-01

    The phenogenetic analysis of the effects of aphakia (ak) gene and its interaction with the ocular retardation (or) and fidget (fi) genes suggests that the ak gene acts in the lens cells with the result of arresting lens fibre differentiation. In mice homozygous for ak, the lens failure leads to secondary retina defects, in particular, to formation of retinal folds. In ak/ak or/or mice, the lens and retina morphogenesis stops at the optic cup stage, the eye is strongly reduced in size and more affected, compared to the corresponding single homozygotes. Unlike ak/ak or/or, in the ak/ak fi/fi mice the eyes are more regular in shape than those in the ak/ak +/+ condition. The fi gene inhibition of the retina anlage growth leads to some improvement of the eye development in double ak/ak fi/fi homozygotes, due to the absence of extensive retina folding.

  2. Dynamic regulatory interactions of Polycomb group genes: MEDEA autoregulation is required for imprinted gene expression in Arabidopsis.

    Science.gov (United States)

    Baroux, Célia; Gagliardini, Valeria; Page, Damian R; Grossniklaus, Ueli

    2006-05-01

    The imprinted Arabidopsis Polycomb group (PcG) gene MEDEA (MEA), which is homologous to Enhancer of Zeste [E(Z)], is maternally required for normal seed development. Here we show that, unlike known mammalian imprinted genes, MEA regulates its own imprinted expression: It down-regulates the maternal allele around fertilization and maintains the paternal allele silent later during seed development. Autorepression of the maternal MEA allele is direct and independent of the MEA-FIE (FERTILIZATION-INDEPENDENT ENDOSPERM) PcG complex, which is similar to the E(Z)-ESC (Extra sex combs) complex of animals, suggesting a novel mechanism. A complex network of cross-regulatory interactions among the other known members of the MEA-FIE PcG complex implies distinct functions that are dynamically regulated during reproduction.

  3. A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions

    NARCIS (Netherlands)

    Sameith, Katrin; Amini, Saman; Groot Koerkamp, Marian J A; van Leenen, Dik|info:eu-repo/dai/nl/304817236; Brok, Mariel; Brabers, Nathalie; Lijnzaad, Philip|info:eu-repo/dai/nl/311462197; van Hooff, Sander R; Benschop, Joris J.; Lenstra, Tineke L.; Apweiler, Eva; van Wageningen, Sake; Snel, Berend; Holstege, Frank C P|info:eu-repo/dai/nl/149308035; Kemmeren, Patrick|info:eu-repo/dai/nl/304817228

    2015-01-01

    Background: Genetic interactions, or non-additive effects between genes, play a crucial role in many cellular processes and disease. Which mechanisms underlie these genetic interactions has hardly been characterized. Understanding the molecular basis of genetic interactions is crucial in deciphering

  4. Gene-environment interaction in progression of AMD: the CFH gene, smoking and exposure to chronic infection.

    Science.gov (United States)

    Baird, Paul N; Robman, Luba D; Richardson, Andrea J; Dimitrov, Peter N; Tikellis, Gabriella; McCarty, Catherine A; Guymer, Robyn H

    2008-05-01

    A number of risk factors including the complement factor H (CFH) gene, smoking and Chlamydia pneumoniae have been associated with age-related macular degeneration (AMD). However, the mechanisms underlying how these risk factors might be involved in disease progression and disease aetiology is poorly understood. A cohort series of 233 individuals followed for AMD progression over a mean period of 7 years underwent a full eye examination, blood was taken for DNA and antibody titre and individuals completed a standard medical and general questionnaire. Y402H variants of the CFH gene were assessed with disease progression as well as examination of interaction between Y402H variants and smoking and Y402H variants and the pathogen C. pneumoniae. The CC risk genotype of Y402H was significantly associated with increased AMD progression [odds ratio (OR) 2.43, 95% confidence interval (95% CI) 1.07-5.49] as was smoking (OR 2.28, 95% CI 1.26-4.12). However, the risk of progression was greatly increased to almost 12-fold (OR 11.8, 95% CI 2.1-65.8) when, in addition to having the C risk allele, subjects also presented with the upper tertile of antibodies to the bacterial pathogen C. pneumoniae compared with those with the T allele of Y402H and the lowest antibody tertile. This demonstrates for the first time the existence of a gene environment-interaction between pathogenic load of C. pneumoniae and the CFH gene in the aetiology of AMD.

  5. Association, haplotype, and gene-gene interactions of the HPA axis genes with suicidal behaviour in affective disorders.

    Science.gov (United States)

    Leszczyńska-Rodziewicz, Anna; Szczepankiewicz, Aleksandra; Pawlak, Joanna; Dmitrzak-Weglarz, Monika; Hauser, Joanna

    2013-01-01

    Family twin and adoption studies have noted the heritability of specific biological factors that influence suicidal behaviour. Exposure to stress is one of the factors that strongly contribute to suicide attempts. The biological response to stress involves the hypothalamic-pituitary-adrenal axis (HPA). Therefore, we found it interesting to study polymorphisms of genes involved in the HPA axis (CRHR1, NR3C1, and AVPBR1). The study was performed on 597 patients, 225 of whom had a history of suicide attempts. We did not observe any significant differences in the studied polymorphisms between the group of patients with a history of suicide attempts and the control subjects. Our haplotype analysis of the AVPR1b gene revealed an association between the GCA haplotype and suicide attempts; however, this association was not significant after correcting for multiple testing. We did not observe any other association in haplotype and MDR analysis. We report here a comprehensive analysis of the HPA axis genes and a lack of association for genetic variations regarding the risk of suicide attempts in affective disorder patients. Nonetheless, the inconsistencies with the previously published results indicate the importance of the further investigation of these polymorphisms with respect to the risk of suicide attempts.

  6. Association, Haplotype, and Gene-Gene Interactions of the HPA Axis Genes with Suicidal Behaviour in Affective Disorders

    Directory of Open Access Journals (Sweden)

    Anna Leszczyńska-Rodziewicz

    2013-01-01

    Full Text Available Family twin and adoption studies have noted the heritability of specific biological factors that influence suicidal behaviour. Exposure to stress is one of the factors that strongly contribute to suicide attempts. The biological response to stress involves the hypothalamic-pituitary-adrenal axis (HPA. Therefore, we found it interesting to study polymorphisms of genes involved in the HPA axis (CRHR1, NR3C1, and AVPBR1. The study was performed on 597 patients, 225 of whom had a history of suicide attempts. We did not observe any significant differences in the studied polymorphisms between the group of patients with a history of suicide attempts and the control subjects. Our haplotype analysis of the AVPR1b gene revealed an association between the GCA haplotype and suicide attempts; however, this association was not significant after correcting for multiple testing. We did not observe any other association in haplotype and MDR analysis. We report here a comprehensive analysis of the HPA axis genes and a lack of association for genetic variations regarding the risk of suicide attempts in affective disorder patients. Nonetheless, the inconsistencies with the previously published results indicate the importance of the further investigation of these polymorphisms with respect to the risk of suicide attempts.

  7. Multiple Gene-Environment Interactions on the Angiogenesis Gene-Pathway Impact Rectal Cancer Risk and Survival

    Directory of Open Access Journals (Sweden)

    Noha Sharafeldin

    2017-09-01

    Full Text Available Characterization of gene-environment interactions (GEIs in cancer is limited. We aimed at identifying GEIs in rectal cancer focusing on a relevant biologic process involving the angiogenesis pathway and relevant environmental exposures: cigarette smoking, alcohol consumption, and animal protein intake. We analyzed data from 747 rectal cancer cases and 956 controls from the Diet, Activity and Lifestyle as a Risk Factor for Rectal Cancer study. We applied a 3-step analysis approach: first, we searched for interactions among single nucleotide polymorphisms on the pathway genes; second, we searched for interactions among the genes, both steps using Logic regression; third, we examined the GEIs significant at the 5% level using logistic regression for cancer risk and Cox proportional hazards models for survival. Permutation-based test was used for multiple testing adjustment. We identified 8 significant GEIs associated with risk among 6 genes adjusting for multiple testing: TNF (OR = 1.85, 95% CI: 1.10, 3.11, TLR4 (OR = 2.34, 95% CI: 1.38, 3.98, and EGR2 (OR = 2.23, 95% CI: 1.04, 4.78 with smoking; IGF1R (OR = 1.69, 95% CI: 1.04, 2.72, TLR4 (OR = 2.10, 95% CI: 1.22, 3.60 and EGR2 (OR = 2.12, 95% CI: 1.01, 4.46 with alcohol; and PDGFB (OR = 1.75, 95% CI: 1.04, 2.92 and MMP1 (OR = 2.44, 95% CI: 1.24, 4.81 with protein. Five GEIs were associated with survival at the 5% significance level but not after multiple testing adjustment: CXCR1 (HR = 2.06, 95% CI: 1.13, 3.75 with smoking; and KDR (HR = 4.36, 95% CI: 1.62, 11.73, TLR2 (HR = 9.06, 95% CI: 1.14, 72.11, EGR2 (HR = 2.45, 95% CI: 1.42, 4.22, and EGFR (HR = 6.33, 95% CI: 1.95, 20.54 with protein. GEIs between angiogenesis genes and smoking, alcohol, and animal protein impact rectal cancer risk. Our results support the importance of considering the biologic hypothesis to characterize GEIs associated with cancer outcomes.

  8. Review of the Gene-Environment Interaction Literature in Cancer: What Do We Know?

    Science.gov (United States)

    Simonds, Naoko I; Ghazarian, Armen A; Pimentel, Camilla B; Schully, Sheri D; Ellison, Gary L; Gillanders, Elizabeth M; Mechanic, Leah E

    2016-07-01

    Risk of cancer is determined by a complex interplay of genetic and environmental factors. Although the study of gene-environment interactions (G×E) has been an active area of research, little is reported about the known findings in the literature. To examine the state of the science in G×E research in cancer, we performed a systematic review of published literature using gene-environment or pharmacogenomic flags from two curated databases of genetic association studies, the Human Genome Epidemiology (HuGE) literature finder and Cancer Genome-Wide Association and Meta Analyses Database (CancerGAMAdb), from January 1, 2001, to January 31, 2011. A supplemental search using HuGE was conducted for articles published from February 1, 2011, to April 11, 2013. A 25% sample of the supplemental publications was reviewed. A total of 3,019 articles were identified in the original search. From these articles, 243 articles were determined to be relevant based on inclusion criteria (more than 3,500 interactions). From the supplemental search (1,400 articles identified), 29 additional relevant articles (1,370 interactions) were included. The majority of publications in both searches examined G×E in colon, rectal, or colorectal; breast; or lung cancer. Specific interactions examined most frequently included environmental factors categorized as energy balance (e.g., body mass index, diet), exogenous (e.g., oral contraceptives) and endogenous hormones (e.g., menopausal status), chemical environment (e.g., grilled meats), and lifestyle (e.g., smoking, alcohol intake). In both searches, the majority of interactions examined were using loci from candidate genes studies and none of the studies were genome-wide interaction studies (GEWIS). The most commonly reported measure was the interaction P-value, of which a sizable number of P-values were considered statistically significant (i.e., article is a U.S. Government work and is in the public domain in the USA.

  9. An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology

    Directory of Open Access Journals (Sweden)

    Jain Shobhit

    2010-11-01

    Full Text Available Abstract Background Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs. They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO. Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity. Results We describe an improved algorithm, Topological Clustering Semantic Similarity (TCSS, to compute semantic similarity between GO terms annotated to proteins in interaction datasets. Our algorithm, considers unequal depth of biological knowledge representation in different branches of the GO graph. The central idea is to divide the GO graph into sub-graphs and score PPIs higher if participating proteins belong to the same sub-graph as compared to if they belong to different sub-graphs. Conclusions The TCSS algorithm performs better than other semantic similarity measurement techniques that we evaluated in terms of their performance on distinguishing true from false protein interactions, and correlation with gene expression and protein families. We show an average improvement of 4.6 times the F1 score over Resnik, the next best method, on our Saccharomyces cerevisiae PPI dataset and 2 times on our Homo sapiens PPI dataset using cellular component, biological process and molecular function GO annotations.

  10. Burkholderia cenocepacia Differential Gene Expression during Host–Pathogen Interactions and Adaptation to the Host Environment

    Science.gov (United States)

    O’Grady, Eoin P.; Sokol, Pamela A.

    2011-01-01

    Members of the Burkholderia cepacia complex (Bcc) are important in medical, biotechnological, and agricultural disciplines. These bacteria naturally occur in soil and water environments and have adapted to survive in association with plants and animals including humans. All Bcc species are opportunistic pathogens including Burkholderia cenocepacia that causes infections in cystic fibrosis and chronic granulomatous disease patients. The adaptation of B. cenocepacia to the host environment was assessed in a rat chronic respiratory infection model and compared to that of high cell-density in vitro grown cultures using transcriptomics. The distribution of genes differentially expressed on chromosomes 1, 2, and 3 was relatively proportional to the size of each genomic element, whereas the proportion of plasmid-encoded genes differentially expressed was much higher relative to its size and most genes were induced in vivo. The majority of genes encoding known virulence factors, components of types II and III secretion systems and chromosome 2-encoded type IV secretion system were similarly expressed between in vitro and in vivo environments. Lower expression in vivo was detected for genes encoding N-acyl-homoserine lactone synthase CepI, orphan LuxR homolog CepR2, zinc metalloproteases ZmpA and ZmpB, LysR-type transcriptional regulator ShvR, nematocidal protein AidA, and genes associated with flagellar motility, Flp type pilus formation, and type VI secretion. Plasmid-encoded type IV secretion genes were markedly induced in vivo. Additional genes induced in vivo included genes predicted to be involved in osmotic stress adaptation or intracellular survival, metal ion, and nutrient transport, as well as those encoding outer membrane proteins. Genes identified in this study are potentially important for virulence during host–pathogen interactions and may be associated with survival and adaptation to the host environment during chronic lung infections. PMID:22919581

  11. Hox gene function and interaction in the milkweed bug Oncopeltus fasciatus (Hemiptera).

    Science.gov (United States)

    Angelini, David R; Liu, Paul Z; Hughes, Cynthia L; Kaufman, Thomas C

    2005-11-15

    Studies in genetic model organisms such as Drosophila have demonstrated that the homeotic complex (Hox) genes impart segmental identity during embryogenesis. Comparative studies in a wide range of other insect taxa have shown that the Hox genes are expressed in largely conserved domains along the anterior-posterior body axis, but whether they are performing the same functions in different insects is an open question. Most of the Hox genes have been studied functionally in only a few holometabolous insects that undergo metamorphosis. Thus, it is unclear how the Hox genes are functioning in the majority of direct-developing insects and other arthropods. To address this question, we used a combination of RNAi and in situ hybridization to reveal the expression, functions, and regulatory interactions of the Hox genes in the milkweed bug Oncopeltus fasciatus. Our results reveal many similarities and some interesting differences compared to Drosophila. We find that the gene Antennapedia is required for the identity of all three thoracic segments, while Ultrabithorax, abdominal-A and Abdominal-B cooperate to pattern the abdomen. The three abdominal genes exhibit posterior prevalence like in Drosophila, but apparently via some post-transcriptional mechanism. The functions of the head genes proboscipedia, Deformed, and Sex combs reduced were shown previously, and here we find that the complex temporal expression of pb in the labium is like that of other insects, but its regulatory relationship with Scr is unique. Overall, our data reveal that the evolution of insect Hox genes has included many small changes within general conservation of expression and function, and that the milkweed bug provides a useful model for understanding the roles of Hox genes in a direct-developing insect.

  12. NRIP enhances HPV gene expression via interaction with either GR or E2

    Energy Technology Data Exchange (ETDEWEB)

    Chang, Szu-Wei; Lu, Pei-Yu; Guo, Jih-Huong [Graduate Institute of Microbiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan (China); Tsai, Tzung-Chieh [Department of Microbiology and Immunology, National Chiayi University, Chiayi 600-04, Taiwan (China); Tsao, Yeou-Ping [Department of Ophthalmology, Mackay Memorial Hospital, Taipei 104, Taiwan (China); Chen, Show-Li, E-mail: showlic@ha.mc.ntu.edu.tw [Graduate Institute of Microbiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan (China)

    2012-02-05

    We previously identified a gene, nuclear receptor-interaction protein (NRIP), which functions as a transcription cofactor in glucocorticoid receptor (GR) and human papillomavirus E2 (HPV E2)-driven gene expression. Here, we comprehensively evaluated the role of NRIP in HPV-16 gene expression. NRIP acts as a transcription cofactor to enhance GR-regulated HPV-16 gene expression in the presence of hormone. NRIP also can form complex with E2 that caused NRIP-induced HPV gene expression via E2-binding sites in a hormone-independent manner. Furthermore, NRIP can associate with GR and E2 to form tri-protein complex to activate HPV gene expression via GRE, not the E2-binding site, in a hormone-dependent manner. These results indicate that NRIP and GR are viral E2-binding proteins and that NRIP regulates HPV gene expression via GRE and/or E2 binding site in the HPV promoter in a hormone-dependent or independent manner, respectively.

  13. Diet-gene interactions between dietary fat intake and common polymorphisms in determining lipid metabolism

    Directory of Open Access Journals (Sweden)

    Corella, Dolores

    2009-03-01

    Full Text Available Current dietary guidelines for fat intake have not taken into consideration the possible genetic differences underlying the individual variability in responsiveness to dietary components. Genetic variability has been identified in humans for all the known lipid metabolim-related genes resulting in a plethora of candidate genes and genetic variants to examine in diet-gene interaction studies focused on fat consumption. Some examples of fat-gene interaction are reviewed. These include: the interaction between total intake and the 514C/T in the hepatic lipase gene promoter in determining high-density lipoprotein cholesterol (HDL-C metabolism; the interaction between polyunsaturated fatty acids (PUFA and the 75G/A polymorphism in the APOA1 gene plasma HDL-C concentrations; the interaction between PUFA and the L162V polymorphism in the PPARA gene in determining triglycerides and APOC3 concentrations; and the interaction between PUFA intake and the 1131TC in the APOA5 gene in determining triglyceride metabolism. Although hundreds of diet-gene interaction studies in lipid metabolism have been published, the level of evidence to make specific nutritional recommendations to the population is still low and more research in nutrigenetics has to be undertaken.Las recomendaciones dietéticas actuales referentes al consumo de grasas en la dieta han sido realizadas sin tener en cuenta las posibles diferencias genéticas de las personas que podrían ser las responsables de las diferentes respuestas interindividuales que frecuentemente se observan ante la misma dieta. La presencia de variabilidad genética ha sido puesta de manifiesto para todos los genes relacionados con el metabolismo lipídico, por lo que existe un ingente número de genes y de variantes genéticas para ser incluidas en los estudios sobre interacciones dieta-genotipo en el ámbito específico del consumo de grasas y aceites. Se revisarán algunos ejemplos sobre interacciones grasa

  14. Gene interactions and genetics for yield and its attributes in grass pea (Lathyrus sativus L.)

    Indian Academy of Sciences (India)

    A. K. PARIHAR; G. P. DIXIT; DEEPAK SINGH

    2016-12-01

    Grain yield is a complex character representing a multiplicative end product of many yield attributes. However, understanding the genetics and inheritance that underlies yield and its component characters pose a prerequisite to attain the actual yieldpotential of any crop species. The knowledge pertaining to gene actions and interactions is likely to direct and strengthen the crop breeding programmes. With this objective, the present investigation was undertaken by using six generations derived from three different crosses in grass pea. The study underscores the significance of additive–dominance model, gene action involved in inheritance of quantitative characters and heritability. Of note, nonallelic interactions influencing the traits were detected by both scaling test and joint scaling test, indicating the inadequacy of the additive–dominance model alone in explaining the manifestation of complex traits such as yield. Besides, additive (d) and dominance (h) gene effects, different types of interallelic interactions (i, j, l) contributed towards the inheritance of traits in the given crosses. Nevertheless, predominanceof additive variance suggests a difference between homozygotes at a locus with positive and negative alleles being distributed between the parents. Duplicate epistasis was prevalent in most of the cases for traits like plant height, seeds/pod,100-seed weight and pod width. In view of the diverse gene actions, i.e. additive, dominant and epistasis, playing important roles in the manifestation of complex traits like yield, we advocate implementation of population improvement techniques inparticular reciprocal recurrent selection to improve productivity gains in grass pea.

  15. Maternal Environment Interacts with Modifier Genes to Influence Progression of Nephrotic Syndrome

    Science.gov (United States)

    Ratelade, Julien; Lavin, Tiphaine Aguirre; Muda, Andrea Onetti; Morisset, Ludivine; Mollet, Géraldine; Boyer, Olivia; Chen, Deborah S.; Henger, Anna; Kretzler, Matthias; Hubner, Norbert; Théry, Clotilde; Gubler, Marie-Claire; Montagutelli, Xavier; Antignac, Corinne; Esquivel, Ernie L.

    2008-01-01

    Mutations in the NPHS2 gene, which encodes podocin, are responsible for some cases of sporadic and familial autosomal recessive steroid-resistant nephrotic syndrome. Inter- and intrafamilial variability in the progression of renal disease among patients bearing NPHS2 mutations suggests a potential role for modifier genes. Using a mouse model in which the podocin gene is constitutively inactivated, we sought to identify genetic determinants of the development and progression of renal disease as a result of the nephrotic syndrome. We report that the evolution of renal disease as a result of nephrotic syndrome in Nphs2-null mice depends on genetic background. Furthermore, the maternal environment significantly interacts with genetic determinants to modify survival and progression of renal disease. Quantitative trait locus mapping suggested that these genetic determinants may be encoded for by genes on the distal end of chromosome 3, which are linked to proteinuria, and on the distal end of chromosome 7, which are linked to a composite trait of urea, creatinine, and potassium. These loci demonstrate epistatic interactions with other chromosomal regions, highlighting the complex genetics of renal disease progression. In summary, constitutive inactivation of podocin models the complex interactions between maternal and genetically determined factors on the progression of renal disease as a result of nephrotic syndrome in mice. PMID:18385421

  16. Diet-gene interactions and PUFA metabolism: a potential contributor to health disparities and human diseases.

    Science.gov (United States)

    Chilton, Floyd H; Murphy, Robert C; Wilson, Bryan A; Sergeant, Susan; Ainsworth, Hannah; Seeds, Michael C; Mathias, Rasika A

    2014-05-21

    The "modern western" diet (MWD) has increased the onset and progression of chronic human diseases as qualitatively and quantitatively maladaptive dietary components give rise to obesity and destructive gene-diet interactions. There has been a three-fold increase in dietary levels of the omega-6 (n-6) 18 carbon (C18), polyunsaturated fatty acid (PUFA) linoleic acid (LA; 18:2n-6), with the addition of cooking oils and processed foods to the MWD. Intense debate has emerged regarding the impact of this increase on human health. Recent studies have uncovered population-related genetic variation in the LCPUFA biosynthetic pathway (especially within the fatty acid desaturase gene (FADS) cluster) that is associated with levels of circulating and tissue PUFAs and several biomarkers and clinical endpoints of cardiovascular disease (CVD). Importantly, populations of African descent have higher frequencies of variants associated with elevated levels of arachidonic acid (ARA), CVD biomarkers and disease endpoints. Additionally, nutrigenomic interactions between dietary n-6 PUFAs and variants in genes that encode for enzymes that mobilize and metabolize ARA to eicosanoids have been identified. These observations raise important questions of whether gene-PUFA interactions are differentially driving the risk of cardiovascular and other diseases in diverse populations, and contributing to health disparities, especially in African American populations.

  17. Diet-Gene Interactions and PUFA Metabolism: A Potential Contributor to Health Disparities and Human Diseases

    Directory of Open Access Journals (Sweden)

    Floyd H. Chilton

    2014-05-01

    Full Text Available The “modern western” diet (MWD has increased the onset and progression of chronic human diseases as qualitatively and quantitatively maladaptive dietary components give rise to obesity and destructive gene-diet interactions. There has been a three-fold increase in dietary levels of the omega-6 (n-6 18 carbon (C18, polyunsaturated fatty acid (PUFA linoleic acid (LA; 18:2n-6, with the addition of cooking oils and processed foods to the MWD. Intense debate has emerged regarding the impact of this increase on human health. Recent studies have uncovered population-related genetic variation in the LCPUFA biosynthetic pathway (especially within the fatty acid desaturase gene (FADS cluster that is associated with levels of circulating and tissue PUFAs and several biomarkers and clinical endpoints of cardiovascular disease (CVD. Importantly, populations of African descent have higher frequencies of variants associated with elevated levels of arachidonic acid (ARA, CVD biomarkers and disease endpoints. Additionally, nutrigenomic interactions between dietary n-6 PUFAs and variants in genes that encode for enzymes that mobilize and metabolize ARA to eicosanoids have been identified. These observations raise important questions of whether gene-PUFA interactions are differentially driving the risk of cardiovascular and other diseases in diverse populations, and contributing to health disparities, especially in African American populations.

  18. The SKP1-like gene family of Arabidopsis exhibits a high degree of differential gene expression and gene product interaction during development.

    Directory of Open Access Journals (Sweden)

    Mohammad H Dezfulian

    Full Text Available The Arabidopsis thaliana genome encodes several families of polypeptides that are known or predicted to participate in the formation of the SCF-class of E3-ubiquitin ligase complexes. One such gene family encodes the Skp1-like class of polypeptide subunits, where 21 genes have been identified and are known to be expressed in Arabidopsis. Phylogenetic analysis based on deduced polypeptide sequence organizes the family of ASK proteins into 7 clades. The complexity of the ASK gene family, together with the close structural similarity among its members raises the prospect of significant functional redundancy among select paralogs. We have assessed the potential for functional redundancy within the ASK gene family by analyzing an expanded set of criteria that define redundancy with higher resolution. The criteria used include quantitative expression of locus-specific transcripts using qRT-PCR, assessment of the sub-cellular localization of individual ASK:YFP auto-fluorescent fusion proteins expressed in vivo as well as the in planta assessment of individual ASK-F-Box protein interactions using bimolecular fluorescent complementation techniques in combination with confocal imagery in live cells. The results indicate significant functional divergence of steady state transcript abundance and protein-protein interaction specificity involving ASK proteins in a pattern that is poorly predicted by sequence-based phylogeny. The information emerging from this and related studies will prove important for defining the functional intersection of expression, localization and gene product interaction that better predicts the formation of discrete SCF complexes, as a prelude to investigating their molecular mode of action.

  19. The SKP1-like gene family of Arabidopsis exhibits a high degree of differential gene expression and gene product interaction during development.

    Science.gov (United States)

    Dezfulian, Mohammad H; Soulliere, Danielle M; Dhaliwal, Rajdeep K; Sareen, Madhulika; Crosby, William L

    2012-01-01

    The Arabidopsis thaliana genome encodes several families of polypeptides that are known or predicted to participate in the formation of the SCF-class of E3-ubiquitin ligase complexes. One such gene family encodes the Skp1-like class of polypeptide subunits, where 21 genes have been identified and are known to be expressed in Arabidopsis. Phylogenetic analysis based on deduced polypeptide sequence organizes the family of ASK proteins into 7 clades. The complexity of the ASK gene family, together with the close structural similarity among its members raises the prospect of significant functional redundancy among select paralogs. We have assessed the potential for functional redundancy within the ASK gene family by analyzing an expanded set of criteria that define redundancy with higher resolution. The criteria used include quantitative expression of locus-specific transcripts using qRT-PCR, assessment of the sub-cellular localization of individual ASK:YFP auto-fluorescent fusion proteins expressed in vivo as well as the in planta assessment of individual ASK-F-Box protein interactions using bimolecular fluorescent complementation techniques in combination with confocal imagery in live cells. The results indicate significant functional divergence of steady state transcript abundance and protein-protein interaction specificity involving ASK proteins in a pattern that is poorly predicted by sequence-based phylogeny. The information emerging from this and related studies will prove important for defining the functional intersection of expression, localization and gene product interaction that better predicts the formation of discrete SCF complexes, as a prelude to investigating their molecular mode of action.

  20. Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

    DEFF Research Database (Denmark)

    Brorsson, C.; Hansen, Niclas Tue; Hansen, Kasper Lage;

    2009-01-01

    region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein...

  1. Gene-Diet Interactions in Type 2 Diabetes: The Chicken and Egg Debate.

    Science.gov (United States)

    Ortega, Ángeles; Berná, Genoveva; Rojas, Anabel; Martín, Franz; Soria, Bernat

    2017-06-02

    Consistent evidence from both experimental and human studies indicates that Type 2 diabetes mellitus (T2DM) is a complex disease resulting from the interaction of genetic, epigenetic, environmental, and lifestyle factors. Nutrients and dietary patterns are important environmental factors to consider in the prevention, development and treatment of this disease. Nutritional genomics focuses on the interaction between bioactive food components and the genome and includes studies of nutrigenetics, nutrigenomics and epigenetic modifications caused by nutrients. There is evidence supporting the existence of nutrient-gene and T2DM interactions coming from animal studies and family-based intervention studies. Moreover, many case-control, cohort, cross-sectional cohort studies and clinical trials have identified relationships between individual genetic load, diet and T2DM. Some of these studies were on a large scale. In addition, studies with animal models and human observational studies, in different countries over periods of time, support a causative relationship between adverse nutritional conditions during in utero development, persistent epigenetic changes and T2DM. This review provides comprehensive information on the current state of nutrient-gene interactions and their role in T2DM pathogenesis, the relationship between individual genetic load and diet, and the importance of epigenetic factors in influencing gene expression and defining the individual risk of T2DM.

  2. Genome-wide gene-environment interactions on quantitative traits using family data.

    Science.gov (United States)

    Sitlani, Colleen M; Dupuis, Josée; Rice, Kenneth M; Sun, Fangui; Pitsillides, Achilleas N; Cupples, L Adrienne; Psaty, Bruce M

    2016-07-01

    Gene-environment interactions may provide a mechanism for targeting interventions to those individuals who would gain the most benefit from them. Searching for interactions agnostically on a genome-wide scale requires large sample sizes, often achieved through collaboration among multiple studies in a consortium. Family studies can contribute to consortia, but to do so they must account for correlation within families by using specialized analytic methods. In this paper, we investigate the performance of methods that account for within-family correlation, in the context of gene-environment interactions with binary exposures and quantitative outcomes. We simulate both cross-sectional and longitudinal measurements, and analyze the simulated data taking family structure into account, via generalized estimating equations (GEE) and linear mixed-effects models. With sufficient exposure prevalence and correct model specification, all methods perform well. However, when models are misspecified, mixed modeling approaches have seriously inflated type I error rates. GEE methods with robust variance estimates are less sensitive to model misspecification; however, when exposures are infrequent, GEE methods require modifications to preserve type I error rate. We illustrate the practical use of these methods by evaluating gene-drug interactions on fasting glucose levels in data from the Framingham Heart Study, a cohort that includes related individuals.

  3. Fungal genes related to calcium homeostasis and signalling are upregulated in symbiotic arbuscular mycorrhiza interactions.

    Science.gov (United States)

    Liu, Yi; Gianinazzi-Pearson, Vivienne; Arnould, Christine; Wipf, Daniel; Zhao, Bin; van Tuinen, Diederik

    2013-01-01

    Fluctuations in intracellular calcium levels generate signalling events and regulate different cellular processes. Whilst the implication of Ca(2+) in plant responses during arbuscular mycorrhiza (AM) interactions is well documented, nothing is known about the regulation or role of this secondary messenger in the fungal symbiont. The spatio-temporal expression pattern of putatively Ca(2+)-related genes of Glomus intraradices BEG141 encoding five proteins involved in membrane transport and one nuclear protein kinase, was investigated during the AM symbiosis. Expression profiles related to successful colonization of host roots were observed in interactions of G. intraradices with roots of wild-type Medicago truncatula (line J5) compared to the mycorrhiza-defective mutant dmi3/Mtsym13. Symbiotic fungal activity was monitored using stearoyl-CoA desaturase and phosphate transporter genes. Laser microdissection based-mapping of fungal gene expression in mycorrhizal root tissues indicated that the Ca(2+)-related genes were differentially upregulated in arbuscules and/or in intercellular hyphae. The spatio-temporal variations in gene expression suggest that the encoded proteins may have different functions in fungal development or function during symbiosis development. Full-length cDNA obtained for two genes with interesting expression profiles confirmed a close similarity with an endoplasmic reticulum P-type ATPase and a Vcx1-like vacuolar Ca(2+) ion transporter functionally characterized in other fungi and involved in the regulation of cell calcium pools. Possible mechanisms are discussed in which Ca(2+)-related proteins G. intraradices BEG141 may play a role in mobilization and perception of the intracellular messenger by the AM fungus during symbiotic interactions with host roots. Copyright © 2012 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.

  4. PTRcombiner: mining combinatorial regulation of gene expression from post-transcriptional interaction maps.

    Science.gov (United States)

    Corrado, Gianluca; Tebaldi, Toma; Bertamini, Giulio; Costa, Fabrizio; Quattrone, Alessandro; Viero, Gabriella; Passerini, Andrea

    2014-04-23

    The progress in mapping RNA-protein and RNA-RNA interactions at the transcriptome-wide level paves the way to decipher possible combinatorial patterns embedded in post-transcriptional regulation of gene expression. Here we propose an innovative computational tool to extract clusters of mRNA trans-acting co-regulators (RNA binding proteins and non-coding RNAs) from pairwise interaction annotations. In addition the tool allows to analyze the binding site similarity of co-regulators belonging to the same cluster, given their positional binding information. The tool has been tested on experimental collections of human and yeast interactions, identifying modules that coordinate functionally related messages. This tool is an original attempt to uncover combinatorial patterns using all the post-transcriptional interaction data available so far. PTRcombiner is available at http://disi.unitn.it/~passerini/software/PTRcombiner/.

  5. Genes related to antioxidant metabolism are involved in Methylobacterium mesophilicum-soybean interaction.

    Science.gov (United States)

    Araújo, Welington Luiz; Santos, Daiene Souza; Dini-Andreote, Francisco; Salgueiro-Londoño, Jennifer Katherine; Camargo-Neves, Aline Aparecida; Andreote, Fernando Dini; Dourado, Manuella Nóbrega

    2015-10-01

    The genus Methylobacterium is composed of pink-pigmented methylotrophic bacterial species that are widespread in natural environments, such as soils, stream water and plants. When in association with plants, this genus colonizes the host plant epiphytically and/or endophytically. This association is known to promote plant growth, induce plant systemic resistance and inhibit plant infection by phytopathogens. In the present study, we focused on evaluating the colonization of soybean seedling-roots by Methylobacterium mesophilicum strain SR1.6/6. We focused on the identification of the key genes involved in the initial step of soybean colonization by methylotrophic bacteria, which includes the plant exudate recognition and adaptation by planktonic bacteria. Visualization by scanning electron microscopy revealed that M. mesophilicum SR1.6/6 colonizes soybean roots surface effectively at 48 h after inoculation, suggesting a mechanism for root recognition and adaptation before this period. The colonization proceeds by the development of a mature biofilm on roots at 96 h after inoculation. Transcriptomic analysis of the planktonic bacteria (with plant) revealed the expression of several genes involved in membrane transport, thus confirming an initial metabolic activation of bacterial responses when in the presence of plant root exudates. Moreover, antioxidant genes were mostly expressed during the interaction with the plant exudates. Further evaluation of stress- and methylotrophic-related genes expression by qPCR showed that glutathione peroxidase and glutathione synthetase genes were up-regulated during the Methylobacterium-soybean interaction. These findings support that glutathione (GSH) is potentially a key molecule involved in cellular detoxification during plant root colonization. In addition to methylotrophic metabolism, antioxidant genes, mainly glutathione-related genes, play a key role during soybean exudate recognition and adaptation, the first step in

  6. Protein Interaction Networks Reveal Novel Autism Risk Genes within GWAS Statistical Noise

    Science.gov (United States)

    Correia, Catarina; Oliveira, Guiomar; Vicente, Astrid M.

    2014-01-01

    Genome-wide association studies (GWAS) for Autism Spectrum Disorder (ASD) thus far met limited success in the identification of common risk variants, consistent with the notion that variants with small individual effects cannot be detected individually in single SNP analysis. To further capture disease risk gene information from ASD association studies, we applied a network-based strategy to the Autism Genome Project (AGP) and the Autism Genetics Resource Exchange GWAS datasets, combining family-based association data with Human Protein-Protein interaction (PPI) data. Our analysis showed that autism-associated proteins at higher than conventional levels of significance (P<0.1) directly interact more than random expectation and are involved in a limited number of interconnected biological processes, indicating that they are functionally related. The functionally coherent networks generated by this approach contain ASD-relevant disease biology, as demonstrated by an improved positive predictive value and sensitivity in retrieving known ASD candidate genes relative to the top associated genes from either GWAS, as well as a higher gene overlap between the two ASD datasets. Analysis of the intersection between the networks obtained from the two ASD GWAS and six unrelated disease datasets identified fourteen genes exclusively present in the ASD networks. These are mostly novel genes involved in abnormal nervous system phenotypes in animal models, and in fundamental biological processes previously implicated in ASD, such as axon guidance, cell adhesion or cytoskeleton organization. Overall, our results highlighted novel susceptibility genes previously hidden within GWAS statistical “noise” that warrant further analysis for causal variants. PMID:25409314

  7. Differential expression of small RNA pathway genes associated with the Biomphalaria glabrata/Schistosoma mansoni interaction.

    Science.gov (United States)

    Queiroz, Fábio Ribeiro; Silva, Luciana Maria; Jeremias, Wander de Jesus; Babá, Élio Hideo; Caldeira, Roberta Lima; Coelho, Paulo Marcos Zech; Gomes, Matheus de Souza

    2017-01-01

    The World Health Organization (WHO) estimates that approximately 240 million people in 78 countries require treatment for schistosomiasis, an endemic disease caused by trematodes of the genus Schistosoma. In Brazil, Schistosoma mansoni is the only species representative of the genus whose passage through an invertebrate host, snails of the genus Biomphalaria, is obligatory before infecting a mammalian host, including humans. The availability of the genome and transcriptome of B. glabrata makes studying the regulation of gene expression, particularly the regulation of miRNA and piRNA processing pathway genes, possible. This might assist in better understanding the biology of B. glabrata as well as its relationship to the parasite S. mansoni. Some aspects of this interaction are still poorly explored, including the participation of non-coding small RNAs, such as miRNAs and piRNAs, with lengths varying from 18 to 30 nucleotides in mature form, which are potent regulators of gene expression. Using bioinformatics tools and quantitative PCR, we characterized and validated the miRNA and piRNA processing pathway genes in B. glabrata. In silico analyses showed that genes involved in miRNA and piRNA pathways were highly conserved in protein domain distribution, catalytic site residue conservation and phylogenetic analysis. Our study showed differential expression of putative Argonaute, Drosha, Piwi, Exportin-5 and Tudor genes at different snail developmental stages and during infection with S. mansoni, suggesting that the machinery is required for miRNA and piRNA processing in B. glabrata at all stages. These data suggested that the silencing pathway mediated by miRNAs and piRNAs can interfere in snail biology throughout the life cycle of the snail, thereby influencing the B. glabrata/S. mansoni interaction. Further studies are needed to confirm the participation of the small RNA processing pathway proteins in the parasite/host relationship, mainly the effective

  8. Genome-wide diet-gene interaction analyses for risk of colorectal cancer.

    Science.gov (United States)

    Figueiredo, Jane C; Hsu, Li; Hutter, Carolyn M; Lin, Yi; Campbell, Peter T; Baron, John A; Berndt, Sonja I; Jiao, Shuo; Casey, Graham; Fortini, Barbara; Chan, Andrew T; Cotterchio, Michelle; Lemire, Mathieu; Gallinger, Steven; Harrison, Tabitha A; Le Marchand, Loic; Newcomb, Polly A; Slattery, Martha L; Caan, Bette J; Carlson, Christopher S; Zanke, Brent W; Rosse, Stephanie A; Brenner, Hermann; Giovannucci, Edward L; Wu, Kana; Chang-Claude, Jenny; Chanock, Stephen J; Curtis, Keith R; Duggan, David; Gong, Jian; Haile, Robert W; Hayes, Richard B; Hoffmeister, Michael; Hopper, John L; Jenkins, Mark A; Kolonel, Laurence N; Qu, Conghui; Rudolph, Anja; Schoen, Robert E; Schumacher, Fredrick R; Seminara, Daniela; Stelling, Deanna L; Thibodeau, Stephen N; Thornquist, Mark; Warnick, Greg S; Henderson, Brian E; Ulrich, Cornelia M; Gauderman, W James; Potter, John D; White, Emily; Peters, Ulrike

    2014-04-01

    Dietary factors, including meat, fruits, vegetables and fiber, are associated with colorectal cancer; however, there is limited information as to whether these dietary factors interact with genetic variants to modify risk of colorectal cancer. We tested interactions between these dietary factors and approximately 2.7 million genetic variants for colorectal cancer risk among 9,287 cases and 9,117 controls from ten studies. We used logistic regression to investigate multiplicative gene-diet interactions, as well as our recently developed Cocktail method that involves a screening step based on marginal associations and gene-diet correlations and a testing step for multiplicative interactions, while correcting for multiple testing using weighted hypothesis testing. Per quartile increment in the intake of red and processed meat were associated with statistically significant increased risks of colorectal cancer and vegetable, fruit and fiber intake with lower risks. From the case-control analysis, we detected a significant interaction between rs4143094 (10p14/near GATA3) and processed meat consumption (OR = 1.17; p = 8.7E-09), which was consistently observed across studies (p heterogeneity = 0.78). The risk of colorectal cancer associated with processed meat was increased among individuals with the rs4143094-TG and -TT genotypes (OR = 1.20 and OR = 1.39, respectively) and null among those with the GG genotype (OR = 1.03). Our results identify a novel gene-diet interaction with processed meat for colorectal cancer, highlighting that diet may modify the effect of genetic variants on disease risk, which may have important implications for prevention.

  9. Genome-wide diet-gene interaction analyses for risk of colorectal cancer.

    Directory of Open Access Journals (Sweden)

    Jane C Figueiredo

    2014-04-01

    Full Text Available Dietary factors, including meat, fruits, vegetables and fiber, are associated with colorectal cancer; however, there is limited information as to whether these dietary factors interact with genetic variants to modify risk of colorectal cancer. We tested interactions between these dietary factors and approximately 2.7 million genetic variants for colorectal cancer risk among 9,287 cases and 9,117 controls from ten studies. We used logistic regression to investigate multiplicative gene-diet interactions, as well as our recently developed Cocktail method that involves a screening step based on marginal associations and gene-diet correlations and a testing step for multiplicative interactions, while correcting for multiple testing using weighted hypothesis testing. Per quartile increment in the intake of red and processed meat were associated with statistically significant increased risks of colorectal cancer and vegetable, fruit and fiber intake with lower risks. From the case-control analysis, we detected a significant interaction between rs4143094 (10p14/near GATA3 and processed meat consumption (OR = 1.17; p = 8.7E-09, which was consistently observed across studies (p heterogeneity = 0.78. The risk of colorectal cancer associated with processed meat was increased among individuals with the rs4143094-TG and -TT genotypes (OR = 1.20 and OR = 1.39, respectively and null among those with the GG genotype (OR = 1.03. Our results identify a novel gene-diet interaction with processed meat for colorectal cancer, highlighting that diet may modify the effect of genetic variants on disease risk, which may have important implications for prevention.

  10. Refining ensembles of predicted gene regulatory networks based on characteristic interaction sets.

    Directory of Open Access Journals (Sweden)

    Lukas Windhager

    Full Text Available Different ensemble voting approaches have been successfully applied for reverse-engineering of gene regulatory networks. They are based on the assumption that a good approximation of true network structure can be derived by considering the frequencies of individual interactions in a large number of predicted networks. Such approximations are typically superior in terms of prediction quality and robustness as compared to considering a single best scoring network only. Nevertheless, ensemble approaches only work well if the predicted gene regulatory networks are sufficiently similar to each other. If the topologies of predicted networks are considerably different, an ensemble of all networks obscures interesting individual characteristics. Instead, networks should be grouped according to local topological similarities and ensemble voting performed for each group separately. We argue that the presence of sets of co-occurring interactions is a suitable indicator for grouping predicted networks. A stepwise bottom-up procedure is proposed, where first mutual dependencies between pairs of interactions are derived from predicted networks. Pairs of co-occurring interactions are subsequently extended to derive characteristic interaction sets that distinguish groups of networks. Finally, ensemble voting is applied separately to the resulting topologically similar groups of networks to create distinct group-ensembles. Ensembles of topologically similar networks constitute distinct hypotheses about the reference network structure. Such group-ensembles are easier to interpret as their characteristic topology becomes clear and dependencies between interactions are known. The availability of distinct hypotheses facilitates the design of further experiments to distinguish between plausible network structures. The proposed procedure is a reasonable refinement step for non-deterministic reverse-engineering applications that produce a large number of candidate

  11. Common and specific signatures of gene expression and protein-protein interactions in autoimmune diseases.

    Science.gov (United States)

    Tuller, T; Atar, S; Ruppin, E; Gurevich, M; Achiron, A

    2013-03-01

    The aim of this study is to understand intracellular regulatory mechanisms in peripheral blood mononuclear cells (PBMCs), which are either common to many autoimmune diseases or specific to some of them. We incorporated large-scale data such as protein-protein interactions, gene expression and demographical information of hundreds of patients and healthy subjects, related to six autoimmune diseases with available large-scale gene expression measurements: multiple sclerosis (MS), systemic lupus erythematosus (SLE), juvenile rheumatoid arthritis (JRA), Crohn's disease (CD), ulcerative colitis (UC) and type 1 diabetes (T1D). These data were analyzed concurrently by statistical and systems biology approaches tailored for this purpose. We found that chemokines such as CXCL1-3, 5, 6 and the interleukin (IL) IL8 tend to be differentially expressed in PBMCs of patients with the analyzed autoimmune diseases. In addition, the anti-apoptotic gene BCL3, interferon-γ (IFNG), and the vitamin D receptor (VDR) gene physically interact with significantly many genes that tend to be differentially expressed in PBMCs of patients with the analyzed autoimmune diseases. In general, similar cellular processes tend to be differentially expressed in PBMC in the analyzed autoimmune diseases. Specifically, the cellular processes related to cell proliferation (for example, epidermal growth factor, platelet-derived growth factor, nuclear factor-κB, Wnt/β-catenin signaling, stress-activated protein kinase c-Jun NH2-terminal kinase), inflammatory response (for example, interleukins IL2 and IL6, the cytokine granulocyte-macrophage colony-stimulating factor and the B-cell receptor), general signaling cascades (for example, mitogen-activated protein kinase, extracellular signal-regulated kinase, p38 and TRK) and apoptosis are activated in most of the analyzed autoimmune diseases. However, our results suggest that in each of the analyzed diseases, apoptosis and chemotaxis are activated via

  12. To control false positives in gene-gene interaction analysis: two novel conditional entropy-based approaches.

    Directory of Open Access Journals (Sweden)

    Xiaoyu Zuo

    Full Text Available Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects.

  13. Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality

    Directory of Open Access Journals (Sweden)

    Ye Ping

    2005-12-01

    Full Text Available Abstract Background Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets. Results We have developed Genetic Interaction Motif Finding (GIMF, an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif category. We demonstrate the capacity to extract known and novel pathways for Saccharomyces cerevisiae (budding yeast. Annotations suggested for several uncharacterized genes are supported by recent experimental evidence. GIMF is efficient in computation, requires no training and automatically down-weights promiscuous genes with high degrees. Conclusion GIMF effectively identifies pathways from synthetic lethality data with several unique features. It is mostly suitable for building gene modules around seed genes. Optimal choice of one single model parameter allows construction of gene networks with different levels of confidence. The impact of hub genes the generic probabilistic framework of GIMF may be used to group other types of biological entities such as proteins based on stochastic motifs. Analysis of the strongest motifs discovered by the algorithm indicates that synthetic lethal interactions are depleted between genes within a motif, suggesting that synthetic

  14. Functional Ecological Gene Networks to Reveal the Changes Among Microbial Interactions Under Elevated Carbon Dioxide Conditions

    Energy Technology Data Exchange (ETDEWEB)

    Deng, Ye; Zhou, Jizhong; Luo, Feng; He, Zhili; Tu, Qichao; Zhi, Xiaoyang

    2010-05-17

    Biodiversity and its responses to environmental changes is a central issue in ecology, and for society. Almost all microbial biodiversity researches focus on species richness and abundance but ignore the interactions among different microbial species/populations. However, determining the interactions and their relationships to environmental changes in microbial communities is a grand challenge, primarily due to the lack of information on the network structure among different microbial species/populations. Here, a novel random matrix theory (RMT)-based conceptual framework for identifying functional ecological gene networks (fEGNs) is developed with the high throughput functional gene array hybridization data from the grassland microbial communities in a long-term FACE (Free Air CO2 Enrichment) experiment. Both fEGNs under elevated CO2 (eCO2) and ambient CO2 (aCO2) possessed general characteristics of many complex systems such as scale-free, small-world, modular and hierarchical. However, the topological structure of the fEGNs is distinctly different between eCO2 and aCO2, suggesting that eCO2 dramatically altered the interactions among different microbial functional groups/populations. In addition, the changes in network structure were significantly correlated with soil carbon and nitrogen dynamics, and plant productivity, indicating the potential importance of network interactions in ecosystem functioning. Elucidating network interactions in microbial communities and their responses to environmental changes are fundamentally important for research in microbial ecology, systems microbiology, and global change.

  15. Differential gene expression in Pycnoporus coccineus during interspecific mycelial interactions with different competitors.

    Science.gov (United States)

    Arfi, Yonathan; Levasseur, Anthony; Record, Eric

    2013-11-01

    Fungi compete against each other for environmental resources. These interspecific combative interactions encompass a wide range of mechanisms. In this study, we highlight the ability of the white-rot fungus Pycnoporus coccineus to quickly overgrow or replace a wide range of competitor fungi, including the gray-mold fungus Botrytis cinerea and the brown-rot fungus Coniophora puteana. To gain a better understanding of the mechanisms deployed by P. coccineus to compete against other fungi and to assess whether common pathways are used to interact with different competitors, differential gene expression in P. coccineus during cocultivation was assessed by transcriptome sequencing and confirmed by quantitative reverse transcription-PCR analysis of a set of 15 representative genes. Compared with the pure culture, 1,343 transcripts were differentially expressed in the interaction with C. puteana and 4,253 were differentially expressed in the interaction with B. cinerea, but only 197 transcripts were overexpressed in both interactions. Overall, the results suggest that a broad array of functions is necessary for P. coccineus to replace its competitors and that different responses are elicited by the two competitors, although a portion of the mechanism is common to both. However, the functions elicited by the expression of specific transcripts appear to converge toward a limited set of roles, including detoxification of secondary metabolites.

  16. Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer Disease Genetics Consortium.

    Science.gov (United States)

    Hohman, Timothy J; Bush, William S; Jiang, Lan; Brown-Gentry, Kristin D; Torstenson, Eric S; Dudek, Scott M; Mukherjee, Shubhabrata; Naj, Adam; Kunkle, Brian W; Ritchie, Marylyn D; Martin, Eden R; Schellenberg, Gerard D; Mayeux, Richard; Farrer, Lindsay A; Pericak-Vance, Margaret A; Haines, Jonathan L; Thornton-Wells, Tricia A

    2016-02-01

    Late-onset Alzheimer disease (AD) has a complex genetic etiology, involving locus heterogeneity, polygenic inheritance, and gene-gene interactions; however, the investigation of interactions in recent genome-wide association studies has been limited. We used a biological knowledge-driven approach to evaluate gene-gene interactions for consistency across 13 data sets from the Alzheimer Disease Genetics Consortium. Fifteen single nucleotide polymorphism (SNP)-SNP pairs within 3 gene-gene combinations were identified: SIRT1 × ABCB1, PSAP × PEBP4, and GRIN2B × ADRA1A. In addition, we extend a previously identified interaction from an endophenotype analysis between RYR3 × CACNA1C. Finally, post hoc gene expression analyses of the implicated SNPs further implicate SIRT1 and ABCB1, and implicate CDH23 which was most recently identified as an AD risk locus in an epigenetic analysis of AD. The observed interactions in this article highlight ways in which genotypic variation related to disease may depend on the genetic context in which it occurs. Further, our results highlight the utility of evaluating genetic interactions to explain additional variance in AD risk and identify novel molecular mechanisms of AD pathogenesis.

  17. Avidin-biotin interaction mediated peptide assemblies as efficient gene delivery vectors for cancer therapy.

    Science.gov (United States)

    Qu, Wei; Chen, Wei-Hai; Kuang, Ying; Zeng, Xuan; Cheng, Si-Xue; Zhou, Xiang; Zhuo, Ren-Xi; Zhang, Xian-Zheng

    2013-01-01

    Gene therapy offers a bright future for the treatment of cancers. One of the research highlights focuses on smart gene delivery vectors with good biocompatibility and tumor-targeting ability. Here, a novel gene vector self-assembled through avidin-biotin interaction with optimized targeting functionality, biotinylated tumor-targeting peptide/avidin/biotinylated cell-penetrating peptide (TAC), was designed and prepared to mediate the in vitro and in vivo delivery of p53 gene. TAC exhibited efficient DNA-binding ability and low cytotoxicity. In in vitro transfection assay, TAC/p53 complexes showed higher transfection efficiency and expression amount of p53 protein in MCF-7 cells as compared with 293T and HeLa cells, primarily due to the specific recognition between tumor-targeting peptides and receptors on MCF-7 cells. Additionally, by in situ administration of TAC/p53 complexes into tumor-bearing mice, the expression of p53 gene was obviously upregulated in tumor cells, and the tumor growth was significantly suppressed. This study provides an alternative and unique strategy to assemble functionalized peptides, and the novel self-assembled vector TAC developed is a promising gene vector for cancer therapy.

  18. Key Considerations and Methods in the Study of Gene-Environment Interactions.

    Science.gov (United States)

    Simon, Paul H G; Sylvestre, Marie-Pierre; Tremblay, Johanne; Hamet, Pavel

    2016-08-01

    With increased involvement of genetic data in most epidemiological investigations, gene-environment (G × E) interactions now stand as a topic, which must be meticulously assessed and thoroughly understood. The level, mode, and outcomes of interactions between environmental factors and genetic traits have the capacity to modulate disease risk. These must, therefore, be carefully evaluated as they have the potential to offer novel insights on the "missing heritability problem", reaching beyond our current limitations. First, we review a definition of G × E interactions. We then explore how concepts such as the early manifestation of the genetic components of a disease, the heterogeneity of complex traits, the clear definition of epidemiological strata, and the effect of varying physiological conditions can affect our capacity to detect (or miss) G × E interactions. Lastly, we discuss the shortfalls of regression models to study G × E interactions and how other methods such as the ReliefF algorithm, pattern recognition methods, or the LASSO (Least Absolute Shrinkage and Selection Operator) method can enable us to more adequately model G × E interactions. Overall, we present the elements to consider and a path to follow when studying genetic determinants of disease in order to uncover potential G × E interactions.

  19. Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering.

    Science.gov (United States)

    Yang, Lei; Tang, Xianglong

    2014-01-01

    Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  20. Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

    Directory of Open Access Journals (Sweden)

    Lei Yang

    2014-01-01

    Full Text Available Cliques (maximal complete subnets in protein-protein interaction (PPI network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  1. The role of gene-environment interactions in the development of food allergy.

    Science.gov (United States)

    Neeland, Melanie R; Martino, David J; Allen, Katrina J

    2015-01-01

    The rates of IgE-mediated food allergy have increased globally, particularly in developed countries. The rising incidence is occurring more rapidly than changes to the genome sequence would allow, suggesting that environmental exposures that alter the immune response play an important role. Genetic factors may also be used to predict an increased predisposition to these environmental risk factors, giving rise to the concept of gene-environment interactions, whereby differential risk of environmental exposures is mediated through the genome. Increasing evidence also suggests a role for epigenetic mechanisms, which are sensitive to environmental exposures, in the development of food allergy. This paper discusses the current state of knowledge regarding the environmental and genetic risk factors for food allergy and how environmental exposures may interact with immune genes to modify disease risk or outcome.

  2. Multivariate dimensionality reduction approaches to identify gene-gene and gene-environment interactions underlying multiple complex traits.

    Directory of Open Access Journals (Sweden)

    Hai-Ming Xu

    Full Text Available The elusive but ubiquitous multifactor interactions represent a stumbling block that urgently needs to be removed in searching for determinants involved in human complex diseases. The dimensionality reduction approaches are a promising tool for this task. Many complex diseases exhibit composite syndromes required to be measured in a cluster of clinical traits with varying correlations and/or are inherently longitudinal in nature (changing over time and measured dynamically at multiple time points. A multivariate approach for detecting interactions is thus greatly needed on the purposes of handling a multifaceted phenotype and longitudinal data, as well as improving statistical power for multiple significance testing via a two-stage testing procedure that involves a multivariate analysis for grouped phenotypes followed by univariate analysis for the phenotypes in the significant group(s. In this article, we propose a multivariate extension of generalized multifactor dimensionality reduction (GMDR based on multivariate generalized linear, multivariate quasi-likelihood and generalized estimating equations models. Simulations and real data analysis for the cohort from the Study of Addiction: Genetics and Environment are performed to investigate the properties and performance of the proposed method, as compared with the univariate method. The results suggest that the proposed multivariate GMDR substantially boosts statistical power.

  3. DNAH6 and Its Interactions with PCD Genes in Heterotaxy and Primary Ciliary Dyskinesia

    Science.gov (United States)

    Onuoha, Ezenwa Obi; Damerla, Rama Rao; Francis, Richard; Furutani, Yoshiyuki; Tariq, Muhammad; King, Stephen M.; Hendricks, Gregory; Cui, Cheng; Saydmohammed, Manush; Lee, Dong Min; Zahid, Maliha; Sami, Iman; Leatherbury, Linda; Pazour, Gregory J.; Ware, Stephanie M.; Nakanishi, Toshio; Goldmuntz, Elizabeth; Tsang, Michael; Lo, Cecilia W.

    2016-01-01

    Heterotaxy, a birth defect involving left-right patterning defects, and primary ciliary dyskinesia (PCD), a sinopulmonary disease with dyskinetic/immotile cilia in the airway are seemingly disparate diseases. However, they have an overlapping genetic etiology involving mutations in cilia genes, a reflection of the common requirement for motile cilia in left-right patterning and airway clearance. While PCD is a monogenic recessive disorder, heterotaxy has a more complex, largely non-monogenic etiology. In this study, we show mutations in the novel dynein gene DNAH6 can cause heterotaxy and ciliary dysfunction similar to PCD. We provide the first evidence that trans-heterozygous interactions between DNAH6 and other PCD genes potentially can cause heterotaxy. DNAH6 was initially identified as a candidate heterotaxy/PCD gene by filtering exome-sequencing data from 25 heterotaxy patients stratified by whether they have airway motile cilia defects. dnah6 morpholino knockdown in zebrafish disrupted motile cilia in Kupffer’s vesicle required for left-right patterning and caused heterotaxy with abnormal cardiac/gut looping. Similarly DNAH6 shRNA knockdown disrupted motile cilia in human and mouse respiratory epithelia. Notably a heterotaxy patient harboring heterozygous DNAH6 mutation was identified to also carry a rare heterozygous PCD-causing DNAI1 mutation, suggesting a DNAH6/DNAI1 trans-heterozygous interaction. Furthermore, sequencing of 149 additional heterotaxy patients showed 5 of 6 patients with heterozygous DNAH6 mutations also had heterozygous mutations in DNAH5 or other PCD genes. We functionally assayed for DNAH6/DNAH5 and DNAH6/DNAI1 trans-heterozygous interactions using subthreshold double-morpholino knockdown in zebrafish and showed this caused heterotaxy. Similarly, subthreshold siRNA knockdown of Dnah6 in heterozygous Dnah5 or Dnai1 mutant mouse respiratory epithelia disrupted motile cilia function. Together, these findings support an oligogenic disease

  4. DNAH6 and Its Interactions with PCD Genes in Heterotaxy and Primary Ciliary Dyskinesia.

    Directory of Open Access Journals (Sweden)

    You Li

    2016-02-01

    Full Text Available Heterotaxy, a birth defect involving left-right patterning defects, and primary ciliary dyskinesia (PCD, a sinopulmonary disease with dyskinetic/immotile cilia in the airway are seemingly disparate diseases. However, they have an overlapping genetic etiology involving mutations in cilia genes, a reflection of the common requirement for motile cilia in left-right patterning and airway clearance. While PCD is a monogenic recessive disorder, heterotaxy has a more complex, largely non-monogenic etiology. In this study, we show mutations in the novel dynein gene DNAH6 can cause heterotaxy and ciliary dysfunction similar to PCD. We provide the first evidence that trans-heterozygous interactions between DNAH6 and other PCD genes potentially can cause heterotaxy. DNAH6 was initially identified as a candidate heterotaxy/PCD gene by filtering exome-sequencing data from 25 heterotaxy patients stratified by whether they have airway motile cilia defects. dnah6 morpholino knockdown in zebrafish disrupted motile cilia in Kupffer's vesicle required for left-right patterning and caused heterotaxy with abnormal cardiac/gut looping. Similarly DNAH6 shRNA knockdown disrupted motile cilia in human and mouse respiratory epithelia. Notably a heterotaxy patient harboring heterozygous DNAH6 mutation was identified to also carry a rare heterozygous PCD-causing DNAI1 mutation, suggesting a DNAH6/DNAI1 trans-heterozygous interaction. Furthermore, sequencing of 149 additional heterotaxy patients showed 5 of 6 patients with heterozygous DNAH6 mutations also had heterozygous mutations in DNAH5 or other PCD genes. We functionally assayed for DNAH6/DNAH5 and DNAH6/DNAI1 trans-heterozygous interactions using subthreshold double-morpholino knockdown in zebrafish and showed this caused heterotaxy. Similarly, subthreshold siRNA knockdown of Dnah6 in heterozygous Dnah5 or Dnai1 mutant mouse respiratory epithelia disrupted motile cilia function. Together, these findings support an

  5. Advances in adult asthma diagnosis and treatment in 2012: potential therapeutics and gene-environment interactions.

    Science.gov (United States)

    Apter, Andrea J

    2013-01-01

    In the Journal of Allergy and Clinical Immunology in 2012, research reports related to asthma in adults clustered around mechanisms of disease, with a special focus on their potential for informing new therapies. There was also consideration of the effect of the environment on health from pollution, climate change, and epigenetic influences, underlining the importance of understanding gene-environment interactions in the pathogenesis of asthma and response to treatment.

  6. The physics of protein-DNA interaction networks in the control of gene expression

    Science.gov (United States)

    Saiz, Leonor

    2012-05-01

    Protein-DNA interaction networks play a central role in many fundamental cellular processes. In gene regulation, physical interactions and reactions among the molecular components together with the physical properties of DNA control how genes are turned on and off. A key player in all these processes is the inherent flexibility of DNA, which provides an avenue for long-range interactions between distal DNA elements through DNA looping. Such versatility enables multiple interactions and results in additional complexity that is remarkably difficult to address with traditional approaches. This topical review considers recent advances in statistical physics methods to study the assembly of protein-DNA complexes with loops, their effects in the control of gene expression, and their explicit application to the prototypical lac operon genetic system of the E. coli bacterium. In the last decade, it has been shown that the underlying physical properties of DNA looping can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including the balance between robustness and sensitivity of the induction process. These physical properties are largely dependent on the free energy of DNA looping, which accounts for DNA bending and twisting effects. These new physical methods have also been used in reverse to uncover the actual in vivo free energy of looping double-stranded DNA in living cells, which was not possible with existing experimental techniques. The results obtained for DNA looping by the lac repressor inside the E. coli bacterium showed a more malleable DNA than expected as a result of the interplay of the simultaneous presence of two distinct conformations of looped DNA.

  7. An interactive tool for visualization of relationships between gene expression profiles

    OpenAIRE

    Jones Steven JM; Ruzanov Peter

    2006-01-01

    Abstract Background Application of phenetic methods to gene expression analysis proved to be a successful approach. Visualizing the results in a 3-dimentional space may further enhance these techniques. Results We designed and built TreeBuilder3D, an interactive viewer for visualizing the hierarchical relationships between expression profiles such as SAGE libraries or microarrays. The program allows loading expression data as plain text files and visualizing the relative differences of the an...

  8. Quantitative Chemical-Genetic Interaction Map Connects Gene Alterations to Drug Responses | Office of Cancer Genomics

    Science.gov (United States)

    In a recent Cancer Discovery report, CTD2 researchers at the University of California in San Francisco developed a new quantitative chemical-genetic interaction mapping approach to evaluate drug sensitivity or resistance in isogenic cell lines. Performing a high-throughput screen with isogenic cell lines allowed the researchers to explore the impact of a panel of emerging and established drugs on cells overexpressing a single cancer-associated gene in isolation.

  9. Gene-Environment Interactions in Preventive Medicine: Current Status and Expectations for the Future.

    Science.gov (United States)

    Narimatsu, Hiroto

    2017-01-30

    The progression of many common disorders involves a complex interplay of multiple factors, including numerous different genes and environmental factors. Gene-environmental cohort studies focus on the identification of risk factors that cannot be discovered by conventional epidemiological methodologies. Such epidemiological methodologies preclude precise predictions, because the exact risk factors can be revealed only after detailed analyses of the interactions among multiple factors, that is, between genes and environmental factors. To date, these cohort studies have reported some promising results. However, the findings do not yet have sufficient clinical significance for the development of precise, personalized preventive medicine. Especially, some promising preliminary studies have been conducted in terms of the prevention of obesity. Large-scale validation studies of those preliminary studies, using a prospective cohort design and long follow-ups, will produce useful and practical evidence for the development of preventive medicine in the future.

  10. Interactions between genes and environmental factors in asthma and atopy: new developments

    Directory of Open Access Journals (Sweden)

    Sengler Claudia

    2001-10-01

    Full Text Available Abstract Asthma and associated phenotypes are complex traits most probably caused by an interaction of multiple disease susceptibility genes and environmental factors. Major achievements have occurred in identifying chromosomal regions and polymorphisms in candidate genes linked to or associated with asthma, atopic dermatitis, IgE levels and response to asthma therapy. The aims of this review are to explain the methodology of genetic studies of multifactorial diseases, to summarize chromosomal regions and polymorphisms in candidate genes linked to or associated with asthma and associated traits, to list genetic alterations that may alter response to asthma therapy, and to outline genetic factors that may render individuals more susceptible to asthma and atopy due to environmental changes.

  11. Gene × environment interaction on intergroup bias: the role of 5-HTTLPR and perceived outgroup threat.

    Science.gov (United States)

    Cheon, Bobby K; Livingston, Robert W; Hong, Ying-Yi; Chiao, Joan Y

    2014-09-01

    Perceived threat from outgroups is a consistent social-environmental antecedent of intergroup bias (i.e. prejudice, ingroup favoritism). The serotonin transporter gene polymorphism (5-HTTLPR) has been associated with individual variations in sensitivity to context, particularly stressful and threatening situations. Here, we examined how 5-HTTLPR and environmental factors signaling potential outgroup threat dynamically interact to shape intergroup bias. Across two studies, we provide novel evidence for a gene-environment interaction on the acquisition of intergroup bias and prejudice. Greater exposure to signals of outgroup threat, such as negative prior contact with outgroups and perceived danger from the social environment, were more predictive of intergroup bias among participants possessing at least one short allele (vs two long alleles) of 5-HTTLPR. Furthermore, this gene x environment interaction was observed for biases directed at diverse ethnic and arbitrarily-defined outgroups across measures reflecting intergroup biases in evaluation and discriminatory behavior. These findings reveal a candidate genetic mechanism for the acquisition of intergroup bias, and suggest that intergroup bias is dually inherited and transmitted through the interplay of social (i.e. contextual cues of outgroup threat) and biological mechanisms (i.e. genetic sensitivity toward threatening contexts) that regulate perceived intergroup threats.

  12. Gene and environment interaction: Is the differential susceptibility hypothesis relevant for obesity?

    Science.gov (United States)

    Dalle Molle, Roberta; Fatemi, Hajar; Dagher, Alain; Levitan, Robert D; Silveira, Patricia P; Dubé, Laurette

    2017-02-01

    The differential susceptibility model states that a given genetic variant is associated with an increased risk of pathology in negative environments but greater than average resilience in enriched ones. While this theory was first implemented in psychiatric-genetic research, it may also help us to unravel the complex ways that genes and environments interact to influence feeding behavior and obesity. We reviewed evidence on gene vs. environment interactions that influence obesity development, aiming to support the applicability of the differential susceptibility model for this condition, and propose that various environmental "layers" relevant for human development should be considered when bearing the differential susceptibility model in mind. Mother-child relationship, socioeconomic status and individual's response are important modifiers of BMI and food intake when interacting with gene variants, "for better and for worse". While only a few studies to date have investigated obesity outcomes using this approach, we propose that the differential susceptibility hypothesis is in fact highly applicable to the study of genetic and environmental influences on feeding behavior and obesity risk.

  13. Interaction between the RGS6 gene and psychosocial stress on obesity-related traits.

    Science.gov (United States)

    Kim, Hyun-Jin; Min, Jin-Young; Min, Kyoung-Bok

    2017-03-31

    Obesity is a major risk factor for chronic diseases and arises from the interactions between environmental factors and multiple genes. Psychosocial stress may affect the risk for obesity, modifying food intake and choice. A recent study suggested regulator of G-protein signaling 6 (RGS6) as a novel candidate gene for obesity in terms of reward-related feeding under stress. In this study, we tried to verify the unidentified connection between RGS6 and human obesity with psychosocial stress in a Korean population. A total of 1,462 adult subjects, who participated in the Korean Association Resource cohort project, were included for this analysis. Obesity-related traits including waist circumference, body mass index, and visceral adipose tissue were recorded. A total of 4 intronic SNPs for the RGS6 gene were used for this study. We found that interactions between SNP rs2239219 and psychosocial stress are significantly associated with abdominal obesity (p = 0.007). As risk allele of this SNP increased, prevalence of abdominal obesity under high-stress conditions gradually increased (p = 0.013). However, we found no SNPs-by-stress interaction effect on other adiposity phenotypes. This study suggests that RGS6 is closely linked to stress-induced abdominal obesity in Korean adults.

  14. A simulation study of gene-by-environment interactions in GWAS implies ample hidden effects

    Directory of Open Access Journals (Sweden)

    Urko M Marigorta

    2014-07-01

    Full Text Available The switch to a modern lifestyle in recent decades has coincided with a rapid increase in prevalence of obesity and other diseases. These shifts in prevalence could be explained by the release of genetic susceptibility for disease in the form of gene-by-environment (GxE interactions. Yet, the detection of interaction effects requires large sample sizes, little replication has been reported, and a few studies have demonstrated environmental effects only after summing the risk of GWAS alleles into genetic risk scores (GRSxE. We performed extensive simulations of a quantitative trait controlled by 2,500 causal variants to inspect the feasibility to detect gene-by-environment interactions in the context of GWAS. The simulated individuals were assigned either to an ancestral or a modern setting that alters the phenotype by increasing the effect size by 1.05 to 2-fold at a varying fraction of perturbed SNPs (from 1% to 20%. We report two main results. First, for a wide range of realistic scenarios, highly significant GRSxE is detected despite the absence of individual genotype GxE evidence at the contributing loci. Second, an increase in phenotypic variance after environmental perturbation reduces the power to discover susceptibility variants by GWAS in mixed cohorts with individuals from both ancestral and modern environments. We conclude that a pervasive presence of gene-by-environment effects can remain hidden even though it contributes to the genetic architecture of complex traits.

  15. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.

    Science.gov (United States)

    Chen, Edward Y; Tan, Christopher M; Kou, Yan; Duan, Qiaonan; Wang, Zichen; Meirelles, Gabriela Vaz; Clark, Neil R; Ma'ayan, Avi

    2013-04-15

    System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

  16. Dairy Product Consumption Interacts with Glucokinase (GCK Gene Polymorphisms Associated with Insulin Resistance

    Directory of Open Access Journals (Sweden)

    Marine S. Da Silva

    2017-08-01

    Full Text Available Dairy product intake and a person’s genetic background have been reported to be associated with the risk of type 2 diabetes (T2D. The objective of this study was to examine the interaction between dairy products and genes related to T2D on glucose-insulin homeostasis parameters. A validated food frequency questionnaire, fasting blood samples, and glucokinase (GCK genotypes were analyzed in 210 healthy participants. An interaction between rs1799884 in GCK and dairy intake on the homeostasis model assessment of insulin resistance was identified. Secondly, human hepatocellular carcinoma cells (HepG2 were grown in a high-glucose medium and incubated with either 1-dairy proteins: whey, caseins, and a mixture of whey and casein; and 2-four amino acids (AA or mixtures of AA. The expression of GCK-related genes insulin receptor substrate-1 (IRS-1 and fatty acid synthase (FASN was increased with whey protein isolate or hydrolysate. Individually, leucine increased IRS-1 expression, whereas isoleucine and valine decreased FASN expression. A branched-chain AA mixture decreased IRS-1 and FASN expression. In conclusion, carriers of the A allele for rs1799884 in the GCK gene may benefit from a higher intake of dairy products to maintain optimal insulin sensitivity. Moreover, the results show that whey proteins affect the expression of genes related to glucose metabolism.

  17. Clustering Gene Expression Data Based on Predicted Differential Effects of G V Interaction

    Institute of Scientific and Technical Information of China (English)

    Hai-Yan Pan; Jun Zhu; Dan-Fu Han

    2005-01-01

    Microarray has become a popular biotechnology in biological and medical research.However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of G V (gene by variety)interaction using the adjusted unbiased prediction (AUP) method. The predicted G V interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

  18. Bisphenol-A and Female Infertility: A Possible Role of Gene-Environment Interactions

    Directory of Open Access Journals (Sweden)

    Xiaona Huo

    2015-09-01

    Full Text Available Background: Bisphenol-A (BPA is widely used and ubiquitous in the environment. Animal studies indicate that BPA affects reproduction, however, the gene-environment interaction mechanism(s involved in this association remains unclear. We performed a literature review to summarize the evidence on this topic. Methods: A comprehensive search was conducted in PubMed using as keywords BPA, gene, infertility and female reproduction. Full-text articles in both human and animals published in English prior to December 2014 were selected. Results: Evidence shows that BPA can interfere with endocrine function of hypothalamic-pituitary axis, such as by changing gonadotropin-releasing hormones (GnRH secretion in hypothalamus and promoting pituitary proliferation. Such actions affect puberty, ovulation and may even result in infertility. Ovary, uterus and other reproductive organs are also targets of BPA. BPA exposure impairs the structure and functions of female reproductive system in different times of life cycle and may contribute to infertility. Both epidemiological and experimental evidences demonstrate that BPA affects reproduction-related gene expression and epigenetic modification that are closely associated with infertility. The detrimental effects on reproduction may be lifelong and transgenerational. Conclusions: Evidence on gene-environment interactions, especially from human studies, is still limited. Further research on this topic is warranted.

  19. Frequency modulation of stochastic gene expression bursts by strongly interacting small RNAs

    Science.gov (United States)

    Kumar, Niraj; Jia, Tao; Zarringhalam, Kourosh; Kulkarni, Rahul V.

    2016-10-01

    The sporadic nature of gene expression at the single-cell level—long periods of inactivity punctuated by bursts of mRNA or protein production—plays a critical role in diverse cellular processes. To elucidate the cellular role of bursting in gene expression, synthetic biology approaches have been used to design simple genetic circuits with bursty mRNA or protein production. Understanding how such genetic circuits can be designed with the ability to control burst-related parameters requires the development of quantitative stochastic models of gene expression. In this work, we analyze stochastic models for the regulation of gene expression bursts by strongly interacting small RNAs. For the parameter range considered, results based on mean-field approaches are significantly inaccurate and alternative analytical approaches are needed. Using simplifying approximations, we obtain analytical results for the corresponding steady-state distributions that are in agreement with results from stochastic simulations. These results indicate that regulation by small RNAs, in the strong interaction limit, can be used to effectively modulate the frequency of bursting. We explore the consequences of such regulation for simple genetic circuits involving feedback effects and switching between promoter states.

  20. Frequency modulation of stochastic gene expression bursts by strongly interacting small RNAs.

    Science.gov (United States)

    Kumar, Niraj; Jia, Tao; Zarringhalam, Kourosh; Kulkarni, Rahul V

    2016-10-01

    The sporadic nature of gene expression at the single-cell level-long periods of inactivity punctuated by bursts of mRNA or protein production-plays a critical role in diverse cellular processes. To elucidate the cellular role of bursting in gene expression, synthetic biology approaches have been used to design simple genetic circuits with bursty mRNA or protein production. Understanding how such genetic circuits can be designed with the ability to control burst-related parameters requires the development of quantitative stochastic models of gene expression. In this work, we analyze stochastic models for the regulation of gene expression bursts by strongly interacting small RNAs. For the parameter range considered, results based on mean-field approaches are significantly inaccurate and alternative analytical approaches are needed. Using simplifying approximations, we obtain analytical results for the corresponding steady-state distributions that are in agreement with results from stochastic simulations. These results indicate that regulation by small RNAs, in the strong interaction limit, can be used to effectively modulate the frequency of bursting. We explore the consequences of such regulation for simple genetic circuits involving feedback effects and switching between promoter states.

  1. A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.

    Science.gov (United States)

    Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M; Worrall, Bradford B; Williams, Stephen R; Hsu, Fang-Chi; Tzeng, Jung-Ying

    2015-09-01

    Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level.

  2. Protein-Protein Interactions: Gene Acronym Redundancies and Current Limitations Precluding Automated Data Integration.

    Science.gov (United States)

    Casado-Vela, Juan; Matthiesen, Rune; Sellés, Susana; Naranjo, José Ramón

    2013-05-31

    Understanding protein interaction networks and their dynamic changes is a major challenge in modern biology. Currently, several experimental and in silico approaches allow the screening of protein interactors in a large-scale manner. Therefore, the bulk of information on protein interactions deposited in databases and peer-reviewed published literature is constantly growing. Multiple databases interfaced from user-friendly web tools recently emerged to facilitate the task of protein interaction data retrieval and data integration. Nevertheless, as we evidence in this report, despite the current efforts towards data integration, the quality of the information on protein interactions retrieved by in silico approaches is frequently incomplete and may even list false interactions. Here we point to some obstacles precluding confident data integration, with special emphasis on protein interactions, which include gene acronym redundancies and protein synonyms. Three human proteins (choline kinase, PPIase and uromodulin) and three different web-based data search engines focused on protein interaction data retrieval (PSICQUIC, DASMI and BIPS) were used to explain the potential occurrence of undesired errors that should be considered by researchers in the field. We demonstrate that, despite the recent initiatives towards data standardization, manual curation of protein interaction networks based on literature searches are still required to remove potential false positives. A three-step workflow consisting of: (i) data retrieval from multiple databases, (ii) peer-reviewed literature searches, and (iii) data curation and integration, is proposed as the best strategy to gather updated information on protein interactions. Finally, this strategy was applied to compile bona fide information on human DREAM protein interactome, which constitutes liable training datasets that can be used to improve computational predictions.

  3. The heritable basis of gene-environment interactions in cardiometabolic traits.

    Science.gov (United States)

    Poveda, Alaitz; Chen, Yan; Brändström, Anders; Engberg, Elisabeth; Hallmans, Göran; Johansson, Ingegerd; Renström, Frida; Kurbasic, Azra; Franks, Paul W

    2017-03-01

    Little is known about the heritable basis of gene-environment interactions in humans. We therefore screened multiple cardiometabolic traits to assess the probability that they are influenced by genotype-environment interactions. Fourteen established environmental risk exposures and 11 cardiometabolic traits were analysed in the VIKING study, a cohort of 16,430 Swedish adults from 1682 extended pedigrees with available detailed genealogical, phenotypic and demographic information, using a maximum likelihood variance decomposition method in Sequential Oligogenic Linkage Analysis Routines software. All cardiometabolic traits had statistically significant heritability estimates, with narrow-sense heritabilities (h (2)) ranging from 24% to 47%. Genotype-environment interactions were detected for age and sex (for the majority of traits), physical activity (for triacylglycerols, 2 h glucose and diastolic BP), smoking (for weight), alcohol intake (for weight, BMI and 2 h glucose) and diet pattern (for weight, BMI, glycaemic traits and systolic BP). Genotype-age interactions for weight and systolic BP, genotype-sex interactions for BMI and triacylglycerols and genotype-alcohol intake interactions for weight remained significant after multiple test correction. Age, sex and alcohol intake are likely to be major modifiers of genetic effects for a range of cardiometabolic traits. This information may prove valuable for studies that seek to identify specific loci that modify the effects of lifestyle in cardiometabolic disease.

  4. PPISEARCHENGINE: gene ontology-based search for protein-protein interactions.

    Science.gov (United States)

    Park, Byungkyu; Cui, Guangyu; Lee, Hyunjin; Huang, De-Shuang; Han, Kyungsook

    2013-01-01

    This paper presents a new search engine called PPISearchEngine which finds protein-protein interactions (PPIs) using the gene ontology (GO) and the biological relations of proteins. For efficient retrieval of PPIs, each GO term is assigned a prime number and the relation between the terms is represented by the product of prime numbers. This representation is hidden from users but facilitates the search for the interactions of a query protein by unique prime factorisation of the number that represents the query protein. For a query protein, PPISearchEngine considers not only the GO term associated with the query protein but also the GO terms at the lower level than the GO term in the GO hierarchy, and finds all the interactions of the query protein which satisfy the search condition. In contrast, the standard keyword-matching or ID-matching search method cannot find the interactions of a protein unless the interactions involve a protein with explicit annotations. To the best of our knowledge, this search engine is the first method that can process queries like 'for protein p with GO [Formula: see text], find p's interaction partners with GO [Formula: see text]'. PPISearchEngine is freely available to academics at http://search.hpid.org/.

  5. Joint genetic analysis using variant sets reveals polygenic gene-context interactions.

    Directory of Open Access Journals (Sweden)

    Francesco Paolo Casale

    2017-04-01

    Full Text Available Joint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. Our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. We first use simulations to validate iSet before applying the model to the analysis of genotype-environment interactions in an eQTL study. Our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. Finally, we apply iSet to test for sub-group specific genetic effects in human lipid levels in a large human cohort, where we identify a gene-sex interaction for C-reactive protein that is missed by alternative methods.

  6. Interaction and Relationship Between Angiotensin Converting Enzyme Gene and Environmental Factors Predisposing to Essential Hypertension in Mongolian Population of China

    Institute of Scientific and Technical Information of China (English)

    QUN XU; HUA FENG; SHUANG-LIAN BAI; HAI-HUA PANG; GUI-RONG HUANG; MING-WU FANG; YONG-HONG ZHANG; ZHENG-LAI WU; CHANG-CHUN QIU; YAN-HUA WANG; WEI-JUN TONG; MING-LIANG GU; GANG WU; BATU BUREN; YONG-YUE LIU; JIAN WANG; YONG-SHAN LI

    2004-01-01

    Objective To investigate the association of specific functional gene ACE (I/D) variants of the renin-angiotensin system with essential hypertension (EH) and interaction between ACE (I/D) gene and risk factors for EH in a genetically homogenous Mongolia rural population of China. Methods Individuals (n=1099) were recruited from general population of Kezuohouqi Banner in Inner Mongolian Autonomous Region. Results The association was found between ACE genotype DD plus ID and EH, with an interaction between ACE genotype DD plus ID and cigarette smoking in an additive model. Cigarette smoking index and ACE gene showed a low exposure-gene (LEG) effect on EH, with interaction indices from 7.10 to 1.16. Interaction between ACE genotype DD plus ID and alcohol drinking on EH appeared an additive model. Alcohol drinking index and ACE gene showed a low exposure-gene (LEG) effect on EH, with interaction indices from 1.66 to 1.09. BMI and ACE gene showed a low exposure-gene (LEG) effect on EH, with interaction indices from 6.15 to 2.49. Interactions between ACE genotype and WHR on EH showed a multiplicative model. In a short, there was an interaction between ACE gene and cigarette smoking, alcohol drinking and BMI on EH, especially in a low dose-exposure effect. Conclusion It is important for individuals who carry ACE D allele gene to prevent EH, and furthermore, to prevent and control coronary heart disease, in a view of population-based prevention.

  7. Isolation and Characterization of Pepper Genes Interacting with the CMV-P1 Helicase Domain.

    Directory of Open Access Journals (Sweden)

    Yoomi Choi

    Full Text Available Cucumber mosaic virus (CMV is a destructive pathogen affecting Capsicum annuum (pepper production. The pepper Cmr1 gene confers resistance to most CMV strains, but is overcome by CMV-P1 in a process dependent on the CMV-P1 RNA1 helicase domain (P1 helicase. Here, to identify host factors involved in CMV-P1 infection in pepper, a yeast two-hybrid library derived from a C. annuum 'Bukang' cDNA library was screened, producing a total of 76 potential clones interacting with the P1 helicase. Beta-galactosidase filter lift assay, PCR screening, and sequencing analysis narrowed the candidates to 10 genes putatively involved in virus infection. The candidate host genes were silenced in Nicotiana benthamiana plants that were then inoculated with CMV-P1 tagged with the green fluorescent protein (GFP. Plants silenced for seven of the genes showed development comparable to N. benthamiana wild type, whereas plants silenced for the other three genes showed developmental defects including stunting and severe distortion. Silencing formate dehydrogenase and calreticulin-3 precursor led to reduced virus accumulation. Formate dehydrogenase-silenced plants showed local infection in inoculated leaves, but not in upper (systemic leaves. In the calreticulin-3 precursor-silenced plants, infection was not observed in either the inoculated or the upper leaves. Our results demonstrate that formate dehydrogenase and calreticulin-3 precursor are required for CMV-P1 infection.

  8. Interaction of rearing environment and reproductive tactic on gene expression profiles in Atlantic salmon

    Science.gov (United States)

    Aubin-Horth, N.; Letcher, B.H.; Hofmann, H.A.

    2005-01-01

    Organisms that share the same genotype can develop into divergent phenotypes, depending on environmental conditions. In Atlantic salmon, young males of the same age can be found either as sneakers or immature males that are future anadromous fish. Just as the organism-level phenotype varies between divergent male developmental trajectories, brain gene expression is expected to vary as well. We hypothesized that rearing environment can also have an important effect on gene expression in the brain and possibly interact with the reproductive tactic adopted. We tested this hypothesis by comparing brain gene expression profiles of the two male tactics in fish from the same population that were reared in either a natural stream or under laboratory conditions. We found that expression of certain genes was affected by rearing environment only, while others varied between male reproductive tactics independent of rearing environment. Finally, more than half of all genes that showed variable expression varied between the two male tactics only in one environment. Thus, in these fish, very different molecular pathways can give rise to similar macro-phenotypes depending on rearing environment. This result gives important insights into the molecular underpinnings of developmental plasticity in relationship to the environment. ?? 2005 The American Genetic Association.

  9. The EHV-1 UL4 protein that tempers viral gene expression interacts with cellular transcription factors.

    Science.gov (United States)

    Zhang, Yunfei; Charvat, Robert A; Kim, Seong K; O'Callaghan, Dennis J

    2014-01-20

    The UL4 gene is conserved within the genome of defective interfering particles of equine herpesvirus type 1 (EHV-1) that mediate persistent infection. Here, we show that the UL4 protein inhibits EHV-1 reporter gene expression by decreasing the level of transcribed mRNA. The UL4 protein did not bind any gene class of EHV-1 promoters in electromobility or chromatin immunoprecipitation assays, but directly interacted with the TATA box-binding protein (TBP) and the carboxy-terminal domain of RNA polymerase II both in vitro (GST-pulldown assays) and in infected cells (coimmunoprecipitation analyses). Microarray analyses of the expression of the 78 EHV-1 genes revealed that viral late genes important for virion assembly displayed enhanced expression in cells infected with UL4-null virus as compared to wild-type or UL4-restored EHV-1. Quantitative PCR analyses showed that viral DNA replication was not retarded in cells infected with the UL4-null virus as compared to wild-type EHV-1.

  10. The ASK1 gene regulates development and interacts with the UFO gene to control floral organ identity in Arabidopsis.

    Science.gov (United States)

    Zhao, D; Yang, M; Solava, J; Ma, H

    1999-09-01

    Normal flower development likely requires both specific and general regulators. We have isolated an Arabidopsis mutant ask1-1 (for -Arabidopsis skp1-like1-1), which exhibits defects in both vegetative and reproductive development. In the ask1-1mutant, rosette leaf growth is reduced, resulting in smaller than normal rosette leaves, and internodes in the floral stem are shorter than normal. Examination of cell sizes in these organs indicates that cell expansion is normal in the mutant, but cell number is reduced. In the mutant, the numbers of petals and stamens are reduced, and many flowers have one or more petals with a reduced size. In addition, all mutant flowers have short stamen filaments. Furthermore, petal/stamen chimeric organs are found in many flowers. These results indicate that the ASK1 gene affects the size of vegetative and floral organs. The ask1 floral phenotype resembles somewhat that of the Arabidopsis ufo mutants in that both genes affect whorls 2 and 3. We therefore tested for possible interactions between ASK1 and UFO by analyzing the phenotypes of ufo-2 ask1-1 double mutant plants. In these plants, vegetative development is similar to that of the ask1-1 single mutant, whereas the floral defects are more severe than those in either single mutant. Interior to the first whorl, the double mutant flowers have more sepals or sepal-like organs than are found in ufo-2, and less petals than ask1-1. Our results suggest that ASK1 interacts with UFO to control floral organ identity in whorls 2 and 3. This is very intriguing because ASK1 is very similar in sequence to the yeast SKP1 protein and UFO contains an F-box, a motif known to interact with SKP1 in yeast. Although the precise mechanism of ASK1 and UFO action is unknown, our results support the hypothesis that these two proteins physically interact in vivo. Copyright 1999 Wiley-Liss, Inc.

  11. Utilizing cell-matrix interactions to modulate gene transfer to stem cells inside hyaluronic acid hydrogels.

    Science.gov (United States)

    Gojgini, Shiva; Tokatlian, Talar; Segura, Tatiana

    2011-10-01

    The effective delivery of DNA locally would increase the applicability of gene therapy in tissue regeneration, where diseased tissue is to be repaired in situ. One promising approach is to use hydrogel scaffolds to encapsulate and deliver plasmid DNA in the form of nanoparticles to the diseased tissue, so that cells infiltrating the scaffold are transfected to induce regeneration. This study focuses on the design of a DNA nanoparticle-loaded hydrogel scaffold. In particular, this study focuses on understanding how cell-matrix interactions affect gene transfer to adult stem cells cultured inside matrix metalloproteinase (MMP) degradable hyaluronic acid (HA) hydrogel scaffolds. HA was cross-linked to form a hydrogel material using a MMP degradable peptide and Michael addition chemistry. Gene transfer inside these hydrogel materials was assessed as a function of polyplex nitrogen to phosphate ratio (N/P = 5 to 12), matrix stiffness (100-1700 Pa), RGD (Arg-Gly-Asp) concentration (10-400 μM), and RGD presentation (0.2-4.7 RGDs per HA molecule). All variables were found to affect gene transfer to mouse mensenchymal stem cells culture inside the DNA loaded hydrogels. As expected, higher N/P ratios lead to higher gene transfer efficiency but also higher toxicity; softer hydrogels resulted in higher transgene expression than stiffer hydrogels, and an intermediate RGD concentration and RGD clustering resulted in higher transgene expression. We believe that the knowledge gained through this in vitro model can be utilized to design better scaffold-mediated gene delivery for local gene therapy.

  12. A methodology to establish a database to study gene environment interactions for childhood asthma

    Directory of Open Access Journals (Sweden)

    McCormick Jonathan

    2010-12-01

    Full Text Available Abstract Background Gene-environment interactions are likely to explain some of the heterogeneity in childhood asthma. Here, we describe the methodology and experiences in establishing a database for childhood asthma designed to study gene-environment interactions (PAGES - Paediatric Asthma Gene Environment Study. Methods Children with asthma and under the care of a respiratory paediatrician are being recruited from 15 hospitals between 2008 and 2011. An asthma questionnaire is completed and returned by post. At a routine clinic visit saliva is collected for DNA extraction. Detailed phenotyping in a proportion of children includes spirometry, bronchodilator response (BDR, skin prick reactivity, exhaled nitric oxide and salivary cotinine. Dietary and quality of life questionnaires are completed. Data are entered onto a purpose-built database. Results To date 1045 children have been invited to participate and data collected in 501 (48%. The mean age (SD of participants is 8.6 (3.9 years, 57% male. DNA has been collected in 436 children. Spirometry has been obtained in 172 children, mean % predicted (SD FEV1 97% (15 and median (IQR BDR is 5% (2, 9. There were differences in age, socioeconomic status, severity and %FEV1 between the different centres (p≤0.024. Reasons for non-participation included parents not having time to take part, children not attending clinics and, in a small proportion, refusal to take part. Conclusions It is feasible to establish a national database to study gene-environment interactions within an asthmatic paediatric population; there are barriers to participation and some different characteristics in individuals recruited from different centres. Recruitment to our study continues and is anticipated to extend current understanding of asthma heterogeneity.

  13. MAOA genotype, social exclusion and aggression: an experimental test of a gene-environment interaction.

    Science.gov (United States)

    Gallardo-Pujol, D; Andrés-Pueyo, A; Maydeu-Olivares, A

    2013-02-01

    In 2002, Caspi and colleagues provided the first epidemiological evidence that genotype may moderate individuals' responses to environmental determinants. However, in a correlational study great care must be taken to ensure the proper estimation of the causal relationship. Here, a randomized experiment was performed to test the hypothesis that the MAOA gene promoter polymorphism (MAOA-LPR) interacts with environmental adversity in determining aggressive behavior using laboratory analogs of real-life conditions. A sample of 57 Caucasian male students of Catalan and Spanish origin was recruited at the University of Barcelona. Ostracism, or social exclusion, was induced as environmental adversity using the Cyberball software. Laboratory aggression was assessed with the Point Subtraction Aggression Paradigm (PSAP), which was used as an analog of antisocial behavior. We also measured aggressiveness by means of the reduced version of the Aggression Questionnaire. The MAOA-LPR polymorphism showed a significant effect on the number of aggressive responses in the PSAP (F(1,53) = 4.63, P = 0.03, partial η(2) = 0.08), as well as social exclusion (F(1,53) = 8.03, P = 0.01, partial η(2) = 0.13). Most notably, however, we found that the MAOA-LPR polymorphism interacts significantly with social exclusion in order to provoke aggressive behavior (F(1,53) = 4.42, P = 0.04, partial η(2) = 0.08), remarkably, the low-activity allele of the MAOA-LPR polymorphism carriers in the ostracized group show significantly higher aggression scores than the rest. Our results support the notion that gene-environment interactions can be successfully reproduced within a laboratory using analogs and an appropriate design. We provide guidelines to test gene-environment interactions hypotheses under controlled, experimental settings.

  14. Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci.

    Science.gov (United States)

    Martin, Paul; McGovern, Amanda; Orozco, Gisela; Duffus, Kate; Yarwood, Annie; Schoenfelder, Stefan; Cooper, Nicholas J; Barton, Anne; Wallace, Chris; Fraser, Peter; Worthington, Jane; Eyre, Steve

    2015-11-30

    Genome-wide association studies have been tremendously successful in identifying genetic variants associated with complex diseases. The majority of association signals are intergenic and evidence is accumulating that a high proportion of signals lie in enhancer regions. We use Capture Hi-C to investigate, for the first time, the interactions between associated variants for four autoimmune diseases and their functional targets in B- and T-cell lines. Here we report numerous looping interactions and provide evidence that only a minority of interactions are common to both B- and T-cell lines, suggesting interactions may be highly cell-type specific; some disease-associated SNPs do not interact with the nearest gene but with more compelling candidate genes (for example, FOXO1, AZI2) often situated several megabases away; and finally, regions associated with different autoimmune diseases interact with each other and the same promoter suggesting common autoimmune gene targets (for example, PTPRC, DEXI and ZFP36L1).

  15. Identification of genes differentially expressed during interaction of Mexican lime tree infected with "Candidatus Phytoplasma aurantifolia".

    Science.gov (United States)

    Zamharir, Maryam Ghayeb; Mardi, Mohsen; Alavi, Seyed Mohammad; Hasanzadeh, Nader; Nekouei, Mojtaba Khayyam; Zamanizadeh, Hamid Reza; Alizadeh, Ali; Salekdeh, Ghasem Hoseini

    2011-01-01

    "Candidatus Phytoplasma aurantifolia", is the causative agent of witches' broom disease in Mexican lime trees (Citrus aurantifolia L.), and is responsible for major losses of Mexican lime trees in Southern Iran and Oman. The pathogen is strictly biotrophic, and thus is completely dependent on living host cells for its survival. The molecular basis of compatibility and disease development in this system is poorly understood. Therefore, we have applied a cDNA- amplified fragment length polymorphism (AFLP) approach to analyze gene expression in Mexican lime trees infected by "Ca. Phytoplasma aurantifolia". We carried out cDNA-AFLP analysis on grafted infected Mexican lime trees of the susceptible cultivar at the representative symptoms stage. Selective amplifications with 43 primer combinations allowed the visualisation of 55 transcript-derived fragments that were expressed differentially between infected and non-infected leaves. We sequenced 51 fragments, 36 of which were identified as lime tree transcripts after homology searching. Of the 36 genes, 70.5% were down-regulated during infection and could be classified into various functional groups. We showed that Mexican lime tree genes that were homologous to known resistance genes tended to be repressed in response to infection. These included the genes for modifier of snc1 and autophagy protein 5. Furthermore, down-regulation of genes involved in metabolism, transcription, transport and cytoskeleton was observed, which included the genes for formin, importin β 3, transducin, L-asparaginase, glycerophosphoryl diester phosphodiesterase, and RNA polymerase β. In contrast, genes that encoded a proline-rich protein, ubiquitin-protein ligase, phosphatidyl glycerol specific phospholipase C-like, and serine/threonine-protein kinase were up-regulated during the infection. The present study identifies a number of candidate genes that might be involved in the interaction of Mexican lime trees with "Candidatus Phytoplasma

  16. Identification of genes differentially expressed during interaction of Mexican lime tree infected with "Candidatus Phytoplasma aurantifolia"

    Directory of Open Access Journals (Sweden)

    Nekouei Mojtaba

    2011-01-01

    interaction of Mexican lime trees with "Candidatus Phytoplasma aurantifolia". These results should help to elucidate the molecular basis of the infection process and to identify genes that could be targeted to increase plant resistance and inhibit the growth and reproduction of the pathogen.

  17. IRF5:RelA Interaction Targets Inflammatory Genes in Macrophages

    Directory of Open Access Journals (Sweden)

    David G. Saliba

    2014-09-01

    Full Text Available Interferon Regulatory Factor 5 (IRF5 plays a major role in setting up an inflammatory macrophage phenotype, but the molecular basis of its transcriptional activity is not fully understood. In this study, we conduct a comprehensive genome-wide analysis of IRF5 recruitment in macrophages stimulated with bacterial lipopolysaccharide and discover that IRF5 binds to regulatory elements of highly transcribed genes. Analysis of protein:DNA microarrays demonstrates that IRF5 recognizes the canonical IRF-binding (interferon-stimulated response element [ISRE] motif in vitro. However, IRF5 binding in vivo appears to rely on its interactions with other proteins. IRF5 binds to a noncanonical composite PU.1:ISRE motif, and its recruitment is aided by RelA. Global gene expression analysis in macrophages deficient in IRF5 and RelA highlights the direct role of the RelA:IRF5 cistrome in regulation of a subset of key inflammatory genes. We map the RelA:IRF5 interaction domain and suggest that interfering with it would offer selective targeting of macrophage inflammatory activities.

  18. Melanopsin Gene Variations Interact With Season to Predict Sleep Onset and Chronotype

    Science.gov (United States)

    Roecklein, Kathryn A.; Wong, Patricia M.; Franzen, Peter L.; Hasler, Brant P.; Wood-Vasey, W. Michael; Nimgaonkar, Vishwajit L.; Miller, Megan A.; Kepreos, Kyle M.; Ferrell, Robert E.; Manuck, Stephen B.

    2013-01-01

    The human melanopsin gene has been reported to mediate risk for seasonal affective disorder (SAD), which is hypothesized to be caused by decreased photic input during winter when light levels fall below threshold, resulting in differences in circadian phase and/or sleep. However, it is unclear if melanopsin increases risk of SAD by causing differences in sleep or circadian phase, or if those differences are symptoms of the mood disorder. To determine if melanopsin sequence variations are associated with differences in sleep-wake behavior among those not suffering from a mood disorder, the authors tested associations between melanopsin gene polymorphisms and self-reported sleep timing (sleep onset and wake time) in a community sample (N = 234) of non-Hispanic Caucasian participants (age 30–54 yrs) with no history of psychological, neurological, or sleep disorders. The authors also tested the effect of melanopsin variations on differences in preferred sleep and activity timing (i.e., chronotype), which may reflect differences in circadian phase, sleep homeostasis, or both. Daylength on the day of assessment was measured and included in analyses. DNA samples were genotyped for melanopsin gene polymorphisms using fluorescence polarization. P10L genotype interacted with daylength to predict self-reported sleep onset (interaction p seasonal patterns of recurrence or exacerbation. PMID:22881342

  19. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets.

    Science.gov (United States)

    Vinayagam, Arunachalam; Gibson, Travis E; Lee, Ho-Joon; Yilmazel, Bahar; Roesel, Charles; Hu, Yanhui; Kwon, Young; Sharma, Amitabh; Liu, Yang-Yu; Perrimon, Norbert; Barabási, Albert-László

    2016-05-03

    The protein-protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as "indispensable," "neutral," or "dispensable," which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network's control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.

  20. Screening of Rice Genes Interacting with p5b of Rice Black-Streaked Dwarf Virus

    Institute of Scientific and Technical Information of China (English)

    LU Ying; YANG Jian; ZHANG Heng-mu; CHEN Jian-ping

    2013-01-01

    Rice black-streaked dwarf virus (RBSDV) is a recognized member of the genus Fijivirus,family Reoviridae.Its genome has ten double-stranded RNA (dsRNA) segments (S1-S10),in which the fifth genome segment (S5) contains two open reading frames (ORFs) with a partially overlapping region.The second ORF of RBSDV S5 encodes a viral nonstructural protein named p5b with unknown function.To reveal the function of p5b,its gene was ligated into the bait plasmid pGBKT7 and an expression library containing rice cDNAs was constructed using plasmid pGADT7 for yeast two-hybrid assay.The bait protein p5b was detected in yeast by western blot,and the result of an auto-activation test showed that p5b could not autonomously activate the expression of reporter genes in yeast.Then the bait protein p5b was used for screening the cDNA expression libraries of rice.Gene fragments of some pivotal enzymes involved in photosynthesis,respiration and other important metabolic processes,were identified to interact with p5b in yeast,suggesting that these interactions may play roles in symptom development in infected plants.

  1. Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

    Science.gov (United States)

    Zhang, Shu-Bo; Tang, Qiang-Rong

    2016-07-21

    Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction.

  2. PARP1 Differentially Interacts with Promoter region of DUX4 Gene in FSHD Myoblasts

    Science.gov (United States)

    Sharma, Vishakha; Pandey, Sachchida Nand; Khawaja, Hunain; Brown, Kristy J; Hathout, Yetrib; Chen, Yi-Wen

    2016-01-01

    Objective The goal of the study is to identity proteins, which interact with the promoter region of double homeobox protein 4 (DUX4) gene known to be causative for the autosomal dominant disorder Facioscapulohumeral Muscular Dystrophy (FSHD). Methods We performed a DNA pull down assay coupled with mass spectrometry analysis to identify proteins that interact with a DUX4 promoter probe in Rhabdomyosarcomca (RD) cells. We selected the top ranked protein poly (ADP-ribose) polymerase 1 (PARP1) from our mass spectrometry data for further ChIP-qPCR validation using patients' myoblasts. We then treated FSHD myoblasts with PARP1 inhibitors to investigate the role of PARP1 in the FSHD myoblasts. Results In our mass spectrometry analysis, PARP1 was found to be the top ranked protein interacting preferentially with the DUX4 promoter probe in RD cells. We further validated this interaction by immunoblotting in RD cells (2-fold enrichment compared to proteins pulled down by a control probe, pfisetin (0.5 mM), a polyphenol compound with PARP1 inhibitory property, for 24 h also suppressed the expression of DUX4 (44.8 fold, p<0.01) and ZSCAN4 (2.2 fold, p<0.05) in the FSHD myoblasts. We further showed that DNA methyltransferase 1 (DNMT1), a gene regulated by PARP1 was also enriched at the DUX4 promoter in RD cells through immunoblotting (2-fold, p<0.01) and immortalized FSHD myoblasts (42-fold, p<0.01) but not control myoblasts through ChIP qPCR. Conclusion Our results showed that PARP1 and DNMT1 interacted with DUX4 promoter and may be involved in modulating DUX4 expression in FSHD. PMID:27722032

  3. Epistatic interactions of genes influence within-individual variation of physical activity traits in mice.

    Science.gov (United States)

    Leamy, Larry J; Pomp, Daniel; Lightfoot, J Timothy

    2011-06-01

    A number of quantitative trait loci (QTLs) recently have been discovered that affect various activity traits in mice, but their collective impact does not appear to explain the consistently moderate to high heritabilities for these traits. We previously suggested interactions of genes, or epistasis, might account for additional genetic variability of activity, and tested this for the average distance, duration and speed run by mice during a 3 week period. We found abundant evidence for epistasis affecting these traits, although, recognized that epistatic effects may well vary within individuals over time. We therefore conducted a full genome scan for epistatic interactions affecting these traits in each of seven three-day intervals. Our intent was to assess the extent and trends in epistasis affecting these traits in each of the intervals. We discovered a number of epistatic interactions of QTLs that influenced the activity traits in the mice, the majority of which were not previously found and appeared to affect the activity traits (especially distance and speed) primarily in the early or in the late age intervals. The overall impact of epistasis was considerable, its contribution to the total phenotypic variance varying from an average of 22-35% in the three traits across all age intervals. It was concluded that epistasis is more important than single-locus effects of genes on activity traits at specific ages and it is therefore an essential component of the genetic architecture of physical activity.

  4. Hybrid male sterility in rice controlled by interaction between divergent alleles of two adjacent genes.

    Science.gov (United States)

    Long, Yunming; Zhao, Lifeng; Niu, Baixiao; Su, Jing; Wu, Hao; Chen, Yuanling; Zhang, Qunyu; Guo, Jingxin; Zhuang, Chuxiong; Mei, Mantong; Xia, Jixing; Wang, Lan; Wu, Haibin; Liu, Yao-Guang

    2008-12-01

    Sterility is common in hybrids between divergent populations, such as the indica and japonica subspecies of Asian cultivated rice (Oryza sativa). Although multiple loci for plant hybrid sterility have been identified, it remains unknown how alleles of the loci interact at the molecular level. Here we show that a locus for indica-japonica hybrid male sterility, Sa, comprises two adjacent genes, SaM and SaF, encoding a small ubiquitin-like modifier E3 ligase-like protein and an F-box protein, respectively. Most indica cultivars contain a haplotype SaM(+)SaF(+), whereas all japonica cultivars have SaM(-)SaF(-) that diverged by nucleotide variations in wild rice. Male semi-sterility in this heterozygous complex locus is caused by abortion of pollen carrying SaM(-). This allele-specific gamete elimination results from a selective interaction of SaF(+) with SaM(-), a truncated protein, but not with SaM(+) because of the presence of an inhibitory domain, although SaM(+) is required for this male sterility. Lack of any one of the three alleles in recombinant plants does not produce male sterility. We propose a two-gene/three-component interaction model for this hybrid male sterility system. The findings have implications for overcoming male sterility in inter-subspecific hybrid rice breeding.

  5. Adult onset asthma and interaction between genes and active tobacco smoking: The GABRIEL consortium

    Science.gov (United States)

    Postma, D. S.; Moffatt, M. F.; Jarvis, D.; Ramasamy, A.; Wjst, M.; Omenaas, E. R.; Bouzigon, E.; Demenais, F.; Nadif, R.; Siroux, V.; Polonikov, A. V.; Solodilova, M.; Ivanov, V. P.; Curjuric, I.; Imboden, M.; Kumar, A.; Probst-Hensch, N.; Ogorodova, L. M.; Puzyrev, V. P.; Bragina, E. Yu; Freidin, M. B.; Nolte, I. M.; Farrall, A. M.; Cookson, W. O. C. M.; Strachan, D. P.; Koppelman, G. H.; Boezen, H. M.

    2017-01-01

    Background Genome-wide association studies have identified novel genetic associations for asthma, but without taking into account the role of active tobacco smoking. This study aimed to identify novel genes that interact with ever active tobacco smoking in adult onset asthma. Methods We performed a genome-wide interaction analysis in six studies participating in the GABRIEL consortium following two meta-analyses approaches based on 1) the overall interaction effect and 2) the genetic effect in subjects with and without smoking exposure. We performed a discovery meta-analysis including 4,057 subjects of European descent and replicated our findings in an independent cohort (LifeLines Cohort Study), including 12,475 subjects. Results First approach: 50 SNPs were selected based on an overall interaction effect at p<10−4. The most pronounced interaction effect was observed for rs9969775 on chromosome 9 (discovery meta-analysis: ORint = 0.50, p = 7.63*10−5, replication: ORint = 0.65, p = 0.02). Second approach: 35 SNPs were selected based on the overall genetic effect in exposed subjects (p <10−4). The most pronounced genetic effect was observed for rs5011804 on chromosome 12 (discovery meta-analysis ORint = 1.50, p = 1.21*10−4; replication: ORint = 1.40, p = 0.03). Conclusions Using two genome-wide interaction approaches, we identified novel polymorphisms in non-annotated intergenic regions on chromosomes 9 and 12, that showed suggestive evidence for interaction with active tobacco smoking in the onset of adult asthma. PMID:28253294

  6. The core planar cell polarity gene prickle interacts with flamingo to promote sensory axon advance in the Drosophila embryo.

    Science.gov (United States)

    Mrkusich, Eli M; Flanagan, Dustin J; Whitington, Paul M

    2011-10-01

    The atypical cadherin Drosophila protein Flamingo and its vertebrate homologues play widespread roles in the regulation of both dendrite and axon growth. However, little is understood about the molecular mechanisms that underpin these functions. Whereas flamingo interacts with a well-defined group of genes in regulating planar cell polarity, previous studies have uncovered little evidence that the other core planar cell polarity genes are involved in regulation of neurite growth. We present data in this study showing that the planar cell polarity gene prickle interacts with flamingo in regulating sensory axon advance at a key choice point - the transition between the peripheral nervous system and the central nervous system. The cytoplasmic tail of the Flamingo protein is not required for this interaction. Overexpression of another core planar cell polarity gene dishevelled produces a similar phenotype to prickle mutants, suggesting that this gene may also play a role in regulation of sensory axon advance.

  7. Role of Cell Membrane-Vector Interactions in Successful Gene Delivery.

    Science.gov (United States)

    Vaidyanathan, Sriram; Orr, Bradford G; Banaszak Holl, Mark M

    2016-08-16

    Cationic polymers have been investigated as nonviral vectors for gene delivery due to their favorable safety profile when compared to viral vectors. However, nonviral vectors are limited by poor efficacy in inducing gene expression. The physicochemical properties of cationic polymers enabling successful gene expression have been investigated in order to improve expression efficiency and safety. Studies over the past several years have focused on five possible rate-limiting processes to explain the differences in gene expression: (1) endosomal release, (2) transport within specific intracellular pathways, (3) protection of DNA from nucleases, (4) transport into the nucleus, and (5) DNA release from vectors. However, determining the relative importance of these processes and the vector properties necessary for optimization remain a challenge to the field. In this Account, we describe over a decade of studies focused on understanding the interaction of cationic polymer and cationic polymer/oligonucleotide (polyplex) interactions with model lipid membranes, cell membranes, and cells in culture. In particular, we have been interested in how the interaction between cationic polymers and the membrane influences the intracellular transport of intact DNA to the nucleus. Recent advances in microfluidic patch clamp techniques enabled us to quantify polyplex cell membrane interactions at the cellular level with precise control over material concentrations and exposure times. In attempting to relate these findings to subsequent intracellular transport of DNA and expression of protein, we needed to develop an approach that could distinguish DNA that was intact and potentially functional for gene expression from the much larger pool of degraded, nonfunctional DNA within the cell. We addressed this need by developing a FRET oligonucleotide molecular beacon (OMB) to monitor intact DNA transport. The research highlighted in this Account builds to the conclusion that polyplex

  8. Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Oded Magger

    Full Text Available The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.

  9. Plant-Agrobacterium interaction mediated by ethylene and super-Agrobacterium conferring efficient gene transfer ability

    Directory of Open Access Journals (Sweden)

    Satoko eNonaka

    2014-12-01

    Full Text Available Agrobacterium tumefaciens has a unique ability to transfer genes into plant genomes. This ability has been utilized for plant genetic engineering. However, the efficiency is not sufficient for all plant species. Several studies have shown that ethylene decreased the Agrobacterium-mediated transformation frequency. Thus, A. tumefaciens with an ability to suppress ethylene evolution would increase the efficiency of Agrobacterium-mediated transformation. Some studies showed that plant growth-promoting rhizobacteria (PGPR can reduce ethylene levels in plants through 1-aminocyclopropane-1-carboxylic acid (ACC deaminase, which cleaves the ethylene precursor ACC into α-ketobutyrate and ammonia, resulting in reduced ethylene production. The whole genome sequence data showed that A. tumefaciens does not possess an ACC deaminase gene in its genome. Therefore, providing ACC deaminase activity to the bacteria would improve gene transfer. As expected, A. tumefaciens with ACC deaminase activity, designated as super-Agrobacterium, could suppress ethylene evolution and increase the gene transfer efficiency in several plant species. In this review, we summarize plant–Agrobacterium interactions and their applications for improving Agrobacterium-mediated genetic engineering techniques via super-Agrobacterium.

  10. Gene - environment interaction in programming hippocampal plasticity: focus on adult neurogenesis

    Directory of Open Access Journals (Sweden)

    Muriel eKoehl

    2015-08-01

    Full Text Available Interactions between genes and environment are a critical feature of development and both contribute to shape individuality. They are at the chore of vulnerability / resiliency for mental illnesses. During the early postnatal period, several brain structures involved in cognitive and emotional processing, such as the hippocampus, still develop and it is likely that interferences with this neuronal development, which is genetically determined, might lead to long-lasting structural and functional consequences and increase the risk of developing psychopathology. One particular target is adult neurogenesis, which is involved in the regulation of cognitive and emotional processes. Insights into the dynamic interplay between genes and environmental factors in setting up individual rates of neurogenesis have come from laboratory studies exploring experience-dependent changes in adult neurogenesis as a function of individual’s genetic makeup. These studies have implications for our understanding of the mechanisms regulating adult neurogenesis, which could constitute a link between environmental challenges and psychopathology.

  11. Protein interactions of the MLL PHD fingers modulate MLL target gene regulation in human cells.

    Science.gov (United States)

    Fair, K; Anderson, M; Bulanova, E; Mi, H; Tropschug, M; Diaz, M O

    2001-05-01

    The PHD fingers of the human MLL and Drosophila trx proteins have strong amino acid sequence conservation but their function is unknown. We have determined that these fingers mediate homodimerization and binding of MLL to Cyp33, a nuclear cyclophilin. These two proteins interact in vitro and in vivo in mammalian cells and colocalize at specific nuclear subdomains. Overexpression of the Cyp33 protein in leukemia cells results in altered expression of HOX genes that are targets for regulation by MLL. These alterations are suppressed by cyclosporine and are not observed in cell lines that express a mutant MLL protein without PHD fingers. These results suggest that binding of Cyp33 to MLL modulates its effects on the expression of target genes.

  12. An interactive tool for visualization of relationships between gene expression profiles

    Directory of Open Access Journals (Sweden)

    Jones Steven JM

    2006-04-01

    Full Text Available Abstract Background Application of phenetic methods to gene expression analysis proved to be a successful approach. Visualizing the results in a 3-dimentional space may further enhance these techniques. Results We designed and built TreeBuilder3D, an interactive viewer for visualizing the hierarchical relationships between expression profiles such as SAGE libraries or microarrays. The program allows loading expression data as plain text files and visualizing the relative differences of the analyzed datasets in 3-dimensional space using various distance metrics. Conclusion TreeBuilder3D provides a simple interface and has a small size. Written in Java, TreeBuilder3D is a platform-independent, open source application, which may be useful in analysis of large-scale gene expression data.

  13. Interactions of adolescent social experiences and dopamine genes to predict physical intimate partner violence perpetration

    Science.gov (United States)

    Parker, Edith A.; Peek-Asa, Corinne

    2017-01-01

    Objectives We examined the interactions between three dopamine gene alleles (DAT1, DRD2, DRD4) previously associated with violent behavior and two components of the adolescent environment (exposure to violence, school social environment) to predict adulthood physical intimate partner violence (IPV) perpetration among white men and women. Methods We used data from Wave IV of the National Longitudinal Study of Adolescent to Adult Health, a cohort study following individuals from adolescence to adulthood. Based on the prior literature, we categorized participants as at risk for each of the three dopamine genes using this coding scheme: two 10-R alleles for DAT1; at least one A-1 allele for DRD2; at least one 7-R or 8-R allele for DRD4. Adolescent exposure to violence and school social environment was measured in 1994 and 1995 when participants were in high school or middle school. Intimate partner violence perpetration was measured in 2008 when participants were 24 to 32 years old. We used simple and multivariable logistic regression models, including interactions of genes and the adolescent environments for the analysis. Results Presence of risk alleles was not independently associated with IPV perpetration but increasing exposure to violence and disconnection from the school social environment was associated with physical IPV perpetration. The effects of these adolescent experiences on physical IPV perpetration varied by dopamine risk allele status. Among individuals with non-risk dopamine alleles, increased exposure to violence during adolescence and perception of disconnection from the school environment were significantly associated with increased odds of physical IPV perpetration, but individuals with high risk alleles, overall, did not experience the same increase. Conclusion Our results suggested the effects of adolescent environment on adulthood physical IPV perpetration varied by genetic factors. This analysis did not find a direct link between risk alleles

  14. Plasma selenium levels and oxidative stress biomarkers: a gene-environment interaction population-based study.

    Science.gov (United States)

    Galan-Chilet, Inmaculada; Tellez-Plaza, Maria; Guallar, Eliseo; De Marco, Griselda; Lopez-Izquierdo, Raul; Gonzalez-Manzano, Isabel; Carmen Tormos, M; Martin-Nuñez, Gracia M; Rojo-Martinez, Gemma; Saez, Guillermo T; Martín-Escudero, Juan C; Redon, Josep; Javier Chaves, F

    2014-09-01

    The role of selenium exposure in preventing chronic disease is controversial, especially in selenium-repleted populations. At high concentrations, selenium exposure may increase oxidative stress. Studies evaluating the interaction of genetic variation in genes involved in oxidative stress pathways and selenium are scarce. We evaluated the cross-sectional association of plasma selenium concentrations with oxidative stress levels, measured as oxidized to reduced glutathione ratio (GSSG/GSH), malondialdehyde (MDA), and 8-oxo-7,8-dihydroguanine (8-oxo-dG) in urine, and the interacting role of genetic variation in oxidative stress candidate genes, in a representative sample of 1445 men and women aged 18-85 years from Spain. The geometric mean of plasma selenium levels in the study sample was 84.76 µg/L. In fully adjusted models the geometric mean ratios for oxidative stress biomarker levels comparing the highest to the lowest quintiles of plasma selenium levels were 0.61 (0.50-0.76) for GSSG/GSH, 0.89 (0.79-1.00) for MDA, and 1.06 (0.96-1.18) for 8-oxo-dG. We observed nonlinear dose-responses of selenium exposure and oxidative stress biomarkers, with plasma selenium concentrations above ~110 μg/L being positively associated with 8-oxo-dG, but inversely associated with GSSG/GSH and MDA. In addition, we identified potential risk genotypes associated with increased levels of oxidative stress markers with high selenium levels. Our findings support that high selenium levels increase oxidative stress in some biological processes. More studies are needed to disentangle the complexity of selenium biology and the relevance of potential gene-selenium interactions in relation to health outcomes in human populations. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Genetic interactions between planar cell polarity genes cause diverse neural tube defects in mice

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    Jennifer N. Murdoch

    2014-10-01

    Full Text Available Neural tube defects (NTDs are among the commonest and most severe forms of developmental defect, characterized by disruption of the early embryonic events of central nervous system formation. NTDs have long been known to exhibit a strong genetic dependence, yet the identity of the genetic determinants remains largely undiscovered. Initiation of neural tube closure is disrupted in mice homozygous for mutations in planar cell polarity (PCP pathway genes, providing a strong link between NTDs and PCP signaling. Recently, missense gene variants have been identified in PCP genes in humans with NTDs, although the range of phenotypes is greater than in the mouse mutants. In addition, the sequence variants detected in affected humans are heterozygous, and can often be detected in unaffected individuals. It has been suggested that interactions between multiple heterozygous gene mutations cause the NTDs in humans. To determine the phenotypes produced in double heterozygotes, we bred mice with all three pairwise combinations of Vangl2Lp, ScribCrc and Celsr1Crsh mutations, the most intensively studied PCP mutants. The majority of double-mutant embryos had open NTDs, with the range of phenotypes including anencephaly and spina bifida, therefore reflecting the defects observed in humans. Strikingly, even on a uniform genetic background, variability in the penetrance and severity of the mutant phenotypes was observed between the different double-heterozygote combinations. Phenotypically, Celsr1Crsh;Vangl2Lp;ScribCrc triply heterozygous mutants were no more severe than doubly heterozygous or singly homozygous mutants. We propose that some of the variation between double-mutant phenotypes could be attributed to the nature of the protein disruption in each allele: whereas ScribCrc is a null mutant and produces no Scrib protein, Celsr1Crsh and Vangl2Lp homozygotes both express mutant proteins, consistent with dominant effects. The variable outcomes of these genetic

  16. Genetic interactions between planar cell polarity genes cause diverse neural tube defects in mice.

    Science.gov (United States)

    Murdoch, Jennifer N; Damrau, Christine; Paudyal, Anju; Bogani, Debora; Wells, Sara; Greene, Nicholas D E; Stanier, Philip; Copp, Andrew J

    2014-10-01

    Neural tube defects (NTDs) are among the commonest and most severe forms of developmental defect, characterized by disruption of the early embryonic events of central nervous system formation. NTDs have long been known to exhibit a strong genetic dependence, yet the identity of the genetic determinants remains largely undiscovered. Initiation of neural tube closure is disrupted in mice homozygous for mutations in planar cell polarity (PCP) pathway genes, providing a strong link between NTDs and PCP signaling. Recently, missense gene variants have been identified in PCP genes in humans with NTDs, although the range of phenotypes is greater than in the mouse mutants. In addition, the sequence variants detected in affected humans are heterozygous, and can often be detected in unaffected individuals. It has been suggested that interactions between multiple heterozygous gene mutations cause the NTDs in humans. To determine the phenotypes produced in double heterozygotes, we bred mice with all three pairwise combinations of Vangl2(Lp), Scrib(Crc) and Celsr1(Crsh) mutations, the most intensively studied PCP mutants. The majority of double-mutant embryos had open NTDs, with the range of phenotypes including anencephaly and spina bifida, therefore reflecting the defects observed in humans. Strikingly, even on a uniform genetic background, variability in the penetrance and severity of the mutant phenotypes was observed between the different double-heterozygote combinations. Phenotypically, Celsr1(Crsh);Vangl2(Lp);Scrib(Crc) triply heterozygous mutants were no more severe than doubly heterozygous or singly homozygous mutants. We propose that some of the variation between double-mutant phenotypes could be attributed to the nature of the protein disruption in each allele: whereas Scrib(Crc) is a null mutant and produces no Scrib protein, Celsr1(Crsh) and Vangl2(Lp) homozygotes both express mutant proteins, consistent with dominant effects. The variable outcomes of these genetic

  17. Interaction of hepatitis B virus with tumor suppressor gene p53: its significance and biological function

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The mechanism of the interaction of hepatitis B virus (HBV) with tumor suppressor p53 and its role in the hepatocarcinogenesis have been studied by PCR-directed sequencing, gel shift assays and in situ ultraviolet cross-linking assay. The biological function of the interaction of HBV with p53 gene was investigated by co-transfection of chloramphenicol acetyltransferase (CAT) reporter gene, p53 and HBV DNA, and quantitative PCR. Among the 16 primary hepatocellular carcinoma (PHC) samples, 13 were HBV-DNA positive,10 HBxAg positive and 9 p53 protein positive. The p53 gene point mutation was found in 5 samples, one of which had a G to T substitution located at codon 249. After analyzing the HBV genome by a computer program, a p53 response element binding sequence was found in HBV genome at upstream of enhancer I, from 1047 to 1059 nucleotides. This sequence could specifically bind to p53 protein, increase p53 protein accumulation in the PHC cells and stimulate the transactivating activity of p53 and HBV replication .The results also revealed that HBxAg could combine with p53 protein to form a complex in the cells and enhance CAT expression. Immunocytochemical staining showed that p53 protein complex was located in the cytoplasm and the process of p53 entry to nuclei was, in part, blocked. From our results, we conclude that the mutation of p53 gene at codon 249 is infrequent in HBV-associated PHC, the DNA-protein binding between HBV and p53, and the protein-protein binding between HBxAg and p53 might lead to the reduction or inactivation of p53 protein, which in turn resulting in HBV-associated hepatocarcinogenesis.

  18. Genotype-Based Bayesian Analysis of Gene-Environment Interactions with Multiple Genetic Markers and Misclassification in Environmental Factors

    OpenAIRE

    Iryna Lobach; Ruzong Fan

    2012-01-01

    A key component to understanding etiology of complex diseases, such as cancer, diabetes, alcohol dependence, is to investigate gene-environment interactions. This work is motivated by the following two concerns in the analysis of gene-environment interactions. First, multiple genetic markers in moderate linkage disequilibrium may be involved in susceptibility to a complex disease. Second, environmental factors may be subject to misclassification. We develop a genotype based Bayesian pseudolik...

  19. Variable Persister Gene Interactions with (pppGpp for Persister Formation in Escherichia coli

    Directory of Open Access Journals (Sweden)

    Shuang Liu

    2017-09-01

    Full Text Available Persisters comprise a group of phenotypically heterogeneous metabolically quiescent bacteria with multidrug tolerance and contribute to the recalcitrance of chronic infections. Although recent work has shown that toxin-antitoxin (TA system HipAB depends on stringent response effector (pppGppin persister formation, whether other persister pathways are also dependent on stringent response has not been explored. Here we examined the relationship of (pppGpp with 15 common persister genes (dnaK, clpB, rpoS, pspF, tnaA, sucB, ssrA, smpB, recA, umuD, uvrA, hipA, mqsR, relE, dinJ using Escherichia coli as a model. By comparing the persister levels of wild type with their single gene knockout and double knockout mutants with relA, we divided their interactions into five types, namely A “dependent” (dnaK, recA, B “positive reinforcement” (rpoS, pspF, ssrA, recA, C “antagonistic” (clpB, sucB, umuD, uvrA, hipA, mqsR, relE, dinJ, D “epistasis” (clpB, rpoS, tnaA, ssrA, smpB, hipA, and E “irrelevant” (dnaK, clpB, rpoS, tnaA, sucB, smpB, umuD, uvrA, hipA, mqsR, relE, dinJ. We found that the persister gene interactions are intimately dependent on bacterial culture age, cell concentrations (diluted versus undiluted culture, and drug classifications, where the same gene may belong to different groups with varying antibiotics, culture age or cell concentrations. Together, this study represents the first attempt to systematically characterize the intricate relationships among the different mechanisms of persistence and as such provide new insights into the complexity of the persistence phenomenon at the level of persister gene network interactions.

  20. Gene-environment interactions in early life and adulthood: implications for cocaine intake

    OpenAIRE

    van der Veen, Rixt

    2008-01-01

    The objective of the research described in this thesis was to demonstrate the role of gene-environment interactions in the emergence of individual differences in cocaine use. For this purpose we used two inbred mouse strains, the C57Bl/6 (C57) and DBA/2 (DBA), which are known to differ in drug-intake and to be differentially sensitive to several stressors. We studied the impact of early life experiences (long-term influence) as well as a later life psychosocial stressor (short-term influence)...

  1. Gene expression profiles of the NCI-60 human tumor cell lines define molecular interaction networks governing cell migration processes.

    Directory of Open Access Journals (Sweden)

    Kurt W Kohn

    Full Text Available Although there is extensive information on gene expression and molecular interactions in various cell types, integrating those data in a functionally coherent manner remains challenging. This study explores the premise that genes whose expression at the mRNA level is correlated over diverse cell lines are likely to function together in a network of molecular interactions. We previously derived expression-correlated gene clusters from the database of the NCI-60 human tumor cell lines and associated each cluster with function categories of the Gene Ontology (GO database. From a cluster rich in genes associated with GO categories related to cell migration, we extracted 15 genes that were highly cross-correlated; prominent among them were RRAS, AXL, ADAM9, FN14, and integrin-beta1. We then used those 15 genes as bait to identify other correlated genes in the NCI-60 database. A survey of current literature disclosed, not only that many of the expression-correlated genes engaged in molecular interactions related to migration, invasion, and metastasis, but that highly cross-correlated subsets of those genes engaged in specific cell migration processes. We assembled this information in molecular interaction maps (MIMs that depict networks governing 3 cell migration processes: degradation of extracellular matrix, production of transient focal complexes at the leading edge of the cell, and retraction of the rear part of the cell. Also depicted are interactions controlling the release and effects of calcium ions, which may regulate migration in a spaciotemporal manner in the cell. The MIMs and associated text comprise a detailed and integrated summary of what is currently known or surmised about the role of the expression cross-correlated genes in molecular networks governing those processes.

  2. Environmental and gene-environment interactions and risk of rheumatoid arthritis

    Science.gov (United States)

    Karlson, Elizabeth W.; Deane, Kevin

    2012-01-01

    Multiple environmental factors including hormones, dietary factors, infections and exposure to tobacco smoke as well as gene-environment interactions have been associated with increased risk for rheumatoid arthritis (RA). Importantly, the growing understanding of the prolonged period prior to the first onset of symptoms of RA suggests that these environmental and genetic factors are likely acting to drive the development of RA-related autoimmunity long before the appearance of the first joint symptoms and clinical findings that are characteristic of RA. Herein we will review these factors and interactions, especially those that have been investigated in a prospective fashion prior to the symptomatic onset of RA. We will also discuss how these factors may be explored in future study to further the understanding of the pathogenesis of RA, and ultimately perhaps develop preventive measures for this disease. PMID:22819092

  3. Drug-Gene Interactions of Antihypertensive Medications and Risk of Incident Cardiovascular Disease

    DEFF Research Database (Denmark)

    Bis, Joshua C; Sitlani, Colleen; Irvin, Ryan

    2015-01-01

    : Using a genome-wide association study among 21,267 participants with pharmaceutically treated hypertension, we explored the hypothesis that genetic variants might influence or modify the effectiveness of common antihypertensive therapies on the risk of major cardiovascular outcomes. The classes of drug...... reference panels, and used additive genetic models in proportional hazards or logistic regression models to evaluate drug-gene interactions for each of four therapeutic drug classes. We used meta-analysis to combine study-specific interaction estimates for approximately 2 million single nucleotide......-analyses restricted to 66 SNPs with significant main effects on coronary artery disease or blood pressure from large published genome-wide association studies (Pinteraction ≥ 0.01). Our results suggest that there are no major pharmacogenetic influences of common SNPs on the relationship between blood pressure...

  4. Influenza NA and PB1 Gene Segments Interact during the Formation of Viral Progeny: Localization of the Binding Region within the PB1 Gene

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    Brad Gilbertson

    2016-08-01

    Full Text Available The influenza A virus genome comprises eight negative-sense viral RNAs (vRNAs that form individual ribonucleoprotein (RNP complexes. In order to incorporate a complete set of each of these vRNAs, the virus uses a selective packaging mechanism that facilitates co-packaging of specific gene segments but whose molecular basis is still not fully understood. Recently, we used a competitive transfection model where plasmids encoding the A/Puerto Rico/8/34 (PR8 and A/Udorn/307/72 (Udorn PB1 gene segments were competed to show that the Udorn PB1 gene segment is preferentially co-packaged into progeny virions with the Udorn NA gene segment. Here we created chimeric PB1 genes combining both Udorn and PR8 PB1 sequences to further define the location within the Udorn PB1 gene that drives co-segregation of these genes and show that nucleotides 1776–2070 of the PB1 gene are crucial for preferential selection. In vitro assays examining specific interactions between Udorn NA vRNA and purified vRNAs transcribed from chimeric PB1 genes also supported the importance of this region in the PB1-NA interaction. Hence, this work identifies an association between viral genes that are co-selected during packaging. It also reveals a region potentially important in the RNP-RNP interactions within the supramolecular complex that is predicted to form prior to budding to allow one of each segment to be packaged in the viral progeny. Our study lays the foundation to understand the co-selection of specific genes, which may be critical to the emergence of new viruses with pandemic potential.

  5. Gene network and familial analyses uncover a gene network involving Tbx5/Osr1/Pcsk6 interaction in the second heart field for atrial septation.

    Science.gov (United States)

    Zhang, Ke K; Xiang, Menglan; Zhou, Lun; Liu, Jielin; Curry, Nathan; Heine Suñer, Damian; Garcia-Pavia, Pablo; Zhang, Xiaohua; Wang, Qin; Xie, Linglin

    2016-03-15

    Atrial septal defects (ASDs) are a common human congenital heart disease (CHD) that can be induced by genetic abnormalities. Our previous studies have demonstrated a genetic interaction between Tbx5 and Osr1 in the second heart field (SHF) for atrial septation. We hypothesized that Osr1 and Tbx5 share a common signaling networking and downstream targets for atrial septation. To identify this molecular networks, we acquired the RNA-Seq transcriptome data from the posterior SHF of wild-type, Tbx5(+/) (-), Osr1(+/-), Osr1(-/-) and Tbx5(+/-)/Osr1(+/-) mutant embryos. Gene set analysis was used to identify the Kyoto Encyclopedia of Genes and Genomes pathways that were affected by the doses of Tbx5 and Osr1. A gene network module involving Tbx5 and Osr1 was identified using a non-parametric distance metric, distance correlation. A subset of 10 core genes and gene-gene interactions in the network module were validated by gene expression alterations in posterior second heart field (pSHF) of Tbx5 and Osr1 transgenic mouse embryos, a time-course gene expression change during P19CL6 cell differentiation. Pcsk6 was one of the network module genes that were linked to Tbx5. We validated the direct regulation of Tbx5 on Pcsk6 using immunohistochemical staining of pSHF, ChIP-quantitative polymerase chain reaction and luciferase reporter assay. Importantly, we identified Pcsk6 as a novel gene associated with ASD via a human genotyping study of an ASD family. In summary, our study implicated a gene network involving Tbx5, Osr1 and Pcsk6 interaction in SHF for atrial septation, providing a molecular framework for understanding the role of Tbx5 in CHD ontogeny.

  6. Melanopsin gene variations interact with season to predict sleep onset and chronotype.

    Science.gov (United States)

    Roecklein, Kathryn A; Wong, Patricia M; Franzen, Peter L; Hasler, Brant P; Wood-Vasey, W Michael; Nimgaonkar, Vishwajit L; Miller, Megan A; Kepreos, Kyle M; Ferrell, Robert E; Manuck, Stephen B

    2012-10-01

    The human melanopsin gene has been reported to mediate risk for seasonal affective disorder (SAD), which is hypothesized to be caused by decreased photic input during winter when light levels fall below threshold, resulting in differences in circadian phase and/or sleep. However, it is unclear if melanopsin increases risk of SAD by causing differences in sleep or circadian phase, or if those differences are symptoms of the mood disorder. To determine if melanopsin sequence variations are associated with differences in sleep-wake behavior among those not suffering from a mood disorder, the authors tested associations between melanopsin gene polymorphisms and self-reported sleep timing (sleep onset and wake time) in a community sample (N = 234) of non-Hispanic Caucasian participants (age 30-54 yrs) with no history of psychological, neurological, or sleep disorders. The authors also tested the effect of melanopsin variations on differences in preferred sleep and activity timing (i.e., chronotype), which may reflect differences in circadian phase, sleep homeostasis, or both. Daylength on the day of assessment was measured and included in analyses. DNA samples were genotyped for melanopsin gene polymorphisms using fluorescence polarization. P10L genotype interacted with daylength to predict self-reported sleep onset (interaction p sleep onset among those with the TT genotype was later in the day when individuals were assessed on longer days and earlier in the day on shorter days, whereas individuals in the other genotype groups (i.e., CC and CT) did not show this interaction effect. P10L genotype also interacted in an analogous way with daylength to predict self-reported morningness (interaction p sleep onset and chronotype as a function of daylength, whereas other genotypes at P10L do not seem to have effects that vary by daylength. A better understanding of how melanopsin confers heightened responsivity to daylength may improve our understanding of a broad range of

  7. Relevance of different prior knowledge sources for inferring gene interaction networks.

    Science.gov (United States)

    Olsen, Catharina; Bontempi, Gianluca; Emmert-Streib, Frank; Quackenbush, John; Haibe-Kains, Benjamin

    2014-01-01

    When inferring networks from high-throughput genomic data, one of the main challenges is the subsequent validation of these networks. In the best case scenario, the true network is partially known from previous research results published in structured databases or research articles. Traditionally, inferred networks are validated against these known interactions. Whenever the recovery rate is gauged to be high enough, subsequent high scoring but unknown inferred interactions are deemed good candidates for further experimental validation. Therefore such validation framework strongly depends on the quantity and quality of published interactions and presents serious pitfalls: (1) availability of these known interactions for the studied problem might be sparse; (2) quantitatively comparing different inference algorithms is not trivial; and (3) the use of these known interactions for validation prevents their integration in the inference procedure. The latter is particularly relevant as it has recently been showed that integration of priors during network inference significantly improves the quality of inferred networks. To overcome these problems when validating inferred networks, we recently proposed a data-driven validation framework based on single gene knock-down experiments. Using this framework, we were able to demonstrate the benefits of integrating prior knowledge and expression data. In this paper we used this framework to assess the quality of different sources of prior knowledge on their own and in combination with different genomic data sets in colorectal cancer. We observed that most prior sources lead to significant F-scores. Furthermore, their integration with genomic data leads to a significant increase in F-scores, especially for priors extracted from full text PubMed articles, known co-expression modules and genetic interactions. Lastly, we observed that the results are consistent for three different data sets: experimental knock-down data and two

  8. Hierarchical Interactions of Homeodomain and Forkhead Transcription Factors in Regulating Odontogenic Gene Expression*

    Science.gov (United States)

    Venugopalan, Shankar R.; Li, Xiao; Amen, Melanie A.; Florez, Sergio; Gutierrez, Diana; Cao, Huojun; Wang, Jianbo; Amendt, Brad A.

    2011-01-01

    FoxJ1 is a forkhead transcription factor expressed in multiple tissues during development and a major regulator of cilia development. FoxJ1−/− mice present with defects in odontogenesis, and we correlate these defects to hierarchical interactions between homeodomain factors Pitx2 and Dlx2 with FoxJ1 in regulating their expression through direct physical interactions. Chromatin immunoprecipitation assays reveal endogenous Pitx2 and Dlx2 binding to the Dlx2 promoter and Dlx2 binding to the FoxJ1 promoter as well as Dlx2 and FoxJ1 binding to the amelogenin promoter. PITX2 activation of the Dlx2 promoter is attenuated by a direct Dlx2 physical interaction with PITX2. Dlx2 autoregulates its promoter, and Dlx2 transcriptionally activates the downstream gene FoxJ1. Dlx2 and FoxJ1 physically interact and synergistically regulate both Dlx2 and FoxJ1 promoters. Dlx2 and FoxJ1 also activate the amelogenin promoter, and amelogenin is required for enamel formation and late stage tooth development. FoxJ1−/− mice maxillary and mandibular incisors are reduced in length and width and have reduced amelogenin expression. FoxJ1−/− mice show a reduced and defective ameloblast layer, revealing a biological effect of these transcription factor hierarchies during tooth morphogenesis. These transcriptional mechanisms may contribute to other developmental processes such as neuronal, pituitary, and heart development. PMID:21504905

  9. Expression QTL mapping in regulatory and helper T cells from the BXD family of strains reveals novel cell-specific genes, gene-gene interactions and candidate genes for auto-immune disease

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    Alberts Rudi

    2011-12-01

    Full Text Available Abstract Background Regulatory T cells (Tregs play an essential role in the control of the immune response. Treg cells represent important targets for therapeutic interventions of the immune system. Therefore, it will be very important to understand in more detail which genes are specifically activated in Treg cells versus T helper (Th cells, and which gene regulatory circuits may be involved in specifying and maintaining Treg cell homeostasis. Results We isolated Treg and Th cells from a genetically diverse family of 31 BXD type recombinant inbred strains and the fully inbred parental strains of this family--C57BL/6J and DBA/2J. Subsequently genome-wide gene expression studies were performed from the isolated Treg and Th cells. A comparative analysis of the transcriptomes of these cell populations allowed us to identify many novel differentially expressed genes. Analysis of cis- and trans-expression Quantitative Trait Loci (eQTLs highlighted common and unique regulatory mechanisms that are active in the two cell types. Trans-eQTL regions were found for the Treg functional genes Nrp1, Stat3 and Ikzf4. Analyses of the respective QTL intervals suggested several candidate genes that may be involved in regulating these genes in Treg cells. Similarly, possible candidate genes were found which may regulate the expression of F2rl1, Ctla4, Klrb1f. In addition, we identified a focused group of candidate genes that may be important for the maintenance of self-tolerance and the prevention of allergy. Conclusions Variation of expression across the strains allowed us to find many novel gene-interaction networks in both T cell subsets. In addition, these two data sets enabled us to identify many differentially expressed genes and to nominate candidate genes that may have important functions for the maintenance of self-tolerance and the prevention of allergy.

  10. Knowledge-driven analysis identifies a gene-gene interaction affecting high-density lipoprotein cholesterol levels in multi-ethnic populations.

    Science.gov (United States)

    Ma, Li; Brautbar, Ariel; Boerwinkle, Eric; Sing, Charles F; Clark, Andrew G; Keinan, Alon

    2012-01-01

    Total cholesterol, low-density lipoprotein cholesterol, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels are among the most important risk factors for coronary artery disease. We tested for gene-gene interactions affecting the level of these four lipids based on prior knowledge of established genome-wide association study (GWAS) hits, protein-protein interactions, and pathway information. Using genotype data from 9,713 European Americans from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and a locus near LIPC in their effect on HDL-C levels (Bonferroni corrected P(c) = 0.002). Using an adaptive locus-based validation procedure, we successfully validated this gene-gene interaction in the European American cohorts from the Framingham Heart Study (P(c) = 0.002) and the Multi-Ethnic Study of Atherosclerosis (MESA; P(c) = 0.006). The interaction between these two loci is also significant in the African American sample from ARIC (P(c) = 0.004) and in the Hispanic American sample from MESA (P(c) = 0.04). Both HMGCR and LIPC are involved in the metabolism of lipids, and genome-wide association studies have previously identified LIPC as associated with levels of HDL-C. However, the effect on HDL-C of the novel gene-gene interaction reported here is twice as pronounced as that predicted by the sum of the marginal effects of the two loci. In conclusion, based on a knowledge-driven analysis of epistasis, together with a new locus-based validation method, we successfully identified and validated an interaction affecting a complex trait in multi-ethnic populations.

  11. Gene-Gene Interactions in Renin-Angiotensin-Aldosterone System Contributes to End-Stage Renal Disease Susceptibility in a Han Chinese Population

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    Sui-Lung Su

    2014-01-01

    Full Text Available Objective. In this study, we investigated whether RAAS gene single nucleotide polymorphisms (SNPs and their interactions were associated with end-stage renal stage (ESRD. Methodology and Results. This was a case-control study for 647 ESRD cases and 644 controls. AGT (M235T (rs699 and T174M (rs4762, AGTR1 (A1166C (rs5186 and C573T (rs5182, ACE (I/D (rs1799752 and G2350A (rs4343, and CYP11B2 C-344T (rs1799998 were genotyped and compared between cases and controls to identify SNPs associated with ESRD susceptibility. Multifactor dimensionality reduction (MDR was used to identify gene-gene interactions. Several RAAS genes were associated with ESRD: AGT M235T, ACE I/D, ACE G2350A, and CYP11B2 C-344T. By MDR analysis, a three-locus model (ACE ID/ACE G2350A/CYP11B2 C-344T of gene-gene interaction was the best for predicting ESRD risk, and its maximum testing accuracy was 56.08% and maximum cross-validation consistency was 9/10. ESRD risk was higher with the simultaneous occurrence of ACE I/D DD-ACE G2350A AA. AGT, ACE, and CYP11B2 gene polymorphisms are associated with ESRD. Conclusions. The gene-gene interaction effects of ACE I/D, ACE G2350A, and CYP11B2 C-344T polymorphisms are more important than individual factors for ESRD development among Han Chinese.

  12. An environmental analysis of genes associated with schizophrenia: hypoxia and vascular factors as interacting elements in the neurodevelopmental model.

    Science.gov (United States)

    Schmidt-Kastner, R; van Os, J; Esquivel, G; Steinbusch, H W M; Rutten, B P F

    2012-12-01

    Investigating and understanding gene-environment interaction (G × E) in a neurodevelopmentally and biologically plausible manner is a major challenge for schizophrenia research. Hypoxia during neurodevelopment is one of several environmental factors related to the risk of schizophrenia, and links between schizophrenia candidate genes and hypoxia regulation or vascular expression have been proposed. Given the availability of a wealth of complex genetic information on schizophrenia in the literature without knowledge on the connections to environmental factors, we now systematically collected genes from candidate studies (using SzGene), genome-wide association studies (GWAS) and copy number variation (CNV) analyses, and then applied four criteria to test for a (theoretical) link to ischemia-hypoxia and/or vascular factors. In all, 55% of the schizophrenia candidate genes (n=42 genes) met the criteria for a link to ischemia-hypoxia and/or vascular factors. Genes associated with schizophrenia showed a significant, threefold enrichment among genes that were derived from microarray studies of the ischemia-hypoxia response (IHR) in the brain. Thus, the finding of a considerable match between genes associated with the risk of schizophrenia and IHR and/or vascular factors is reproducible. An additional survey of genes identified by GWAS and CNV analyses suggested novel genes that match the criteria. Findings for interactions between specific variants of genes proposed to be IHR and/or vascular factors with obstetric complications in patients with schizophrenia have been reported in the literature. Therefore, the extended gene set defined here may form a reasonable and evidence-based starting point for hypothesis-based testing of G × E interactions in clinical genetic and translational neuroscience studies.

  13. Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

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    Bi-Qing Li

    2013-01-01

    Full Text Available Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC and nonsmall cell lung cancer (NSCLC. In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutation P value less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

  14. No interactions between previously associated 2-hour glucose gene variants and physical activity or BMI on 2-hour glucose levels

    DEFF Research Database (Denmark)

    Scott, Robert A; Chu, Audrey Y; Grarup, Niels

    2012-01-01

    Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to dete...

  15. Evidence of gene-environment interactions between common breast cancer susceptibility loci and established environmental risk factors

    DEFF Research Database (Denmark)

    Nickels, Stefan; Truong, Thérèse; Hein, Rebecca

    2013-01-01

    ratio tests to assess gene-environment interactions. All statistical tests were two-sided. We replicated previously reported potential interactions between LSP1-rs3817198 and parity (Pinteraction = 2.4 × 10(-6)) and between CASP8-rs17468277 and alcohol consumption (Pinteraction = 3.1 × 10(-4)). Overall...

  16. Interaction between effects of genes coding for dopamine and glutamate transmission on striatal and parahippocampal function.

    Science.gov (United States)

    Pauli, Andreina; Prata, Diana P; Mechelli, Andrea; Picchioni, Marco; Fu, Cynthia H Y; Chaddock, Christopher A; Kane, Fergus; Kalidindi, Sridevi; McDonald, Colm; Kravariti, Eugenia; Toulopoulou, Timothea; Bramon, Elvira; Walshe, Muriel; Ehlert, Natascha; Georgiades, Anna; Murray, Robin; Collier, David A; McGuire, Philip

    2013-09-01

    The genes for the dopamine transporter (DAT) and the D-Amino acid oxidase activator (DAOA or G72) have been independently implicated in the risk for schizophrenia and in bipolar disorder and/or their related intermediate phenotypes. DAT and G72 respectively modulate central dopamine and glutamate transmission, the two systems most robustly implicated in these disorders. Contemporary studies have demonstrated that elevated dopamine function is associated with glutamatergic dysfunction in psychotic disorders. Using functional magnetic resonance imaging we examined whether there was an interaction between the effects of genes that influence dopamine and glutamate transmission (DAT and G72) on regional brain activation during verbal fluency, which is known to be abnormal in psychosis, in 80 healthy volunteers. Significant interactions between the effects of G72 and DAT polymorphisms on activation were evident in the striatum, parahippocampal gyrus, and supramarginal/angular gyri bilaterally, the right insula, in the right pre-/postcentral and the left posterior cingulate/retrosplenial gyri (P dopamine and the glutamate system, thought to be altered in psychosis, have an impact in executive processing which can be modulated by common genetic variation. Copyright © 2012 Wiley Periodicals, Inc., a Wiley company.

  17. Population stratification bias in the case-only study for gene-environment interactions.

    Science.gov (United States)

    Wang, Liang-Yi; Lee, Wen-Chung

    2008-07-15

    The case-only study is a convenient approach and provides increased statistical efficiency in detecting gene-environment interactions. The validity of a case-only study hinges on one well-recognized assumption: The susceptibility genotypes and the environmental exposures of interest are independent in the population. Otherwise, the study will be biased. The authors show that hidden stratification in the study population could also ruin a case-only study. They derive the formulas for population stratification bias. The bias involves three terms: 1) the coefficient of variation of the exposure prevalence odds, 2) the coefficient of variation of the genotype frequency odds, and 3) the correlation coefficient between the exposure prevalence odds and the genotype frequency odds. The authors perform simulation to investigate the magnitude of bias over a wide range of realistic scenarios. It is found that the estimated interaction effect is frequently biased by more than 5%. For a rarer gene and a rarer exposure, the bias becomes even larger (>30%). Because of the potentially large bias, researchers conducting case-only studies should use the boundary formula presented in this paper to make more prudent interpretations of their results, or they should use stratified analysis or a modeling approach to adjust for population stratification bias in their studies.

  18. A case-control study of Parkinson's disease and tobacco use: gene-tobacco interactions.

    Science.gov (United States)

    De Palma, Giuseppe; Dick, Finlay D; Calzetti, Stefano; Scott, Neil W; Prescott, Gordon J; Osborne, Aileen; Haites, Neva; Mozzoni, Paola; Negrotti, Anna; Scaglioni, Augusto; Mutti, Antonio

    2010-05-15

    A case-control study of genetic, environmental, and occupational risk factors for Parkinson's disease (PD) was carried out in five European countries (Italy, Malta, Romania, Scotland, and Sweden) to explore the possible contribution of interactions among host and environmental factors in sporadic PD. Whereas smoking habits confirmed its negative association with PD, a possible modulatory role of genetic polymorphisms was investigated to obtain further mechanistic insights. We recruited 767 cases of PD and 1989 age-matched and gender-matched controls. Participants completed an interviewer-administered questionnaire including the history of smoking habits. The polymorphisms of genes involved either in metabolism of compounds contained in tobacco smoke (CYP2D6, CYP1B1, GSTM1, GSTT1, GSTM3, GSTP1, NQO1, SOD2, EPHX and NAT2) or in dopaminergic neurotransmission (MAOA, MAOB, DAT1 and DRD2) were characterized by PCR based methods on genomic DNA. We found evidence of statistically significant gene-tobacco interaction for GSTM1, NAT2, and GSTP1, the negative association between tobacco smoking and PD being significantly enhanced in subjects expressing GSTM1-1 activity, in NAT2 fast acetylators, and in those with the GSTP1*B*C haplotype. Owing to the retrospective design of the study, these results require confirmation.

  19. Consilient research approaches in studying gene x environment interactions in alcohol research.

    Science.gov (United States)

    Sher, Kenneth J; Dick, Danielle M; Crabbe, John C; Hutchison, Kent E; O'Malley, Stephanie S; Heath, Andrew C

    2010-04-01

    This review article discusses the importance of identifying gene-environment interactions for understanding the etiology and course of alcohol use disorders and related conditions. A number of critical challenges are discussed, including the fact that there is no organizing typology for classifying different types of environmental exposures, many key human environmental risk factors for alcohol dependence have no clear equivalents in other species, much of the genetic variance of alcohol dependence in human is not 'alcohol specific', and the potential range of gene-environment interactions that could be considered is so vast that maintaining statistical control of Type 1 errors is a daunting task. Despite these and other challenges, there appears to be a number of promising approaches that could be taken in order to achieve consilience and ecologically valid translation between human alcohol dependence and animal models. Foremost among these is to distinguish environmental exposures that are thought to have enduring effects on alcohol use motivation (and self-regulation) from situational environmental exposures that facilitate the expression of such motivations but do not, by themselves, have enduring effects. In order to enhance consilience, various domains of human approach motivation should be considered so that relevant environmental exposures can be sampled, as well as the appropriate species to study them in (i.e. where such motivations are ecologically relevant). Foremost among these are social environments, which are central to the initiation and escalation of human alcohol consumption. The value of twin studies, human laboratory studies and pharmacogenetic studies is also highlighted.

  20. Gene-Environment Interaction Research and Transgenic Mouse Models of Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    L. Chouliaras

    2010-01-01

    Full Text Available The etiology of the sporadic form of Alzheimer's disease (AD remains largely unknown. Recent evidence has suggested that gene-environment interactions (GxE may play a crucial role in its development and progression. Whereas various susceptibility loci have been identified, like the apolipoprotein E4 allele, these cannot fully explain the increasing prevalence of AD observed with aging. In addition to such genetic risk factors, various environmental factors have been proposed to alter the risk of developing AD as well as to affect the rate of cognitive decline in AD patients. Nevertheless, aside from the independent effects of genetic and environmental risk factors, their synergistic participation in increasing the risk of developing AD has been sparsely investigated, even though evidence points towards such a direction. Advances in the genetic manipulation of mice, modeling various aspects of the AD pathology, have provided an excellent tool to dissect the effects of genes, environment, and their interactions. In this paper we present several environmental factors implicated in the etiology of AD that have been tested in transgenic animal models of the disease. The focus lies on the concept of GxE and its importance in a multifactorial disease like AD. Additionally, possible mediating mechanisms and future challenges are discussed.

  1. Effect of Interaction between Chromatin Loops on Cell-to-Cell Variability in Gene Expression.

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    Tuoqi Liu

    2016-05-01

    Full Text Available According to recent experimental evidence, the interaction between chromatin loops, which can be characterized by three factors-connection pattern, distance between regulatory elements, and communication form, play an important role in determining the level of cell-to-cell variability in gene expression. These quantitative experiments call for a corresponding modeling effect that addresses the question of how changes in these factors affect variability at the expression level in a systematic rather than case-by-case fashion. Here we make such an effort, based on a mechanic model that maps three fundamental patterns for two interacting DNA loops into a 4-state model of stochastic transcription. We first show that in contrast to side-by-side loops, nested loops enhance mRNA expression and reduce expression noise whereas alternating loops have just opposite effects. Then, we compare effects of facilitated tracking and direct looping on gene expression. We find that the former performs better than the latter in controlling mean expression and in tuning expression noise, but this control or tuning is distance-dependent, remarkable for moderate loop lengths, and there is a limit loop length such that the difference in effect between two communication forms almost disappears. Our analysis and results justify the facilitated chromatin-looping hypothesis.

  2. Gene-physical activity interactions in the etiology of obesity: behavioral considerations.

    Science.gov (United States)

    Dishman, Rod K

    2008-12-01

    Understanding how genes, environment, and personal motivation operate to influence physical activity will require (i) inclusion of properly validated measures of putative mediators (e.g., cultural values, efficacy and control beliefs, goals, intentions, enjoyment, and self-management skills) and moderators (e.g., age or maturation, personality, race/ethnicity, fitness, fatness, skill, and competing behaviors) of physical activity, (ii) a search for candidate genes involved with motivational systems of energy expenditure in addition to energy intake pathways, (iii) assessment of specific features physical activity exposure (i.e., type, intensity, timing, and context), (iv) manipulation of physical activity or prospective observation of change in physical activity at multiple times, rather than cross-sectional association and linkage studies, and (v) use of statistical procedures that permit multilevel modeling (i.e., personal and group-level variables) of direct, indirect (i.e., mediated), and moderated (i.e., interactions of mediators with external factors) relations with physical activity within theoretical gene-environment networks.

  3. Shame and Guilt-Proneness in Adolescents: Gene-Environment Interactions.

    Directory of Open Access Journals (Sweden)

    Aurora Szentágotai-Tătar

    Full Text Available Rooted in people's preoccupation with how they are perceived and evaluated, shame and guilt are self-conscious emotions that play adaptive roles in social behavior, but can also contribute to psychopathology when dysregulated. Shame and guilt-proneness develop during childhood and adolescence, and are influenced by genetic and environmental factors that are little known to date. This study investigated the effects of early traumatic events and functional polymorphisms in the brain-derived neurotrophic factor (BDNF gene and the serotonin transporter gene promoter (5-HTTLPR on shame and guilt in adolescents. A sample of N = 271 healthy adolescents between 14 and 17 years of age filled in measures of early traumatic events and proneness to shame and guilt, and were genotyped for the BDNF Val66Met and 5-HTTLPR polymorphisms. Results of moderator analyses indicated that trauma intensity was positively associated with guilt-proneness only in carriers of the low-expressing Met allele of BDNF Val66Met. This is the first study that identifies a gene-environment interaction that significantly contributes to guilt proneness in adolescents, with potential implications for developmental psychopathology.

  4. Shame and Guilt-Proneness in Adolescents: Gene-Environment Interactions.

    Science.gov (United States)

    Szentágotai-Tătar, Aurora; Chiș, Adina; Vulturar, Romana; Dobrean, Anca; Cândea, Diana Mirela; Miu, Andrei C

    2015-01-01

    Rooted in people's preoccupation with how they are perceived and evaluated, shame and guilt are self-conscious emotions that play adaptive roles in social behavior, but can also contribute to psychopathology when dysregulated. Shame and guilt-proneness develop during childhood and adolescence, and are influenced by genetic and environmental factors that are little known to date. This study investigated the effects of early traumatic events and functional polymorphisms in the brain-derived neurotrophic factor (BDNF) gene and the serotonin transporter gene promoter (5-HTTLPR) on shame and guilt in adolescents. A sample of N = 271 healthy adolescents between 14 and 17 years of age filled in measures of early traumatic events and proneness to shame and guilt, and were genotyped for the BDNF Val66Met and 5-HTTLPR polymorphisms. Results of moderator analyses indicated that trauma intensity was positively associated with guilt-proneness only in carriers of the low-expressing Met allele of BDNF Val66Met. This is the first study that identifies a gene-environment interaction that significantly contributes to guilt proneness in adolescents, with potential implications for developmental psychopathology.

  5. Computational modeling identifies key gene regulatory interactions underlying phenobarbital-mediated tumor promotion

    Science.gov (United States)

    Luisier, Raphaëlle; Unterberger, Elif B.; Goodman, Jay I.; Schwarz, Michael; Moggs, Jonathan; Terranova, Rémi; van Nimwegen, Erik

    2014-01-01

    Gene regulatory interactions underlying the early stages of non-genotoxic carcinogenesis are poorly understood. Here, we have identified key candidate regulators of phenobarbital (PB)-mediated mouse liver tumorigenesis, a well-characterized model of non-genotoxic carcinogenesis, by applying a new computational modeling approach to a comprehensive collection of in vivo gene expression studies. We have combined our previously developed motif activity response analysis (MARA), which models gene expression patterns in terms of computationally predicted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif activities, to disentangle the roles that different transcriptional regulators play in specific biological pathways of tumor promotion. Furthermore, transgenic mouse models enabled us to identify which of these regulatory activities was downstream of constitutive androstane receptor and β-catenin signaling, both crucial components of PB-mediated liver tumorigenesis. We propose novel roles for E2F and ZFP161 in PB-mediated hepatocyte proliferation and suggest that PB-mediated suppression of ESR1 activity contributes to the development of a tumor-prone environment. Our study shows that combining MARA with SVD allows for automated identification of independent transcription regulatory programs within a complex in vivo tissue environment and provides novel mechanistic insights into PB-mediated hepatocarcinogenesis. PMID:24464994

  6. Interactions between the nuclear matrix and an enhancer of the tryptophan oxygenase gene

    Energy Technology Data Exchange (ETDEWEB)

    Kaneoka, Hidenori [Department of Biotechnology, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603 (Japan); Miyake, Katsuhide, E-mail: miyake@nubio.nagoya-u.ac.jp [Department of Biotechnology, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603 (Japan); Iijima, Shinji [Department of Biotechnology, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603 (Japan)

    2009-10-02

    The gene for tryptophan oxygenase (TO) is expressed in adult hepatocytes in a tissue- and differentiation-specific manner. The TO promoter has two glucocorticoid-responsive elements (GREs), and its expression is regulated by glucocorticoid hormone in the liver. We found a novel GRE in close proximity to a scaffold/matrix attachment region (S/MAR) that was located around -8.5 kb from the transcriptional start site of the TO gene by electrophoretic mobility shift and chromatin immunoprecipitation (ChIP) assays. A combination of nuclear fractionation and quantitative PCR analysis showed that the S/MAR was tethered to the nuclear matrix in both fetal and adult hepatocytes. ChIP assay showed that, in adult hepatocytes, the S/MAR-GRE and the promoter proximal regions interacted with lamin and heterogeneous nuclear ribonucleoprotein U in a dexamethasone dependent manner, but this was not the case in fetal cells, suggesting that developmental stage-specific expression of the TO gene might rely on the binding of the enhancer (the -8.5 kb S/MAR-GRE) and the promoter to the inner nuclear matrix.

  7. The BET family member BRD4 interacts with OCT4 and regulates pluripotency gene expression.

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    Wu, Tao; Pinto, Hugo Borges; Kamikawa, Yasunao F; Donohoe, Mary E

    2015-03-10

    Embryonic stem cell (ESC) pluripotency is controlled by defined transcription factors. During cellular differentiation, ESCs undergo a global epigenetic reprogramming. Female ESCs exemplify this process as one of the two X-chromosomes is globally silenced during X chromosome inactivation (XCI) to balance the X-linked gene disparity with XY males. The pluripotent factor OCT4 regulates XCI by triggering X chromosome pairing and counting. OCT4 directly binds Xite and Tsix, which encode two long noncoding RNAs (lncRNAs) that suppress the silencer lncRNA, Xist. To control its activity as a master regulator in pluripotency and XCI, OCT4 must have chromatin protein partners. Here we show that BRD4, a member of the BET protein subfamily, interacts with OCT4. BRD4 occupies the regulatory regions of pluripotent genes and the lncRNAs of XCI. BET inhibition or depletion of BRD4 reduces the expression of many pluripotent genes and shifts cellular fate showing that BRD4 is pivotal for transcription in ESCs.

  8. Using linkage analysis to detect gene-gene interaction by stratifying family data on known disease, or disease-associated, alleles.

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    Barbara Corso

    Full Text Available Detecting gene-gene interaction in complex diseases is a major challenge for common disease genetics. Most interaction detection approaches use disease-marker associations and such methods have low power and unknown reliability in real data. We developed and tested a powerful linkage-analysis-based gene-gene interaction detection strategy based on conditioning the family data on a known disease-causing allele or disease-associated marker allele. We computer-generated multipoint linkage data for a disease caused by two epistatically interacting loci (A and B. We examined several two-locus epistatic inheritance models: dominant-dominant, dominant-recessive, recessive-dominant, recessive-recessive. At one of the loci (A, there was a known disease-related allele. We stratified the family data on the presence of this allele, eliminating family members who were without it. This elimination step has the effect of raising the "penetrance" at the second locus (B. We then calculated the lod score at the second locus (B and compared the pre- and post-stratification lod scores at B. A positive difference indicated interaction. We also examined if it was possible to detect interaction with locus B based on a disease-marker association (instead of an identified disease allele at locus A. We also tested whether the presence of genetic heterogeneity would generate false positive evidence of interaction. The power to detect interaction for a known disease allele was 60-90%. The probability of false positives, based on heterogeneity, was low. Decreasing linkage disequilibrium between the disease and marker at locus A decreased the likelihood of detecting interaction. The allele frequency of the associated marker made little difference to the power.

  9. Sample size requirements for indirect association studies of gene-environment interactions (G x E).

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    Hein, Rebecca; Beckmann, Lars; Chang-Claude, Jenny

    2008-04-01

    Association studies accounting for gene-environment interactions (G x E) may be useful for detecting genetic effects. Although current technology enables very dense marker spacing in genetic association studies, the true disease variants may not be genotyped. Thus, causal genes are searched for by indirect association using genetic markers in linkage disequilibrium (LD) with the true disease variants. Sample sizes needed to detect G x E effects in indirect case-control association studies depend on the true genetic main effects, disease allele frequencies, whether marker and disease allele frequencies match, LD between loci, main effects and prevalence of environmental exposures, and the magnitude of interactions. We explored variables influencing sample sizes needed to detect G x E, compared these sample sizes with those required to detect genetic marginal effects, and provide an algorithm for power and sample size estimations. Required sample sizes may be heavily inflated if LD between marker and disease loci decreases. More than 10,000 case-control pairs may be required to detect G x E. However, given weak true genetic main effects, moderate prevalence of environmental exposures, as well as strong interactions, G x E effects may be detected with smaller sample sizes than those needed for the detection of genetic marginal effects. Moreover, in this scenario, rare disease variants may only be detectable when G x E is included in the analyses. Thus, the analysis of G x E appears to be an attractive option for the detection of weak genetic main effects of rare variants that may not be detectable in the analysis of genetic marginal effects only.

  10. ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data.

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    Brett A McKinney

    Full Text Available Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k for each gene to optimize the Relief-F test statistics (importance scores for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to

  11. ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data.

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    McKinney, Brett A; White, Bill C; Grill, Diane E; Li, Peter W; Kennedy, Richard B; Poland, Gregory A; Oberg, Ann L

    2013-01-01

    Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k) for each gene to optimize the Relief-F test statistics (importance scores) for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak) Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to detect both main

  12. Lack of evidence for intermolecular epistatic interactions between adiponectin and resistin gene polymorphisms in Malaysian male subjects

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    Cia-Hin Lau

    2012-01-01

    Full Text Available Epistasis (gene-gene interaction is a ubiquitous component of the genetic architecture of complex traits such as susceptibility to common human diseases. Given the strong negative correlation between circulating adiponectin and resistin levels, the potential intermolecular epistatic interactions between ADIPOQ (SNP+45T > G, SNP+276G > T, SNP+639T > C and SNP+1212A > G and RETN (SNP-420C > G and SNP+299G > A gene polymorphisms in the genetic risk underlying type 2 diabetes (T2DM and metabolic syndrome (MS were assessed. The potential mutual influence of the ADIPOQ and RETN genes on their adipokine levels was also examined. The rare homozygous genotype (risk alleles of SNP-420C > G at the RETN locus tended to be co-inherited together with the common homozygous genotypes (protective alleles of SNP+639T > C and SNP+1212A > G at the ADIPOQ locus. Despite the close structural relationship between the ADIPOQ and RETN genes, there was no evidence of an intermolecular epistatic interaction between these genes. There was also no reciprocal effect of the ADIPOQ and RETN genes on their adipokine levels, i.e., ADIPOQ did not affect resistin levels nor did RETN affect adiponectin levels. The possible influence of the ADIPOQ gene on RETN expression warrants further investigation.

  13. Different genes interact with particulate matter and tobacco smoke exposure in affecting lung function decline in the general population.

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    Ivan Curjuric

    Full Text Available BACKGROUND: Oxidative stress related genes modify the effects of ambient air pollution or tobacco smoking on lung function decline. The impact of interactions might be substantial, but previous studies mostly focused on main effects of single genes. OBJECTIVES: We studied the interaction of both exposures with a broad set of oxidative-stress related candidate genes and pathways on lung function decline and contrasted interactions between exposures. METHODS: For 12679 single nucleotide polymorphisms (SNPs, change in forced expiratory volume in one second (FEV(1, FEV(1 over forced vital capacity (FEV(1/FVC, and mean forced expiratory flow between 25 and 75% of the FVC (FEF(25-75 was regressed on interval exposure to particulate matter 10% in 3320 SAPALDIA participants without GWAS. RESULTS: On the SNP-level, rs2035268 in gene SNCA accelerated FEV(1/FVC decline by 3.8% (p(interaction = 2.5×10(-6, and rs12190800 in PARK2 attenuated FEV1 decline by 95.1 ml p(interaction = 9.7×10(-8 over 11 years, while interacting with PM10. Genes and pathways nominally interacting with PM10 and packyears exposure differed substantially. Gene CRISP2 presented a significant interaction with PM10 (p(interaction = 3.0×10(-4 on FEV(1/FVC decline. Pathway interactions were weak. Replications for the strongest SNPs in PARK2 and CRISP2 were not successful. CONCLUSIONS: Consistent with a stratified response to increasing oxidative stress, different genes and pathways potentially mediate PM10 and tobacco smoke effects on lung function decline. Ignoring environmental exposures would miss these patterns, but achieving sufficient sample size and comparability across study samples is challenging.

  14. Synergistic interactions between Drosophila orthologues of genes spanned by de novo human CNVs support multiple-hit models of autism.

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    Stuart J Grice

    2015-03-01

    Full Text Available Autism spectrum disorders (ASDs are highly heritable and characterised by deficits in social interaction and communication, as well as restricted and repetitive behaviours. Although a number of highly penetrant ASD gene variants have been identified, there is growing evidence to support a causal role for combinatorial effects arising from the contributions of multiple loci. By examining synaptic and circadian neurological phenotypes resulting from the dosage variants of unique human:fly orthologues in Drosophila, we observe numerous synergistic interactions between pairs of informatically-identified candidate genes whose orthologues are jointly affected by large de novo copy number variants (CNVs. These CNVs were found in the genomes of individuals with autism, including a patient carrying a 22q11.2 deletion. We first demonstrate that dosage alterations of the unique Drosophila orthologues of candidate genes from de novo CNVs that harbour only a single candidate gene display neurological defects similar to those previously reported in Drosophila models of ASD-associated variants. We then considered pairwise dosage changes within the set of orthologues of candidate genes that were affected by the same single human de novo CNV. For three of four CNVs with complete orthologous relationships, we observed significant synergistic effects following the simultaneous dosage change of gene pairs drawn from a single CNV. The phenotypic variation observed at the Drosophila synapse that results from these interacting genetic variants supports a concordant phenotypic outcome across all interacting gene pairs following the direction of human gene copy number change. We observe both specificity and transitivity between interactors, both within and between CNV candidate gene sets, supporting shared and distinct genetic aetiologies. We then show that different interactions affect divergent synaptic processes, demonstrating distinct molecular aetiologies. Our

  15. Interaction between serotonin 5-HT2A receptor gene and dopamine transporter (DAT1) gene polymorphisms influences personality trait of persistence in Austrian Caucasians.

    Science.gov (United States)

    Schosser, Alexandra; Fuchs, Karoline; Scharl, Theresa; Schloegelhofer, Monika; Kindler, Jochen; Mossaheb, Nilufar; Kaufmann, Rainer M; Leisch, Friedrich; Kasper, Siegfried; Sieghart, Werner; Aschauer, Harald N

    2010-03-01

    We examined 89 normal volunteers using Cloninger's Temperament and Character Inventory (TCI). Genotyping the 102T/C polymorphism of the serotonin 5HT2A receptor gene and the ser9gly polymorphism in exon 1 of the dopamine D3 receptor (DRD3) gene was performed using PCR-RFLP, whereas the dopamine transporter (DAT1) gene variable number of tandem repeats (VNTR) polymorphism was investigated using PCR amplification followed by electrophoresis in an 8% acrylamide gel with a set of size markers. We found a nominally significant association between gender and harm avoidance (P=0.017; women showing higher scores). There was no association of either DAT1, DRD3 or 5HT2A alleles or genotypes with any dimension of the TCI applying Kruskal-Wallis rank-sum tests. Comparing homozygote and heterozygote DAT1 genotypes, we found higher novelty seeking scores in homozygotes (P=0.054). We further found a nominally significant interaction between DAT1 and 5HT2A homo-/heterozygous gene variants (P=0.0071; DAT1 and 5HT2A genotypes P value of 0.05), performing multivariate analysis of variance (MANOVA). Examining the temperamental TCI subscales, this interaction was associated with persistence (genotypes: P=0.004; homo-/heterozygous gene variants: P=0.0004). We conclude that an interaction between DAT1 and 5HT2A genes might influence the temperamental personality trait persistence.

  16. DISC1 mouse models as a tool to decipher gene-environment interactions in psychiatric disorders

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    Tyler eCash-Padgett

    2013-09-01

    Full Text Available DISC1 was discovered in a Scottish pedigree in which a chromosomal translocation that breaks this gene segregates with psychiatric disorders, mainly depression and schizophrenia. Linkage and association studies in diverse populations support DISC1 as a susceptibility gene to a variety of neuropsychiatric disorders. Many Disc1 mouse models have been generated to study its neuronal functions. These mouse models display variable phenotypes, some of them relevant to schizophrenia, others to depression.The Disc1 mouse models are popular genetic models for studying gene-environment interactions in schizophrenia. Five different Disc1 models have been combined with environmental factors. The environmental stressors employed can be classified as either early immune activation or later social paradigms. These studies cover major time points along the neurodevelopmental trajectory: prenatal, early postnatal, adolescence, and adulthood. Various combinations of molecular, anatomical and behavioral methods have been used to assess the outcomes. Additionally, three of the studies sought to rescue the resulting abnormalities.Here we provide background on the environmental paradigms used, summarize the results of these studies combining Disc1 mouse models with environmental stressors and discuss what we can learn and how to proceed. A major question is how the genetic and environmental factors determine which psychiatric disorder will be clinically manifested. To address this we can take advantage of the many Disc1 models available and expose them to the same environmental stressor. The complementary experiment would be to expose the same model to different environmental stressors. DISC1 is an ideal gene for this approach, since in the Scottish pedigree the same chromosomal translocation results in different psychiatric conditions.

  17. Growing functional modules from a seed protein via integration of protein interaction and gene expression data

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    Dimitrakopoulou Konstantina

    2007-10-01

    Full Text Available Abstract Background Nowadays modern biology aims at unravelling the strands of complex biological structures such as the protein-protein interaction (PPI networks. A key concept in the organization of PPI networks is the existence of dense subnetworks (functional modules in them. In recent approaches clustering algorithms were applied at these networks and the resulting subnetworks were evaluated by estimating the coverage of well-established protein complexes they contained. However, most of these algorithms elaborate on an unweighted graph structure which in turn fails to elevate those interactions that would contribute to the construction of biologically more valid and coherent functional modules. Results In the current study, we present a method that corroborates the integration of protein interaction and microarray data via the discovery of biologically valid functional modules. Initially the gene expression information is overlaid as weights onto the PPI network and the enriched PPI graph allows us to exploit its topological aspects, while simultaneously highlights enhanced functional association in specific pairs of proteins. Then we present an algorithm that unveils the functional modules of the weighted graph by expanding a kernel protein set, which originates from a given 'seed' protein used as starting-point. Conclusion The integrated data and the concept of our approach provide reliable functional modules. We give proofs based on yeast data that our method manages to give accurate results in terms both of structural coherency, as well as functional consistency.

  18. Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network

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    Lv Jie

    2011-10-01

    Full Text Available Abstract Background As an important epigenetic modification, DNA methylation plays a crucial role in the development of mammals and in the occurrence of complex diseases. Genes that interact directly or indirectly may have the same or similar functions in the biological processes in which they are involved and together contribute to the related disease phenotypes. The complicated relations between genes can be clearly represented using network theory. A protein-protein interaction (PPI network offers a platform from which to systematically identify disease-related genes from the relations between genes with similar functions. Results We constructed a weighted human PPI network (WHPN using DNA methylation correlations based on human protein-protein interactions. WHPN represents the relationships of DNA methylation levels in gene pairs for four cancer types. A cancer-associated subnetwork (CASN was obtained from WHPN by selecting genes associated with seed genes which were known to be methylated in the four cancers. We found that CASN had a more densely connected network community than WHPN, indicating that the genes in CASN were much closer to seed genes. We prioritized 154 potential cancer-related genes with aberrant methylation in CASN by neighborhood-weighting decision rule. A function enrichment analysis for GO and KEGG indicated that the optimized genes were mainly involved in the biological processes of regulating cell apoptosis and programmed cell death. An analysis of expression profiling data revealed that many of the optimized genes were expressed differentially in the four cancers. By examining the PubMed co-citations, we found 43 optimized genes were related with cancers and aberrant methylation, and 10 genes were validated to be methylated aberrantly in cancers. Of 154 optimized genes, 27 were as diagnostic markers and 20 as prognostic markers previously identified in literature for cancers and other complex diseases by searching Pub

  19. Gene-gene interactions of the INSIG1 and INSIG2 in metabolic syndrome in schizophrenic patients treated with atypical antipsychotics.

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    Liou, Y-J; Bai, Y M; Lin, E; Chen, J-Y; Chen, T-T; Hong, C-J; Tsai, S-J

    2012-02-01

    The use of atypical antipsychotics (AAPs) is associated with increasing the risk of the metabolic syndrome (MetS), which is an important risk factor for cardiovascular disease and diabetes. Two insulin-induced gene (INSIG) isoforms, designated INSIG-1 and INSIG-2 encode two proteins that mediate feedback control of lipid metabolism. In this genetic case-control study, we investigated whether the common variants in INSIG1 and INSIG2 genes were associated with MetS in schizophrenic patients treated with atypical antipsychctics. The study included 456 schizophrenia patients treated with clozapine (n=171), olanzapine (n=91) and risperidone (n=194), for an average of 45.5±27.6 months. The prevalence of MetS among all subjects was 22.8% (104/456). Two single-nucleotide polymorphisms (SNPs) of the INSIG1 gene and seven SNPs of the INSIG2 gene were chosen as haplotype-tagging SNPs. In single-marker-based analysis, the INSIG2 rs11123469-C homozygous genotype was found to be more frequent in the patients with MetS than those without MetS (P=0.001). In addition, haplotype analysis showed that the C-C-C haplotype of rs11123469-rs10185316- rs1559509 of the INSIG2 gene significantly increased the risk of MetS (P=0.0023). No significant associations were found between polymorphisms of INSIG1 gene and MetS, however, INSIG1 and INSIG2 interactions were found in the significant 3-locus and 4-locus gene-gene interaction models (P=0.003 and 0.012, respectively). The results suggest that the INSIG2 gene may be associated with MetS in patients treated with AAPs independently or in an interactive manner with INSIG1.

  20. Association of Combined Maternal-Fetal TNF-α Gene G308A Genotypes with Preterm Delivery: A Gene-Gene Interaction Study

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    Mingbin Liang

    2010-01-01

    Full Text Available Preterm delivery (PTD is a complicated perinatal adverse event. We were interested in association of G308A polymorphism in tumor necrosis factor-α (TNF-α gene with PTD; so we conducted a genetic epidemiology study in Anqing City, Anhui Province, China. Case families and control families were all collected between July 1999 and June 2002. To control potential population stratification as we could, all eligible subjects were ethnic Han Chinese. 250 case families and 247 control families were included in data analysis. A hybrid design which combines case-parent triads and control parents was employed, to test maternal-fetal genotype (MFG incompatibility. The method is based on a log-linear modeling approach. In summary, we found that when the mother's or child's genotype was G/A, there was a reduced risk of PTD; however when the mother's or child's genotype was genotype A/A, there was a relatively higher risk of PTD. Combined maternal-fetal genotype GA/GA showed the most reduced risk of PTD. Comparison of the LRTs showed that the model with maternal-fetal genotype effects fits significantly better than the model with only maternal and fetal genotype main effects (log-likelihood = −719.4, P=.023, significant at 0.05 level. That means that the combined maternal-fetal genotype incompatibility was significantly associated with PTD. The model with maternal-fetal genotype effects can be considered a gene-gene interaction model. We claim that both maternal effects and fetal effects should be considered together while investigating genetic factors of certain perinatal diseases.

  1. A maximum likelihood method for studying gene-environment interactions under conditional independence of genotype and exposure.

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    Cheng, K F

    2006-09-30

    Given the biomedical interest in gene-environment interactions along with the difficulties inherent in gathering genetic data from controls, epidemiologists need methodologies that can increase precision of estimating interactions while minimizing the genotyping of controls. To achieve this purpose, many epidemiologists suggested that one can use case-only design. In this paper, we present a maximum likelihood method for making inference about gene-environment interactions using case-only data. The probability of disease development is described by a logistic risk model. Thus the interactions are model parameters measuring the departure of joint effects of exposure and genotype from multiplicative odds ratios. We extend the typical inference method derived under the assumption of independence between genotype and exposure to that under a more general assumption of conditional independence. Our maximum likelihood method can be applied to analyse both categorical and continuous environmental factors, and generalized to make inference about gene-gene-environment interactions. Moreover, the application of this method can be reduced to simply fitting a multinomial logistic model when we have case-only data. As a consequence, the maximum likelihood estimates of interactions and likelihood ratio tests for hypotheses concerning interactions can be easily computed. The methodology is illustrated through an example based on a study about the joint effects of XRCC1 polymorphisms and smoking on bladder cancer. We also give two simulation studies to show that the proposed method is reliable in finite sample situation.

  2. Characterization of genome-wide enhancer-promoter interactions reveals co-expression of interacting genes and modes of higher order chromatin organization

    Institute of Scientific and Technical Information of China (English)

    Iouri Chepelev; Gang Wei; Dara Wangsa; Qingsong Tang; Keji Zhao

    2012-01-01

    Recent epigenomic studies have predicted thousands of potential enhancers in the human genome.However,there has not been systematic characterization of target promoters for these potential enhancers.Using H3K4me2 as a mark for active enhancers,we identified genome-wide EP interactions in human CD4+ T cells.Among the 6 520 longdistance chromatin interactions,we identify 2 067 enhancers that interact with 1 619 promoters and enhance their expression.These enhancers exist in accessible chromatin regions and are associated with various histone modifications and polymerase Ⅱ binding.The promoters with interacting enhancers are expressed at higher levels than those without interacting enhancers,and their expression levels are positively correlated with the number of interacting enhancers.Interestingly,interacting promoters are co-expressed in a tissue-specific manner.We also find that chromosomes are organized into multiple levels of interacting domains.Our results define a global view of EP interactions and provide a data set to further understand mechanisms of enhancer targeting and long-range chromatin organization.The Gene Expression Omnibus accession number for the raw and analyzed chromatin interaction data is GSE32677.

  3. Association of peroxisome proliferator-activated receptor single-nucleotide polymorphisms and gene-gene interactions with the lipoprotein(a)

    Institute of Scientific and Technical Information of China (English)

    解惠坚

    2014-01-01

    Objective To examine the associations of 10 singlenucleotide polymorphisms(SNPs)in peroxisome proliferator-activated receptor(PPARs)gene with lipoprotein(a)level,and to investigate if there is gene-gene interaction among the SNPs on lipoprotein(a)level.Methods Totally 644 subjects(234 men and 410 women)were enrolled from Prevention of Multiple Metabolic Disorders and Metabolic Syndrome Study Cohort,which was an urban community survey study conducted in Jiangsu province.Ten SNPs in PPARα(rs135539,rs4253778,

  4. The Dopamine D2 Receptor Gene, Perceived Parental Support, and Adolescent Loneliness: Longitudinal Evidence for Gene-Environment Interactions

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    van Roekel, Eeske; Goossens, Luc; Scholte, Ron H. J.; Engels, Rutger C. M. E.; Verhagen, Maaike

    2011-01-01

    Background: Loneliness is a common problem in adolescence. Earlier research focused on genes within the serotonin and oxytocin systems, but no studies have examined the role of dopamine-related genes in loneliness. In the present study, we focused on the dopamine D2 receptor gene (DRD2). Methods: Associations among the DRD2, sex, parental support,…

  5. The Dopamine D2 Receptor Gene, Perceived Parental Support, and Adolescent Loneliness: Longitudinal Evidence for Gene-Environment Interactions

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    van Roekel, Eeske; Goossens, Luc; Scholte, Ron H. J.; Engels, Rutger C. M. E.; Verhagen, Maaike

    2011-01-01

    Background: Loneliness is a common problem in adolescence. Earlier research focused on genes within the serotonin and oxytocin systems, but no studies have examined the role of dopamine-related genes in loneliness. In the present study, we focused on the dopamine D2 receptor gene (DRD2). Methods: Associations among the DRD2, sex, parental support,…

  6. Genome-wide gene-environment study identifies glutamate receptor gene GRIN2A as a Parkinson's disease modifier gene via interaction with coffee.

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    Hamza, Taye H; Chen, Honglei; Hill-Burns, Erin M; Rhodes, Shannon L; Montimurro, Jennifer; Kay, Denise M; Tenesa, Albert; Kusel, Victoria I; Sheehan, Patricia; Eaaswarkhanth, Muthukrishnan; Yearout, Dora; Samii, Ali; Roberts, John W; Agarwal, Pinky; Bordelon, Yvette; Park, Yikyung; Wang, Liyong; Gao, Jianjun; Vance, Jeffery M; Kendler, Kenneth S; Bacanu, Silviu-Alin; Scott, William K; Ritz, Beate; Nutt, John; Factor, Stewart A; Zabetian, Cyrus P; Payami, Haydeh

    2011-08-01

    Our aim was to identify genes that influence the inverse association of coffee with the risk of developing Parkinson's disease (PD). We used genome-wide genotype data and lifetime caffeinated-coffee-consumption data on 1,458 persons with PD and 931 without PD from the NeuroGenetics Research Consortium (NGRC), and we performed a genome-wide association and interaction study (GWAIS), testing each SNP's main-effect plus its interaction with coffee, adjusting for sex, age, and two principal components. We then stratified subjects as heavy or light coffee-drinkers and performed genome-wide association study (GWAS) in each group. We replicated the most significant SNP. Finally, we imputed the NGRC dataset, increasing genomic coverage to examine the region of interest in detail. The primary analyses (GWAIS, GWAS, Replication) were performed using genotyped data. In GWAIS, the most significant signal came from rs4998386 and the neighboring SNPs in GRIN2A. GRIN2A encodes an NMDA-glutamate-receptor subunit and regulates excitatory neurotransmission in the brain. Achieving P(2df) = 10(-6), GRIN2A surpassed all known PD susceptibility genes in significance in the GWAIS. In stratified GWAS, the GRIN2A signal was present in heavy coffee-drinkers (OR = 0.43; P = 6×10(-7)) but not in light coffee-drinkers. The a priori Replication hypothesis that "Among heavy coffee-drinkers, rs4998386_T carriers have lower PD risk than rs4998386_CC carriers" was confirmed: OR(Replication) = 0.59, P(Replication) = 10(-3); OR(Pooled) = 0.51, P(Pooled) = 7×10(-8). Compared to light coffee-drinkers with rs4998386_CC genotype, heavy coffee-drinkers with rs4998386_CC genotype had 18% lower risk (P = 3×10(-3)), whereas heavy coffee-drinkers with rs4998386_TC genotype had 59% lower risk (P = 6×10(-13)). Imputation revealed a block of SNPs that achieved P(2df)coffee-drinkers. This study is proof of concept that inclusion of environmental factors can help identify

  7. Genome-Wide Gene-Environment Study Identifies Glutamate Receptor Gene GRIN2A as a Parkinson's Disease Modifier Gene via Interaction with Coffee

    Science.gov (United States)

    Hamza, Taye H.; Chen, Honglei; Hill-Burns, Erin M.; Rhodes, Shannon L.; Montimurro, Jennifer; Kay, Denise M.; Tenesa, Albert; Kusel, Victoria I.; Sheehan, Patricia; Eaaswarkhanth, Muthukrishnan; Yearout, Dora; Samii, Ali; Roberts, John W.; Agarwal, Pinky; Bordelon, Yvette; Park, Yikyung; Wang, Liyong; Gao, Jianjun; Vance, Jeffery M.; Kendler, Kenneth S.; Bacanu, Silviu-Alin; Scott, William K.; Ritz, Beate; Nutt, John; Factor, Stewart A.; Zabetian, Cyrus P.; Payami, Haydeh

    2011-01-01

    Our aim was to identify genes that influence the inverse association of coffee with the risk of developing Parkinson's disease (PD). We used genome-wide genotype data and lifetime caffeinated-coffee-consumption data on 1,458 persons with PD and 931 without PD from the NeuroGenetics Research Consortium (NGRC), and we performed a genome-wide association and interaction study (GWAIS), testing each SNP's main-effect plus its interaction with coffee, adjusting for sex, age, and two principal components. We then stratified subjects as heavy or light coffee-drinkers and performed genome-wide association study (GWAS) in each group. We replicated the most significant SNP. Finally, we imputed the NGRC dataset, increasing genomic coverage to examine the region of interest in detail. The primary analyses (GWAIS, GWAS, Replication) were performed using genotyped data. In GWAIS, the most significant signal came from rs4998386 and the neighboring SNPs in GRIN2A. GRIN2A encodes an NMDA-glutamate-receptor subunit and regulates excitatory neurotransmission in the brain. Achieving P2df = 10−6, GRIN2A surpassed all known PD susceptibility genes in significance in the GWAIS. In stratified GWAS, the GRIN2A signal was present in heavy coffee-drinkers (OR = 0.43; P = 6×10−7) but not in light coffee-drinkers. The a priori Replication hypothesis that “Among heavy coffee-drinkers, rs4998386_T carriers have lower PD risk than rs4998386_CC carriers” was confirmed: ORReplication = 0.59, PReplication = 10−3; ORPooled = 0.51, PPooled = 7×10−8. Compared to light coffee-drinkers with rs4998386_CC genotype, heavy coffee-drinkers with rs4998386_CC genotype had 18% lower risk (P = 3×10−3), whereas heavy coffee-drinkers with rs4998386_TC genotype had 59% lower risk (P = 6×10−13). Imputation revealed a block of SNPs that achieved P2dfcoffee-drinkers. This study is proof of concept that inclusion of environmental factors can help identify genes that

  8. Characterization of aid1, a novel gene involved in Fusobacterium nucleatum interspecies interactions.

    Science.gov (United States)

    Kaplan, Aida; Kaplan, Christopher W; He, Xuesong; McHardy, Ian; Shi, Wenyuan; Lux, Renate

    2014-08-01

    The oral opportunistic pathogen Fusobacterium nucleatum is known to interact with a large number of different bacterial species residing in the oral cavity. It adheres to a variety of Gram-positive bacteria, including oral streptococci via the arginine-inhibitable adhesin RadD. In this study, we describe a novel protein encoded by the predicted open reading frame FN1253 that appears to play a role in interspecies interactions of F. nucleatum, particularly with oral streptococci and related Gram-positive species. We designated FN1253 as aid1 (Adherence Inducing Determinant 1). Expression analyses demonstrated that this gene was induced in F. nucleatum single species biofilms, while the presence of representative members of the oral microbiota known to adhere to F. nucleatum triggered its suppression. Inactivation as well as overexpression of aid1 affected the ability of F. nucleatum to coaggregate with oral streptococci and the closely related Enterococcus faecalis, but not other Gram-positive oral species tested. Furthermore, overexpression of aid1 led to a drastic change in the structure of dual species biofilms of F. nucleatum with oral streptococci. Aid1 function was abolished in the presence of arginine and found to be dependent on RadD. Interestingly, differential expression of aid1 did not affect messenger RNA and protein levels of RadD. These findings indicate that RadD-mediated adhesion to oral streptococci involves more complex cellular processes than the simple interaction of adhesins on the surface of partner strains. Aid1 could potentially play an important role in facilitating RadD-mediated interaction with oral streptococci by increasing binding specificity of F. nucleatum to other microbial species.

  9. Interaction between the FTO gene, body mass index and depression: meta-analysis of 13701 individuals.

    Science.gov (United States)

    Rivera, Margarita; Locke, Adam E; Corre, Tanguy; Czamara, Darina; Wolf, Christiane; Ching-Lopez, Ana; Milaneschi, Yuri; Kloiber, Stefan; Cohen-Woods, Sara; Rucker, James; Aitchison, Katherine J; Bergmann, Sven; Boomsma, Dorret I; Craddock, Nick; Gill, Michael; Holsboer, Florian; Hottenga, Jouke-Jan; Korszun, Ania; Kutalik, Zoltan; Lucae, Susanne; Maier, Wolfgang; Mors, Ole; Müller-Myhsok, Bertram; Owen, Michael J; Penninx, Brenda W J H; Preisig, Martin; Rice, John; Rietschel, Marcella; Tozzi, Federica; Uher, Rudolf; Vollenweider, Peter; Waeber, Gerard; Willemsen, Gonneke; Craig, Ian W; Farmer, Anne E; Lewis, Cathryn M; Breen, Gerome; McGuffin, Peter

    2017-08-01

    BackgroundDepression and obesity are highly prevalent, and major impacts on public health frequently co-occur. Recently, we reported that having depression moderates the effect of the FTO gene, suggesting its implication in the association between depression and obesity.AimsTo confirm these findings by investigating the FTO polymorphism rs9939609 in new cohorts, and subsequently in a meta-analysis.MethodThe sample consists of 6902 individuals with depression and 6799 controls from three replication cohorts and two original discovery cohorts. Linear regression models were performed to test for association between rs9939609 and body mass index (BMI), and for the interaction between rs9939609 and depression status for an effect on BMI. Fixed and random effects meta-analyses were performed using METASOFT.ResultsIn the replication cohorts, we observed a significant interaction between FTO, BMI and depression with fixed effects meta-analysis (β = 0.12, P = 2.7 × 10(-4)) and with the Han/Eskin random effects method (P = 1.4 × 10(-7)) but not with traditional random effects (β = 0.1, P = 0.35). When combined with the discovery cohorts, random effects meta-analysis also supports the interaction (β = 0.12, P = 0.027) being highly significant based on the Han/Eskin model (P = 6.9 × 10(-8)). On average, carriers of the risk allele who have depression have a 2.2% higher BMI for each risk allele, over and above the main effect of FTOConclusionsThis meta-analysis provides additional support for a significant interaction between FTO, depression and BMI, indicating that depression increases the effect of FTO on BMI. The findings provide a useful starting point in understanding the biological mechanism involved in the association between obesity and depression. © The Royal College of Psychiatrists 2017.

  10. The CYBA gene A640G polymorphism influences predispositions to coronary artery disease through interactions with cigarette smoking and hypercholesterolemia.

    Science.gov (United States)

    Niemiec, Pawel; Nowak, Tomasz; Balcerzyk, Anna; Krauze, Jolanta; Zak, Iwona

    2011-08-01

    The CYBA gene encodes the p22phox peptide, an essential subunit of vascular NADPH oxidases. The aim of the study was to analyze potential interactions between CYBA gene A640G polymorphism and traditional risk factors of atherosclerosis. We studied 320 subjects: 160 patients with coronary artery disease (CAD) and 160 controls. The results of interactions were interpreted on the basis of synergy index values (SI, SIM). The 640G allele interacted with cigarette smoking (SI = 2.02, SIM = 2.32). Even greater increase of the CAD risk was found whenever the 640G allele interacted with both smoking and hypercholesterolemia (SI = 2.70, SIM = 3.60). The results suggest that the A640G polymorphism may influence individual predispositions to CAD through interactions with smoking and hypercholesterolemia.

  11. Potential role of gene-environment interactions in ion transport mechanisms in the etiology of renal cell cancer

    Science.gov (United States)

    Deckers, Ivette A. G.; van den Brandt, Piet A.; van Engeland, Manon; van Schooten, Frederik J.; Godschalk, Roger W. L.; Keszei, András P.; Hogervorst, Janneke G. F.; Schouten, Leo J.

    2016-01-01

    We investigated the ion transport mechanism (ITM) in renal cell cancer (RCC) etiology using gene-environment interactions between candidate single nucleotide polymorphisms (SNPs) and associated environmental factors, including dietary intakes of sodium, potassium and fluid, hypertension and diuretic medication. A literature-based selection of 13 SNPs in ten ITM genes were successfully genotyped in toenail DNA of 3,048 subcohort members and 419 RCC cases from the Netherlands Cohort Study. Diet and lifestyle were measured with baseline questionnaires. Cox regression analyses were conducted for main effects and gene-environment interactions. ADD1_rs4961 was significantly associated with RCC risk, showing a Hazard Ratio (HR) of 1.24 (95% confidence intervals (CI): 1.01–1.53) for the GT + TT (versus GG) genotype. Four of 65 tested gene-environment interactions were statistically significant. Three of these interactions clustered in SLC9A3_rs4957061, including the ones with fluid and potassium intake, and diuretic medication. For fluid intake, the RCC risk was significantly lower for high versus low intake in participants with the CC genotype (HR(95% CI): 0.47(0.26–0.86)), but not for the CT + TT genotype (P-interaction = 0.002). None of the main genetic effects and gene-environment interactions remained significant after adjustment for multiple testing. Data do not support the general hypothesis that the ITM is a disease mechanism in RCC etiology. PMID:27686058

  12. Characterizing genes with distinct methylation patterns in the context of protein-protein interaction network: application to human brain tissues.

    Science.gov (United States)

    Li, Yongsheng; Xu, Juan; Chen, Hong; Zhao, Zheng; Li, Shengli; Bai, Jing; Wu, Aiwei; Jiang, Chunjie; Wang, Yuan; Su, Bin; Li, Xia

    2013-01-01

    DNA methylation is an essential epigenetic mechanism involved in transcriptional control. However, how genes with different methylation patterns are assembled in the protein-protein interaction network (PPIN) remains a mystery. In the present study, we systematically dissected the characterization of genes with different methylation patterns in the PPIN. A negative association was detected between the methylation levels in the brain tissues and topological centralities. By focusing on two classes of genes with considerably different methylation levels in the brain tissues, namely the low methylated genes (LMGs) and high methylated genes (HMGs), we found that their organizing principles in the PPIN are distinct. The LMGs tend to be the center of the PPIN, and attacking them causes a more deleterious effect on the network integrity. Furthermore, the LMGs express their functions in a modular pattern and substantial differences in functions are observed between the two types of genes. The LMGs are enriched in the basic biological functions, such as binding activity and regulation of transcription. More importantly, cancer genes, especially recessive cancer genes, essential genes, and aging-related genes were all found more often in the LMGs. Additionally, our analysis presented that the intra-classes communications are enhanced, but inter-classes communications are repressed. Finally, a functional complementation was revealed between methylation and miRNA regulation in the human genome. We have elucidated the assembling principles of genes with different methylation levels in the context of the PPIN, providing key insights into the complex epigenetic regulation mechanisms.

  13. Developmental and organ-specific changes in promoter DNA-protein interactions in the tomato rbcS gene family.

    Science.gov (United States)

    Manzara, T; Carrasco, P; Gruissem, W

    1991-12-01

    The five genes encoding ribulose-1,5-bisphosphate carboxylase (rbcS) from tomato are differentially expressed. Transcription of the genes is organ specific and developmentally regulated in fruit and light regulated in cotyledons and leaves. DNase I footprinting assays were used to map multiple sites of DNA-protein interaction in the promoter regions of all five genes and to determine whether the differential transcriptional activity of each gene correlated with developmental or organ-specific changes in DNA-protein interactions. We show organ-specific differences in DNase I protection patterns, suggesting that differential transcription of rbcS genes is controlled at least in part at the level of DNA-protein interactions. In contrast, no changes were detected in the DNase I footprint pattern generated with nuclear extracts from dark-grown cotyledons versus cotyledons exposed to light, implying that light-dependent regulation of rbcS transcription is controlled by protein-protein interactions or modification of DNA binding proteins. During development of tomato fruit, most DNA-protein interactions in the rbcS promoter regions disappear, coincident with the transcriptional inactivation of the rbcS genes. In nuclear extracts from nonphotosynthetic roots and red fruit, the only detectable DNase I protection corresponds to a G-box binding activity. Detection of other DNA binding proteins in extracts from these organs and expression of nonphotosynthetic genes exclude the possibility that roots and red fruit are transcriptionally inactive. The absence of complex promoter protection patterns in these organs suggests either that cooperative interactions between different DNA binding proteins are necessary to form functional transcription complexes or that there is developmental and organ-specific regulation of several rbcS-specific transcription factors in these organs. The DNase I-protected DNA sequences defined in this study are discussed in the context of conserved DNA

  14. The properties of genome conformation and spatial gene interaction and regulation networks of normal and malignant human cell types.

    Directory of Open Access Journals (Sweden)

    Zheng Wang

    Full Text Available The spatial conformation of a genome plays an important role in the long-range regulation of genome-wide gene expression and methylation, but has not been extensively studied due to lack of genome conformation data. The recently developed chromosome conformation capturing techniques such as the Hi-C method empowered by next generation sequencing can generate unbiased, large-scale, high-resolution chromosomal interaction (contact data, providing an unprecedented opportunity to investigate the spatial structure of a genome and its applications in gene regulation, genomics, epigenetics, and cell biology. In this work, we conducted a comprehensive, large-scale computational analysis of this new stream of genome conformation data generated for three different human leukemia cells or cell lines by the Hi-C technique. We developed and applied a set of bioinformatics methods to reliably generate spatial chromosomal contacts from high-throughput sequencing data and to effectively use them to study the properties of the genome structures in one-dimension (1D and two-dimension (2D. Our analysis demonstrates that Hi-C data can be effectively applied to study tissue-specific genome conformation, chromosome-chromosome interaction, chromosomal translocations, and spatial gene-gene interaction and regulation in a three-dimensional genome of primary tumor cells. Particularly, for the first time, we constructed genome-scale spatial gene-gene interaction network, transcription factor binding site (TFBS - TFBS interaction network, and TFBS-gene interaction network from chromosomal contact information. Remarkably, all these networks possess the properties of scale-free modular networks.

  15. Diversity and interactions of microbial functional genes under differing environmental conditions: insights from a membrane bioreactor and an oxidation ditch

    Science.gov (United States)

    Xia, Yu; Hu, Man; Wen, Xianghua; Wang, Xiaohui; Yang, Yunfeng; Zhou, Jizhong

    2016-01-01

    The effect of environmental conditions on the diversity and interactions of microbial communities has caused tremendous interest in microbial ecology. Here, we found that with identical influents but differing operational parameters (mainly mixed liquor suspended solid (MLSS) concentrations, solid retention time (SRT) and dissolved oxygen (DO) concentrations), two full-scale municipal wastewater treatment systems applying oxidation ditch (OD) and membrane bioreactor (MBR) processes harbored a majority of shared genes (87.2%) but had different overall functional gene structures as revealed by two datasets of 12-day time-series generated by a functional gene array-GeoChip 4.2. Association networks of core carbon, nitrogen and phosphorus cycling genes in each system based on random matrix theory (RMT) showed different topological properties and the MBR nodes showed an indication of higher connectivity. MLSS and DO were shown to be effective in shaping functional gene structures of the systems by statistical analyses. Higher MLSS concentrations resulting in decreased resource availability of the MBR system were thought to promote positive interactions of important functional genes. Together, these findings show the differences of functional potentials of some bioprocesses caused by differing environmental conditions and suggest that higher stress of resource limitation increased positive gene interactions in the MBR system.

  16. Somatic polyploidy is associated with the upregulation of c-MYC interacting genes and EMT-like signature.

    Science.gov (United States)

    Vazquez-Martin, Alejandro; Anatskaya, Olga V; Giuliani, Alessandro; Erenpreisa, Jekaterina; Huang, Sui; Salmina, Kristine; Inashkina, Inna; Huna, Anda; Nikolsky, Nikolai N; Vinogradov, Alexander E

    2016-11-15

    The dependence of cancer on overexpressed c-MYC and its predisposition for polyploidy represents a double puzzle. We address this conundrum by cross-species transcription analysis of c-MYC interacting genes in polyploid vs. diploid tissues and cells, including human vs. mouse heart, mouse vs. human liver and purified 4n vs. 2n mouse decidua cells. Gene-by-gene transcriptome comparison and principal component analysis indicated that c-MYC interactants are significantly overrepresented among ploidy-associated genes. Protein interaction networks and gene module analysis revealed that the most upregulated genes relate to growth, stress response, proliferation, stemness and unicellularity, as well as to the pathways of cancer supported by MAPK and RAS coordinated pathways. A surprising feature was the up-regulation of epithelial-mesenchymal transition (EMT) modules embodied by the N-cadherin pathway and EMT regulators from SNAIL and TWIST families. Metabolic pathway analysis also revealed the EMT-linked features, such as global proteome remodeling, oxidative stress, DNA repair and Warburg-like energy metabolism. Genes associated with apoptosis, immunity, energy demand and tumour suppression were mostly down-regulated. Noteworthy, despite the association between polyploidy and ample features of cancer, polyploidy does not trigger it. Possibly it occurs because normal polyploidy does not go that far in embryonalisation and linked genome destabilisation. In general, the analysis of polyploid transcriptome explained the evolutionary relation of c-MYC and polyploidy to cancer.

  17. Diversity and interactions of microbial functional genes under differing environmental conditions: insights from a membrane bioreactor and an oxidation ditch

    Science.gov (United States)

    Xia, Yu; Hu, Man; Wen, Xianghua; Wang, Xiaohui; Yang, Yunfeng; Zhou, Jizhong

    2016-01-01

    The effect of environmental conditions on the diversity and interactions of microbial communities has caused tremendous interest in microbial ecology. Here, we found that with identical influents but differing operational parameters (mainly mixed liquor suspended solid (MLSS) concentrations, solid retention time (SRT) and dissolved oxygen (DO) concentrations), two full-scale municipal wastewater treatment systems applying oxidation ditch (OD) and membrane bioreactor (MBR) processes harbored a majority of shared genes (87.2%) but had different overall functional gene structures as revealed by two datasets of 12-day time-series generated by a functional gene array-GeoChip 4.2. Association networks of core carbon, nitrogen and phosphorus cycling genes in each system based on random matrix theory (RMT) showed different topological properties and the MBR nodes showed an indication of higher connectivity. MLSS and DO were shown to be effective in shaping functional gene structures of the systems by statistical analyses. Higher MLSS concentrations resulting in decreased resource availability of the MBR system were thought to promote positive interactions of important functional genes. Together, these findings show the differences of functional potentials of some bioprocesses caused by differing environmental conditions and suggest that higher stress of resource limitation increased positive gene interactions in the MBR system. PMID:26743465

  18. Gene-environment interaction effects on lung function- a genome-wide association study within the Framingham heart study

    Science.gov (United States)

    2013-01-01

    Background Previous studies in occupational exposure and lung function have focused only on the main effect of occupational exposure or genetics on lung function. Some disease-susceptible genes may be missed due to their low marginal effects, despite potential involvement in the disease process through interactions with the environment. Through comprehensive genome-wide gene-environment interaction studies, we can uncover these susceptibility genes. Our objective in this study was to explore gene by occupational exposure interaction effects on lung function using both the individual SNPs approach and the genetic network approach. Methods The study population comprised the Offspring Cohort and the Third Generation from the Framingham Heart Study. We used forced expiratory volume in one second (FEV1) and ratio of FEV1 to forced vital capacity (FVC) as outcomes. Occupational exposures were classified using a population-specific job exposure matrix. We performed genome-wide gene-environment interaction analysis, using the Affymetrix 550 K mapping array for genotyping. A linear regression-based generalized estimating equation was applied to account for within-family relatedness. Network analysis was conducted using results from single-nucleotide polymorphism (SNP)-level analyses and from gene expression study results. Results There were 4,785 participants in total. SNP-level analysis and network analysis identified SNP rs9931086 (Pinteraction =1.16 × 10-7) in gene SLC38A8, which may significantly modify the effects of occupational exposure on FEV1. Genes identified from the network analysis included CTLA-4, HDAC, and PPAR-alpha. Conclusions Our study implies that SNP rs9931086 in SLC38A8 and genes CTLA-4, HDAC, and PPAR-alpha, which are related to inflammatory processes, may modify the effect of occupational exposure on lung function. PMID:24289273

  19. Cross-fostering: Elucidating the effects of gene×environment interactions on phenotypic development.

    Science.gov (United States)

    McCarty, Richard

    2017-02-01

    Cross-fostering of litters from soon after birth until weaning is a valuable tool to study the ways in which gene×environment interactions program the development of neural, physiological and behavioral characteristics of mammalian species. In laboratory mice and rats, the primary focus of this review, cross-fostering of litters between mothers of different strains or treatment groups (intraspecific) or between mothers of different species (interspecific) has been conducted over the past 9 decades. Areas of particular interest have included maternal effects on emotionality, social preferences, responses to stressful stimulation, nutrition and growth, blood pressure regulation, and epigenetic effects on brain development and behavior. Results from these areas of research highlight the critical role of the postnatal maternal environment in programming the development of offspring phenotypic characteristics. In addition, experimental paradigms that have included cross-fostering have permitted investigators to tease apart prenatal versus postnatal effects of various treatments on offspring development and behavior.

  20. Gene-environment interaction in the onset of eczema in infancy

    DEFF Research Database (Denmark)

    Bisgaard, Hans; Simpson, Angela; Palmer, Colin N A

    2008-01-01

    BACKGROUND: Loss-of-function variants in the gene encoding filaggrin (FLG) are major determinants of eczema. We hypothesized that weakening of the physical barrier in FLG-deficient individuals may potentiate the effect of environmental exposures. Therefore, we investigated whether...... there is an interaction between FLG loss-of-function mutations with environmental exposures (pets and dust mites) in relation to the development of eczema. METHODS AND FINDINGS: We used data obtained in early life in a high-risk birth cohort in Denmark and replicated the findings in an unselected birth cohort...... in the United Kingdom. Primary outcome was age of onset of eczema; environmental exposures included pet ownership and mite and pet allergen levels. In Copenhagen (n = 379), FLG mutation increased the risk of eczema during the first year of life (hazard ratio [HR] 2.26, 95% confidence interval [CI] 1.27-4.00, p...

  1. In situ gene expression in mixed-culture biofilms: Evidence of metabolic interactions between community members

    DEFF Research Database (Denmark)

    Møller, Søren; Sternberg, Claus; Andersen, Jens Bo

    1998-01-01

    in both community and pure-culture biofilms, while the Pm promoter was induced in the mixed community but not in a pure-culture biofilm. By sequentially adding community members, induction of Pm was shown to be a consequence of direct metabolic interactions between an Acinetobacter species and P. putida...... are involved in the biodegradation of toluene and related aromatic compounds. The upper-pathway promoter (Pu) and the meta-pathway promoter (Pm) from the TOL plasmid were fused independently to the gene coding for the green fluorescent protein (GFP), and expression from these promoters was studied in P. putida......, which was a dominant community member. Biofilms were cultured in flow chambers, which in combination with scanning confocal laser microscopy allowed direct monitoring of promoter activity with single-cell spatial resolution. Expression from the Pu promoter was homogeneously induced by benzyl alcohol...

  2. Behavior of QQ-plots and genomic control in studies of gene-environment interaction.

    Directory of Open Access Journals (Sweden)

    Arend Voorman

    Full Text Available Genome-wide association studies of gene-environment interaction (GxE GWAS are becoming popular. As with main effects GWAS, quantile-quantile plots (QQ-plots and Genomic Control are being used to assess and correct for population substructure. However, in G x E work these approaches can be seriously misleading, as we illustrate; QQ-plots may give strong indications of substructure when absolutely none is present. Using simulation and theory, we show how and why spurious QQ-plot inflation occurs in G x E GWAS, and how this differs from main-effects analyses. We also explain how simple adjustments to standard regression-based methods used in G x E GWAS can alleviate this problem.

  3. Gene-environment interactions in psychopathology throughout early childhood: a systematic review.

    Science.gov (United States)

    Pinto, Raquel Q; Soares, Isabel; Carvalho-Correia, Eduarda; Mesquita, Ana R

    2015-12-01

    Up to 20% of children and adolescents worldwide suffer from mental health problems. Epidemiological studies have shown that some of these problems are already present at an early age. The recognition that psychopathology is a result of an interaction between individual experiences and genetic characteristics has led to an increase in the number of studies using a gene-environment approach (G×E). However, to date, there has been no systematic review of G×E studies on psychopathology in the first 6 years of life. Following a literature search and a selection process, 14 studies were identified and most (n=12) of the studies found at least one significant G×E effect. This review provides a systematic characterization of the published G×E studies, providing insights into the neurobiological and environmental determinants involved in the etiology of children's psychopathology.

  4. Metabolic syndrome, diabetes and atherosclerosis: Influence of gene-environment interaction

    Energy Technology Data Exchange (ETDEWEB)

    Andreassi, Maria Grazia, E-mail: andreas@ifc.cnr.it [CNR Institute of Clinical Physiology, G. Pasquinucci Hospital, Via Aurelia Sud, Massa (Italy)

    2009-07-10

    Despite remarkable progress in diagnosis and understanding of risk factors, cardiovascular disease (CVD) remains still the leading cause of morbidity and mortality in the world's developed countries. The metabolic syndrome, a cluster of risk factors (visceral obesity, insulin resistance, dyslipidaemia, and hypertension), is increasingly being recognized as a new risk factor for type 2 diabetes and atherosclerotic cardiovascular disease. Nevertheless, there is wide variation in both the occurrence of disease and age of onset, even in individuals who display very similar risk profiles. There is now compelling evidence that a complex interplay between genetic determinants and environmental factors (still largely unknown) is the reason for this large inter-individual variation in disease susceptibility. The purpose of the present review is to describe the current status of our knowledge concerning the gene-environment interactions potentially implicated in the pathogenesis of metabolic syndrome, diabetes and cardiovascular disease. It focuses predominantly on studies of genes (peroxisome proliferator-activated receptor-gamma, alcohol dehydrogenase type 1C, apolipoprotein E, glutathione S-transferases T1 and M1) that are known to be modified by dietary and lifestyle habits (fat diet, intake of alcohol and smoking habit). It also describes the limited current understanding of the role of genetic variants of xenobiotic metabolizing enzymes and their interactions with environmental toxicants. Additional studies are needed in order to clarify whether inter-individual differences in detoxification of environmental toxicants may have an essential role in the development of CVD and contribute to the emerging field of 'environmental cardiology'. Such knowledge may be particularly relevant for improving cardiovascular risk stratification and conceiving the development of 'personalized intervention program'.

  5. Relating diseases by integrating gene associations and information flow through protein interaction network.

    Science.gov (United States)

    Hamaneh, Mehdi Bagheri; Yu, Yi-Kuo

    2014-01-01

    Identifying similar diseases could potentially provide deeper understanding of their underlying causes, and may even hint at possible treatments. For this purpose, it is necessary to have a similarity measure that reflects the underpinning molecular interactions and biological pathways. We have thus devised a network-based measure that can partially fulfill this goal. Our method assigns weights to all proteins (and consequently their encoding genes) by using information flow from a disease to the protein interaction network and back. Similarity between two diseases is then defined as the cosine of the angle between their corresponding weight vectors. The proposed method also provides a way to suggest disease-pathway associations by using the weights assigned to the genes to perform enrichment analysis for each disease. By calculating pairwise similarities between 2534 diseases, we show that our disease similarity measure is strongly correlated with the probability of finding the diseases in the same disease family and, more importantly, sharing biological pathways. We have also compared our results to those of MimMiner, a text-mining method that assigns pairwise similarity scores to diseases. We find the results of the two methods to be complementary. It is also shown that clustering diseases based on their similarities and performing enrichment analysis for the cluster centers significantly increases the term association rate, suggesting that the cluster centers are better representatives for biological pathways than the diseases themselves. This lends support to the view that our similarity measure is a good indicator of relatedness of biological processes involved in causing the diseases. Although not needed for understanding this paper, the raw results are available for download for further study at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbpmn/DiseaseRelations/.

  6. Gene interactions in the DNA damage-response pathway identified by genome-wide RNA-interference analysis of synthetic lethality

    NARCIS (Netherlands)

    van Haaften, Gijs; Vastenhouw, Nadine L; Nollen, Ellen A A; Plasterk, Ronald H A; Tijsterman, Marcel

    2004-01-01

    Here, we describe a systematic search for synthetic gene interactions in a multicellular organism, the nematode Caenorhabditis elegans. We established a high-throughput method to determine synthetic gene interactions by genome-wide RNA interference and identified genes that are required to protect t

  7. Cognitive endophenotypes, gene-environment interactions and experience-dependent plasticity in animal models of schizophrenia.

    Science.gov (United States)

    Burrows, Emma L; Hannan, Anthony J

    2016-04-01

    Schizophrenia is a devastating brain disorder caused by a complex and heterogeneous combination of genetic and environmental factors. In order to develop effective new strategies to prevent and treat schizophrenia, valid animal models are required which accurately model the disorder, and ideally provide construct, face and predictive validity. The cognitive deficits in schizophrenia represent some of the most debilitating symptoms and are also currently the most poorly treated. Therefore it is crucial that animal models are able to capture the cognitive dysfunction that characterizes schizophrenia, as well as the negative and psychotic symptoms. The genomes of mice have, prior to the recent gene-editing revolution, proven the most easily manipulable of mammalian laboratory species, and hence most genetic targeting has been performed using mouse models. Importantly, when key environmental factors of relevance to schizophrenia are experimentally manipulated, dramatic changes in the phenotypes of these animal models are often observed. We will review recent studies in rodent models which provide insight into gene-environment interactions in schizophrenia. We will focus specifically on environmental factors which modulate levels of experience-dependent plasticity, including environmental enrichment, cognitive stimulation, physical activity and stress. The insights provided by this research will not only help refine the establishment of optimally valid animal models which facilitate development of novel therapeutics, but will also provide insight into the pathogenesis of schizophrenia, thus identifying molecular and cellular targets for future preclinical and clinical investigations.

  8. Drug metabolism and liver disease: a drug-gene-environment interaction.

    Science.gov (United States)

    Zgheib, Nathalie K; Branch, Robert A

    2017-02-01

    Despite the central role of the liver in drug metabolism, surprisingly there is lack of certainty in anticipating the extent of modification of the clearance of a given drug in a given patient. The intent of this review is to provide a conceptual framework in considering the impact of liver disease on drug disposition and reciprocally the impact of drug disposition on liver disease. It is proposed that improved understanding of the situation is gained by considering the issue as a special example of a drug-gene-environment interaction. This requires an integration of knowledge of the drug's properties, knowledge of the gene products involved in its metabolism, and knowledge of the pathophysiology of its disposition. This will enhance the level of predictability of drug disposition and toxicity for a drug of interest in an individual patient. It is our contention that advances in pharmacology, pharmacogenomics, and hepatology, together with concerted interests in the academic, regulatory, and pharmaceutical industry communities provide an ideal immediate environment to move from a qualitative reactive approach to quantitative proactive approach in individualizing patient therapy in liver disease.

  9. Predicting childhood effortful control from interactions between early parenting quality and children's dopamine transporter gene haplotypes.

    Science.gov (United States)

    Li, Yi; Sulik, Michael J; Eisenberg, Nancy; Spinrad, Tracy L; Lemery-Chalfant, Kathryn; Stover, Daryn A; Verrelli, Brian C

    2016-02-01

    Children's observed effortful control (EC) at 30, 42, and 54 months (n = 145) was predicted from the interaction between mothers' observed parenting with their 30-month-olds and three variants of the solute carrier family C6, member 3 (SLC6A3) dopamine transporter gene (single nucleotide polymorphisms in intron8 and intron13, and a 40 base pair variable number tandem repeat [VNTR] in the 3'-untranslated region [UTR]), as well as haplotypes of these variants. Significant moderating effects were found. Children without the intron8-A/intron13-G, intron8-A/3'-UTR VNTR-10, or intron13-G/3'-UTR VNTR-10 haplotypes (i.e., haplotypes associated with the reduced SLC6A3 gene expression and thus lower dopamine functioning) appeared to demonstrate altered levels of EC as a function of maternal parenting quality, whereas children with these haplotypes demonstrated a similar EC level regardless of the parenting quality. Children with these haplotypes demonstrated a trade-off, such that they showed higher EC, relative to their counterparts without these haplotypes, when exposed to less supportive maternal parenting. The findings revealed a diathesis-stress pattern and suggested that different SLC6A3 haplotypes, but not single variants, might represent different levels of young children's sensitivity/responsivity to early parenting.

  10. Reducing aggression and impulsivity through school-based prevention programs: a gene by intervention interaction.

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    Musci, Rashelle J; Bradshaw, Catherine P; Maher, Brion; Uhl, George R; Kellam, Sheppard G; Ialongo, Nicholas S

    2014-12-01

    A variety of school-based, universal preventive interventions have been developed to address behavioral and mental health problems. Unfortunately, few have been evaluated within the context of randomized controlled trials with long-term follow-up. Even fewer still have examined the potential genetic factors that may drive differential impact of the intervention. In the present analysis, we examine the extent to which the longitudinal effects of two elementary school-based interventions were moderated by the brain-derived neurotrophic factor (BDNF) gene, which has been linked with aggression and impulsive behaviors. The sample included 678 urban, primarily African American children who were randomly assigned along with their teachers to one of three first grade classroom conditions: classroom-centered (CC) intervention, Family School Partnership (FSP), or a control condition. The teacher ratings of the youth's aggressive and impulsive behavior were obtained at baseline and in grades 6-12. Single-nucleotide polymorphisms (SNPs) from the BDNF gene were extracted from the genome-wide data. Longitudinal latent trait-state-error models indicated a significant interaction between a particular profile of the BDNF SNP cluster (46 % of sample) and CC intervention on impulsivity (β = -.27, p aggression (β = -.14, p school on late adolescent outcomes of impulsivity and aggression can be potentially modified by genetic factors, such as BDNF. However, replication of these results is necessary before firm conclusions can be drawn.

  11. Identification of expressed genes during compatible interaction between stripe rust (Puccinia striiformis and wheat using a cDNA library

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    Huang Lili

    2009-12-01

    Full Text Available Abstract Background Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst, is one of the most destructive diseases of wheat worldwide. To establish compatibility with the host, Pst forms special infection structures to invade the plant with minimal damage to host cells. Although compatible interaction between wheat and Pst has been studied using various approaches, research on molecular mechanisms of the interaction is limited. The aim of this study was to develop an EST database of wheat infected by Pst in order to determine transcription profiles of genes involved in compatible wheat-Pst interaction. Results Total RNA, extracted from susceptible infected wheat leaves harvested at 3, 5 and 8 days post inoculation (dpi, was used to create a cDNA library, from which 5,793 ESTs with high quality were obtained and clustered into 583 contigs and 2,160 singletons to give a set of 2,743 unisequences (GenBank accessions: GR302385 to GR305127. The BLASTx program was used to search for homologous genes of the unisequences in the GenBank non-redundant protein database. Of the 2,743 unisequences, 52.8% (the largest category were highly homologous to plant genes; 16.3% to fungal genes and 30% of no-hit. The functional classification of all ESTs was established based on the database entry giving the best E-value using the Bevan's classification categories. About 50% of the ESTs were significantly homologous to genes encoding proteins with known functions; 20% were similar to genes encoding proteins with unknown functions and 30% did not have significant homology to any sequence in the database. The quantitative real-time PCR (qRT-PCR analysis determined the transcription profiles and their involvement in the wheat-Pst interaction for seven of the gene. Conclusion The cDNA library is useful for identifying the functional genes involved in the wheat-Pst compatible interaction, and established a new database for studying Pst pathogenesis genes

  12. Dictionary and Gene Ontology Based Similarity for Named Entity Relationship Protein-protein Interaction Prediction from Biotext Corpus

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    Smt K. Prabavathy

    2014-12-01

    Full Text Available Protein-protein interactions functions as a significant key role in several biological systems. These involves in complex formation and many pathways which are used to perform biological processes. By accurate identification of the set of interacting proteins can get rid of new light on the functional role of various proteins in the complex surroundings of the cell. The ability to construct biologically consequential gene networks and identification of the exact relationship in the gene network is critical for present-day systems biology. In earlier research, the power of presented gene modules to shed light on the functioning of complex biological systems is studied. Most of modules in these networks have shown small link with meaningful biological function, because these methods doesn’t exactly calculate the semantic relationship between the entities. In order to overcome these problems and improve the PPI results in the biotext corpus a new method is proposed in this research. The proposed method which directly incorporates Gene Ontology (GO annotation in construction of gene modules and Dictionary-based text is proposed to extract biotext information. Dictionary-Based Text and Gene Ontology (DBTGO approach that integrates with various gene-gene pairwise similarity values, protein-protein interaction relationship obtained from gene expression, in order to gain better biotext information retrieval result. A result analysis has been carried out on Biotext Project at UC Berkley. Testing the DBTGO algorithm indicates that it is able to improve PPI relationship identification result with all previously suggested methods in terms of the precision, recall, F measure and Normalized Discounted Cumulative Gain (NDCG. The proposed DBTGO algorithm can facilitate comprehensive and in-depth analysis of high throughput experimental data at the gene network level.

  13. Plastid-Nuclear Interaction and Accelerated Coevolution in Plastid Ribosomal Genes in Geraniaceae.

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    Weng, Mao-Lun; Ruhlman, Tracey A; Jansen, Robert K

    2016-06-27

    Plastids and mitochondria have many protein complexes that include subunits encoded by organelle and nuclear genomes. In animal cells, compensatory evolution between mitochondrial and nuclear-encoded subunits was identified and the high mitochondrial mutation rates were hypothesized to drive compensatory evolution in nuclear genomes. In plant cells, compensatory evolution between plastid and nucleus has rarely been investigated in a phylogenetic framework. To investigate plastid-nuclear coevolution, we focused on plastid ribosomal protein genes that are encoded by plastid and nuclear genomes from 27 Geraniales species. Substitution rates were compared for five sets of genes representing plastid- and nuclear-encoded ribosomal subunit proteins targeted to the cytosol or the plastid as well as nonribosomal protein controls. We found that nonsynonymous substitution rates (dN) and the ratios of nonsynonymous to synonymous substitution rates (ω) were accelerated in both plastid- (CpRP) and nuclear-encoded subunits (NuCpRP) of the plastid ribosome relative to control sequences. Our analyses revealed strong signals of cytonuclear coevolution between plastid- and nuclear-encoded subunits, in which nonsynonymous substitutions in CpRP and NuCpRP tend to occur along the same branches in the Geraniaceae phylogeny. This coevolution pattern cannot be explained by physical interaction between amino acid residues. The forces driving accelerated coevolution varied with cellular compartment of the sequence. Increased ω in CpRP was mainly due to intensified positive selection whereas increased ω in NuCpRP was caused by relaxed purifying selection. In addition, the many indels identified in plastid rRNA genes in Geraniaceae may have contributed to changes in plastid subunits.

  14. Epistatic Interactions between apolipoprotein E and hemoglobin S Genes in regulation of malaria parasitemia.

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    Virginie Rougeron

    Full Text Available Apolipoprotein E is a monomeric protein secreted by the liver and responsible for the transport of plasma cholesterol and triglycerides. The APOE gene encodes 3 isoforms Ɛ4, Ɛ3 and Ɛ2 with APOE Ɛ4 associated with higher plasma cholesterol levels and increased pathogenesis in several infectious diseases (HIV, HSV. Given that cholesterol is an important nutrient for malaria parasites, we examined whether APOE Ɛ4 was a risk factor for Plasmodium infection, in terms of prevalence or parasite density. A cross sectional survey was performed in 508 children aged 1 to 12 years in Gabon during the wet season. Children were screened for Plasmodium spp. infection, APOE and hemoglobin S (HbS polymorphisms. Median parasite densities were significantly higher in APOE Ɛ4 children for Plasmodium spp. densities compared to non-APOE Ɛ4 children. When stratified for HbS polymorphisms, median Plasmodium spp. densities were significantly higher in HbAA children if they had an APOE Ɛ4 allele compared to those without an APOE Ɛ4 allele. When considering non-APOE Ɛ4 children, there was no quantitative reduction of Plasmodium spp. parasite densities for HbAS compared to HbAA phenotypes. No influence of APOE Ɛ4 on successful Plasmodium liver cell invasion was detected by multiplicity of infection. These results show that the APOE Ɛ4 allele is associated with higher median malaria parasite densities in children likely due to the importance of cholesterol availability to parasite growth and replication. Results suggest an epistatic interaction between APOE and HbS genes such that sickle cell trait only had an effect on parasite density in APOE Ɛ4 children. This suggests a linked pathway of regulation of parasite density involving expression of these genes. These findings have significance for understanding host determinants of regulation of malaria parasite density, the design of clinical trials as well as studies of co-infection with Plasmodium and other

  15. Interaction effects between estrogen receptor α and vitamin D receptor genes on age at menarche in Chinese women

    Institute of Scientific and Technical Information of China (English)

    Hong XU; Ji-rong LONG; Miao-xin LI; Hong-wen DENG

    2005-01-01

    Aim: To evaluate whether estrogen receptor α (ER-α) and vitamin D receptor (VDR) genes are associated with the age at menarche in Chinese women.Methods:A total of 390 pre-menopausal Chinese women were genotyped at the ER-α PvuⅡ,XbaⅠ, and VDR ApaⅠ loci using polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP).Results: Neither the ER-α gene nor the VDR gene individually had significant effects on the age at menarche in our subjects (P>0.10).However, evidence of interaction effects between the two genes were observed: with the aa genotype at the VDR ApaⅠ locus, subjects with haplotype PX at the ER-α gene had, on average, 6 months later onset of menarche than the non-carriers (P=0.01).Conclusion: We found that neither the ER-α gene or the VDR gene had a significant association with the age at menarche individually.However, potential interaction effects between the two genes were observed in Chinese women.

  16. Global gene expression analysis of the zoonotic parasite Trichinella spiralis revealed novel genes in host parasite interaction.

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    Xiaolei Liu

    Full Text Available BACKGROUND: Trichinellosis is a typical food-borne zoonotic disease which is epidemic worldwide and the nematode Trichinella spiralis is the main pathogen. The life cycle of T. spiralis contains three developmental stages, i.e. adult worms, new borne larva (new borne L1 larva and muscular larva (infective L1 larva. Stage-specific gene expression in the parasites has been investigated with various immunological and cDNA cloning approaches, whereas the genome-wide transcriptome and expression features of the parasite have been largely unknown. The availability of the genome sequence information of T. spiralis has made it possible to deeply dissect parasite biology in association with global gene expression and pathogenesis. METHODOLOGY AND PRINCIPAL FINDINGS: In this study, we analyzed the global gene expression patterns in the three developmental stages of T. spiralis using digital gene expression (DGE analysis. Almost 15 million sequence tags were generated with the Illumina RNA-seq technology, producing expression data for more than 9,000 genes, covering 65% of the genome. The transcriptome analysis revealed thousands of differentially expressed genes within the genome, and importantly, a panel of genes encoding functional proteins associated with parasite invasion and immuno-modulation were identified. More than 45% of the genes were found to be transcribed from both strands, indicating the importance of RNA-mediated gene regulation in the development of the parasite. Further, based on gene ontological analysis, over 3000 genes were functionally categorized and biological pathways in the three life cycle stage were elucidated. CONCLUSIONS AND SIGNIFICANCE: The global transcriptome of T. spiralis in three developmental stages has been profiled, and most gene activity in the genome was found to be developmentally regulated. Many metabolic and biological pathways have been revealed. The findings of the differential expression of several protein

  17. AEG-1/MTDH/LYRIC: signaling pathways, downstream genes, interacting proteins, and regulation of tumor angiogenesis.

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    Emdad, Luni; Das, Swadesh K; Dasgupta, Santanu; Hu, Bin; Sarkar, Devanand; Fisher, Paul B

    2013-01-01

    Astrocyte elevated gene-1 (AEG-1), also known as metadherin (MTDH) and lysine-rich CEACAM1 coisolated (LYRIC), was initially cloned in 2002. AEG-1/MTDH/LYRIC has emerged as an important oncogene that is overexpressed in multiple types of human cancer. Expanded research on AEG-1/MTDH/LYRIC has established a functional role of this molecule in several crucial aspects of tumor progression, including transformation, proliferation, cell survival, evasion of apoptosis, migration and invasion, metastasis, angiogenesis, and chemoresistance. The multifunctional role of AEG-1/MTDH/LYRIC in tumor development and progression is associated with a number of signaling cascades, and recent studies identified several important interacting partners of AEG-1/MTDH/LYRIC in regulating cancer promotion and other biological functions. This review evaluates the current literature on AEG-1/MTDH/LYRIC function relative to signaling changes, interacting partners, and angiogenesis and highlights new perspectives of this molecule, indicating its potential as a significant target for the clinical treatment of various cancers and other diseases.

  18. Gene by Sex Interaction for Measures of Obesity in the Framingham Heart Study

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    Ashlee M. Benjamin

    2011-01-01

    Full Text Available Obesity is an increasingly prevalent and severe health concern with a substantial heritable component and marked sex differences. We sought to determine if the effect of genetic variants also differed by sex by performing a genome-wide association study modeling the effect of genotype-by-sex interaction on obesity phenotypes. Genotype data from individuals in the Framingham Heart Study Offspring cohort were analyzed across five exams. Although no variants showed genome-wide significant gene-by-sex interaction in any individual exam, four polymorphisms displayed a consistent BMI association (P-values .00186 to .00010 across all five exams. These variants were clustered downstream of LYPLAL1, which encodes a lipase/esterase expressed in adipose tissue, a locus previously identified as having sex-specific effects on central obesity. Primary effects in males were in the opposite direction from females and were replicated in Framingham Generation 3. Our data support a sex-influenced association between genetic variation at the LYPLAL1 locus and obesity-related traits.

  19. Common oxytocin receptor gene (OXTR) polymorphism and social support interact to reduce stress in humans.

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    Chen, Frances S; Kumsta, Robert; von Dawans, Bernadette; Monakhov, Mikhail; Ebstein, Richard P; Heinrichs, Markus

    2011-12-13

    The neuropeptide oxytocin has played an essential role in the regulation of social behavior and attachment throughout mammalian evolution. Because recent studies in humans have shown that oxytocin administration reduces stress responses and increases prosocial behavior, we investigated whether a common single nucleotide polymorphism (rs53576) in the oxytocin receptor gene (OXTR) might interact with stress-protective effects of social support. Salivary cortisol samples and subjective stress ratings were obtained from 194 healthy male participants before, during, and after a standardized psychosocial laboratory stress procedure. Participants were randomly assigned either to prepare alone or to receive social support from their female partner or close female friend while preparing for the stressful task. Differential stress responses between the genotype groups were observed depending on the presence or absence of social support. Only individuals with one or two copies of the G allele of rs53576 showed lower cortisol responses to stress after social support, compared with individuals with the same genotype receiving no social support. These results indicate that genetic variation of the oxytocin system modulates the e