Genetic and environmental interactions

International Nuclear Information System (INIS)

Strong, L.C.

1977-01-01

Cancer may result from a multistage process occurring over a long period of time. Presumably, initial and progressive stages of carcinogenesis may be modified by both genetic and environmental factors. Theoretically, genetic factors may alter susceptibility to the carcinogenic effects of an environmental agent at the initial exposure due to variation in metabolism of the carcinogen or variation in specific target cell response to the active carcinogen, or during the latent phase due to numerous factors that might increase the probability of tumor expression, including growth-promoting factors or immunodeficiency states. Observed genetic and environmental interactions in carcinogenesis include an association between genetically determined inducibility of aryl hydrocarbon hydroxylase and smoking-related cancers, familial susceptibility to certain environmental carcinogens, an association between hereditary disorders of mutagenesis and carcinogenesis, and enhancement of tissue-specific, dominantly inherited tumor predisposition by radiation. Multiple primary tumors occur frequently in genetically predisposed individuals. Specific markers for susceptibility must be sought in order that high-risk individuals be identified and appropriate measures taken for early cancer detection or prevention. Study of the nature of the genetically determined susceptibility and interactions with environmental agents may be revealing in the understanding of carcinogenesis in general

Parameter determination for quantitative PIXE analysis using genetic algorithms

International Nuclear Information System (INIS)

Aspiazu, J.; Belmont-Moreno, E.

1996-01-01

For biological and environmental samples, PIXE technique is in particular advantage for elemental analysis, but the quantitative analysis implies accomplishing complex calculations that require the knowledge of more than a dozen parameters. Using a genetic algorithm, the authors give here an account of the procedure to obtain the best values for the parameters necessary to fit the efficiency for a X-ray detector. The values for some variables involved in quantitative PIXE analysis, were manipulated in a similar way as the genetic information is treated in a biological process. The authors carried out the algorithm until they reproduce, within the confidence interval, the elemental concentrations corresponding to a reference material

Contribution and perspectives of quantitative genetics to plant breeding in Brazil

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Fernando Henrique Ribeiro Barrozo Toledo

2012-12-01

Full Text Available The purpose of this article is to show how quantitative genetics has contributed to the huge genetic progress obtained inplant breeding in Brazil in the last forty years. The information obtained through quantitative genetics has given Brazilian breedersthe possibility of responding to innumerable questions in their work in a much more informative way, such as the use or not of hybridcultivars, which segregating population to use, which breeding method to employ, alternatives for improving the efficiency of selectionprograms, and how to handle the data of progeny and/or cultivars evaluations to identify the most stable ones and thus improverecommendations.

The Genetics Underlying Natural Variation in the Biotic Interactions of Arabidopsis thaliana: The Challenges of Linking Evolutionary Genetics and Community Ecology.

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Roux, F; Bergelson, J

2016-01-01

In the context of global change, predicting the responses of plant communities in an ever-changing biotic environment calls for a multipronged approach at the interface of evolutionary genetics and community ecology. However, our understanding of the genetic basis of natural variation involved in mediating biotic interactions, and associated adaptive dynamics of focal plants in their natural communities, is still in its infancy. Here, we review the genetic and molecular bases of natural variation in the response to biotic interactions (viruses, bacteria, fungi, oomycetes, herbivores, and plants) in the model plant Arabidopsis thaliana as well as the adaptive value of these bases. Among the 60 identified genes are a number that encode nucleotide-binding site leucine-rich repeat (NBS-LRR)-type proteins, consistent with early examples of plant defense genes. However, recent studies have revealed an extensive diversity in the molecular mechanisms of defense. Many types of genetic variants associate with phenotypic variation in biotic interactions, even among the genes of large effect that tend to be identified. In general, we found that (i) balancing selection rather than directional selection explains the observed patterns of genetic diversity within A. thaliana and (ii) the cost/benefit tradeoffs of adaptive alleles can be strongly dependent on both genomic and environmental contexts. Finally, because A. thaliana rarely interacts with only one biotic partner in nature, we highlight the benefit of exploring diffuse biotic interactions rather than tightly associated host-enemy pairs. This challenge would help to improve our understanding of coevolutionary quantitative genetics within the context of realistic community complexity. © 2016 Elsevier Inc. All rights reserved.

Mapping Protein-Protein Interactions by Quantitative Proteomics

DEFF Research Database (Denmark)

Dengjel, Joern; Kratchmarova, Irina; Blagoev, Blagoy

2010-01-01

spectrometry (MS)-based proteomics in combination with affinity purification protocols has become the method of choice to map and track the dynamic changes in protein-protein interactions, including the ones occurring during cellular signaling events. Different quantitative MS strategies have been used...... to characterize protein interaction networks. In this chapter we describe in detail the use of stable isotope labeling by amino acids in cell culture (SILAC) for the quantitative analysis of stimulus-dependent dynamic protein interactions.......Proteins exert their function inside a cell generally in multiprotein complexes. These complexes are highly dynamic structures changing their composition over time and cell state. The same protein may thereby fulfill different functions depending on its binding partners. Quantitative mass...

Genetic toxicology at the crossroads-from qualitative hazard evaluation to quantitative risk assessment.

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White, Paul A; Johnson, George E

2016-05-01

Applied genetic toxicology is undergoing a transition from qualitative hazard identification to quantitative dose-response analysis and risk assessment. To facilitate this change, the Health and Environmental Sciences Institute (HESI) Genetic Toxicology Technical Committee (GTTC) sponsored a workshop held in Lancaster, UK on July 10-11, 2014. The event included invited speakers from several institutions and the contents was divided into three themes-1: Point-of-departure Metrics for Quantitative Dose-Response Analysis in Genetic Toxicology; 2: Measurement and Estimation of Exposures for Better Extrapolation to Humans and 3: The Use of Quantitative Approaches in Genetic Toxicology for human health risk assessment (HHRA). A host of pertinent issues were discussed relating to the use of in vitro and in vivo dose-response data, the development of methods for in vitro to in vivo extrapolation and approaches to use in vivo dose-response data to determine human exposure limits for regulatory evaluations and decision-making. This Special Issue, which was inspired by the workshop, contains a series of papers that collectively address topics related to the aforementioned themes. The Issue includes contributions that collectively evaluate, describe and discuss in silico, in vitro, in vivo and statistical approaches that are facilitating the shift from qualitative hazard evaluation to quantitative risk assessment. The use and application of the benchmark dose approach was a central theme in many of the workshop presentations and discussions, and the Special Issue includes several contributions that outline novel applications for the analysis and interpretation of genetic toxicity data. Although the contents of the Special Issue constitutes an important step towards the adoption of quantitative methods for regulatory assessment of genetic toxicity, formal acceptance of quantitative methods for HHRA and regulatory decision-making will require consensus regarding the

Quantitative genetic methods depending on the nature of the phenotypic trait.

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de Villemereuil, Pierre

2018-01-24

A consequence of the assumptions of the infinitesimal model, one of the most important theoretical foundations of quantitative genetics, is that phenotypic traits are predicted to be most often normally distributed (so-called Gaussian traits). But phenotypic traits, especially those interesting for evolutionary biology, might be shaped according to very diverse distributions. Here, I show how quantitative genetics tools have been extended to account for a wider diversity of phenotypic traits using first the threshold model and then more recently using generalized linear mixed models. I explore the assumptions behind these models and how they can be used to study the genetics of non-Gaussian complex traits. I also comment on three recent methodological advances in quantitative genetics that widen our ability to study new kinds of traits: the use of "modular" hierarchical modeling (e.g., to study survival in the context of capture-recapture approaches for wild populations); the use of aster models to study a set of traits with conditional relationships (e.g., life-history traits); and, finally, the study of high-dimensional traits, such as gene expression. © 2018 New York Academy of Sciences.

The quantitative genetics of phenotypic variation in animals

NARCIS (Netherlands)

Hill, W.G.; Mulder, H.A.; Zhang, X.S.

2007-01-01

Considerable attention has been paid to estimating genetic variability in quantitative traits and to how it is maintained and changed by selection in natural and domesticated populations, but rather little attention has been paid to how levels of environmental and phenotypic variance are influenced.

Quantitative studies of antimicrobial peptide-lipid membrane interactions

DEFF Research Database (Denmark)

Kristensen, Kasper

antimicrobial peptides interact with phospholipid membranes. Motivated by that fact, the scope of this thesis is to study these antimicrobial peptide-lipid membrane interactions. In particular, we attempt to study these interactions with a quantitative approach. For that purpose, we consider the three...... a significant problem for quantitative studies of antimicrobial peptide-lipid membrane interactions; namely that antimicrobial peptides adsorb to surfaces of glass and plastic. Specifically, we demonstrate that under standard experimental conditions, this effect is significant for mastoparan X, melittin...... lead to inaccurate conclusions, or even completely wrong conclusions, when interpreting the FCS data. We show that, if all of the pitfalls are avoided, then FCS is a technique with a large potential for quantitative studies of antimicrobial peptide-induced leakage of fluorescent markers from large...

Quantitative genetic tools for insecticide resistance risk assessment: estimating the heritability of resistance

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Michael J. Firko; Jane Leslie Hayes

1990-01-01

Quantitative genetic studies of resistance can provide estimates of genetic parameters not available with other types of genetic analyses. Three methods are discussed for estimating the amount of additive genetic variation in resistance to individual insecticides and subsequent estimation of heritability (h2) of resistance. Sibling analysis and...

The current and future use of ridge regression for prediction in quantitative genetics

OpenAIRE

Vlaming, Ronald; Groenen, Patrick

2015-01-01

textabstractIn recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i) the theoretical foundations of ridge regression, (ii) its link to...

Prediction of quantitative phenotypes based on genetic networks: a case study in yeast sporulation

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Shen Li

2010-09-01

Full Text Available Abstract Background An exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations. However, de novo prediction for quantitative phenotypes based on network topology is always a challenging task. Results Using yeast sporulation as a model system, we have assembled a genetic network from literature and exploited Boolean network to predict sporulation efficiency change upon deleting individual genes. We observe that predictions based on the curated network correlate well with the experimentally measured values. In addition, computational analysis reveals the robustness and hysteresis of the yeast sporulation network and uncovers several patterns of sporulation efficiency change caused by double gene deletion. These discoveries may guide future investigation of underlying mechanisms. We have also shown that a hybridized genetic network reconstructed from both temporal microarray data and literature is able to achieve a satisfactory prediction accuracy of the same quantitative phenotypes. Conclusions This case study illustrates the value of predicting quantitative phenotypes based on genetic network and provides a generic approach.

Tests for genetic interactions in type 1 diabetes

DEFF Research Database (Denmark)

Morahan, Grant; Mehta, Munish; James, Ian

2011-01-01

Interactions between genetic and environmental factors lead to immune dysregulation causing type 1 diabetes and other autoimmune disorders. Recently, many common genetic variants have been associated with type 1 diabetes risk, but each has modest individual effects. Familial clustering of type 1...... diabetes has not been explained fully and could arise from many factors, including undetected genetic variation and gene interactions....

A double-mutant collection targeting MAP kinase related genes in Arabidopsis for studying genetic interactions.

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Su, Shih-Heng; Krysan, Patrick J

2016-12-01

Mitogen-activated protein kinase cascades are conserved in all eukaryotes. In Arabidopsis thaliana there are approximately 80 genes encoding MAP kinase kinase kinases (MAP3K), 10 genes encoding MAP kinase kinases (MAP2K), and 20 genes encoding MAP kinases (MAPK). Reverse genetic analysis has failed to reveal abnormal phenotypes for a majority of these genes. One strategy for uncovering gene function when single-mutant lines do not produce an informative phenotype is to perform a systematic genetic interaction screen whereby double-mutants are created from a large library of single-mutant lines. Here we describe a new collection of 275 double-mutant lines derived from a library of single-mutants targeting genes related to MAP kinase signaling. To facilitate this study, we developed a high-throughput double-mutant generating pipeline using a system for growing Arabidopsis seedlings in 96-well plates. A quantitative root growth assay was used to screen for evidence of genetic interactions in this double-mutant collection. Our screen revealed four genetic interactions, all of which caused synthetic enhancement of the root growth defects observed in a MAP kinase 4 (MPK4) single-mutant line. Seeds for this double-mutant collection are publicly available through the Arabidopsis Biological Resource Center. Scientists interested in diverse biological processes can now screen this double-mutant collection under a wide range of growth conditions in order to search for additional genetic interactions that may provide new insights into MAP kinase signaling. © 2016 The Authors The Plant Journal © 2016 John Wiley & Sons Ltd.

A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis.

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Tucker, George; Loh, Po-Ru; Berger, Bonnie

2013-10-04

Comprehensive protein-protein interaction (PPI) maps are a powerful resource for uncovering the molecular basis of genetic interactions and providing mechanistic insights. Over the past decade, high-throughput experimental techniques have been developed to generate PPI maps at proteome scale, first using yeast two-hybrid approaches and more recently via affinity purification combined with mass spectrometry (AP-MS). Unfortunately, data from both protocols are prone to both high false positive and false negative rates. To address these issues, many methods have been developed to post-process raw PPI data. However, with few exceptions, these methods only analyze binary experimental data (in which each potential interaction tested is deemed either observed or unobserved), neglecting quantitative information available from AP-MS such as spectral counts. We propose a novel method for incorporating quantitative information from AP-MS data into existing PPI inference methods that analyze binary interaction data. Our approach introduces a probabilistic framework that models the statistical noise inherent in observations of co-purifications. Using a sampling-based approach, we model the uncertainty of interactions with low spectral counts by generating an ensemble of possible alternative experimental outcomes. We then apply the existing method of choice to each alternative outcome and aggregate results over the ensemble. We validate our approach on three recent AP-MS data sets and demonstrate performance comparable to or better than state-of-the-art methods. Additionally, we provide an in-depth discussion comparing the theoretical bases of existing approaches and identify common aspects that may be key to their performance. Our sampling framework extends the existing body of work on PPI analysis using binary interaction data to apply to the richer quantitative data now commonly available through AP-MS assays. This framework is quite general, and many enhancements are likely

Quantitative genetic activity graphical profiles for use in chemical evaluation

Energy Technology Data Exchange (ETDEWEB)

Waters, M.D. [Environmental Protection Agency, Washington, DC (United States); Stack, H.F.; Garrett, N.E.; Jackson, M.A. [Environmental Health Research and Testing, Inc., Research Triangle Park, NC (United States)

1990-12-31

A graphic approach, terms a Genetic Activity Profile (GAP), was developed to display a matrix of data on the genetic and related effects of selected chemical agents. The profiles provide a visual overview of the quantitative (doses) and qualitative (test results) data for each chemical. Either the lowest effective dose or highest ineffective dose is recorded for each agent and bioassay. Up to 200 different test systems are represented across the GAP. Bioassay systems are organized according to the phylogeny of the test organisms and the end points of genetic activity. The methodology for producing and evaluating genetic activity profile was developed in collaboration with the International Agency for Research on Cancer (IARC). Data on individual chemicals were compiles by IARC and by the US Environmental Protection Agency (EPA). Data are available on 343 compounds selected from volumes 1-53 of the IARC Monographs and on 115 compounds identified as Superfund Priority Substances. Software to display the GAPs on an IBM-compatible personal computer is available from the authors. Structurally similar compounds frequently display qualitatively and quantitatively similar profiles of genetic activity. Through examination of the patterns of GAPs of pairs and groups of chemicals, it is possible to make more informed decisions regarding the selection of test batteries to be used in evaluation of chemical analogs. GAPs provided useful data for development of weight-of-evidence hazard ranking schemes. Also, some knowledge of the potential genetic activity of complex environmental mixtures may be gained from an assessment of the genetic activity profiles of component chemicals. The fundamental techniques and computer programs devised for the GAP database may be used to develop similar databases in other disciplines. 36 refs., 2 figs.

GENETIC AND MORPHOAGRONOMIC DIVERSITY OF Passiflora spp. BASED ON QUANTITATIVE MEASUREMENTS OF FLOWERS AND FRUITS

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JAMILE DA SILVA OLIVEIRA

Full Text Available ABSTRACT The aim of this study was to characterize Passiflora spp. accessions and its genetic diversity based on quantitative morphological descriptors of flowers and fruits. The study was conducted at Embrapa Cerrados, Planaltina-DF. Fifteen Passiflora spp. accessions were characterized using 14 quantitative morphological descriptors. Genetic distances among accessions were estimated based on Mahalanobis’ generalized distance. Cluster analysis via dendrogram and graphic dispersion was analyzed. The relative contribution of characters for accession divergence was also calculated. The morphoagronomic characterization based on quantitative descriptors of flowers and fruits contributed to the differentiation of Passiflora spp. accessions, serving as an important tool for variability quantification. This information is useful to perform Passiflora spp. characterization and genetic diversity studies.

Coevolutionary genetic variation in the legume-rhizobium transcriptome.

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Heath, Katy D; Burke, Patricia V; Stinchcombe, John R

2012-10-01

Coevolutionary change requires reciprocal selection between interacting species, where the partner genotypes that are favoured in one species depend on the genetic composition of the interacting species. Coevolutionary genetic variation is manifested as genotype × genotype (G × G) interactions for fitness in interspecific interactions. Although quantitative genetic approaches have revealed abundant evidence for G × G interactions in symbioses, the molecular basis of this variation remains unclear. Here we study the molecular basis of G × G interactions in a model legume-rhizobium mutualism using gene expression microarrays. We find that, like quantitative traits such as fitness, variation in the symbiotic transcriptome may be partitioned into additive and interactive genetic components. Our results suggest that plant genetic variation had the largest influence on nodule gene expression and that plant genotype and the plant genotype × rhizobium genotype interaction determine global shifts in rhizobium gene expression that in turn feedback to influence plant fitness benefits. Moreover, the transcriptomic variation we uncover implicates regulatory changes in both species as drivers of symbiotic gene expression variation. Our study is the first to partition genetic variation in a symbiotic transcriptome and illuminates potential molecular routes of coevolutionary change. © 2012 Blackwell Publishing Ltd.

Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

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Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

2017-09-21

Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

Cracking anxiety in the mouse : a quantitative (epi)genetic approach

NARCIS (Netherlands)

Labots, M.

2017-01-01

The aim of this thesis was to improve existing methodologies and apply genetic strategies in order to identify (main-effect, epistatic, multiple and pleiotropic) quantitative trait loci and to decipher functional candidate genes for anxiety-related behavior and baseline blood plasma total

A genetic ensemble approach for gene-gene interaction identification

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Ho Joshua WK

2010-10-01

Full Text Available Abstract Background It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging. Methods In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA and an ensemble of classifiers (called genetic ensemble. Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise double fault is designed to quantify the degree of complementarity. Conclusions Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR and is slightly better than Polymorphism Interaction Analysis (PIA, which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of

On coding genotypes for genetic markers with multiple alleles in genetic association study of quantitative traits

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Wang Tao

2011-09-01

Full Text Available Abstract Background In genetic association study of quantitative traits using F∞ models, how to code the marker genotypes and interpret the model parameters appropriately is important for constructing hypothesis tests and making statistical inferences. Currently, the coding of marker genotypes in building F∞ models has mainly focused on the biallelic case. A thorough work on the coding of marker genotypes and interpretation of model parameters for F∞ models is needed especially for genetic markers with multiple alleles. Results In this study, we will formulate F∞ genetic models under various regression model frameworks and introduce three genotype coding schemes for genetic markers with multiple alleles. Starting from an allele-based modeling strategy, we first describe a regression framework to model the expected genotypic values at given markers. Then, as extension from the biallelic case, we introduce three coding schemes for constructing fully parameterized one-locus F∞ models and discuss the relationships between the model parameters and the expected genotypic values. Next, under a simplified modeling framework for the expected genotypic values, we consider several reduced one-locus F∞ models from the three coding schemes on the estimability and interpretation of their model parameters. Finally, we explore some extensions of the one-locus F∞ models to two loci. Several fully parameterized as well as reduced two-locus F∞ models are addressed. Conclusions The genotype coding schemes provide different ways to construct F∞ models for association testing of multi-allele genetic markers with quantitative traits. Which coding scheme should be applied depends on how convenient it can provide the statistical inferences on the parameters of our research interests. Based on these F∞ models, the standard regression model fitting tools can be used to estimate and test for various genetic effects through statistical contrasts with the

General Methods for Evolutionary Quantitative Genetic Inference from Generalized Mixed Models.

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de Villemereuil, Pierre; Schielzeth, Holger; Nakagawa, Shinichi; Morrissey, Michael

2016-11-01

Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioral traits, have inherently nonnormal distributions. The generalized linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for nonnormal traits. However, whereas GLMMs provide inference on a statistically convenient latent scale, it is often desirable to express quantitative genetic parameters on the scale upon which traits are measured. The parameters of fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal with this by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulas for quantities such as heritability of traits with binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation and apply our approach to data from a wild pedigreed vertebrate population. Copyright © 2016 de Villemereuil et al.

Huntingtin interacting proteins are genetic modifiers of neurodegeneration.

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Linda S Kaltenbach

2007-05-01

Full Text Available Huntington's disease (HD is a fatal neurodegenerative condition caused by expansion of the polyglutamine tract in the huntingtin (Htt protein. Neuronal toxicity in HD is thought to be, at least in part, a consequence of protein interactions involving mutant Htt. We therefore hypothesized that genetic modifiers of HD neurodegeneration should be enriched among Htt protein interactors. To test this idea, we identified a comprehensive set of Htt interactors using two complementary approaches: high-throughput yeast two-hybrid screening and affinity pull down followed by mass spectrometry. This effort led to the identification of 234 high-confidence Htt-associated proteins, 104 of which were found with the yeast method and 130 with the pull downs. We then tested an arbitrary set of 60 genes encoding interacting proteins for their ability to behave as genetic modifiers of neurodegeneration in a Drosophila model of HD. This high-content validation assay showed that 27 of 60 orthologs tested were high-confidence genetic modifiers, as modification was observed with more than one allele. The 45% hit rate for genetic modifiers seen among the interactors is an order of magnitude higher than the 1%-4% typically observed in unbiased genetic screens. Genetic modifiers were similarly represented among proteins discovered using yeast two-hybrid and pull-down/mass spectrometry methods, supporting the notion that these complementary technologies are equally useful in identifying biologically relevant proteins. Interacting proteins confirmed as modifiers of the neurodegeneration phenotype represent a diverse array of biological functions, including synaptic transmission, cytoskeletal organization, signal transduction, and transcription. Among the modifiers were 17 loss-of-function suppressors of neurodegeneration, which can be considered potential targets for therapeutic intervention. Finally, we show that seven interacting proteins from among 11 tested were able to

Scaling laws and universality for the strength of genetic interactions in yeast

Science.gov (United States)

Velenich, Andrea; Dai, Mingjie; Gore, Jeff

2012-02-01

Genetic interactions provide a window to the organization of the thousands of biochemical reactions in living cells. If two mutations affect unrelated cellular functions, the fitness effects of their combination can be easily predicted from the two separate fitness effects. However, because of interactions, for some pairs of mutations their combined fitness effect deviates from the naive prediction. We study genetic interactions in yeast cells by analyzing a publicly available database containing experimental growth rates of 5 million double mutants. We show that the characteristic strength of genetic interactions has a simple power law dependence on the fitness effects of the two interacting mutations and that the probability distribution of genetic interactions is a universal function. We further argue that the strength of genetic interactions depends only on the fitness effects of the interacting mutations and not on their biological origin in terms of single point mutations, entire gene knockouts or even more complicated physiological perturbations. Finally, we discuss the implications of the power law scaling of genetic interactions on the ruggedness of fitness landscapes and the consequent evolutionary dynamics.

Analysis of conditional genetic effects and variance components in developmental genetics.

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Zhu, J

1995-12-01

A genetic model with additive-dominance effects and genotype x environment interactions is presented for quantitative traits with time-dependent measures. The genetic model for phenotypic means at time t conditional on phenotypic means measured at previous time (t-1) is defined. Statistical methods are proposed for analyzing conditional genetic effects and conditional genetic variance components. Conditional variances can be estimated by minimum norm quadratic unbiased estimation (MINQUE) method. An adjusted unbiased prediction (AUP) procedure is suggested for predicting conditional genetic effects. A worked example from cotton fruiting data is given for comparison of unconditional and conditional genetic variances and additive effects.

Genetic Interaction Maps in Escherichia coli Reveal Functional Crosstalk among Cell Envelope Biogenesis Pathways

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Vlasblom, James; Gagarinova, Alla; Phanse, Sadhna; Graham, Chris; Yousif, Fouad; Ding, Huiming; Xiong, Xuejian; Nazarians-Armavil, Anaies; Alamgir, Md; Ali, Mehrab; Pogoutse, Oxana; Pe'er, Asaf; Arnold, Roland; Michaut, Magali; Parkinson, John; Golshani, Ashkan; Whitfield, Chris; Wodak, Shoshana J.; Moreno-Hagelsieb, Gabriel; Greenblatt, Jack F.; Emili, Andrew

2011-01-01

As the interface between a microbe and its environment, the bacterial cell envelope has broad biological and clinical significance. While numerous biosynthesis genes and pathways have been identified and studied in isolation, how these intersect functionally to ensure envelope integrity during adaptive responses to environmental challenge remains unclear. To this end, we performed high-density synthetic genetic screens to generate quantitative functional association maps encompassing virtually the entire cell envelope biosynthetic machinery of Escherichia coli under both auxotrophic (rich medium) and prototrophic (minimal medium) culture conditions. The differential patterns of genetic interactions detected among >235,000 digenic mutant combinations tested reveal unexpected condition-specific functional crosstalk and genetic backup mechanisms that ensure stress-resistant envelope assembly and maintenance. These networks also provide insights into the global systems connectivity and dynamic functional reorganization of a universal bacterial structure that is both broadly conserved among eubacteria (including pathogens) and an important target. PMID:22125496

Genetic interaction maps in Escherichia coli reveal functional crosstalk among cell envelope biogenesis pathways.

Directory of Open Access Journals (Sweden)

Mohan Babu

2011-11-01

Full Text Available As the interface between a microbe and its environment, the bacterial cell envelope has broad biological and clinical significance. While numerous biosynthesis genes and pathways have been identified and studied in isolation, how these intersect functionally to ensure envelope integrity during adaptive responses to environmental challenge remains unclear. To this end, we performed high-density synthetic genetic screens to generate quantitative functional association maps encompassing virtually the entire cell envelope biosynthetic machinery of Escherichia coli under both auxotrophic (rich medium and prototrophic (minimal medium culture conditions. The differential patterns of genetic interactions detected among > 235,000 digenic mutant combinations tested reveal unexpected condition-specific functional crosstalk and genetic backup mechanisms that ensure stress-resistant envelope assembly and maintenance. These networks also provide insights into the global systems connectivity and dynamic functional reorganization of a universal bacterial structure that is both broadly conserved among eubacteria (including pathogens and an important target.

A global genetic interaction network maps a wiring diagram of cellular function.

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Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N; Baryshnikova, Anastasia; Pons, Carles; Tan, Guihong; Wang, Wen; Usaj, Matej; Hanchard, Julia; Lee, Susan D; Pelechano, Vicent; Styles, Erin B; Billmann, Maximilian; van Leeuwen, Jolanda; van Dyk, Nydia; Lin, Zhen-Yuan; Kuzmin, Elena; Nelson, Justin; Piotrowski, Jeff S; Srikumar, Tharan; Bahr, Sondra; Chen, Yiqun; Deshpande, Raamesh; Kurat, Christoph F; Li, Sheena C; Li, Zhijian; Usaj, Mojca Mattiazzi; Okada, Hiroki; Pascoe, Natasha; San Luis, Bryan-Joseph; Sharifpoor, Sara; Shuteriqi, Emira; Simpkins, Scott W; Snider, Jamie; Suresh, Harsha Garadi; Tan, Yizhao; Zhu, Hongwei; Malod-Dognin, Noel; Janjic, Vuk; Przulj, Natasa; Troyanskaya, Olga G; Stagljar, Igor; Xia, Tian; Ohya, Yoshikazu; Gingras, Anne-Claude; Raught, Brian; Boutros, Michael; Steinmetz, Lars M; Moore, Claire L; Rosebrock, Adam P; Caudy, Amy A; Myers, Chad L; Andrews, Brenda; Boone, Charles

2016-09-23

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell. Copyright © 2016, American Association for the Advancement of Science.

Genetic influences on attention deficit hyperactivity disorder symptoms from age 2 to 3: A quantitative and molecular genetic investigation

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Saudino Kimberly J

2010-12-01

Full Text Available Abstract Background A twin study design was used to assess the degree to which additive genetic variance influences ADHD symptom scores across two ages during infancy. A further objective in the study was to observe whether genetic association with a number of candidate markers reflects results from the quantitative genetic analysis. Method We have studied 312 twin pairs at two time-points, age 2 and age 3. A composite measure of ADHD symptoms from two parent-rating scales: The Child Behavior Checklist/1.5 - 5 years (CBCL hyperactivity scale and the Revised Rutter Parent Scale for Preschool Children (RRPSPC was used for both quantitative and molecular genetic analyses. Results At ages 2 and 3 ADHD symptoms are highly heritable (h2 = 0.79 and 0.78, respectively with a high level of genetic stability across these ages. However, we also observe a significant level of genetic change from age 2 to age 3. There are modest influences of non-shared environment at each age independently (e2 = 0.22 and 0.21, respectively, with these influences being largely age-specific. In addition, we find modest association signals in DAT1 and NET1 at both ages, along with suggestive specific effects of 5-HTT and DRD4 at age 3. Conclusions ADHD symptoms are heritable at ages 2 and 3. Additive genetic variance is largely shared across these ages, although there are significant new effects emerging at age 3. Results from our genetic association analysis reflect these levels of stability and change and, more generally, suggest a requirement for consideration of age-specific genotypic effects in future molecular studies.

Genetic analysis and hybrid vigor study of grain yield and other quantitative traits in auto tetraploid rice

International Nuclear Information System (INIS)

Shahid, M.Q.; Xiong, C.Z.; Juan, L.Y.; Ming, X.H.

2011-01-01

Genetic analysis and genotype-by-environment interaction for important traits of auto tetraploid rice were evaluated by additive, dominance and additive X additive model. It was show n that genetic effects had more influence on grain yield and other quantitative traits of auto tetraploid rice than genotypic environment interaction. Plant height, panicle length, seed set , grain yield, dry matter production and 1000-grain weight we re mainly regulated by dominance variance. Additive and additive X additive gene action constructed the main proportion of genetic variance for heading date (flowering), number of panicles, grains per panicle, grain length, however grain width was supposed to be affected by additive X additive and dominance variance. Flag leaf length and width, fresh weight, peduncle length, unfilled grains and awn length were greatly influenced by genotypic environment interaction. Heading date produced highly negative heterosis over mid parent (H pm) and better parent ( H pb), whereas H pm and H pb were detected to be highly positive and significant for grain yield, seed set, peduncle length, filled grains and 1000-grain weight in F/sub 1/ and F/sub 2/ generations. The results indicated that auto tetraploid hybrids 96025 X Jackson (indica/japonica), 96025 X Linglun (indica/indica) and Linglun X Jackson (indica/japonica) showed highly significant hybrid vigor with improved seed set percentage and grain yield. These results suggest that intra-specific auto tetraploid rice hybrids have more hybrid vigor as compared to intra-sub specific auto tetraploid rice hybrids and auto tetraploid rice has the potential to be used for further studies and commercial application. (author)

Quantitative analysis of intermolecular interactions in orthorhombic rubrene

Directory of Open Access Journals (Sweden)

Venkatesha R. Hathwar

2015-09-01

Full Text Available Rubrene is one of the most studied organic semiconductors to date due to its high charge carrier mobility which makes it a potentially applicable compound in modern electronic devices. Previous electronic device characterizations and first principles theoretical calculations assigned the semiconducting properties of rubrene to the presence of a large overlap of the extended π-conjugated core between molecules. We present here the electron density distribution in rubrene at 20 K and at 100 K obtained using a combination of high-resolution X-ray and neutron diffraction data. The topology of the electron density and energies of intermolecular interactions are studied quantitatively. Specifically, the presence of Cπ...Cπ interactions between neighbouring tetracene backbones of the rubrene molecules is experimentally confirmed from a topological analysis of the electron density, Non-Covalent Interaction (NCI analysis and the calculated interaction energy of molecular dimers. A significant contribution to the lattice energy of the crystal is provided by H—H interactions. The electron density features of H—H bonding, and the interaction energy of molecular dimers connected by H—H interaction clearly demonstrate an importance of these weak interactions in the stabilization of the crystal structure. The quantitative nature of the intermolecular interactions is virtually unchanged between 20 K and 100 K suggesting that any changes in carrier transport at these low temperatures would have a different origin. The obtained experimental results are further supported by theoretical calculations.

Genetic and Molecular Mechanisms of Quantitative Trait Loci Controlling Maize Inflorescence Architecture.

Science.gov (United States)

Li, Manfei; Zhong, Wanshun; Yang, Fang; Zhang, Zuxin

2018-03-01

The establishment of inflorescence architecture is critical for the reproduction of flowering plant species. The maize plant generates two types of inflorescences, the tassel and the ear, and their architectures have a large effect on grain yield and yield-related traits that are genetically controlled by quantitative trait loci (QTLs). Since ear and tassel architecture are deeply affected by the activity of inflorescence meristems, key QTLs and genes regulating meristematic activity have important impacts on inflorescence development and show great potential for optimizing grain yield. Isolation of yield trait-related QTLs is challenging, but these QTLs have direct application in maize breeding. Additionally, characterization and functional dissection of QTLs can provide genetic and molecular knowledge of quantitative variation in inflorescence architecture. In this review, we summarize currently identified QTLs responsible for the establishment of ear and tassel architecture and discuss the potential genetic control of four ear-related and four tassel-related traits. In recent years, several inflorescence architecture-related QTLs have been characterized at the gene level. We review the mechanisms of these characterized QTLs.

Quantitative autistic trait measurements index background genetic risk for ASD in Hispanic families.

Science.gov (United States)

Page, Joshua; Constantino, John Nicholas; Zambrana, Katherine; Martin, Eden; Tunc, Ilker; Zhang, Yi; Abbacchi, Anna; Messinger, Daniel

2016-01-01

Recent studies have indicated that quantitative autistic traits (QATs) of parents reflect inherited liabilities that may index background genetic risk for clinical autism spectrum disorder (ASD) in their offspring. Moreover, preferential mating for QATs has been observed as a potential factor in concentrating autistic liabilities in some families across generations. Heretofore, intergenerational studies of QATs have focused almost exclusively on Caucasian populations-the present study explored these phenomena in a well-characterized Hispanic population. The present study examined QAT scores in siblings and parents of 83 Hispanic probands meeting research diagnostic criteria for ASD, and 64 non-ASD controls, using the Social Responsiveness Scale-2 (SRS-2). Ancestry of the probands was characterized by genotype, using information from 541,929 single nucleotide polymorphic markers. In families of Hispanic children with an ASD diagnosis, the pattern of quantitative trait correlations observed between ASD-affected children and their first-degree relatives (ICCs on the order of 0.20), between unaffected first-degree relatives in ASD-affected families (sibling/mother ICC = 0.36; sibling/father ICC = 0.53), and between spouses (mother/father ICC = 0.48) were in keeping with the influence of transmitted background genetic risk and strong preferential mating for variation in quantitative autistic trait burden. Results from analysis of ancestry-informative genetic markers among probands in this sample were consistent with that from other Hispanic populations. Quantitative autistic traits represent measurable indices of inherited liability to ASD in Hispanic families. The accumulation of autistic traits occurs within generations, between spouses, and across generations, among Hispanic families affected by ASD. The occurrence of preferential mating for QATs-the magnitude of which may vary across cultures-constitutes a mechanism by which background genetic liability

Quantitative Assessment of Eye Phenotypes for Functional Genetic Studies Using Drosophila melanogaster

Directory of Open Access Journals (Sweden)

Janani Iyer

2016-05-01

Full Text Available About two-thirds of the vital genes in the Drosophila genome are involved in eye development, making the fly eye an excellent genetic system to study cellular function and development, neurodevelopment/degeneration, and complex diseases such as cancer and diabetes. We developed a novel computational method, implemented as Flynotyper software (http://flynotyper.sourceforge.net, to quantitatively assess the morphological defects in the Drosophila eye resulting from genetic alterations affecting basic cellular and developmental processes. Flynotyper utilizes a series of image processing operations to automatically detect the fly eye and the individual ommatidium, and calculates a phenotypic score as a measure of the disorderliness of ommatidial arrangement in the fly eye. As a proof of principle, we tested our method by analyzing the defects due to eye-specific knockdown of Drosophila orthologs of 12 neurodevelopmental genes to accurately document differential sensitivities of these genes to dosage alteration. We also evaluated eye images from six independent studies assessing the effect of overexpression of repeats, candidates from peptide library screens, and modifiers of neurotoxicity and developmental processes on eye morphology, and show strong concordance with the original assessment. We further demonstrate the utility of this method by analyzing 16 modifiers of sine oculis obtained from two genome-wide deficiency screens of Drosophila and accurately quantifying the effect of its enhancers and suppressors during eye development. Our method will complement existing assays for eye phenotypes, and increase the accuracy of studies that use fly eyes for functional evaluation of genes and genetic interactions.

A map of directional genetic interactions in a metazoan cell.

Science.gov (United States)

Fischer, Bernd; Sandmann, Thomas; Horn, Thomas; Billmann, Maximilian; Chaudhary, Varun; Huber, Wolfgang; Boutros, Michael

2015-03-06

Gene-gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene-gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks and provides a resource for the interpretation of functional consequences of genetic alterations.

Introduction to focus issue: quantitative approaches to genetic networks.

Science.gov (United States)

Albert, Réka; Collins, James J; Glass, Leon

2013-06-01

All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks

Introduction to a Protein Interaction System Used for Quantitative Evaluation of Biomolecular Interactions

OpenAIRE

Yamniuk, Aaron

2013-01-01

A central goal of molecular biology is the determination of biomolecular function. This comes largely from a knowledge of the non-covalent interactions that biological small and macro-molecules experience. The fundamental mission of the Molecular Interactions Research Group (MIRG) of the ABRF is to show how solution biophysical tools are used to quantitatively characterize molecular interactions, and to educate the ABRF members and scientific community on the utility and limitations of core t...

Quantitative analysis of protein-ligand interactions by NMR.

Science.gov (United States)

Furukawa, Ayako; Konuma, Tsuyoshi; Yanaka, Saeko; Sugase, Kenji

2016-08-01

Protein-ligand interactions have been commonly studied through static structures of the protein-ligand complex. Recently, however, there has been increasing interest in investigating the dynamics of protein-ligand interactions both for fundamental understanding of the underlying mechanisms and for drug development. NMR is a versatile and powerful tool, especially because it provides site-specific quantitative information. NMR has widely been used to determine the dissociation constant (KD), in particular, for relatively weak interactions. The simplest NMR method is a chemical-shift titration experiment, in which the chemical-shift changes of a protein in response to ligand titration are measured. There are other quantitative NMR methods, but they mostly apply only to interactions in the fast-exchange regime. These methods derive the dissociation constant from population-averaged NMR quantities of the free and bound states of a protein or ligand. In contrast, the recent advent of new relaxation-based experiments, including R2 relaxation dispersion and ZZ-exchange, has enabled us to obtain kinetic information on protein-ligand interactions in the intermediate- and slow-exchange regimes. Based on R2 dispersion or ZZ-exchange, methods that can determine the association rate, kon, dissociation rate, koff, and KD have been developed. In these approaches, R2 dispersion or ZZ-exchange curves are measured for multiple samples with different protein and/or ligand concentration ratios, and the relaxation data are fitted to theoretical kinetic models. It is critical to choose an appropriate kinetic model, such as the two- or three-state exchange model, to derive the correct kinetic information. The R2 dispersion and ZZ-exchange methods are suitable for the analysis of protein-ligand interactions with a micromolar or sub-micromolar dissociation constant but not for very weak interactions, which are typical in very fast exchange. This contrasts with the NMR methods that are used

[The study of tomato fruit weight quantitative trait locus and its application in genetics teaching].

Science.gov (United States)

Wang, Hai-yan

2015-08-01

The classical research cases, which have greatly promoted the development of genetics in history, can be combined with the content of courses in genetics teaching to train students' ability of scientific thinking and genetic analysis. The localization and clone of gene controlling tomato fruit weight is a pioneer work in quantitative trait locus (QTL) studies and represents a complete process of QTL research in plants. Application of this integrated case in genetics teaching, which showed a wonderful process of scientific discovery and the fascination of genetic research, has inspired students' interest in genetics and achieved a good teaching effect.

[Analytic methods for seed models with genotype x environment interactions].

Science.gov (United States)

Zhu, J

1996-01-01

Genetic models with genotype effect (G) and genotype x environment interaction effect (GE) are proposed for analyzing generation means of seed quantitative traits in crops. The total genetic effect (G) is partitioned into seed direct genetic effect (G0), cytoplasm genetic of effect (C), and maternal plant genetic effect (Gm). Seed direct genetic effect (G0) can be further partitioned into direct additive (A) and direct dominance (D) genetic components. Maternal genetic effect (Gm) can also be partitioned into maternal additive (Am) and maternal dominance (Dm) genetic components. The total genotype x environment interaction effect (GE) can also be partitioned into direct genetic by environment interaction effect (G0E), cytoplasm genetic by environment interaction effect (CE), and maternal genetic by environment interaction effect (GmE). G0E can be partitioned into direct additive by environment interaction (AE) and direct dominance by environment interaction (DE) genetic components. GmE can also be partitioned into maternal additive by environment interaction (AmE) and maternal dominance by environment interaction (DmE) genetic components. Partitions of genetic components are listed for parent, F1, F2 and backcrosses. A set of parents, their reciprocal F1 and F2 seeds is applicable for efficient analysis of seed quantitative traits. MINQUE(0/1) method can be used for estimating variance and covariance components. Unbiased estimation for covariance components between two traits can also be obtained by the MINQUE(0/1) method. Random genetic effects in seed models are predictable by the Adjusted Unbiased Prediction (AUP) approach with MINQUE(0/1) method. The jackknife procedure is suggested for estimation of sampling variances of estimated variance and covariance components and of predicted genetic effects, which can be further used in a t-test for parameter. Unbiasedness and efficiency for estimating variance components and predicting genetic effects are tested by

Genetic effects of ionising radiation

International Nuclear Information System (INIS)

Saunders, P.A.H.

1991-12-01

Ionizing radiation effects on the gem cells, which can result in genetic abnormalities, are described. The basic mechanisms of radiation interactions with chromosomes, or specifically DNA, which can result in radiation induced mutation are discussed. Methods of estimating genetic risks, and some values for quantitative risk estimates are given. (U.K.). 13 refs., 2 figs., 1 tab

Mapping the genetic basis of symbiotic variation in legume-rhizobium interactions in Medicago truncatula.

Science.gov (United States)

Gorton, Amanda J; Heath, Katy D; Pilet-Nayel, Marie-Laure; Baranger, Alain; Stinchcombe, John R

2012-11-01

Mutualisms are known to be genetically variable, where the genotypes differ in the fitness benefits they gain from the interaction. To date, little is known about the loci that underlie such genetic variation in fitness or whether the loci influencing fitness are partner specific, and depend on the genotype of the interaction partner. In the legume-rhizobium mutualism, one set of potential candidate genes that may influence the fitness benefits of the symbiosis are the plant genes involved in the initiation of the signaling pathway between the two partners. Here we performed quantitative trait loci (QTL) mapping in Medicago truncatula in two different rhizobium strain treatments to locate regions of the genome influencing plant traits, assess whether such regions are dependent on the genotype of the rhizobial mutualist (QTL × rhizobium strain), and evaluate the contribution of sequence variation at known symbiosis signaling genes. Two of the symbiotic signaling genes, NFP and DMI3, colocalized with two QTL affecting average fruit weight and leaf number, suggesting that natural variation in nodulation genes may potentially influence plant fitness. In both rhizobium strain treatments, there were QTL that influenced multiple traits, indicative of either tight linkage between loci or pleiotropy, including one QTL with opposing effects on growth and reproduction. There was no evidence for QTL × rhizobium strain or genotype × genotype interactions, suggesting either that such interactions are due to small-effect loci or that more genotype-genotype combinations need to be tested in future mapping studies.

Complex Genotype by Environment interactions and changing genetic architectures across thermal environments in the Australian field cricket, Teleogryllus oceanicus

Directory of Open Access Journals (Sweden)

Dowling Damian K

2011-07-01

Full Text Available Abstract Background Biologists studying adaptation under sexual selection have spent considerable effort assessing the relative importance of two groups of models, which hinge on the idea that females gain indirect benefits via mate discrimination. These are the good genes and genetic compatibility models. Quantitative genetic studies have advanced our understanding of these models by enabling assessment of whether the genetic architectures underlying focal phenotypes are congruent with either model. In this context, good genes models require underlying additive genetic variance, while compatibility models require non-additive variance. Currently, we know very little about how the expression of genotypes comprised of distinct parental haplotypes, or how levels and types of genetic variance underlying key phenotypes, change across environments. Such knowledge is important, however, because genotype-environment interactions can have major implications on the potential for evolutionary responses to selection. Results We used a full diallel breeding design to screen for complex genotype-environment interactions, and genetic architectures underlying key morphological traits, across two thermal environments (the lab standard 27°C, and the cooler 23°C in the Australian field cricket, Teleogryllus oceanicus. In males, complex three-way interactions between sire and dam parental haplotypes and the rearing environment accounted for up to 23 per cent of the scaled phenotypic variance in the traits we measured (body mass, pronotum width and testes mass, and each trait harboured significant additive genetic variance in the standard temperature (27°C only. In females, these three-way interactions were less important, with interactions between the paternal haplotype and rearing environment accounting for about ten per cent of the phenotypic variance (in body mass, pronotum width and ovary mass. Of the female traits measured, only ovary mass for crickets

Replicated analysis of the genetic architecture of quantitative traits in two wild great tit populations

NARCIS (Netherlands)

Santure, Anna W.; Poissant, Jocelyn; Cauwer, De Isabelle; Oers, Van Kees; Robinson, Matthew R.; Quinn, John L.; Groenen, M.A.M.; Visser, M.E.; Sheldon, Ben C.; Slate, Jon

2015-01-01

Currently, there is much debate on the genetic architecture of quantitative traits in wild populations. Is trait variation influenced by many genes of small effect or by a few genes of major effect? Where is additive genetic variation located in the genome? Do the same loci cause similar

Replicated analysis of the genetic architecture of quantitative traits in two wild great tit populations

NARCIS (Netherlands)

Santure, Anna W; Poissant, Jocelyn; De Cauwer, Isabelle; van Oers, Kees; Robinson, Matthew R; Quinn, John L; Groenen, Martien A M; Visser, Marcel E; Sheldon, Ben C; Slate, Jon

2015-01-01

Currently, there is much debate on the genetic architecture of quantitative traits in wild populations. Is trait variation influenced by many genes of small effect or by a few genes of major effect? Where is additive genetic variation located in the genome? Do the same loci cause similar phenotypic

Using genetic markers to orient the edges in quantitative trait networks: the NEO software.

Science.gov (United States)

Aten, Jason E; Fuller, Tova F; Lusis, Aldons J; Horvath, Steve

2008-04-15

Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers. We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue. The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait

Coffee, Genetic Variants, and Parkinson's Disease: Gene–Environment Interactions

OpenAIRE

Yamada-Fowler, Naomi; Söderkvist, Peter

2015-01-01

Studies of gene–environment interactions may help us to understand the disease mechanisms of common and complex diseases such as Parkinson's disease (PD). Sporadic PD, the common form of PD, is thought to be a multifactorial disorder caused by combinations of multiple genetic factors and environmental or life-style exposures. Since one of the most extensively studied life-style factors in PD is coffee/caffeine intake, here, the studies of genetic polymorphisms with life-style interactions of ...

Effects of functionally asexual reproduction on quantitative genetic variation in the evening primroses (Oenothera, Onagraceae).

Science.gov (United States)

Godfrey, Ryan M; Johnson, Marc T J

2014-11-01

It has long been predicted that a loss of sexual reproduction leads to decreased heritable variation within populations and increased differentiation between populations. Despite an abundance of theory, there are few empirical tests of how sex affects genetic variation in phenotypic traits, especially for plants. Here we test whether repeated losses of two critical components of sex (recombination and segregation) in the evening primroses (Oenothera L., Onagraceae) affect quantitative genetic variation within and between populations. We sampled multiple genetic families from 3-5 populations from each of eight Oenothera species, which represented four independent transitions between sexual reproduction and a functionally asexual genetic system called "permanent translocation heterozygosity." We used quantitative genetics methods to partition genetic variation within and between populations for eight plant traits related to growth, leaf physiology, flowering, and resistance to herbivores. Heritability was, on average, 74% higher in sexual Oenothera populations than in functionally asexual populations, with plant growth rate, specific leaf area, and the percentage of leaf water content showing the strongest differences. By contrast, genetic differentiation among populations was 2.8× higher in functionally asexual vs. sexual Oenothera species. This difference was particularly strong for specific leaf area. Sexual populations tended to exhibit higher genetic correlations among traits, but this difference was weakly supported. These results support the prediction that sexual reproduction maintains higher genetic variation within populations, which may facilitate adaptive evolution. We also found partial support for the prediction that a loss of sex leads to greater population differentiation, which may elevate speciation rates. © 2014 Botanical Society of America, Inc.

A quantitative genetic approach to assess the evolutionary potential of a coastal marine fish to ocean acidification

KAUST Repository

Malvezzi, Alex J.; Murray, Christopher S.; Feldheim, Kevin A.; DiBattista, Joseph; Garant, Dany; Gobler, Christopher J.; Chapman, Demian D.; Baumann, Hannes

2015-01-01

Assessing the potential of marine organisms to adapt genetically to increasing oceanic CO2 levels requires proxies such as heritability of fitness-related traits under ocean acidification (OA). We applied a quantitative genetic method to derive

Social interactions predict genetic diversification: an experimental manipulation in shorebirds.

Science.gov (United States)

Cunningham, Charles; Parra, Jorge E; Coals, Lucy; Beltrán, Marcela; Zefania, Sama; Székely, Tamás

2018-01-01

Mating strategy and social behavior influence gene flow and hence affect levels of genetic differentiation and potentially speciation. Previous genetic analyses of closely related plovers Charadrius spp. found strikingly different population genetic structure in Madagascar: Kittlitz's plovers are spatially homogenous whereas white-fronted plovers have well segregated and geographically distinct populations. Here, we test the hypotheses that Kittlitz's plovers are spatially interconnected and have extensive social interactions that facilitate gene flow, whereas white-fronted plovers are spatially discrete and have limited social interactions. By experimentally removing mates from breeding pairs and observing the movements of mate-searching plovers in both species, we compare the spatial behavior of Kittlitz's and white-fronted plovers within a breeding season. The behavior of experimental birds was largely consistent with expectations: Kittlitz's plovers travelled further, sought new mates in larger areas, and interacted with more individuals than white-fronted plovers, however there was no difference in breeding dispersal. These results suggest that mating strategies, through spatial behavior and social interactions, are predictors of gene flow and thus genetic differentiation and speciation. Our study highlights the importance of using social behavior to understand gene flow. However, further work is needed to investigate the relative importance of social structure, as well as intra- and inter-season dispersal, in influencing the genetic structures of populations.

Genetic Analysis of Embryo, Cytoplasm and Maternal Effects and Their Environment Interactions for Isoflavone Content in Soybean [Glycine max(L.) Merr.

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

Soybean seed products contain isoflavones (genistein, daidzein, and glycitein) that display biological effects when ingested by humans and animals. These effects are species, dose and age dependent. Therefore, the content and quality of isoflavones in soybeans is a key factor to the biological effect. Our objective was to identify the genetic effects that underlie the isoflavone content in soybean seeds. A genetic model for quantitative traits of seeds in diploid plants was applied to estimate the genetic main effects and genotype × environment (GE) interaction effects for the isoflavone content (IC) of soybean seeds by using two years experimental data with an incomplete diallel mating design of six parents. Results showed that the IC of soybean seeds was simultaneously controlled by the genetic effects of maternal,embryo, and cytoplasm, of which maternal genetic effects were most important, followed by embryo and cytoplasmic genetic effects. The main effects of different genetic systems on IC trait were more important than environment interaction effects. The strong dominance effects on isoflavone from residual was made easily by environment conditions. Therefore,the improvement of the IC of soybean seeds would be more efficient when selection is based on maternal plants than that on the single seed. Maternal heritability (65.73%) was most important for IC, followed by embryo heritability (25.87%) and cytoplasmic heritability (8.39%). Based on predicated genetic effects, Yudou 29 and Zheng 90007 were better than other parents for increasing IC in the progeny and improving the quality of soybean. The significant effects of maternal and embryo dominance effects in variance show that the embryo heterosis and maternal heterosis are existent and uninfluenced by environment interaction effects.

A two-step genetic study on quantitative precursors of coronary ...

Indian Academy of Sciences (India)

Genetic and environmental factors interact in the precipita- tion of the disease. .... 2002a), and decreases the production of the potent vasodilator prostacyclin ...... Fibrinogen and factor VII in the prediction of coronary risk, results from the ...

Genetic Allee effects and their interaction with ecological Allee effects.

Science.gov (United States)

Wittmann, Meike J; Stuis, Hanna; Metzler, Dirk

2018-01-01

It is now widely accepted that genetic processes such as inbreeding depression and loss of genetic variation can increase the extinction risk of small populations. However, it is generally unclear whether extinction risk from genetic causes gradually increases with decreasing population size or whether there is a sharp transition around a specific threshold population size. In the ecological literature, such threshold phenomena are called 'strong Allee effects' and they can arise for example from mate limitation in small populations. In this study, we aim to (i) develop a meaningful notion of a 'strong genetic Allee effect', (ii) explore whether and under what conditions such an effect can arise from inbreeding depression due to recessive deleterious mutations, and (iii) quantify the interaction of potential genetic Allee effects with the well-known mate-finding Allee effect. We define a strong genetic Allee effect as a genetic process that causes a population's survival probability to be a sigmoid function of its initial size. The inflection point of this function defines the critical population size. To characterize survival-probability curves, we develop and analyse simple stochastic models for the ecology and genetics of small populations. Our results indicate that inbreeding depression can indeed cause a strong genetic Allee effect, but only if individuals carry sufficiently many deleterious mutations (lethal equivalents). Populations suffering from a genetic Allee effect often first grow, then decline as inbreeding depression sets in and then potentially recover as deleterious mutations are purged. Critical population sizes of ecological and genetic Allee effects appear to be often additive, but even superadditive interactions are possible. Many published estimates for the number of lethal equivalents in birds and mammals fall in the parameter range where strong genetic Allee effects are expected. Unfortunately, extinction risk due to genetic Allee effects

Sex-specific genetic effects in physical activity: results from a quantitative genetic analysis.

Science.gov (United States)

Diego, Vincent P; de Chaves, Raquel Nichele; Blangero, John; de Souza, Michele Caroline; Santos, Daniel; Gomes, Thayse Natacha; dos Santos, Fernanda Karina; Garganta, Rui; Katzmarzyk, Peter T; Maia, José A R

2015-08-01

The objective of this study is to present a model to estimate sex-specific genetic effects on physical activity (PA) levels and sedentary behaviour (SB) using three generation families. The sample consisted of 100 families covering three generations from Portugal. PA and SB were assessed via the International Physical Activity Questionnaire short form (IPAQ-SF). Sex-specific effects were assessed by genotype-by-sex interaction (GSI) models and sex-specific heritabilities. GSI effects and heterogeneity were tested in the residual environmental variance. SPSS 17 and SOLAR v. 4.1 were used in all computations. The genetic component for PA and SB domains varied from low to moderate (11% to 46%), when analyzing both genders combined. We found GSI effects for vigorous PA (p = 0.02) and time spent watching television (WT) (p < 0.001) that showed significantly higher additive genetic variance estimates in males. The heterogeneity in the residual environmental variance was significant for moderate PA (p = 0.02), vigorous PA (p = 0.006) and total PA (p = 0.001). Sex-specific heritability estimates were significantly higher in males only for WT, with a male-to-female difference in heritability of 42.5 (95% confidence interval: 6.4, 70.4). Low to moderate genetic effects on PA and SB traits were found. Results from the GSI model show that there are sex-specific effects in two phenotypes, VPA and WT with a stronger genetic influence in males.

EvolQG - An R package for evolutionary quantitative genetics [version 2; referees: 1 approved, 2 approved with reservations

Directory of Open Access Journals (Sweden)

Diogo Melo

2016-06-01

Full Text Available We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the EvolQG package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification.

Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies.

Science.gov (United States)

Hüls, Anke; Krämer, Ursula; Carlsten, Christopher; Schikowski, Tamara; Ickstadt, Katja; Schwender, Holger

2017-12-16

Weighted genetic risk scores (GRS), defined as weighted sums of risk alleles of single nucleotide polymorphisms (SNPs), are statistically powerful for detection gene-environment (GxE) interactions. To assign weights, the gold standard is to use external weights from an independent study. However, appropriate external weights are not always available. In such situations and in the presence of predominant marginal genetic effects, we have shown in a previous study that GRS with internal weights from marginal genetic effects ("GRS-marginal-internal") are a powerful and reliable alternative to single SNP approaches or the use of unweighted GRS. However, this approach might not be appropriate for detecting predominant interactions, i.e. interactions showing an effect stronger than the marginal genetic effect. In this paper, we present a weighting approach for such predominant interactions ("GRS-interaction-training") in which parts of the data are used to estimate the weights from the interaction terms and the remaining data are used to determine the GRS. We conducted a simulation study for the detection of GxE interactions in which we evaluated power, type I error and sign-misspecification. We compared this new weighting approach to the GRS-marginal-internal approach and to GRS with external weights. Our simulation study showed that in the absence of external weights and with predominant interaction effects, the highest power was reached with the GRS-interaction-training approach. If marginal genetic effects were predominant, the GRS-marginal-internal approach was more appropriate. Furthermore, the power to detect interactions reached by the GRS-interaction-training approach was only slightly lower than the power achieved by GRS with external weights. The power of the GRS-interaction-training approach was confirmed in a real data application to the Traffic, Asthma and Genetics (TAG) Study (N = 4465 observations). When appropriate external weights are unavailable, we

Genetics of simple and complex host-parasite interactions

International Nuclear Information System (INIS)

Sidhu, G.S.; Webster, J.M.

1977-01-01

In nature a host plant can be viewed as a miniature replica of an ecological system where true and incidental parasites share the same habitat. Consequently, they influence each other's presence directly by interspecific interaction, and indirectly by inducing changes in the host's physiology and so form disease complexes. Since all physiological phenomena have their counterpart in the respective genetic systems of interacting organisms, valuable genetic information can be derived from the analysis of complex parasitic systems. Disease complexes may be classified according to the nature of interaction between various parasites on the same host. One parasite may nullify the host's resistance to another (e.g. Tomato - Meloidogyne incognita + Fusarium oxysporum lycopersici system). Conversely, a parasite may invoke resistance in the host against another parasite (e.g. Tomato - Fusarium oxysporum lycopersici + Verticillium albo atrum system). From the study of simple parasitic systems we know that resistance versus susceptibility against a single parasite is normally monogenically controlled. However, when more than one parasite interacts to invoke or nullify each other's responses on the same host plant, the genetic results suggest epistatic ratios. Nevertheless, epistatic ratios have been obtained also from simple parasitic systems owing to gene interaction. The epistatic ratios obtained from complex and simple parasitic systems are contrasted and compared. It is suggested that epistatic ratios obtained from simple parasitic systems may, in fact, be artifacts resulting from complex parasitic associations that often occur in nature. Polygenic inheritance and the longevity of a cultivar is also discussed briefly in relation to complex parasitic associations. Induced mutations can play a significant role in the study of complex parasitic associations, and thus can be very useful in controlling plant diseases

Fashion sketch design by interactive genetic algorithms

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Mok, P. Y.; Wang, X. X.; Xu, J.; Kwok, Y. L.

2012-11-01

Computer aided design is vitally important for the modern industry, particularly for the creative industry. Fashion industry faced intensive challenges to shorten the product development process. In this paper, a methodology is proposed for sketch design based on interactive genetic algorithms. The sketch design system consists of a sketch design model, a database and a multi-stage sketch design engine. First, a sketch design model is developed based on the knowledge of fashion design to describe fashion product characteristics by using parameters. Second, a database is built based on the proposed sketch design model to define general style elements. Third, a multi-stage sketch design engine is used to construct the design. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch design process. The experimental results have demonstrated that the proposed method is effective in helping laypersons achieve satisfied fashion design sketches.

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

Science.gov (United States)

Moran, Paula; Stokes, Jennifer; Marr, Julia; Bock, Gavin; Desbonnet, Lieve; Waddington, John; O'Tuathaigh, Colm

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.

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

Social and genetic interactions drive fitness variation in a free-living dolphin population.

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Frère, Celine H; Krützen, Michael; Mann, Janet; Connor, Richard C; Bejder, Lars; Sherwin, William B

2010-11-16

The evolutionary forces that drive fitness variation in species are of considerable interest. Despite this, the relative importance and interactions of genetic and social factors involved in the evolution of fitness traits in wild mammalian populations are largely unknown. To date, a few studies have demonstrated that fitness might be influenced by either social factors or genes in natural populations, but none have explored how the combined effect of social and genetic parameters might interact to influence fitness. Drawing from a long-term study of wild bottlenose dolphins in the eastern gulf of Shark Bay, Western Australia, we present a unique approach to understanding these interactions. Our study shows that female calving success depends on both genetic inheritance and social bonds. Moreover, we demonstrate that interactions between social and genetic factors also influence female fitness. Therefore, our study represents a major methodological advance, and provides critical insights into the interplay of genetic and social parameters of fitness.

Quantitative chemogenomics: machine-learning models of protein-ligand interaction.

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Andersson, Claes R; Gustafsson, Mats G; Strömbergsson, Helena

2011-01-01

Chemogenomics is an emerging interdisciplinary field that lies in the interface of biology, chemistry, and informatics. Most of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand interaction is therefore central to drug discovery and design. In the subfield of chemogenomics known as proteochemometrics, protein-ligand-interaction models are induced from data matrices that consist of both protein and ligand information along with some experimentally measured variable. The two general aims of this quantitative multi-structure-property-relationship modeling (QMSPR) approach are to exploit sparse/incomplete information sources and to obtain more general models covering larger parts of the protein-ligand space, than traditional approaches that focuses mainly on specific targets or ligands. The data matrices, usually obtained from multiple sparse/incomplete sources, typically contain series of proteins and ligands together with quantitative information about their interactions. A useful model should ideally be easy to interpret and generalize well to new unseen protein-ligand combinations. Resolving this requires sophisticated machine-learning methods for model induction, combined with adequate validation. This review is intended to provide a guide to methods and data sources suitable for this kind of protein-ligand-interaction modeling. An overview of the modeling process is presented including data collection, protein and ligand descriptor computation, data preprocessing, machine-learning-model induction and validation. Concerns and issues specific for each step in this kind of data-driven modeling will be discussed. © 2011 Bentham Science Publishers

A population genetic interpretation of GWAS findings for human quantitative traits

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Bullaughey, Kevin; Hudson, Richard R.; Sella, Guy

2018-01-01

Human genome-wide association studies (GWASs) are revealing the genetic architecture of anthropomorphic and biomedical traits, i.e., the frequencies and effect sizes of variants that contribute to heritable variation in a trait. To interpret these findings, we need to understand how genetic architecture is shaped by basic population genetics processes—notably, by mutation, natural selection, and genetic drift. Because many quantitative traits are subject to stabilizing selection and because genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multidimensional trait space. We solve the model for the phenotypic distribution and allelic dynamics at steady state and derive robust, closed-form solutions for summary statistics of the genetic architecture. Our results provide a simple interpretation for missing heritability and why it varies among traits. They predict that the distribution of variances contributed by loci identified in GWASs is well approximated by a simple functional form that depends on a single parameter: the expected contribution to genetic variance of a strongly selected site affecting the trait. We test this prediction against the results of GWASs for height and body mass index (BMI) and find that it fits the data well, allowing us to make inferences about the degree of pleiotropy and mutational target size for these traits. Our findings help to explain why the GWAS for height explains more of the heritable variance than the similarly sized GWAS for BMI and to predict the increase in explained heritability with study sample size. Considering the demographic history of European populations, in which these GWASs were performed, we further find that most of the associations they identified likely involve mutations that arose shortly before or during the Out-of-Africa bottleneck at sites with selection coefficients around s = 10−3. PMID

Genetic variability of hull-less barley accessions based on molecular and quantitative data

Directory of Open Access Journals (Sweden)

Ricardo Meneses Sayd

2015-02-01

Full Text Available The objective of this work was to characterize and quantify the genetic, molecular, and agronomic variability of hull-less barley genotypes, for the selection of parents and identification of genotypes adapted to the irrigated production system in the Brazilian Cerrado. Eighteen hull-less barley accessions were evaluated, and three covered barley accessions served as reference. The characterization was based on 157 RAPD molecular markers and ten agronomic traits. Genetic distance matrices were obtained based on molecular markers and quantitative traits. Graphic grouping and dispersion analyses were performed. Genetic, molecular, and agronomic variability was high among genotypes. Ethiopian accessions were genetically more similar, and the Brazilian ones were genetically more distant. For agronomic traits, two more consistent groupings were obtained, one with the most two-rowed materials, and the other with six-rowed materials. The more diverging materials were the two-rowed CI 13453, CN Cerrado 5, CN Cerrado 1, and CN Cerrado 2. The PI 356466, CN Cerrado 1, PI 370799, and CI 13453 genotypes show agronomic traits of interest and, as genetically different genotypes, they are indicated for crossing, in breeding programs.

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

Directory of Open Access Journals (Sweden)

Paula Moran

2016-01-01

Full Text Available 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.

Measuring the genetic influence on human life span: gene-environment interaction and sex-specific genetic effects

DEFF Research Database (Denmark)

Tan, Qihua; De Benedictis, G; Yashin, Annatoli

2001-01-01

New approaches are needed to explore the different ways in which genes affect the human life span. One needs to assess the genetic effects themselves, as well as gene–environment interactions and sex dependency. In this paper, we present a new model that combines both genotypic and demographicinf......New approaches are needed to explore the different ways in which genes affect the human life span. One needs to assess the genetic effects themselves, as well as gene–environment interactions and sex dependency. In this paper, we present a new model that combines both genotypic...

Filling the knowledge gap: Integrating quantitative genetics and genomics in graduate education and outreach

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The genomics revolution provides vital tools to address global food security. Yet to be incorporated into livestock breeding, molecular techniques need to be integrated into a quantitative genetics framework. Within the U.S., with shrinking faculty numbers with the requisite skills, the capacity to ...

The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics

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Ronald de Vlaming

2015-01-01

Full Text Available In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i the theoretical foundations of ridge regression, (ii its link to commonly used methods in animal breeding, (iii the computational feasibility, and (iv the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis. Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N<10,000 the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP. However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially.

Genetic interaction network of the Saccharomyces cerevisiae type 1 phosphatase Glc7

Directory of Open Access Journals (Sweden)

Neszt Michael

2008-07-01

Full Text Available Abstract Background Protein kinases and phosphatases regulate protein phosphorylation, a critical means of modulating protein function, stability and localization. The identification of functional networks for protein phosphatases has been slow due to their redundant nature and the lack of large-scale analyses. We hypothesized that a genome-scale analysis of genetic interactions using the Synthetic Genetic Array could reveal protein phosphatase functional networks. We apply this approach to the conserved type 1 protein phosphatase Glc7, which regulates numerous cellular processes in budding yeast. Results We created a novel glc7 catalytic mutant (glc7-E101Q. Phenotypic analysis indicates that this novel allele exhibits slow growth and defects in glucose metabolism but normal cell cycle progression and chromosome segregation. This suggests that glc7-E101Q is a hypomorphic glc7 mutant. Synthetic Genetic Array analysis of glc7-E101Q revealed a broad network of 245 synthetic sick/lethal interactions reflecting that many processes are required when Glc7 function is compromised such as histone modification, chromosome segregation and cytokinesis, nutrient sensing and DNA damage. In addition, mitochondrial activity and inheritance and lipid metabolism were identified as new processes involved in buffering Glc7 function. An interaction network among 95 genes genetically interacting with GLC7 was constructed by integration of genetic and physical interaction data. The obtained network has a modular architecture, and the interconnection among the modules reflects the cooperation of the processes buffering Glc7 function. Conclusion We found 245 genes required for the normal growth of the glc7-E101Q mutant. Functional grouping of these genes and analysis of their physical and genetic interaction patterns bring new information on Glc7-regulated processes.

Quantitative heartbeat coupling measures in human-horse interaction.

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Lanata, Antonio; Guidi, Andrea; Valenza, Gaetano; Baragli, Paolo; Scilingo, Enzo Pasquale

2016-08-01

We present a study focused on a quantitative estimation of a human-horse dynamic interaction. A set of measures based on magnitude and phase coupling between heartbeat dynamics of both humans and horses in three different conditions is reported: no interaction, visual/olfactory interaction and grooming. Specifically, Magnitude Squared Coherence (MSC), Mean Phase Coherence (MPC) and Dynamic Time Warping (DTW) have been used as estimators of the amount of coupling between human and horse through the analysis of their heart rate variability (HRV) time series in a group of eleven human subjects, and one horse. The rationale behind this study is that the interaction of two complex biological systems go towards a coupling process whose dynamical evolution is modulated by the kind and time duration of the interaction itself. We achieved a congruent and consistent statistical significant difference for all of the three indices. Moreover, a Nearest Mean Classifier was able to recognize the three classes of interaction with an accuracy greater than 70%. Although preliminary, these encouraging results allow a discrimination of three distinct phases in a real human-animal interaction opening to the characterization of the empirically proven relationship between human and horse.

Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes

Science.gov (United States)

Trinkle-Mulcahy, Laura; Boulon, Séverine; Lam, Yun Wah; Urcia, Roby; Boisvert, François-Michel; Vandermoere, Franck; Morrice, Nick A.; Swift, Sam; Rothbauer, Ulrich; Leonhardt, Heinrich; Lamond, Angus

2008-01-01

The identification of interaction partners in protein complexes is a major goal in cell biology. Here we present a reliable affinity purification strategy to identify specific interactors that combines quantitative SILAC-based mass spectrometry with characterization of common contaminants binding to affinity matrices (bead proteomes). This strategy can be applied to affinity purification of either tagged fusion protein complexes or endogenous protein complexes, illustrated here using the well-characterized SMN complex as a model. GFP is used as the tag of choice because it shows minimal nonspecific binding to mammalian cell proteins, can be quantitatively depleted from cell extracts, and allows the integration of biochemical protein interaction data with in vivo measurements using fluorescence microscopy. Proteins binding nonspecifically to the most commonly used affinity matrices were determined using quantitative mass spectrometry, revealing important differences that affect experimental design. These data provide a specificity filter to distinguish specific protein binding partners in both quantitative and nonquantitative pull-down and immunoprecipitation experiments. PMID:18936248

Interactions between genetic background, insulin resistance and β-cell function.

Science.gov (United States)

Kahn, S E; Suvag, S; Wright, L A; Utzschneider, K M

2012-10-01

An interaction between genes and the environment is a critical component underlying the pathogenesis of the hyperglycaemia of type 2 diabetes. The development of more sophisticated techniques for studying gene variants and for analysing genetic data has led to the discovery of some 40 genes associated with type 2 diabetes. Most of these genes are related to changes in β-cell function, with a few associated with decreased insulin sensitivity and obesity. Interestingly, using quantitative traits based on continuous measures rather than dichotomous ones, it has become evident that not all genes associated with changes in fasting or post-prandial glucose are also associated with a diagnosis of type 2 diabetes. Identification of these gene variants has provided novel insights into the physiology and pathophysiology of the β-cell, including the identification of molecules involved in β-cell function that were not previously recognized as playing a role in this critical cell. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

Genetic and genomic interactions of animals with different ploidy levels.

Science.gov (United States)

Bogart, J P; Bi, K

2013-01-01

Polyploid animals have independently evolved from diploids in diverse taxa across the tree of life. We review a few polyploid animal species or biotypes where recently developed molecular and cytogenetic methods have significantly improved our understanding of their genetics, reproduction and evolution. Mitochondrial sequences that target the maternal ancestor of a polyploid show that polyploids may have single (e.g. unisexual salamanders in the genus Ambystoma) or multiple (e.g. parthenogenetic polyploid lizards in the genus Aspidoscelis) origins. Microsatellites are nuclear markers that can be used to analyze genetic recombinations, reproductive modes (e.g. Ambystoma) and recombination events (e.g. polyploid frogs such as Pelophylax esculentus). Hom(e)ologous chromosomes and rare intergenomic exchanges in allopolyploids have been distinguished by applying genome-specific fluorescent probes to chromosome spreads. Polyploids arise, and are maintained, through perturbations of the 'normal' meiotic program that would include pre-meiotic chromosome replication and genomic integrity of homologs. When possible, asexual, unisexual and bisexual polyploid species or biotypes interact with diploid relatives, and genes are passed from diploid to polyploid gene pools, which increase genetic diversity and ultimately evolutionary flexibility in the polyploid. When diploid relatives do not exist, polyploids can interact with another polyploid (e.g. species of African Clawed Frogs in the genus Xenopus). Some polyploid fish (e.g. salmonids) and frogs (Xenopus) represent independent lineages whose ancestors experienced whole genome duplication events. Some tetraploid frogs (P. esculentus) and fish (Squaliusalburnoides) may be in the process of becoming independent species, but diploid and triploid forms of these 'species' continue to genetically interact with the comparatively few tetraploid populations. Genetic and genomic interaction between polyploids and diploids is a complex

Genetics-based interactions among plants, pathogens, and herbivores define arthropod community structure.

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Busby, Posy E; Lamit, Louis J; Keith, Arthur R; Newcombe, George; Gehring, Catherine A; Whitham, Thomas G; Dirzo, Rodolfo

2015-07-01

Plant resistance to pathogens or insect herbivores is common, but its potential for indirectly influencing plant-associated communities is poorly known. Here, we test whether pathogens' indirect effects on arthropod communities and herbivory depend on plant resistance to pathogens and/or herbivores, and address the overarching interacting foundation species hypothesis that genetics-based interactions among a few highly interactive species can structure a much larger community. In a manipulative field experiment using replicated genotypes of two Populus species and their interspecific hybrids, we found that genetic variation in plant resistance to both pathogens and insect herbivores modulated the strength of pathogens' indirect effects on arthropod communities and insect herbivory. First, due in part to the pathogens' differential impacts on leaf biomass among the two Populus species and the hybrids, the pathogen most strongly impacted arthropod community composition, richness, and abundance on the pathogen-susceptible tree species. Second, we found similar patterns comparing pathogen-susceptible and pathogen-resistant genotypes within species. Third, within a plant species, pathogens caused a fivefold greater reduction in herbivory on insect-herbivore-susceptible plant genotypes than on herbivore-resistant genotypes, demonstrating that the pathogen-herbivore interaction is genotype dependent. We conclude that interactions among plants, pathogens, and herbivores can structure multitrophic communities, supporting the interacting foundation species hypothesis. Because these interactions are genetically based, evolutionary changes in genetic resistance could result in ecological changes in associated communities, which may in turn feed back to affect plant fitness.

Quantitative genetic analysis of anxiety trait in bipolar disorder.

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Contreras, J; Hare, E; Chavarría, G; Raventós, H

2018-01-01

Bipolar disorder type I (BPI) affects approximately 1% of the world population. Although genetic influences on bipolar disorder are well established, identification of genes that predispose to the illness has been difficult. Most genetic studies are based on categorical diagnosis. One strategy to overcome this obstacle is the use of quantitative endophenotypes, as has been done for other medical disorders. We studied 619 individuals, 568 participants from 61 extended families and 51 unrelated healthy controls. The sample was 55% female and had a mean age of 43.25 (SD 13.90; range 18-78). Heritability and genetic correlation of the trait scale from the Anxiety State and Trait Inventory (STAI) was computed by using the general linear model (SOLAR package software). we observed that anxiety trait meets the following criteria for an endophenotype of bipolar disorder type I (BPI): 1) association with BPI (individuals with BPI showed the highest trait score (F = 15.20 [5,24], p = 0.009), 2) state-independence confirmed after conducting a test-retest in 321 subjects, 3) co-segregation within families 4) heritability of 0.70 (SE: 0.060), p = 2.33 × 10 -14 and 5) genetic correlation with BPI was 0.20, (SE = 0.17, p = 3.12 × 10 -5 ). Confounding factors such as comorbid disorders and pharmacological treatment could affect the clinical relationship between BPI and anxiety trait. Further research is needed to evaluate if anxiety traits are specially related to BPI in comparison with other traits such as anger, attention or response inhibition deficit, pathological impulsivity or low self-directedness. Anxiety trait is a heritable phenotype that follows a normal distribution when measured not only in subjects with BPI but also in unrelated healthy controls. It could be used as an endophenotype in BPI for the identification of genomic regions with susceptibility genes for this disorder. Published by Elsevier B.V.

Genome complexity, robustness and genetic interactions in digital organisms

Science.gov (United States)

Lenski, Richard E.; Ofria, Charles; Collier, Travis C.; Adami, Christoph

1999-08-01

Digital organisms are computer programs that self-replicate, mutate and adapt by natural selection. They offer an opportunity to test generalizations about living systems that may extend beyond the organic life that biologists usually study. Here we have generated two classes of digital organism: simple programs selected solely for rapid replication, and complex programs selected to perform mathematical operations that accelerate replication through a set of defined `metabolic' rewards. To examine the differences in their genetic architecture, we introduced millions of single and multiple mutations into each organism and measured the effects on the organism's fitness. The complex organisms are more robust than the simple ones with respect to the average effects of single mutations. Interactions among mutations are common and usually yield higher fitness than predicted from the component mutations assuming multiplicative effects; such interactions are especially important in the complex organisms. Frequent interactions among mutations have also been seen in bacteria, fungi and fruitflies. Our findings support the view that interactions are a general feature of genetic systems.

A quantitative genetic approach to assess the evolutionary potential of a coastal marine fish to ocean acidification

KAUST Repository

Malvezzi, Alex J.

2015-02-01

Assessing the potential of marine organisms to adapt genetically to increasing oceanic CO2 levels requires proxies such as heritability of fitness-related traits under ocean acidification (OA). We applied a quantitative genetic method to derive the first heritability estimate of survival under elevated CO2 conditions in a metazoan. Specifically, we reared offspring, selected from a wild coastal fish population (Atlantic silverside, Menidia menidia), at high CO2 conditions (~2300 μatm) from fertilization to 15 days posthatch, which significantly reduced survival compared to controls. Perished and surviving offspring were quantitatively sampled and genotyped along with their parents, using eight polymorphic microsatellite loci, to reconstruct a parent-offspring pedigree and estimate variance components. Genetically related individuals were phenotypically more similar (i.e., survived similarly long at elevated CO2 conditions) than unrelated individuals, which translated into a significantly nonzero heritability (0.20 ± 0.07). The contribution of maternal effects was surprisingly small (0.05 ± 0.04) and nonsignificant. Survival among replicates was positively correlated with genetic diversity, particularly with observed heterozygosity. We conclude that early life survival of M. menidia under high CO2 levels has a significant additive genetic component that could elicit an evolutionary response to OA, depending on the strength and direction of future selection.

The quantitative basis of the Arabidopsis innate immune system to endemic pathogens depends on pathogen genetics

DEFF Research Database (Denmark)

Corwin, Jason A; Copeland, Daniel; Feusier, Julie

2016-01-01

The most established model of the eukaryotic innate immune system is derived from examples of large effect monogenic quantitative resistance to pathogens. However, many host-pathogen interactions involve many genes of small to medium effect and exhibit quantitative resistance. We used the Arabido......The most established model of the eukaryotic innate immune system is derived from examples of large effect monogenic quantitative resistance to pathogens. However, many host-pathogen interactions involve many genes of small to medium effect and exhibit quantitative resistance. We used....... cinerea, we identified a total of 2,982 genes associated with quantitative resistance using lesion area and 3,354 genes associated with camalexin production as measures of the interaction. Most genes were associated with resistance to a specific Botrytis isolate, which demonstrates the influence...... genes associated with quantitative resistance. Using publically available co-expression data, we condensed the quantitative resistance associated genes into co-expressed gene networks. GO analysis of these networks implicated several biological processes commonly connected to disease resistance...

Genetic evolution, plasticity, and bet-hedging as adaptive responses to temporally autocorrelated fluctuating selection: A quantitative genetic model.

Science.gov (United States)

Tufto, Jarle

2015-08-01

Adaptive responses to autocorrelated environmental fluctuations through evolution in mean reaction norm elevation and slope and an independent component of the phenotypic variance are analyzed using a quantitative genetic model. Analytic approximations expressing the mutual dependencies between all three response modes are derived and solved for the joint evolutionary outcome. Both genetic evolution in reaction norm elevation and plasticity are favored by slow temporal fluctuations, with plasticity, in the absence of microenvironmental variability, being the dominant evolutionary outcome for reasonable parameter values. For fast fluctuations, tracking of the optimal phenotype through genetic evolution and plasticity is limited. If residual fluctuations in the optimal phenotype are large and stabilizing selection is strong, selection then acts to increase the phenotypic variance (bet-hedging adaptive). Otherwise, canalizing selection occurs. If the phenotypic variance increases with plasticity through the effect of microenvironmental variability, this shifts the joint evolutionary balance away from plasticity in favor of genetic evolution. If microenvironmental deviations experienced by each individual at the time of development and selection are correlated, however, more plasticity evolves. The adaptive significance of evolutionary fluctuations in plasticity and the phenotypic variance, transient evolution, and the validity of the analytic approximations are investigated using simulations. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Genetic and environmental determinants of violence risk in psychotic disorders: a multivariate quantitative genetic study of 1.8 million Swedish twins and siblings.

Science.gov (United States)

Sariaslan, A; Larsson, H; Fazel, S

2016-09-01

Patients diagnosed with psychotic disorders (for example, schizophrenia and bipolar disorder) have elevated risks of committing violent acts, particularly if they are comorbid with substance misuse. Despite recent insights from quantitative and molecular genetic studies demonstrating considerable pleiotropy in the genetic architecture of these phenotypes, there is currently a lack of large-scale studies that have specifically examined the aetiological links between psychotic disorders and violence. Using a sample of all Swedish individuals born between 1958 and 1989 (n=3 332 101), we identified a total of 923 259 twin-sibling pairs. Patients were identified using the National Patient Register using validated algorithms based on International Classification of Diseases (ICD) 8-10. Univariate quantitative genetic models revealed that all phenotypes (schizophrenia, bipolar disorder, substance misuse, and violent crime) were highly heritable (h(2)=53-71%). Multivariate models further revealed that schizophrenia was a stronger predictor of violence (r=0.32; 95% confidence interval: 0.30-0.33) than bipolar disorder (r=0.23; 0.21-0.25), and large proportions (51-67%) of these phenotypic correlations were explained by genetic factors shared between each disorder, substance misuse, and violence. Importantly, we found that genetic influences that were unrelated to substance misuse explained approximately a fifth (21%; 20-22%) of the correlation with violent criminality in bipolar disorder but none of the same correlation in schizophrenia (Pbipolar disordergenetically similar phenotypes as the latter sources may include aetiologically important clues. Clinically, these findings underline the importance of assessing risk of different phenotypes together and integrating interventions for psychiatric disorders, substance misuse, and violence.

CRISPR genetic screens to discover host-virus interactions.

Science.gov (United States)

McDougall, William M; Perreira, Jill M; Reynolds, Erin C; Brass, Abraham L

2018-04-01

Viruses impose an immense burden on human health. With the goal of treating and preventing viral infections, researchers have carried out genetic screens to improve our understanding of viral dependencies and identify potential anti-viral strategies. The emergence of CRISPR genetic screening tools has facilitated this effort by enabling host-virus screens to be undertaken in a more versatile and fidelitous manner than previously possible. Here we review the growing number of CRISPR screens which continue to increase our understanding of host-virus interactions. Copyright © 2018 Elsevier B.V. All rights reserved.

Environmental and genetic interactions in human cancer

International Nuclear Information System (INIS)

Paterson, M.C.

Humans, depending upon their genetic make-up, differ in their susceptibility to the cancer-causing effects of extrinsic agents. Clinical and laboratory studies on the hereditary disorder, ataxia telangiectasia (AT) show that persons afflicted with this are cancer-prone and unusually sensitive to conventional radiotherapy. Their skin cells, when cultured, are hypersensitive to killing by ionizing radiation, being defective in the enzymatic repair of radiation-induced damange to the genetic material, deoxyribonucleic acid (DNA). This molecular finding implicates DNA damage and its imperfect repair as an early step in the induction of human cancer by radiation and other carcinogens. The parents of AT patients are clincally normal but their cultured cells are often moderately radiosensitive. The increased radiosensitivity of cultured cells offers a means of identifying a presumed cancer-prone subpopulation that should avoid undue exposure to certain carcinogens. The radioresponse of cells from patients with other cancer-associated genetic disorders and persons suspected of being genetically predisposed to radiation-induced cancer has also been measured. Increased cell killing by γ-rays appears in the complex genetic disease, tuberous sclerosis. Cells from cancer-stricken members of a leukemia-prone family are also radiosensitive, as are cells from one patient with radiation-associated breast cancer. These radiobiological data, taken together, strongly suggest that genetic factors can interact with extrinsic agents and thereby play a greater causative role in the development of common cancers in man than previously thought. (L.L.)

A UV-Induced Genetic Network Links the RSC Complex to Nucleotide Excision Repair and Shows Dose-Dependent Rewiring

Directory of Open Access Journals (Sweden)

Rohith Srivas

2013-12-01

Full Text Available Efficient repair of UV-induced DNA damage requires the precise coordination of nucleotide excision repair (NER with numerous other biological processes. To map this crosstalk, we generated a differential genetic interaction map centered on quantitative growth measurements of >45,000 double mutants before and after different doses of UV radiation. Integration of genetic data with physical interaction networks identified a global map of 89 UV-induced functional interactions among 62 protein complexes, including a number of links between the RSC complex and several NER factors. We show that RSC is recruited to both silenced and transcribed loci following UV damage where it facilitates efficient repair by promoting nucleosome remodeling. Finally, a comparison of the response to high versus low levels of UV shows that the degree of genetic rewiring correlates with dose of UV and reveals a network of dose-specific interactions. This study makes available a large resource of UV-induced interactions, and it illustrates a methodology for identifying dose-dependent interactions based on quantitative shifts in genetic networks.

Analysis of Nanobody-Epitope Interactions in Living Cells via Quantitative Protein Transport Assays.

Science.gov (United States)

Früholz, Simone; Pimpl, Peter

2017-01-01

Over the past few decades, quantitative protein transport analyses have been used to elucidate the sorting and transport of proteins in the endomembrane system of plants. Here, we have applied our knowledge about transport routes and the corresponding sorting signals to establish an in vivo system for testing specific interactions between soluble proteins.Here, we describe the use of quantitative protein transport assays in tobacco mesophyll protoplasts to test for interactions occurring between a GFP-binding nanobody and its GFP epitope. For this, we use a secreted GFP-tagged α-amylase as a reporter together with a vacuolar-targeted RFP-tagged nanobody. The interaction between these proteins is then revealed by a transport alteration of the secretory reporter due to the interaction-triggered attachment of the vacuolar sorting signal.

Study of quantitative genetics of gum arabic production complicated by variability in ploidy level of Acacia senegal (L.) Willd

DEFF Research Database (Denmark)

Diallo, Adja Madjiguene; Nielsen, Lene Rostgaard; Hansen, Jon Kehlet

2015-01-01

Gum arabic is an important international commodity produced by trees of Acacia senegal across Sahelian Africa, but documented results of breeding activities are limited. The objective of this study was to provide reliable estimates of quantitative genetic parameters in order to shed light on the ...... stress the importance of testing ploidy levels of selected material and use of genetic markers to qualify the assumptions in the quantitative genetic analysis....... that progenies consisted of both diploid and polyploid trees, and growth, gum yield, and gum quality varied substantially among ploidy level, populations, and progenies. Analysis of molecular variance and estimates of outcrossing rate supported that trees within open-pollinated families of diploids were half...... sibs, while the open-pollinated families of polyploids showed low variation within families. The difference in sibling relationship observed between ploidy levels complicated estimation of genetic parameters. However, based on the diploid trees, we conclude that heritability in gum arabic production...

Genetic interaction analysis of point mutations enables interrogation of gene function at a residue-level resolution

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Braberg, Hannes; Moehle, Erica A.; Shales, Michael; Guthrie, Christine; Krogan, Nevan J.

2014-01-01

We have achieved a residue-level resolution of genetic interaction mapping – a technique that measures how the function of one gene is affected by the alteration of a second gene – by analyzing point mutations. Here, we describe how to interpret point mutant genetic interactions, and outline key applications for the approach, including interrogation of protein interaction interfaces and active sites, and examination of post-translational modifications. Genetic interaction analysis has proven effective for characterizing cellular processes; however, to date, systematic high-throughput genetic interaction screens have relied on gene deletions or knockdowns, which limits the resolution of gene function analysis and poses problems for multifunctional genes. Our point mutant approach addresses these issues, and further provides a tool for in vivo structure-function analysis that complements traditional biophysical methods. We also discuss the potential for genetic interaction mapping of point mutations in human cells and its application to personalized medicine. PMID:24842270

Skype Synchronous Interaction Effectiveness in a Quantitative Management Science Course

Science.gov (United States)

Strang, Kenneth David

2012-01-01

An experiment compared asynchronous versus synchronous instruction in an online quantitative course. Mann-Whitney U-tests, correlation, analysis of variance, t tests, and multivariate analysis of covariance (MANCOVA) were utilized to test the hypothesis that more high-quality online experiential learning interactions would increase grade.…

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

Science.gov (United States)

Sameith, Katrin; Amini, Saman; Groot Koerkamp, Marian J A; van Leenen, Dik; Brok, Mariel; Brabers, Nathalie; Lijnzaad, Philip; van Hooff, Sander R; Benschop, Joris J; Lenstra, Tineke L; Apweiler, Eva; van Wageningen, Sake; Snel, Berend; Holstege, Frank C P; Kemmeren, Patrick

2015-12-23

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 pathway organization and understanding the relationship between genotype, phenotype and disease. To investigate the nature of genetic interactions between gene-specific transcription factors (GSTFs) in Saccharomyces cerevisiae, we systematically analyzed 72 GSTF pairs by gene expression profiling double and single deletion mutants. These pairs were selected through previously published growth-based genetic interactions as well as through similarity in DNA binding properties. The result is a high-resolution atlas of gene expression-based genetic interactions that provides systems-level insight into GSTF epistasis. The atlas confirms known genetic interactions and exposes new ones. Importantly, the data can be used to investigate mechanisms that underlie individual genetic interactions. Two molecular mechanisms are proposed, "buffering by induced dependency" and "alleviation by derepression". These mechanisms indicate how negative genetic interactions can occur between seemingly unrelated parallel pathways and how positive genetic interactions can indirectly expose parallel rather than same-pathway relationships. The focus on GSTFs is important for understanding the transcription regulatory network of yeast as it uncovers details behind many redundancy relationships, some of which are completely new. In addition, the study provides general insight into the complex nature of epistasis and proposes mechanistic models for genetic interactions, the majority of which do not fall into easily recognizable within- or between-pathway relationships.

Quantitative genetic models of sexual selection by male choice.

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Nakahashi, Wataru

2008-09-01

There are many examples of male mate choice for female traits that tend to be associated with high fertility. I develop quantitative genetic models of a female trait and a male preference to show when such a male preference can evolve. I find that a disagreement between the fertility maximum and the viability maximum of the female trait is necessary for directional male preference (preference for extreme female trait values) to evolve. Moreover, when there is a shortage of available male partners or variance in male nongenetic quality, strong male preference can evolve. Furthermore, I also show that males evolve to exhibit a stronger preference for females that are more feminine (less resemblance to males) than the average female when there is a sexual dimorphism caused by fertility selection which acts only on females.

A Qualitative and Quantitative Assay to Study DNA/Drug Interaction ...

African Journals Online (AJOL)

Research Article. A Qualitative and Quantitative Assay to Study. DNA/Drug Interaction Based on Sequence Selective. Inhibition of Restriction Endonucleases. Syed A Hassan1*, Lata Chauhan2, Ritu Barthwal2 and Aparna Dixit3. 1 Faculty of Computing and Information Technology, King Abdul Aziz University, Rabigh-21911 ...

Genetic Risk by Experience Interaction for Childhood Internalizing Problems: Converging Evidence across Multiple Methods

Science.gov (United States)

Vendlinski, Matthew K.; Lemery-Chalfant, Kathryn; Essex, Marilyn J.; Goldsmith, H. Hill

2011-01-01

Background: Identifying how genetic risk interacts with experience to predict psychopathology is an important step toward understanding the etiology of mental health problems. Few studies have examined genetic risk by experience interaction (GxE) in the development of childhood psychopathology. Methods: We used both co-twin and parent mental…

Quantitative genetics of plastron shape in slider turtles (Trachemys scripta).

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Myers, Erin M; Janzen, Fredric J; Adams, Dean C; Tucker, John K

2006-03-01

Shape variation is widespread in nature and embodies both a response to and a source for evolution and natural selection. To detect patterns of shape evolution, one must assess the quantitative genetic underpinnings of shape variation as well as the selective environment that the organisms have experienced. Here we used geometric morphometrics to assess variation in plastron shell shape in 1314 neonatal slider turtles (Trachemys scripta) from 162 clutches of laboratory-incubated eggs from two nesting areas. Multivariate analysis of variance indicated that nesting area has a limited role in describing plastron shape variation among clutches, whereas differences between individual clutches were highly significant, suggesting a prominent clutch effect. The covariation between plastron shape and several possible maternal effect variables (yolk hormone levels and egg dimensions) was assessed for a subset of clutches and found to be negligible. We subsequently employed several recently proposed methods for estimating heritability from shape variables, and generalized a univariate approach to accommodate unequal sample sizes. Univariate estimates of shape heritability based on Procrustes distances yielded large values for both nesting populations (h2 approximately 0.86), and multivariate estimates of maximal additive heritability were also large for both nesting populations (h2max approximately 0.57). We also estimated the dominant trend in heritable shape change for each nesting population and found that the direction of shape evolution was not the same for the two sites. Therefore, although the magnitude of shape evolution was similar between nesting populations, the manner in which plastron shape is evolving is not. We conclude that the univariate approach for assessing quantitative genetic parameters from geometric morphometric data has limited utility, because it is unable to accurately describe how shape is evolving.

Quantitative sociodynamics stochastic methods and models of social interaction processes

CERN Document Server

Helbing, Dirk

1995-01-01

Quantitative Sociodynamics presents a general strategy for interdisciplinary model building and its application to a quantitative description of behavioural changes based on social interaction processes. Originally, the crucial methods for the modeling of complex systems (stochastic methods and nonlinear dynamics) were developed in physics but they have very often proved their explanatory power in chemistry, biology, economics and the social sciences. Quantitative Sociodynamics provides a unified and comprehensive overview of the different stochastic methods, their interrelations and properties. In addition, it introduces the most important concepts from nonlinear dynamics (synergetics, chaos theory). The applicability of these fascinating concepts to social phenomena is carefully discussed. By incorporating decision-theoretical approaches a very fundamental dynamic model is obtained which seems to open new perspectives in the social sciences. It includes many established models as special cases, e.g. the log...

Quantitative Sociodynamics Stochastic Methods and Models of Social Interaction Processes

CERN Document Server

Helbing, Dirk

2010-01-01

This new edition of Quantitative Sociodynamics presents a general strategy for interdisciplinary model building and its application to a quantitative description of behavioral changes based on social interaction processes. Originally, the crucial methods for the modeling of complex systems (stochastic methods and nonlinear dynamics) were developed in physics and mathematics, but they have very often proven their explanatory power in chemistry, biology, economics and the social sciences as well. Quantitative Sociodynamics provides a unified and comprehensive overview of the different stochastic methods, their interrelations and properties. In addition, it introduces important concepts from nonlinear dynamics (e.g. synergetics, chaos theory). The applicability of these fascinating concepts to social phenomena is carefully discussed. By incorporating decision-theoretical approaches, a fundamental dynamic model is obtained, which opens new perspectives in the social sciences. It includes many established models a...

A quantitative study of nanoparticle skin penetration with interactive segmentation.

Science.gov (United States)

Lee, Onseok; Lee, See Hyun; Jeong, Sang Hoon; Kim, Jaeyoung; Ryu, Hwa Jung; Oh, Chilhwan; Son, Sang Wook

2016-10-01

In the last decade, the application of nanotechnology techniques has expanded within diverse areas such as pharmacology, medicine, and optical science. Despite such wide-ranging possibilities for implementation into practice, the mechanisms behind nanoparticle skin absorption remain unknown. Moreover, the main mode of investigation has been qualitative analysis. Using interactive segmentation, this study suggests a method of objectively and quantitatively analyzing the mechanisms underlying the skin absorption of nanoparticles. Silica nanoparticles (SNPs) were assessed using transmission electron microscopy and applied to the human skin equivalent model. Captured fluorescence images of this model were used to evaluate degrees of skin penetration. These images underwent interactive segmentation and image processing in addition to statistical quantitative analyses of calculated image parameters including the mean, integrated density, skewness, kurtosis, and area fraction. In images from both groups, the distribution area and intensity of fluorescent silica gradually increased in proportion to time. Since statistical significance was achieved after 2 days in the negative charge group and after 4 days in the positive charge group, there is a periodic difference. Furthermore, the quantity of silica per unit area showed a dramatic change after 6 days in the negative charge group. Although this quantitative result is identical to results obtained by qualitative assessment, it is meaningful in that it was proven by statistical analysis with quantitation by using image processing. The present study suggests that the surface charge of SNPs could play an important role in the percutaneous absorption of NPs. These findings can help achieve a better understanding of the percutaneous transport of NPs. In addition, these results provide important guidance for the design of NPs for biomedical applications.

Quantitative analysis of terahertz spectra for illicit drugs using adaptive-range micro-genetic algorithm

Science.gov (United States)

Chen, Yi; Ma, Yong; Lu, Zheng; Peng, Bei; Chen, Qin

2011-08-01

In the field of anti-illicit drug applications, many suspicious mixture samples might consist of various drug components—for example, a mixture of methamphetamine, heroin, and amoxicillin—which makes spectral identification very difficult. A terahertz spectroscopic quantitative analysis method using an adaptive range micro-genetic algorithm with a variable internal population (ARVIPɛμGA) has been proposed. Five mixture cases are discussed using ARVIPɛμGA driven quantitative terahertz spectroscopic analysis in this paper. The devised simulation results show agreement with the previous experimental results, which suggested that the proposed technique has potential applications for terahertz spectral identifications of drug mixture components. The results show agreement with the results obtained using other experimental and numerical techniques.

The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study.

Science.gov (United States)

Lekman, Magnus; Hössjer, Ola; Andrews, Peter; Källberg, Henrik; Uvehag, Daniel; Charney, Dennis; Manji, Husseini; Rush, John A; McMahon, Francis J; Moore, Jason H; Kockum, Ingrid

2014-01-01

Genetic contributions to major depressive disorder (MDD) are thought to result from multiple genes interacting with each other. Different procedures have been proposed to detect such interactions. Which approach is best for explaining the risk of developing disease is unclear. This study sought to elucidate the genetic interaction landscape in candidate genes for MDD by conducting a SNP-SNP interaction analysis using an exhaustive search through 3,704 SNP-markers in 1,732 cases and 1,783 controls provided from the GAIN MDD study. We used three different methods to detect interactions, two logistic regressions models (multiplicative and additive) and one data mining and machine learning (MDR) approach. Although none of the interaction survived correction for multiple comparisons, the results provide important information for future genetic interaction studies in complex disorders. Among the 0.5% most significant observations, none had been reported previously for risk to MDD. Within this group of interactions, less than 0.03% would have been detectable based on main effect approach or an a priori algorithm. We evaluated correlations among the three different models and conclude that all three algorithms detected the same interactions to a low degree. Although the top interactions had a surprisingly large effect size for MDD (e.g. additive dominant model Puncorrected = 9.10E-9 with attributable proportion (AP) value = 0.58 and multiplicative recessive model with Puncorrected = 6.95E-5 with odds ratio (OR estimated from β3) value = 4.99) the area under the curve (AUC) estimates were low (< 0.54). Moreover, the population attributable fraction (PAF) estimates were also low (< 0.15). We conclude that the top interactions on their own did not explain much of the genetic variance of MDD. The different statistical interaction methods we used in the present study did not identify the same pairs of interacting markers. Genetic interaction studies may uncover previously

Social evolution and genetic interactions in the short and long term.

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Van Cleve, Jeremy

2015-08-01

The evolution of social traits remains one of the most fascinating and feisty topics in evolutionary biology even after half a century of theoretical research. W.D. Hamilton shaped much of the field initially with his 1964 papers that laid out the foundation for understanding the effect of genetic relatedness on the evolution of social behavior. Early theoretical investigations revealed two critical assumptions required for Hamilton's rule to hold in dynamical models: weak selection and additive genetic interactions. However, only recently have analytical approaches from population genetics and evolutionary game theory developed sufficiently so that social evolution can be studied under the joint action of selection, mutation, and genetic drift. We review how these approaches suggest two timescales for evolution under weak mutation: (i) a short-term timescale where evolution occurs between a finite set of alleles, and (ii) a long-term timescale where a continuum of alleles are possible and populations evolve continuously from one monomorphic trait to another. We show how Hamilton's rule emerges from the short-term analysis under additivity and how non-additive genetic interactions can be accounted for more generally. This short-term approach reproduces, synthesizes, and generalizes many previous results including the one-third law from evolutionary game theory and risk dominance from economic game theory. Using the long-term approach, we illustrate how trait evolution can be described with a diffusion equation that is a stochastic analogue of the canonical equation of adaptive dynamics. Peaks in the stationary distribution of the diffusion capture classic notions of convergence stability from evolutionary game theory and generally depend on the additive genetic interactions inherent in Hamilton's rule. Surprisingly, the peaks of the long-term stationary distribution can predict the effects of simple kinds of non-additive interactions. Additionally, the peaks

Decay Of Bacterial Pathogens, Fecal Indicators, And Real-Time Quantitative PCR Genetic Markers In Manure-Amended Soils

Science.gov (United States)

This study examined persistence and decay of bacterial pathogens, fecal indicator bacteria (FIB), and emerging real-time quantitative PCR (qPCR) genetic markers for rapid detection of fecal pollution in manure-amended agricultural soils. Known concentrations of transformed green...

Decay Of Bacterial Pathogen, Fecal Indicators, And Real-Time Quantitative PCR Genetic Markers In Manure Amended Soils

Science.gov (United States)

This study examined persistence and decay of bacterial pathogens, fecal indicator bacteria, and emerging real-time quantitative PCR (qPCR) genetic markers for rapid detection of fecal pollution in manre-amended agricultural soils. Known concentrations of transformed green fluore...

Selection Transforms the Landscape of Genetic Variation Interacting with Hsp90.

Science.gov (United States)

Geiler-Samerotte, Kerry A; Zhu, Yuan O; Goulet, Benjamin E; Hall, David W; Siegal, Mark L

2016-10-01

The protein-folding chaperone Hsp90 has been proposed to buffer the phenotypic effects of mutations. The potential for Hsp90 and other putative buffers to increase robustness to mutation has had major impact on disease models, quantitative genetics, and evolutionary theory. But Hsp90 sometimes contradicts expectations for a buffer by potentiating rapid phenotypic changes that would otherwise not occur. Here, we quantify Hsp90's ability to buffer or potentiate (i.e., diminish or enhance) the effects of genetic variation on single-cell morphological features in budding yeast. We corroborate reports that Hsp90 tends to buffer the effects of standing genetic variation in natural populations. However, we demonstrate that Hsp90 tends to have the opposite effect on genetic variation that has experienced reduced selection pressure. Specifically, Hsp90 tends to enhance, rather than diminish, the effects of spontaneous mutations and recombinations. This result implies that Hsp90 does not make phenotypes more robust to the effects of genetic perturbation. Instead, natural selection preferentially allows buffered alleles to persist and thereby creates the false impression that Hsp90 confers greater robustness.

Quantitative genetic variance and multivariate clines in the Ivyleaf morning glory, Ipomoea hederacea.

Science.gov (United States)

Stock, Amanda J; Campitelli, Brandon E; Stinchcombe, John R

2014-08-19

Clinal variation is commonly interpreted as evidence of adaptive differentiation, although clines can also be produced by stochastic forces. Understanding whether clines are adaptive therefore requires comparing clinal variation to background patterns of genetic differentiation at presumably neutral markers. Although this approach has frequently been applied to single traits at a time, we have comparatively fewer examples of how multiple correlated traits vary clinally. Here, we characterize multivariate clines in the Ivyleaf morning glory, examining how suites of traits vary with latitude, with the goal of testing for divergence in trait means that would indicate past evolutionary responses. We couple this with analysis of genetic variance in clinally varying traits in 20 populations to test whether past evolutionary responses have depleted genetic variance, or whether genetic variance declines approaching the range margin. We find evidence of clinal differentiation in five quantitative traits, with little evidence of isolation by distance at neutral loci that would suggest non-adaptive or stochastic mechanisms. Within and across populations, the traits that contribute most to population differentiation and clinal trends in the multivariate phenotype are genetically variable as well, suggesting that a lack of genetic variance will not cause absolute evolutionary constraints. Our data are broadly consistent theoretical predictions of polygenic clines in response to shallow environmental gradients. Ecologically, our results are consistent with past findings of natural selection on flowering phenology, presumably due to season-length variation across the range. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Development of a Model Protein Interaction Pair as a Benchmarking Tool for the Quantitative Analysis of 2-Site Protein-Protein Interactions.

Science.gov (United States)

Yamniuk, Aaron P; Newitt, John A; Doyle, Michael L; Arisaka, Fumio; Giannetti, Anthony M; Hensley, Preston; Myszka, David G; Schwarz, Fred P; Thomson, James A; Eisenstein, Edward

2015-12-01

A significant challenge in the molecular interaction field is to accurately determine the stoichiometry and stepwise binding affinity constants for macromolecules having >1 binding site. The mission of the Molecular Interactions Research Group (MIRG) of the Association of Biomolecular Resource Facilities (ABRF) is to show how biophysical technologies are used to quantitatively characterize molecular interactions, and to educate the ABRF members and scientific community on the utility and limitations of core technologies [such as biosensor, microcalorimetry, or analytic ultracentrifugation (AUC)]. In the present work, the MIRG has developed a robust model protein interaction pair consisting of a bivalent variant of the Bacillus amyloliquefaciens extracellular RNase barnase and a variant of its natural monovalent intracellular inhibitor protein barstar. It is demonstrated that this system can serve as a benchmarking tool for the quantitative analysis of 2-site protein-protein interactions. The protein interaction pair enables determination of precise binding constants for the barstar protein binding to 2 distinct sites on the bivalent barnase binding partner (termed binase), where the 2 binding sites were engineered to possess affinities that differed by 2 orders of magnitude. Multiple MIRG laboratories characterized the interaction using isothermal titration calorimetry (ITC), AUC, and surface plasmon resonance (SPR) methods to evaluate the feasibility of the system as a benchmarking model. Although general agreement was seen for the binding constants measured using solution-based ITC and AUC approaches, weaker affinity was seen for surface-based method SPR, with protein immobilization likely affecting affinity. An analysis of the results from multiple MIRG laboratories suggests that the bivalent barnase-barstar system is a suitable model for benchmarking new approaches for the quantitative characterization of complex biomolecular interactions.

Dual gene activation and knockout screen reveals directional dependencies in genetic networks. | Office of Cancer Genomics

Science.gov (United States)

Understanding the direction of information flow is essential for characterizing how genetic networks affect phenotypes. However, methods to find genetic interactions largely fail to reveal directional dependencies. We combine two orthogonal Cas9 proteins from Streptococcus pyogenes and Staphylococcus aureus to carry out a dual screen in which one gene is activated while a second gene is deleted in the same cell. We analyze the quantitative effects of activation and knockout to calculate genetic interaction and directionality scores for each gene pair.

Family Conflict Interacts with Genetic Liability in Predicting Childhood and Adolescent Depression

Science.gov (United States)

Rice, Frances; Harold, Gordon T.; Shelton, Katherine H.; Thapar, Anita

2006-01-01

Objective: To test for gene-environment interaction with depressive symptoms and family conflict. Specifically, to first examine whether the influence of family conflict in predicting depressive symptoms is increased in individuals at genetic risk of depression. Second, to test whether the genetic component of variance in depressive symptoms…

Quantitative trait loci and metabolic pathways

Science.gov (United States)

McMullen, M. D.; Byrne, P. F.; Snook, M. E.; Wiseman, B. R.; Lee, E. A.; Widstrom, N. W.; Coe, E. H.

1998-01-01

The interpretation of quantitative trait locus (QTL) studies is limited by the lack of information on metabolic pathways leading to most economic traits. Inferences about the roles of the underlying genes with a pathway or the nature of their interaction with other loci are generally not possible. An exception is resistance to the corn earworm Helicoverpa zea (Boddie) in maize (Zea mays L.) because of maysin, a C-glycosyl flavone synthesized in silks via a branch of the well characterized flavonoid pathway. Our results using flavone synthesis as a model QTL system indicate: (i) the importance of regulatory loci as QTLs, (ii) the importance of interconnecting biochemical pathways on product levels, (iii) evidence for “channeling” of intermediates, allowing independent synthesis of related compounds, (iv) the utility of QTL analysis in clarifying the role of specific genes in a biochemical pathway, and (v) identification of a previously unknown locus on chromosome 9S affecting flavone level. A greater understanding of the genetic basis of maysin synthesis and associated corn earworm resistance should lead to improved breeding strategies. More broadly, the insights gained in relating a defined genetic and biochemical pathway affecting a quantitative trait should enhance interpretation of the biological basis of variation for other quantitative traits. PMID:9482823

A Simple Interactive Introduction to Teaching Genetic Engineering

Science.gov (United States)

Child, Paula

2013-01-01

In the UK, at key stage 4, students aged 14-15 studying GCSE Core Science or Unit 1 of the GCSE Biology course are required to be able to describe the process of genetic engineering to produce bacteria that can produce insulin. The simple interactive introduction described in this article allows students to consider the problem, devise a model and…

Quantitative magnetic resonance imaging traits as endophenotypes for genetic mapping in epilepsy

Directory of Open Access Journals (Sweden)

Saud Alhusaini

2016-01-01

Full Text Available Over the last decade, the field of imaging genomics has combined high-throughput genotype data with quantitative magnetic resonance imaging (QMRI measures to identify genes associated with brain structure, cognition, and several brain-related disorders. Despite its successful application in different psychiatric and neurological disorders, the field has yet to be advanced in epilepsy. In this article we examine the relevance of imaging genomics for future genetic studies in epilepsy from three perspectives. First, we discuss prior genome-wide genetic mapping efforts in epilepsy, considering the possibility that some studies may have been constrained by inherent theoretical and methodological limitations of the genome-wide association study (GWAS method. Second, we offer a brief overview of the imaging genomics paradigm, from its original inception, to its role in the discovery of important risk genes in a number of brain-related disorders, and its successful application in large-scale multinational research networks. Third, we provide a comprehensive review of past studies that have explored the eligibility of brain QMRI traits as endophenotypes for epilepsy. While the breadth of studies exploring QMRI-derived endophenotypes in epilepsy remains narrow, robust syndrome-specific neuroanatomical QMRI traits have the potential to serve as accessible and relevant intermediate phenotypes for future genetic mapping efforts in epilepsy.

Systems Level Dissection of Anaerobic Methane Cycling: Quantitative Measurements of Single Cell Ecophysiology, Genetic Mechanisms, and Microbial Interactions

Energy Technology Data Exchange (ETDEWEB)

Orphan, Victoria [California Inst. of Technology (CalTech), Pasadena, CA (United States); Tyson, Gene [University of Queensland, Brisbane Australia; Meile, Christof [University of Georgia, Athens, Georgia; McGlynn, Shawn [California Inst. of Technology (CalTech), Pasadena, CA (United States); Yu, Hang [California Inst. of Technology (CalTech), Pasadena, CA (United States); Chadwick, Grayson [California Inst. of Technology (CalTech), Pasadena, CA (United States); Marlow, Jeffrey [California Inst. of Technology (CalTech), Pasadena, CA (United States); Trembath-Reichert, Elizabeth [California Inst. of Technology (CalTech), Pasadena, CA (United States); Dekas, Anne [California Inst. of Technology (CalTech), Pasadena, CA (United States); Hettich, Robert [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Pan, Chongle [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Ellisman, Mark [University of California San Diego; Hatzenpichler, Roland [California Inst. of Technology (CalTech), Pasadena, CA (United States); Skennerton, Connor [California Inst. of Technology (CalTech), Pasadena, CA (United States); Scheller, Silvan [California Inst. of Technology (CalTech), Pasadena, CA (United States)

2017-12-25

The global biological CH4 cycle is largely controlled through coordinated and often intimate microbial interactions between archaea and bacteria, the majority of which are still unknown or have been only cursorily identified. Members of the methanotrophic archaea, aka ‘ANME’, are believed to play a major role in the cycling of methane in anoxic environments coupled to sulfate, nitrate, and possibly iron and manganese oxides, frequently forming diverse physical and metabolic partnerships with a range of bacteria. The thermodynamic challenges overcome by the ANME and their bacterial partners and corresponding slow rates of growth are common characteristics in anaerobic ecosystems, and, in stark contrast to most cultured microorganisms, this type of energy and resource limited microbial lifestyle is likely the norm in the environment. While we have gained an in-depth systems level understanding of fast-growing, energy-replete microorganisms, comparatively little is known about the dynamics of cell respiration, growth, protein turnover, gene expression, and energy storage in the slow-growing microbial majority. These fundamental properties, combined with the observed metabolic and symbiotic versatility of methanotrophic ANME, make these cooperative microbial systems a relevant (albeit challenging) system to study and for which to develop and optimize culture-independent methodologies, which enable a systems-level understanding of microbial interactions and metabolic networks. We used an integrative systems biology approach to study anaerobic sediment microcosms and methane-oxidizing bioreactors and expanded our understanding of the methanotrophic ANME archaea, their interactions with physically-associated bacteria, ecophysiological characteristics, and underlying genetic basis for cooperative microbial methane-oxidation linked with different terminal electron acceptors. Our approach is inherently multi-disciplinary and multi-scaled, combining transcriptional and

Developments in statistical analysis in quantitative genetics

DEFF Research Database (Denmark)

Sorensen, Daniel

2009-01-01

of genetic means and variances, models for the analysis of categorical and count data, the statistical genetics of a model postulating that environmental variance is partly under genetic control, and a short discussion of models that incorporate massive genetic marker information. We provide an overview......A remarkable research impetus has taken place in statistical genetics since the last World Conference. This has been stimulated by breakthroughs in molecular genetics, automated data-recording devices and computer-intensive statistical methods. The latter were revolutionized by the bootstrap...... and by Markov chain Monte Carlo (McMC). In this overview a number of specific areas are chosen to illustrate the enormous flexibility that McMC has provided for fitting models and exploring features of data that were previously inaccessible. The selected areas are inferences of the trajectories over time...

Estimation of genetic parameters and detection of quantitative trait loci for metabolites in Danish Holstein milk

DEFF Research Database (Denmark)

Buitenhuis, Albert Johannes; Sundekilde, Ulrik; Poulsen, Nina Aagaard

2013-01-01

Small components and metabolites in milk are significant for the utilization of milk, not only in dairy food production but also as disease predictors in dairy cattle. This study focused on estimation of genetic parameters and detection of quantitative trait loci for metabolites in bovine milk. F...... for lactic acid to >0.8 for orotic acid and β-hydroxybutyrate. A single SNP association analysis revealed 7 genome-wide significant quantitative trait loci [malonate: Bos taurus autosome (BTA)2 and BTA7; galactose-1-phosphate: BTA2; cis-aconitate: BTA11; urea: BTA12; carnitine: BTA25...

Genetic Interactions of STAT3 and Anticancer Drug Development

International Nuclear Information System (INIS)

Fang, Bingliang

2014-01-01

Signal transducer and activator of transcription 3 (STAT3) plays critical roles in tumorigenesis and malignant evolution and has been intensively studied as a therapeutic target for cancer. A number of STAT3 inhibitors have been evaluated for their antitumor activity in vitro and in vivo in experimental tumor models and several approved therapeutic agents have been reported to function as STAT3 inhibitors. Nevertheless, most STAT3 inhibitors have yet to be translated to clinical evaluation for cancer treatment, presumably because of pharmacokinetic, efficacy, and safety issues. In fact, a major cause of failure of anticancer drug development is lack of efficacy. Genetic interactions among various cancer-related pathways often provide redundant input from parallel and/or cooperative pathways that drives and maintains survival environments for cancer cells, leading to low efficacy of single-target agents. Exploiting genetic interactions of STAT3 with other cancer-related pathways may provide molecular insight into mechanisms of cancer resistance to pathway-targeted therapies and strategies for development of more effective anticancer agents and treatment regimens. This review focuses on functional regulation of STAT3 activity; possible interactions of the STAT3, RAS, epidermal growth factor receptor, and reduction-oxidation pathways; and molecular mechanisms that modulate therapeutic efficacies of STAT3 inhibitors

Lab-on-capillary: a rapid, simple and quantitative genetic analysis platform integrating nucleic acid extraction, amplification and detection.

Science.gov (United States)

Fu, Yu; Zhou, Xiaoming; Xing, Da

2017-12-05

In this work, we describe for the first time a genetic diagnosis platform employing a polydiallyldimethylammonium chloride (PDDA)-modified capillary and a liquid-based thermalization system for rapid, simple and quantitative DNA analysis with minimal user interaction. Positively charged PDDA is modified on the inner surface of the silicon dioxide capillary by using an electrostatic self-assembly approach that allows the negatively charged DNA to be separated from the lysate in less than 20 seconds. The capillary loaded with the PCR mix is incorporated in the thermalization system, which can achieve on-site real-time PCR. This system is based on the circulation of pre-heated liquids in the chamber, allowing for high-speed thermalization of the capillary and fast amplification. Multiple targets can be simultaneously analysed with multiplex spatial melting. Starting with live Escherichia coli (E. coli) cells in milk, as a realistic sample, the current method can achieve DNA extraction, amplification, and detection within 40 min.

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

Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis.

Directory of Open Access Journals (Sweden)

Brett A McKinney

2009-03-01

Full Text Available Evidence from human genetic studies of several disorders suggests that interactions between alleles at multiple genes play an important role in influencing phenotypic expression. Analytical methods for identifying Mendelian disease genes are not appropriate when applied to common multigenic diseases, because such methods investigate association with the phenotype only one genetic locus at a time. New strategies are needed that can capture the spectrum of genetic effects, from Mendelian to multifactorial epistasis. Random Forests (RF and Relief-F are two powerful machine-learning methods that have been studied as filters for genetic case-control data due to their ability to account for the context of alleles at multiple genes when scoring the relevance of individual genetic variants to the phenotype. However, when variants interact strongly, the independence assumption of RF in the tree node-splitting criterion leads to diminished importance scores for relevant variants. Relief-F, on the other hand, was designed to detect strong interactions but is sensitive to large backgrounds of variants that are irrelevant to classification of the phenotype, which is an acute problem in genome-wide association studies. To overcome the weaknesses of these data mining approaches, we develop Evaporative Cooling (EC feature selection, a flexible machine learning method that can integrate multiple importance scores while removing irrelevant genetic variants. To characterize detailed interactions, we construct a genetic-association interaction network (GAIN, whose edges quantify the synergy between variants with respect to the phenotype. We use simulation analysis to show that EC is able to identify a wide range of interaction effects in genetic association data. We apply the EC filter to a smallpox vaccine cohort study of single nucleotide polymorphisms (SNPs and infer a GAIN for a collection of SNPs associated with adverse events. Our results suggest an important

Mapping determinants of gene expression plasticity by genetical genomics in C. elegans.

Directory of Open Access Journals (Sweden)

Yang Li

2006-12-01

Full Text Available Recent genetical genomics studies have provided intimate views on gene regulatory networks. Gene expression variations between genetically different individuals have been mapped to the causal regulatory regions, termed expression quantitative trait loci. Whether the environment-induced plastic response of gene expression also shows heritable difference has not yet been studied. Here we show that differential expression induced by temperatures of 16 degrees C and 24 degrees C has a strong genetic component in Caenorhabditis elegans recombinant inbred strains derived from a cross between strains CB4856 (Hawaii and N2 (Bristol. No less than 59% of 308 trans-acting genes showed a significant eQTL-by-environment interaction, here termed plasticity quantitative trait loci. In contrast, only 8% of an estimated 188 cis-acting genes showed such interaction. This indicates that heritable differences in plastic responses of gene expression are largely regulated in trans. This regulation is spread over many different regulators. However, for one group of trans-genes we found prominent evidence for a common master regulator: a transband of 66 coregulated genes appeared at 24 degrees C. Our results suggest widespread genetic variation of differential expression responses to environmental impacts and demonstrate the potential of genetical genomics for mapping the molecular determinants of phenotypic plasticity.

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation.

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Nath, Artika P; Ritchie, Scott C; Byars, Sean G; Fearnley, Liam G; Havulinna, Aki S; Joensuu, Anni; Kangas, Antti J; Soininen, Pasi; Wennerström, Annika; Milani, Lili; Metspalu, Andres; Männistö, Satu; Würtz, Peter; Kettunen, Johannes; Raitoharju, Emma; Kähönen, Mika; Juonala, Markus; Palotie, Aarno; Ala-Korpela, Mika; Ripatti, Samuli; Lehtimäki, Terho; Abraham, Gad; Raitakari, Olli; Salomaa, Veikko; Perola, Markus; Inouye, Michael

2017-08-01

Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up. We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus. This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.

Genetic variability, heritability and genetic advance of quantitative ...

African Journals Online (AJOL)

ONOS

2010-05-10

May 10, 2010 ... coefficient of variation; h2, heritability; GA, genetic advance;. EMS, ethyl methane ... The analysis of variance (ANOVA) revealed the significance degree among the ... fullest extent. The estimates of range, phenotypic and.

On the use of sibling recurrence risks to select environmental factors liable to interact with genetic risk factors. : GxE interaction and sibling recurrence risk

OpenAIRE

Kazma, Rémi; Bonaïti-Pellié, Catherine; Norris, Jill,; Génin, Emmanuelle

2010-01-01

International audience; Gene-environment interactions are likely to be involved in the susceptibility to multifactorial diseases but are difficult to detect. Available methods usually concentrate on some particular genetic and environmental factors. In this paper, we propose a new method to determine whether a given exposure is susceptible to interact with unknown genetic factors. Rather than focusing on a specific genetic factor, the degree of familial aggregation is used as a surrogate for ...

Interactive genetic counseling role-play: a novel educational strategy for family physicians.

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Blaine, Sean M; Carroll, June C; Rideout, Andrea L; Glendon, Gord; Meschino, Wendy; Shuman, Cheryl; Telner, Deanna; Van Iderstine, Natasha; Permaul, Joanne

2008-04-01

Family physicians (FPs) are increasingly involved in delivering genetic services. Familiarization with aspects of genetic counseling may enable FPs to help patients make informed choices. Exploration of interactive role-play as a means to raise FPs' awareness of the process and content of genetic counseling. FPs attending two large Canadian family medicine conferences in 2005 were eligible -- 93 participated. FPs discussed a case during a one-on-one session with a genetic counselor. Evaluation involved pre and post intervention questionnaires FPs' baseline genetic knowledge was self-rated as uniformly poor. Baseline confidence was highest in eliciting family history and providing psychosocial support and lowest in discussing risks/benefits of genetic testing and counseling process. Post-intervention, 80% of FPs had better appreciation of family history and 97% indicated this was an effective learning experience. Role-play with FPs is effective in raising awareness of the process and content of genetic counseling and may be applied to other health disciplines.

The genetics of music accomplishment: evidence for gene-environment correlation and interaction.

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Hambrick, David Z; Tucker-Drob, Elliot M

2015-02-01

Theories of skilled performance that emphasize training history, such as K. Anders Ericsson and colleagues' deliberate-practice theory, have received a great deal of recent attention in both the scientific literature and the popular press. Twin studies, however, have demonstrated evidence for moderate-to-strong genetic influences on skilled performance. Focusing on musical accomplishment in a sample of over 800 pairs of twins, we found evidence for gene-environment correlation, in the form of a genetic effect on music practice. However, only about one quarter of the genetic effect on music accomplishment was explained by this genetic effect on music practice, suggesting that genetically influenced factors other than practice contribute to individual differences in music accomplishment. We also found evidence for gene-environment interaction, such that genetic effects on music accomplishment were most pronounced among those engaging in music practice, suggesting that genetic potentials for skilled performance are most fully expressed and fostered by practice.

Genetics: A New Landscape for Medical Geography

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Carrel, Margaret; Emch, Michael

2014-01-01

The emergence and re-emergence of human pathogens resistant to medical treatment will present a challenge to the international public health community in the coming decades. Geography is uniquely positioned to examine the progressive evolution of pathogens across space and through time, and to link molecular change to interactions between population and environmental drivers. Landscape as an organizing principle for the integration of natural and cultural forces has a long history in geography, and, more specifically, in medical geography. Here, we explore the role of landscape in medical geography, the emergent field of landscape genetics, and the great potential that exists in the combination of these two disciplines. We argue that landscape genetics can enhance medical geographic studies of local-level disease environments with quantitative tests of how human-environment interactions influence pathogenic characteristics. In turn, such analyses can expand theories of disease diffusion to the molecular scale and distinguish the important factors in ecologies of disease that drive genetic change of pathogens. PMID:24558292

Genetic control of environmental variation of two quantitative traits of Drosophila melanogaster revealed by whole-genome sequencing

DEFF Research Database (Denmark)

Sørensen, Peter; de los Campos, Gustavo; Morgante, Fabio

2015-01-01

and others more volatile performance. Understanding the mechanisms responsible for environmental variability not only informs medical questions but is relevant in evolution and in agricultural science. In this work fully sequenced inbred lines of Drosophila melanogaster were analyzed to study the nature...... of genetic control of environmental variance for two quantitative traits: starvation resistance (SR) and startle response (SL). The evidence for genetic control of environmental variance is compelling for both traits. Sequence information is incorporated in random regression models to study the underlying...... genetic signals, which are shown to be different in the two traits. Genomic variance in sexual dimorphism was found for SR but not for SL. Indeed, the proportion of variance captured by sequence information and the contribution to this variance from four chromosome segments differ between sexes in SR...

G×E Interaction and Pluralism in the Postgenomic Era

OpenAIRE

Perbal, Laurence

2013-01-01

Behavioral genetics is in a postgenomic era, and this paper illustrates this epistemological evolution using the debate between developmental criticism and traditional biometric genetics about gene x environment interaction. Quantitative geneticists are blamed for failing to respect the complexity of development; as a response, they claim a defensive position, called isolationist pluralism, which supports the idea that studying development is not their problem. But postgenomics seems to have ...

Expression Profiling of Human Genetic and Protein Interaction Networks in Type 1 Diabetes

DEFF Research Database (Denmark)

Brunak, Søren; Bergholdt, R; Brorsson, C

2009-01-01

Proteins contributing to a complex disease are often members of the same functional pathways. Elucidation of such pathways may provide increased knowledge about functional mechanisms underlying disease. By combining genetic interactions in Type 1 Diabetes (T1D) with protein interaction data we have...

Limits to behavioral evolution: the quantitative genetics of a complex trait under directional selection.

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Careau, Vincent; Wolak, Matthew E; Carter, Patrick A; Garland, Theodore

2013-11-01

Replicated selection experiments provide a powerful way to study how "multiple adaptive solutions" may lead to differences in the quantitative-genetic architecture of selected traits and whether this may translate into differences in the timing at which evolutionary limits are reached. We analyze data from 31 generations (n=17,988) of selection on voluntary wheel running in house mice. The rate of initial response, timing of selection limit, and height of the plateau varied significantly between sexes and among the four selected lines. Analyses of litter size and realized selection differentials seem to rule out counterposing natural selection as a cause of the selection limits. Animal-model analyses showed that although the additive genetic variance was significantly lower in selected than control lines, both before and after the limits, the decrease was not sufficient to explain the limits. Moreover, directional selection promoted a negative covariance between additive and maternal genetic variance over the first 10 generations. These results stress the importance of replication in selection studies of higher-level traits and highlight the fact that long-term predictions of response to selection are not necessarily expected to be linear because of the variable effects of selection on additive genetic variance and maternal effects. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.

Characterizing Male–Female Interactions Using Natural Genetic Variation in Drosophila melanogaster

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Reinhart, Michael; Carney, Tara; Clark, Andrew G.

2015-01-01

Drosophila melanogaster females commonly mate with multiple males establishing the opportunity for pre- and postcopulatory sexual selection. Traits impacting sexual selection can be affected by a complex interplay of the genotypes of the competing males, the genotype of the female, and compatibilities between the males and females. We scored males from 96 2nd and 94 3rd chromosome substitution lines for traits affecting reproductive success when mated with females from 3 different genetic backgrounds. The traits included male-induced female refractoriness, male remating ability, the proportion of offspring sired under competitive conditions and male-induced female fecundity. We observed significant effects of male line, female genetic background, and strong male by female interactions. Some males appeared to be “generalists” and performed consistently across the different females; other males appeared to be “specialists” and performed very well with a particular female and poorly with others. “Specialist” males did not, however, prefer to court those females with whom they had the highest reproductive fitness. Using 143 polymorphisms in male reproductive genes, we mapped several genes that had consistent effects across the different females including a derived, high fitness allele in Acp26Aa that may be the target of adaptive evolution. We also identified a polymorphism upstream of PebII that may interact with the female genetic background to affect male-induced refractoriness to remating. These results suggest that natural variation in PebII might contribute to the observed male–female interactions. PMID:25425680

Identification of genetic components involved in Lotus-endophyte interactions

DEFF Research Database (Denmark)

Zgadzaj, Rafal Lukasz

of growth hormones or nitrogen fixation. However, the genes involved in plant-endophyte interactions and bacterial accomodation within plant tissues are not known. In order to shed some light on such processes, an approach “one host-one endophyte” was chosen. The focus on a single plant species and a single......Endophytes are microorganisms capable of colonising plant tissues without inducing host defense responses. They have a large impact on plants, since they can modulate plant responses to pathogens, herbivores and environmental stress. They can also induce plant growth promotion through synthesis...... bacterial strain aimed at obtaining a reliable and easy to handle system for plant-microsymbiont interaction research. Two different methods were tested for their usefulness in identification of genetic components involved in plant-endophyte interactions. The first method was based on measuring growth...

Bigger Is Fitter? Quantitative Genetic Decomposition of Selection Reveals an Adaptive Evolutionary Decline of Body Mass in a Wild Rodent Population.

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Timothée Bonnet

2017-01-01

Full Text Available In natural populations, quantitative trait dynamics often do not appear to follow evolutionary predictions. Despite abundant examples of natural selection acting on heritable traits, conclusive evidence for contemporary adaptive evolution remains rare for wild vertebrate populations, and phenotypic stasis seems to be the norm. This so-called "stasis paradox" highlights our inability to predict evolutionary change, which is especially concerning within the context of rapid anthropogenic environmental change. While the causes underlying the stasis paradox are hotly debated, comprehensive attempts aiming at a resolution are lacking. Here, we apply a quantitative genetic framework to individual-based long-term data for a wild rodent population and show that despite a positive association between body mass and fitness, there has been a genetic change towards lower body mass. The latter represents an adaptive response to viability selection favouring juveniles growing up to become relatively small adults, i.e., with a low potential adult mass, which presumably complete their development earlier. This selection is particularly strong towards the end of the snow-free season, and it has intensified in recent years, coinciding which a change in snowfall patterns. Importantly, neither the negative evolutionary change, nor the selective pressures that drive it, are apparent on the phenotypic level, where they are masked by phenotypic plasticity and a non causal (i.e., non genetic positive association between body mass and fitness, respectively. Estimating selection at the genetic level enabled us to uncover adaptive evolution in action and to identify the corresponding phenotypic selective pressure. We thereby demonstrate that natural populations can show a rapid and adaptive evolutionary response to a novel selective pressure, and that explicitly (quantitative genetic models are able to provide us with an understanding of the causes and consequences of

Bigger Is Fitter? Quantitative Genetic Decomposition of Selection Reveals an Adaptive Evolutionary Decline of Body Mass in a Wild Rodent Population

Science.gov (United States)

Wandeler, Peter; Camenisch, Glauco

2017-01-01

In natural populations, quantitative trait dynamics often do not appear to follow evolutionary predictions. Despite abundant examples of natural selection acting on heritable traits, conclusive evidence for contemporary adaptive evolution remains rare for wild vertebrate populations, and phenotypic stasis seems to be the norm. This so-called “stasis paradox” highlights our inability to predict evolutionary change, which is especially concerning within the context of rapid anthropogenic environmental change. While the causes underlying the stasis paradox are hotly debated, comprehensive attempts aiming at a resolution are lacking. Here, we apply a quantitative genetic framework to individual-based long-term data for a wild rodent population and show that despite a positive association between body mass and fitness, there has been a genetic change towards lower body mass. The latter represents an adaptive response to viability selection favouring juveniles growing up to become relatively small adults, i.e., with a low potential adult mass, which presumably complete their development earlier. This selection is particularly strong towards the end of the snow-free season, and it has intensified in recent years, coinciding which a change in snowfall patterns. Importantly, neither the negative evolutionary change, nor the selective pressures that drive it, are apparent on the phenotypic level, where they are masked by phenotypic plasticity and a non causal (i.e., non genetic) positive association between body mass and fitness, respectively. Estimating selection at the genetic level enabled us to uncover adaptive evolution in action and to identify the corresponding phenotypic selective pressure. We thereby demonstrate that natural populations can show a rapid and adaptive evolutionary response to a novel selective pressure, and that explicitly (quantitative) genetic models are able to provide us with an understanding of the causes and consequences of selection that is

Modelling the co-evolution of indirect genetic effects and inherited variability.

Science.gov (United States)

Marjanovic, Jovana; Mulder, Han A; Rönnegård, Lars; Bijma, Piter

2018-03-28

When individuals interact, their phenotypes may be affected not only by their own genes but also by genes in their social partners. This phenomenon is known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, however, competition not only affects trait levels of individuals, but also inflates variability of trait values among individuals. In the field of quantitative genetics, the variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Such studies, however, consider only the genetic effect of the focal individual on trait variability and do not make a connection to competition. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, the current quantitative genetic models of IGE and inherited variability do not allow for such a relationship. The lack of quantitative genetic models that connect IGEs to inherited variability limits our understanding of the potential of variability to respond to selection, both in nature and agriculture. Models of trait levels, for example, show that IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to variability of trait values. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of the genetic and environmental effects of the focal individual and of the difference in trait value between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait, which is a measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. In contrast to the existing quantitative genetic models, our model allows for co-evolution of

The genetics of childhood obesity and interaction with dietary macronutrients.

Science.gov (United States)

Garver, William S; Newman, Sara B; Gonzales-Pacheco, Diana M; Castillo, Joseph J; Jelinek, David; Heidenreich, Randall A; Orlando, Robert A

2013-05-01

The genes contributing to childhood obesity are categorized into three different types based on distinct genetic and phenotypic characteristics. These types of childhood obesity are represented by rare monogenic forms of syndromic or non-syndromic childhood obesity, and common polygenic childhood obesity. In some cases, genetic susceptibility to these forms of childhood obesity may result from different variations of the same gene. Although the prevalence for rare monogenic forms of childhood obesity has not increased in recent times, the prevalence of common childhood obesity has increased in the United States and developing countries throughout the world during the past few decades. A number of recent genome-wide association studies and mouse model studies have established the identification of susceptibility genes contributing to common childhood obesity. Accumulating evidence suggests that this type of childhood obesity represents a complex metabolic disease resulting from an interaction with environmental factors, including dietary macronutrients. The objective of this article is to provide a review on the origins, mechanisms, and health consequences of obesity susceptibility genes and interaction with dietary macronutrients that predispose to childhood obesity. It is proposed that increased knowledge of these obesity susceptibility genes and interaction with dietary macronutrients will provide valuable insight for individual, family, and community preventative lifestyle intervention, and eventually targeted nutritional and medicinal therapies.

Quantitative Seq-LGS: Genome-Wide Identification of Genetic Drivers of Multiple Phenotypes in Malaria Parasites

KAUST Repository

Abkallo, Hussein M.

2016-10-01

Identifying the genetic determinants of phenotypes that impact on disease severity is of fundamental importance for the design of new interventions against malaria. Traditionally, such discovery has relied on labor-intensive approaches that require significant investments of time and resources. By combining Linkage Group Selection (LGS), quantitative whole genome population sequencing and a novel mathematical modeling approach (qSeq-LGS), we simultaneously identified multiple genes underlying two distinct phenotypes, identifying novel alleles for growth rate and strain specific immunity (SSI), while removing the need for traditionally required steps such as cloning, individual progeny phenotyping and marker generation. The detection of novel variants, verified by experimental phenotyping methods, demonstrates the remarkable potential of this approach for the identification of genes controlling selectable phenotypes in malaria and other apicomplexan parasites for which experimental genetic crosses are amenable.

Genomic value prediction for quantitative traits under the epistatic model

Directory of Open Access Journals (Sweden)

Xu Shizhong

2011-01-01

Full Text Available Abstract Background Most quantitative traits are controlled by multiple quantitative trait loci (QTL. The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects and marker pairs (epistatic effects to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement. Results In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive effects were used for prediction. When the interaction (epistatic effects were also included in the model, the squared correlation coefficient reached 0.78. Conclusions This study provided an excellent example for the application of genome selection to plant breeding.

Development and evaluation of event-specific quantitative PCR method for genetically modified soybean A2704-12.

Science.gov (United States)

Takabatake, Reona; Akiyama, Hiroshi; Sakata, Kozue; Onishi, Mari; Koiwa, Tomohiro; Futo, Satoshi; Minegishi, Yasutaka; Teshima, Reiko; Mano, Junichi; Furui, Satoshi; Kitta, Kazumi

2011-01-01

A novel real-time PCR-based analytical method was developed for the event-specific quantification of a genetically modified (GM) soybean event; A2704-12. During the plant transformation, DNA fragments derived from pUC19 plasmid were integrated in A2704-12, and the region was found to be A2704-12 specific. The pUC19-derived DNA sequences were used as primers for the specific detection of A2704-12. We first tried to construct a standard plasmid for A2704-12 quantification using pUC19. However, non-specific signals appeared with both qualitative and quantitative PCR analyses using the specific primers with pUC19 as a template, and we then constructed a plasmid using pBR322. The conversion factor (C(f)), which is required to calculate the amount of the genetically modified organism (GMO), was experimentally determined with two real-time PCR instruments, the Applied Biosystems 7900HT and the Applied Biosystems 7500. The determined C(f) values were both 0.98. The quantitative method was evaluated by means of blind tests in multi-laboratory trials using the two real-time PCR instruments. The limit of quantitation for the method was estimated to be 0.1%. The trueness and precision were evaluated as the bias and reproducibility of relative standard deviation (RSD(R)), and the determined bias and RSD(R) values for the method were each less than 20%. These results suggest that the developed method would be suitable for practical analyses for the detection and quantification of A2704-12.

Detecting high-order interactions of single nucleotide polymorphisms using genetic programming.

Science.gov (United States)

Nunkesser, Robin; Bernholt, Thorsten; Schwender, Holger; Ickstadt, Katja; Wegener, Ingo

2007-12-15

Not individual single nucleotide polymorphisms (SNPs), but high-order interactions of SNPs are assumed to be responsible for complex diseases such as cancer. Therefore, one of the major goals of genetic association studies concerned with such genotype data is the identification of these high-order interactions. This search is additionally impeded by the fact that these interactions often are only explanatory for a relatively small subgroup of patients. Most of the feature selection methods proposed in the literature, unfortunately, fail at this task, since they can either only identify individual variables or interactions of a low order, or try to find rules that are explanatory for a high percentage of the observations. In this article, we present a procedure based on genetic programming and multi-valued logic that enables the identification of high-order interactions of categorical variables such as SNPs. This method called GPAS cannot only be used for feature selection, but can also be employed for discrimination. In an application to the genotype data from the GENICA study, an association study concerned with sporadic breast cancer, GPAS is able to identify high-order interactions of SNPs leading to a considerably increased breast cancer risk for different subsets of patients that are not found by other feature selection methods. As an application to a subset of the HapMap data shows, GPAS is not restricted to association studies comprising several 10 SNPs, but can also be employed to analyze whole-genome data. Software can be downloaded from http://ls2-www.cs.uni-dortmund.de/~nunkesser/#Software

Interaction between common breast cancer susceptibility variants, genetic ancestry, and nongenetic risk factors in Hispanic women.

Science.gov (United States)

Fejerman, Laura; Stern, Mariana C; John, Esther M; Torres-Mejía, Gabriela; Hines, Lisa M; Wolff, Roger K; Baumgartner, Kathy B; Giuliano, Anna R; Ziv, Elad; Pérez-Stable, Eliseo J; Slattery, Martha L

2015-11-01

Most genetic variants associated with breast cancer risk have been discovered in women of European ancestry, and only a few genome-wide association studies (GWAS) have been conducted in minority groups. This research disparity persists in post-GWAS gene-environment interaction analyses. We tested the interaction between hormonal and lifestyle risk factors for breast cancer, and ten GWAS-identified SNPs among 2,107 Hispanic women with breast cancer and 2,587 unaffected controls, to gain insight into a previously reported gene by ancestry interaction in this population. We estimated genetic ancestry with a set of 104 ancestry-informative markers selected to discriminate between Indigenous American and European ancestry. We used logistic regression models to evaluate main effects and interactions. We found that the rs13387042-2q35(G/A) SNP was associated with breast cancer risk only among postmenopausal women who never used hormone therapy [per A allele OR: 0.94 (95% confidence intervals, 0.74-1.20), 1.20 (0.94-1.53), and 1.49 (1.28-1.75) for current, former, and never hormone therapy users, respectively, Pinteraction 0.002] and premenopausal women who breastfed >12 months [OR: 1.01 (0.72-1.42), 1.19 (0.98-1.45), and 1.69 (1.26-2.26) for never, 12 months breastfeeding, respectively, Pinteraction 0.014]. The correlation between genetic ancestry, hormone replacement therapy use, and breastfeeding behavior partially explained a previously reported interaction between a breast cancer risk variant and genetic ancestry in Hispanic women. These results highlight the importance of understanding the interplay between genetic ancestry, genetics, and nongenetic risk factors and their contribution to breast cancer risk. ©2015 American Association for Cancer Research.

Quantitative image analysis for investigating cell-matrix interactions

Science.gov (United States)

Burkel, Brian; Notbohm, Jacob

2017-07-01

The extracellular matrix provides both chemical and physical cues that control cellular processes such as migration, division, differentiation, and cancer progression. Cells can mechanically alter the matrix by applying forces that result in matrix displacements, which in turn may localize to form dense bands along which cells may migrate. To quantify the displacements, we use confocal microscopy and fluorescent labeling to acquire high-contrast images of the fibrous material. Using a technique for quantitative image analysis called digital volume correlation, we then compute the matrix displacements. Our experimental technology offers a means to quantify matrix mechanics and cell-matrix interactions. We are now using these experimental tools to modulate mechanical properties of the matrix to study cell contraction and migration.

Genome-wide conserved non-coding microsatellite (CNMS) marker-based integrative genetical genomics for quantitative dissection of seed weight in chickpea.

Science.gov (United States)

Bajaj, Deepak; Saxena, Maneesha S; Kujur, Alice; Das, Shouvik; Badoni, Saurabh; Tripathi, Shailesh; Upadhyaya, Hari D; Gowda, C L L; Sharma, Shivali; Singh, Sube; Tyagi, Akhilesh K; Parida, Swarup K

2015-03-01

Phylogenetic footprinting identified 666 genome-wide paralogous and orthologous CNMS (conserved non-coding microsatellite) markers from 5'-untranslated and regulatory regions (URRs) of 603 protein-coding chickpea genes. The (CT)n and (GA)n CNMS carrying CTRMCAMV35S and GAGA8BKN3 regulatory elements, respectively, are abundant in the chickpea genome. The mapped genic CNMS markers with robust amplification efficiencies (94.7%) detected higher intraspecific polymorphic potential (37.6%) among genotypes, implying their immense utility in chickpea breeding and genetic analyses. Seventeen differentially expressed CNMS marker-associated genes showing strong preferential and seed tissue/developmental stage-specific expression in contrasting genotypes were selected to narrow down the gene targets underlying seed weight quantitative trait loci (QTLs)/eQTLs (expression QTLs) through integrative genetical genomics. The integration of transcript profiling with seed weight QTL/eQTL mapping, molecular haplotyping, and association analyses identified potential molecular tags (GAGA8BKN3 and RAV1AAT regulatory elements and alleles/haplotypes) in the LOB-domain-containing protein- and KANADI protein-encoding transcription factor genes controlling the cis-regulated expression for seed weight in the chickpea. This emphasizes the potential of CNMS marker-based integrative genetical genomics for the quantitative genetic dissection of complex seed weight in chickpea. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology.

PCR-free quantitative detection of genetically modified organism from raw materials. An electrochemiluminescence-based bio bar code method.

Science.gov (United States)

Zhu, Debin; Tang, Yabing; Xing, Da; Chen, Wei R

2008-05-15

A bio bar code assay based on oligonucleotide-modified gold nanoparticles (Au-NPs) provides a PCR-free method for quantitative detection of nucleic acid targets. However, the current bio bar code assay requires lengthy experimental procedures including the preparation and release of bar code DNA probes from the target-nanoparticle complex and immobilization and hybridization of the probes for quantification. Herein, we report a novel PCR-free electrochemiluminescence (ECL)-based bio bar code assay for the quantitative detection of genetically modified organism (GMO) from raw materials. It consists of tris-(2,2'-bipyridyl) ruthenium (TBR)-labeled bar code DNA, nucleic acid hybridization using Au-NPs and biotin-labeled probes, and selective capture of the hybridization complex by streptavidin-coated paramagnetic beads. The detection of target DNA is realized by direct measurement of ECL emission of TBR. It can quantitatively detect target nucleic acids with high speed and sensitivity. This method can be used to quantitatively detect GMO fragments from real GMO products.

Quantitative analysis of fatty-acid-based biofuels produced by wild-type and genetically engineered cyanobacteria by gas chromatography-mass spectrometry.

Science.gov (United States)

Guan, Wenna; Zhao, Hui; Lu, Xuefeng; Wang, Cong; Yang, Menglong; Bai, Fali

2011-11-11

Simple and rapid quantitative determination of fatty-acid-based biofuels is greatly important for the study of genetic engineering progress for biofuels production by microalgae. Ideal biofuels produced from biological systems should be chemically similar to petroleum, like fatty-acid-based molecules including free fatty acids, fatty acid methyl esters, fatty acid ethyl esters, fatty alcohols and fatty alkanes. This study founded a gas chromatography-mass spectrometry (GC-MS) method for simultaneous quantification of seven free fatty acids, nine fatty acid methyl esters, five fatty acid ethyl esters, five fatty alcohols and three fatty alkanes produced by wild-type Synechocystis PCC 6803 and its genetically engineered strain. Data obtained from GC-MS analyses were quantified using internal standard peak area comparisons. The linearity, limit of detection (LOD) and precision (RSD) of the method were evaluated. The results demonstrated that fatty-acid-based biofuels can be directly determined by GC-MS without derivation. Therefore, rapid and reliable quantitative analysis of fatty-acid-based biofuels produced by wild-type and genetically engineered cyanobacteria can be achieved using the GC-MS method founded in this work. Copyright © 2011 Elsevier B.V. All rights reserved.

Genetic Diversity of Globally Dispersed Lacustrine Group I Haptophytes: Implications for Quantitative Temperature Reconstructions

Science.gov (United States)

Richter, N.; Longo, W. M.; Amaral-Zettler, L. A.; Huang, Y.

2017-12-01

There are significant uncertainties surrounding the forcings that drive terrestrial temperature changes on local and regional scales. Quantitative temperature reconstructions from terrestrial sites, such as lakes, help to unravel the fundamental processes that drive changes in temperature on different temporal and spatial scales. Recent studies at Brown University show that distinct alkenones, long chain ketones produced by haptophytes, are found in many freshwater, alkaline lakes in the Northern Hemisphere, highlighting these systems as targets for quantitative continental temperature reconstructions. These freshwater alkenones are produced by the Group I haptophyte phylotype and are characterized by a distinct signature: the presence of isomeric tri-unsaturated ketones and absence of alkenoates. There are currently no cultured representatives of the "Group I" haptophytes, hence they are only known based on their rRNA gene signatures. Here we present robust evidence that Northern Hemispheric freshwater, alkaline lakes with the characteristic "Group I" alkenone signature all host the same clade of Isochrysidales haptophytes. We employed next generation DNA amplicon sequencing to target haptophyte specific hypervariable regions of the large and small-subunit ribosomal RNA gene from 13 different lakes from three continents (i.e., North America, Europe, and Asia). Combined with previously published sequences, our genetic data show that the Group I haptophyte is genetically diverse on a regional and global scale, and even within the same lake. We present two case studies from a suite of five lakes in Alaska and three in Iceland to assess the impact of various environmental factors affecting Group I diversity and alkenone production. Despite the genetic diversity in this group, the overall ketone signature is conserved. Based on global surface sediment samples and in situ Alaskan lake calibrations, alkenones produced by different operational taxonomic units of the Group

Establishment of a quantitative ELISA capable of determining peptide - MHC class I interaction

DEFF Research Database (Denmark)

Sylvester-Hvid, C; Kristensen, N; Blicher, T

2002-01-01

dependent manner. Here, we exploit the availability of these molecules to generate a quantitative ELISA-based assay capable of measuring the affinity of the interaction between peptide and MHC-I. This assay is simple and sensitive, and one can easily envisage that the necessary reagents, standards...

Genetic variants influencing phenotypic variance heterogeneity.

Science.gov (United States)

Ek, Weronica E; Rask-Andersen, Mathias; Karlsson, Torgny; Enroth, Stefan; Gyllensten, Ulf; Johansson, Åsa

2018-03-01

Most genetic studies identify genetic variants associated with disease risk or with the mean value of a quantitative trait. More rarely, genetic variants associated with variance heterogeneity are considered. In this study, we have identified such variance single-nucleotide polymorphisms (vSNPs) and examined if these represent biological gene × gene or gene × environment interactions or statistical artifacts caused by multiple linked genetic variants influencing the same phenotype. We have performed a genome-wide study, to identify vSNPs associated with variance heterogeneity in DNA methylation levels. Genotype data from over 10 million single-nucleotide polymorphisms (SNPs), and DNA methylation levels at over 430 000 CpG sites, were analyzed in 729 individuals. We identified vSNPs for 7195 CpG sites (P mean DNA methylation levels. We further showed that variance heterogeneity between genotypes mainly represents additional, often rare, SNPs in linkage disequilibrium (LD) with the respective vSNP and for some vSNPs, multiple low frequency variants co-segregating with one of the vSNP alleles. Therefore, our results suggest that variance heterogeneity of DNA methylation mainly represents phenotypic effects by multiple SNPs, rather than biological interactions. Such effects may also be important for interpreting variance heterogeneity of more complex clinical phenotypes.

A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits

OpenAIRE

Gui, Jiang; Moore, Jason H.; Williams, Scott M.; Andrews, Peter; Hillege, Hans L.; van der Harst, Pim; Navis, Gerjan; Van Gilst, Wiek H.; Asselbergs, Folkert W.; Gilbert-Diamond, Diane

2013-01-01

We present an extension of the two-class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP-SNP interactions in the context of a quantitative trait. The proposed Quantitative MDR (QMDR) method handles continuous data by modifying MDR's constructive induction algorithm to use a T-test. QMDR replaces the balanced accuracy metric with a T-test statistic as the score to determine the best interaction model. We used a simulation to ide...

Mouse IDGenes: a reference database for genetic interactions in the developing mouse brain.

Science.gov (United States)

Matthes, Michaela; Preusse, Martin; Zhang, Jingzhong; Schechter, Julia; Mayer, Daniela; Lentes, Bernd; Theis, Fabian; Prakash, Nilima; Wurst, Wolfgang; Trümbach, Dietrich

2014-01-01

The study of developmental processes in the mouse and other vertebrates includes the understanding of patterning along the anterior-posterior, dorsal-ventral and medial- lateral axis. Specifically, neural development is also of great clinical relevance because several human neuropsychiatric disorders such as schizophrenia, autism disorders or drug addiction and also brain malformations are thought to have neurodevelopmental origins, i.e. pathogenesis initiates during childhood and adolescence. Impacts during early neurodevelopment might also predispose to late-onset neurodegenerative disorders, such as Parkinson's disease. The neural tube develops from its precursor tissue, the neural plate, in a patterning process that is determined by compartmentalization into morphogenetic units, the action of local signaling centers and a well-defined and locally restricted expression of genes and their interactions. While public databases provide gene expression data with spatio-temporal resolution, they usually neglect the genetic interactions that govern neural development. Here, we introduce Mouse IDGenes, a reference database for genetic interactions in the developing mouse brain. The database is highly curated and offers detailed information about gene expressions and the genetic interactions at the developing mid-/hindbrain boundary. To showcase the predictive power of interaction data, we infer new Wnt/β-catenin target genes by machine learning and validate one of them experimentally. The database is updated regularly. Moreover, it can easily be extended by the research community. Mouse IDGenes will contribute as an important resource to the research on mouse brain development, not exclusively by offering data retrieval, but also by allowing data input. http://mouseidgenes.helmholtz-muenchen.de. © The Author(s) 2014. Published by Oxford University Press.

Genetic Vulnerability Interacts with Parenting and Early Care and Education to Predict Increasing Externalizing Behavior

Science.gov (United States)

Lipscomb, Shannon T.; Laurent, Heidemarie; Neiderhiser, Jenae M.; Shaw, Daniel S.; Natsuaki, Misaki N.; Reiss, David; Leve, Leslie D.

2014-01-01

The current study examined interactions among genetic influences and children's early environments on the development of externalizing behaviors from 18 months to 6 years of age. Participants included 233 families linked through adoption (birth parents and adoptive families). Genetic influences were assessed by birth parent temperamental…

A quantitative genetic analysis of intermediate asthma phenotypes

DEFF Research Database (Denmark)

Thomsen, S.F.; Ferreira, M.A.R.; Kyvik, K.O.

2009-01-01

to the observed data using maximum likelihood methods. RESULTS: Additive genetic factors explained 67% of the variation in FeNO, 43% in airway responsiveness, 22% in airway obstruction, and 81% in serum total IgE. In general, traits had genetically and environmentally distinct variance structures. The most......AIM: To study the relative contribution of genetic and environmental factors to the correlation between exhaled nitric oxide (FeNO), airway responsiveness, airway obstruction, and serum total immunoglobulin E (IgE). METHODS: Within a sampling frame of 21,162 twin subjects, 20-49 years of age, from...... substantial genetic similarity was observed between FeNO and serum total IgE, genetic correlation (rhoA) = 0.37, whereas the strongest environmental resemblance was observed between airway responsiveness and airway obstruction, specific environmental correlation (rhoE) = -0.46, and between FeNO and airway...

A quantitative genetic analysis of intermediate asthma phenotypes

DEFF Research Database (Denmark)

Thomsen, S F; Ferreira, M A R; Kyvik, K O

2009-01-01

to the observed data using maximum likelihood methods. Results: Additive genetic factors explained 67% of the variation in FeNO, 43% in airway responsiveness, 22% in airway obstruction, and 81% in serum total IgE. In general, traits had genetically and environmentally distinct variance structures. The most......Aim: To study the relative contribution of genetic and environmental factors to the correlation between exhaled nitric oxide (FeNO), airway responsiveness, airway obstruction, and serum total immunoglobulin E (IgE). Methods: Within a sampling frame of 21 162 twin subjects, 20-49 years of age, from...... substantial genetic similarity was observed between FeNO and serum total IgE, genetic correlation (rho(A)) = 0.37, whereas the strongest environmental resemblance was observed between airway responsiveness and airway obstruction, specific environmental correlation (rho(E)) = -0.46, and between FeNO and airway...

Effect of genetic variants and traits related to glucose metabolism and their interaction with obesity on breast and colorectal cancer risk among postmenopausal women.

Science.gov (United States)

Jung, Su Yon; Sobel, Eric M; Papp, Jeanette C; Zhang, Zuo-Feng

2017-04-26

Impaired glucose metabolism-related genetic variants and traits likely interact with obesity and related lifestyle factors, influencing postmenopausal breast and colorectal cancer (CRC), but their interconnected pathways are not fully understood. By stratifying via obesity and lifestyles, we partitioned the total effect of glucose metabolism genetic variants on cancer risk into two putative mechanisms: 1) indirect (risk-associated glucose metabolism genetic variants mediated by glucose metabolism traits) and 2) direct (risk-associated glucose metabolism genetic variants through pathways other than glucose metabolism traits) effects. Using 16 single-nucleotide polymorphisms (SNPs) associated with glucose metabolism and data from 5379 postmenopausal women in the Women's Health Initiative Harmonized and Imputed Genome-Wide Association Studies, we retrospectively assessed the indirect and direct effects of glucose metabolism-traits (fasting glucose, insulin, and homeostatic model assessment-insulin resistance [HOMA-IR]) using two quantitative tests. Several SNPs were associated with breast cancer and CRC risk, and these SNP-cancer associations differed between non-obese and obese women. In both strata, the direct effect of cancer risk associated with the SNP accounted for the majority of the total effect for most SNPs, with roughly 10% of cancer risk due to the SNP that was from an indirect effect mediated by glucose metabolism traits. No apparent differences in the indirect (glucose metabolism-mediated) effects were seen between non-obese and obese women. It is notable that among obese women, 50% of cancer risk was mediated via glucose metabolism trait, owing to two SNPs: in breast cancer, in relation to GCKR through glucose, and in CRC, in relation to DGKB/TMEM195 through HOMA-IR. Our findings suggest that glucose metabolism genetic variants interact with obesity, resulting in altered cancer risk through pathways other than those mediated by glucose metabolism traits.

On the use of sibling recurrence risks to select environmental factors liable to interact with genetic risk factors.

Science.gov (United States)

Kazma, Rémi; Bonaïti-Pellié, Catherine; Norris, Jill M; Génin, Emmanuelle

2010-01-01

Gene-environment interactions are likely to be involved in the susceptibility to multifactorial diseases but are difficult to detect. Available methods usually concentrate on some particular genetic and environmental factors. In this paper, we propose a new method to determine whether a given exposure is susceptible to interact with unknown genetic factors. Rather than focusing on a specific genetic factor, the degree of familial aggregation is used as a surrogate for genetic factors. A test comparing the recurrence risks in sibs according to the exposure of indexes is proposed and its power is studied for varying values of model parameters. The Exposed versus Unexposed Recurrence Analysis (EURECA) is valuable for common diseases with moderate familial aggregation, only when the role of exposure has been clearly outlined. Interestingly, accounting for a sibling correlation for the exposure increases the power of EURECA. An application on a sample ascertained through one index affected with type 2 diabetes is presented where gene-environment interactions involving obesity and physical inactivity are investigated. Association of obesity with type 2 diabetes is clearly evidenced and a potential interaction involving this factor is suggested in Hispanics (P=0.045), whereas a clear gene-environment interaction is evidenced involving physical inactivity only in non-Hispanic whites (P=0.028). The proposed method might be of particular interest before genetic studies to help determine the environmental risk factors that will need to be accounted for to increase the power to detect genetic risk factors and to select the most appropriate samples to genotype.

Genetic Analysis of Micro-environmental Plasticity in Drosophila melanogaster

DEFF Research Database (Denmark)

Morgante, Fabio; Sorensen, Daniel A; Sørensen, Peter

Quantitative genetic models recognize the potential for genotype by environment interaction, whereby different genotypes have different plastic responses to changes in macro-environmental conditions. Recently, it has been recognized that micro-environmental plasticity (‘residual’ variance) may also...... be genetically variable. This study utilized the Drosophila Genetic Reference Panel (DGRP) to accurately estimate the genetic variance of micro-environmental plasticity for chill coma recovery time and startle response. Estimates of broad sense heritabilities for both traits are substantial (from 0.51 to 0.......77), of the same order as the heritability at the level of the trait mean for startle response and even larger for chill coma recovery. Genome wide association analyses identified molecular variants (from 15 to 31 depending on the sex and the trait) associated with micro-environmental plasticity. These findings...

Quantitative model for the blood pressure‐lowering interaction of valsartan and amlodipine

Science.gov (United States)

Heo, Young‐A; Holford, Nick; Kim, Yukyung; Son, Mijeong

2016-01-01

Aims The objective of this study was to develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model to quantitatively describe the antihypertensive effect of combined therapy with amlodipine and valsartan. Methods PK modelling was used with data collected from 48 healthy volunteers receiving a single dose of combined formulation of 10 mg amlodipine and 160 mg valsartan. Systolic (SBP) and diastolic blood pressure (DBP) were recorded during combined administration. SBP and DBP data for each drug alone were gathered from the literature. PKPD models of each drug and for combined administration were built with NONMEM 7.3. Results A two‐compartment model with zero order absorption best described the PK data of both drugs. Amlodipine and valsartan monotherapy effects on SBP and DBP were best described by an I max model with an effect compartment delay. Combined therapy was described using a proportional interaction term as follows: (D1 + D2) +ALPHA×(D1 × D2). D1 and D2 are the predicted drug effects of amlodipine and valsartan monotherapy respectively. ALPHA is the interaction term for combined therapy. Quantitative estimates of ALPHA were −0.171 (95% CI: −0.218, −0.143) for SBP and −0.0312 (95% CI: −0.07739, −0.00283) for DBP. These infra‐additive interaction terms for both SBP and DBP were consistent with literature results for combined administration of drugs in these classes. Conclusion PKPD models for SBP and DBP successfully described the time course of the antihypertensive effects of amlodipine and valsartan. An infra‐additive interaction between amlodipine and valsartan when used in combined administration was confirmed and quantified. PMID:27504853

Gene-based testing of interactions in association studies of quantitative traits.

Directory of Open Access Journals (Sweden)

Li Ma

Full Text Available Various methods have been developed for identifying gene-gene interactions in genome-wide association studies (GWAS. However, most methods focus on individual markers as the testing unit, and the large number of such tests drastically erodes statistical power. In this study, we propose novel interaction tests of quantitative traits that are gene-based and that confer advantage in both statistical power and biological interpretation. The framework of gene-based gene-gene interaction (GGG tests combine marker-based interaction tests between all pairs of markers in two genes to produce a gene-level test for interaction between the two. The tests are based on an analytical formula we derive for the correlation between marker-based interaction tests due to linkage disequilibrium. We propose four GGG tests that extend the following P value combining methods: minimum P value, extended Simes procedure, truncated tail strength, and truncated P value product. Extensive simulations point to correct type I error rates of all tests and show that the two truncated tests are more powerful than the other tests in cases of markers involved in the underlying interaction not being directly genotyped and in cases of multiple underlying interactions. We applied our tests to pairs of genes that exhibit a protein-protein interaction to test for gene-level interactions underlying lipid levels using genotype data from the Atherosclerosis Risk in Communities study. We identified five novel interactions that are not evident from marker-based interaction testing and successfully replicated one of these interactions, between SMAD3 and NEDD9, in an independent sample from the Multi-Ethnic Study of Atherosclerosis. We conclude that our GGG tests show improved power to identify gene-level interactions in existing, as well as emerging, association studies.

Gene interactions and genetics for yield and its attributes in grass pea

Indian Academy of Sciences (India)

[Parihar A. K., Dixit G. P. and Singh D. 2016 Gene interactions and genetics for yield and its attributes .... Biological yield. Seed yield factors. Plant height. Primary branches plant pod ..... indicates that these traits are under the control of several.

Genetic architecture of resistance in Daphnia hosts against two species of host-specific parasites.

Science.gov (United States)

Routtu, J; Ebert, D

2015-02-01

Understanding the genetic architecture of host resistance is key for understanding the evolution of host-parasite interactions. Evolutionary models often assume simple genetics based on few loci and strong epistasis. It is unknown, however, whether these assumptions apply to natural populations. Using a quantitative trait loci (QTL) approach, we explore the genetic architecture of resistance in the crustacean Daphnia magna to two of its natural parasites: the horizontally transmitted bacterium Pasteuria ramosa and the horizontally and vertically transmitted microsporidium Hamiltosporidium tvaerminnensis. These two systems have become models for studies on the evolution of host-parasite interactions. In the QTL panel used here, Daphnia's resistance to P. ramosa is controlled by a single major QTL (which explains 50% of the observed variation). Resistance to H. tvaerminnensis horizontal infections shows a signature of a quantitative trait based in multiple loci with weak epistatic interactions (together explaining 38% variation). Resistance to H. tvaerminnensis vertical infections, however, shows only one QTL (explaining 13.5% variance) that colocalizes with one of the QTLs for horizontal infections. QTLs for resistance to Pasteuria and Hamiltosporidium do not colocalize. We conclude that the genetics of resistance in D. magna are drastically different for these two parasites. Furthermore, we infer that based on these and earlier results, the mechanisms of coevolution differ strongly for the two host-parasite systems. Only the Pasteuria-Daphnia system is expected to follow the negative frequency-dependent selection (Red Queen) model. How coevolution works in the Hamiltosporidium-Daphnia system remains unclear.

Piezoelectric tuning fork biosensors for the quantitative measurement of biomolecular interactions

International Nuclear Information System (INIS)

Gonzalez, Laura; Maria Benito, Angel; Puig-Vidal, Manel; Otero, Jorge; Rodrigues, Mafalda; Pérez-García, Lluïsa

2015-01-01

The quantitative measurement of biomolecular interactions is of great interest in molecular biology. Atomic force microscopy (AFM) has proved its capacity to act as a biosensor and determine the affinity between biomolecules of interest. Nevertheless, the detection scheme presents certain limitations when it comes to developing a compact biosensor. Recently, piezoelectric quartz tuning forks (QTFs) have been used as laser-free detection sensors for AFM. However, only a few studies along these lines have considered soft biological samples, and even fewer constitute quantified molecular recognition experiments. Here, we demonstrate the capacity of QTF probes to perform specific interaction measurements between biotin–streptavidin complexes in buffer solution. We propose in this paper a variant of dynamic force spectroscopy based on representing adhesion energies E (aJ) against pulling rates v (nm s"–"1). Our results are compared with conventional AFM measurements and show the great potential of these sensors in molecular interaction studies. (paper)

Establishment of a quantitative ELISA capable of determining peptide - MHC class I interaction

DEFF Research Database (Denmark)

Sylvester-Hvid, C; Kristensen, N; Blicher, T

2002-01-01

dependent manner. Here, we exploit the availability of these molecules to generate a quantitative ELISA-based assay capable of measuring the affinity of the interaction between peptide and MHC-I. This assay is simple and sensitive, and one can easily envisage that the necessary reagents, standards......Many different assays for measuring peptide-MHC interactions have been suggested over the years. Yet, there is no generally accepted standard method available. We have recently generated preoxidized recombinant MHC class I molecules (MHC-I) which can be purified to homogeneity under denaturing...

Quantitative Imaging in Cancer Evolution and Ecology

Science.gov (United States)

Grove, Olya; Gillies, Robert J.

2013-01-01

Cancer therapy, even when highly targeted, typically fails because of the remarkable capacity of malignant cells to evolve effective adaptations. These evolutionary dynamics are both a cause and a consequence of cancer system heterogeneity at many scales, ranging from genetic properties of individual cells to large-scale imaging features. Tumors of the same organ and cell type can have remarkably diverse appearances in different patients. Furthermore, even within a single tumor, marked variations in imaging features, such as necrosis or contrast enhancement, are common. Similar spatial variations recently have been reported in genetic profiles. Radiologic heterogeneity within tumors is usually governed by variations in blood flow, whereas genetic heterogeneity is typically ascribed to random mutations. However, evolution within tumors, as in all living systems, is subject to Darwinian principles; thus, it is governed by predictable and reproducible interactions between environmental selection forces and cell phenotype (not genotype). This link between regional variations in environmental properties and cellular adaptive strategies may permit clinical imaging to be used to assess and monitor intratumoral evolution in individual patients. This approach is enabled by new methods that extract, report, and analyze quantitative, reproducible, and mineable clinical imaging data. However, most current quantitative metrics lack spatialness, expressing quantitative radiologic features as a single value for a region of interest encompassing the whole tumor. In contrast, spatially explicit image analysis recognizes that tumors are heterogeneous but not well mixed and defines regionally distinct habitats, some of which appear to harbor tumor populations that are more aggressive and less treatable than others. By identifying regional variations in key environmental selection forces and evidence of cellular adaptation, clinical imaging can enable us to define intratumoral

Alleles versus genotypes: Genetic interactions and the dynamics of selection in sexual populations

Science.gov (United States)

Neher, Richard

2010-03-01

Physical interactions between amino-acids are essential for protein structure and activity, while protein-protein interactions and regulatory interactions are central to cellular function. As a consequence of these interactions, the combined effect of two mutations can differ from the sum of the individual effects of the mutations. This phenomenon of genetic interaction is known as epistasis. However, the importance of epistasis and its effects on evolutionary dynamics are poorly understood, especially in sexual populations where recombination breaks up existing combinations of alleles to produce new ones. Here, we present a computational model of selection dynamics involving many epistatic loci in a recombining population. We demonstrate that a large number of polymorphic interacting loci can, despite frequent recombination, exhibit cooperative behavior that locks alleles into favorable genotypes leading to a population consisting of a set of competing clones. As the recombination rate exceeds a certain critical value this ``genotype selection'' phase disappears in an abrupt transition giving way to ``allele selection'' - the phase where different loci are only weakly correlated as expected in sexually reproducing populations. Clustering of interacting sets of genes on a chromosome leads to the emergence of an intermediate regime, where localized blocks of cooperating alleles lock into genetic modules. Large populations attain highest fitness at a recombination rate just below critical, suggesting that natural selection might tune recombination rates to balance the beneficial aspect of exploration of genotype space with the breaking up of synergistic allele combinations.

Cryptic Genetic Variation in Evolutionary Developmental Genetics

Directory of Open Access Journals (Sweden)

Annalise B. Paaby

2016-06-01

Full Text Available Evolutionary developmental genetics has traditionally been conducted by two groups: Molecular evolutionists who emphasize divergence between species or higher taxa, and quantitative geneticists who study variation within species. Neither approach really comes to grips with the complexities of evolutionary transitions, particularly in light of the realization from genome-wide association studies that most complex traits fit an infinitesimal architecture, being influenced by thousands of loci. This paper discusses robustness, plasticity and lability, phenomena that we argue potentiate major evolutionary changes and provide a bridge between the conceptual treatments of macro- and micro-evolution. We offer cryptic genetic variation and conditional neutrality as mechanisms by which standing genetic variation can lead to developmental system drift and, sheltered within canalized processes, may facilitate developmental transitions and the evolution of novelty. Synthesis of the two dominant perspectives will require recognition that adaptation, divergence, drift and stability all depend on similar underlying quantitative genetic processes—processes that cannot be fully observed in continuously varying visible traits.

Testing for biases in selection on avian reproductive traits and partitioning direct and indirect selection using quantitative genetic models.

Science.gov (United States)

Reed, Thomas E; Gienapp, Phillip; Visser, Marcel E

2016-10-01

Key life history traits such as breeding time and clutch size are frequently both heritable and under directional selection, yet many studies fail to document microevolutionary responses. One general explanation is that selection estimates are biased by the omission of correlated traits that have causal effects on fitness, but few valid tests of this exist. Here, we show, using a quantitative genetic framework and six decades of life-history data on two free-living populations of great tits Parus major, that selection estimates for egg-laying date and clutch size are relatively unbiased. Predicted responses to selection based on the Robertson-Price Identity were similar to those based on the multivariate breeder's equation (MVBE), indicating that unmeasured covarying traits were not missing from the analysis. Changing patterns of phenotypic selection on these traits (for laying date, linked to climate change) therefore reflect changing selection on breeding values, and genetic constraints appear not to limit their independent evolution. Quantitative genetic analysis of correlational data from pedigreed populations can be a valuable complement to experimental approaches to help identify whether apparent associations between traits and fitness are biased by missing traits, and to parse the roles of direct versus indirect selection across a range of environments. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Glucose levels and genetic variants across transcriptional pathways: interaction effects with BMI

NARCIS (Netherlands)

Povel, C.M.; Feskens, E.J.M.; Imholz, S.; Blaak, E.E.; Boer, J.M.A.; Dollé, M.E.T.

2010-01-01

Objective: Much of the genetic variation in glucose levels remains to be discovered. Especially, research on gene–environment interactions is scarce. Overweight is one of the main risk factors for hyperglycemia. As transcriptional regulation is important for both weight maintenance and glucose

PCR-free quantitative detection of genetically modified organism from raw materials – A novel electrochemiluminescence-based bio-barcode method

Science.gov (United States)

Zhu, Debin; Tang, Yabing; Xing, Da; Chen, Wei R.

2018-01-01

Bio-barcode assay based on oligonucleotide-modified gold nanoparticles (Au-NPs) provides a PCR-free method for quantitative detection of nucleic acid targets. However, the current bio-barcode assay requires lengthy experimental procedures including the preparation and release of barcode DNA probes from the target-nanoparticle complex, and immobilization and hybridization of the probes for quantification. Herein, we report a novel PCR-free electrochemiluminescence (ECL)-based bio-barcode assay for the quantitative detection of genetically modified organism (GMO) from raw materials. It consists of tris-(2’2’-bipyridyl) ruthenium (TBR)-labele barcode DNA, nucleic acid hybridization using Au-NPs and biotin-labeled probes, and selective capture of the hybridization complex by streptavidin-coated paramagnetic beads. The detection of target DNA is realized by direct measurement of ECL emission of TBR. It can quantitatively detect target nucleic acids with high speed and sensitivity. This method can be used to quantitatively detect GMO fragments from real GMO products. PMID:18386909

Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics

NARCIS (Netherlands)

Lundby, Alicia; Rossin, Elizabeth J.; Steffensen, Annette B.; Acha, Moshe Ray; Newton-Cheh, Christopher; Pfeufer, Arne; Lyneh, Stacey N.; Olesen, Soren-Peter; Brunak, Soren; Ellinor, Patrick T.; Jukema, J. Wouter; Trompet, Stella; Ford, Ian; Macfarlane, Peter W.; Krijthe, Bouwe P.; Hofman, Albert; Uitterlinden, Andre G.; Stricker, Bruno H.; Nathoe, Hendrik M.; Spiering, Wilko; Daly, Mark J.; Asselbergs, Ikea W.; van der Harst, Pim; Milan, David J.; de Bakker, Paul I. W.; Lage, Kasper; Olsen, Jesper V.

Genome-wide association studies (GWAS) have identified thousands of loci associated with complex traits, but it is challenging to pinpoint causal genes in these loci and to exploit subtle association signals. We used tissue-specific quantitative interaction proteomics to map a network of five genes

Controlled initiation and quantitative visualization of cell interaction dynamics - a novel hybrid microscopy method -

NARCIS (Netherlands)

Snijder-van As, M.I.

2010-01-01

This thesis describes the development, validation, and application of a hybrid microscopy technique to study cell-substrate and cell-cell interactions in a controlled and quantitative manner. We studied the spatial and temporal dynamics of the selected membrane molecules CD6 and the activated

Development and Evaluation of Event-Specific Quantitative PCR Method for Genetically Modified Soybean MON87701.

Science.gov (United States)

Tsukahara, Keita; Takabatake, Reona; Masubuchi, Tomoko; Futo, Satoshi; Minegishi, Yasutaka; Noguchi, Akio; Kondo, Kazunari; Nishimaki-Mogami, Tomoko; Kurashima, Takeyo; Mano, Junichi; Kitta, Kazumi

2016-01-01

A real-time PCR-based analytical method was developed for the event-specific quantification of a genetically modified (GM) soybean event, MON87701. First, a standard plasmid for MON87701 quantification was constructed. The conversion factor (C f ) required to calculate the amount of genetically modified organism (GMO) was experimentally determined for a real-time PCR instrument. The determined C f for the real-time PCR instrument was 1.24. For the evaluation of the developed method, a blind test was carried out in an inter-laboratory trial. The trueness and precision were evaluated as the bias and reproducibility of relative standard deviation (RSDr), respectively. The determined biases and the RSDr values were less than 30 and 13%, respectively, at all evaluated concentrations. The limit of quantitation of the method was 0.5%, and the developed method would thus be applicable for practical analyses for the detection and quantification of MON87701.

Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer.

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Jian Gong

2016-10-01

Full Text Available Genome-wide association studies (GWAS have identified many genetic susceptibility loci for colorectal cancer (CRC. However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO. Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10-8; permuted p-value 3.51x10-8 region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74-0.91]; P = 2.1×10-4 and TT genotypes (OR,0.62 [95% CI, 0.51-0.75]; P = 1.3×10-6 but not associated among those with the CC genotype (p = 0.059. No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk.

Gene-particulate matter-health interactions

International Nuclear Information System (INIS)

Kleeberger, Steven R.; Ohtsuka, Yoshinori

2005-01-01

Inter-individual variation in human responses to air pollutants suggests that some subpopulations are at increased risk to the detrimental effects of pollutant exposure. Extrinsic factors such as previous exposure and nutritional status may influence individual susceptibility. Intrinsic (host) factors that determine susceptibility include age, gender, and pre-existing disease (e.g., asthma), and it is becoming clear that genetic background also contributes to individual susceptibility. Environmental exposures to particulates and genetic factors associated with disease risk likely interact in a complex fashion that varies from one population and one individual to another. The relationships between genetic background and disease risk and severity are often evaluated through traditional family-based linkage studies and positional cloning techniques. However, case-control studies based on association of disease or disease subphenotypes with candidate genes have advantages over family pedigree studies for complex disease phenotypes. This is based in part on continued development of quantitative analysis and the discovery and availability of simple sequence repeats and single nucleotide polymorphisms. Linkage analyses with genetically standardized animal models also provide a useful tool to identify genetic determinants of responses to environmental pollutants. These approaches have identified significant susceptibility quantitative trait loci on mouse chromosomes 1, 6, 11, and 17. Physical mapping and comparative mapping between human and mouse genomes will yield candidate susceptibility genes that may be tested by association studies in human subjects. Human studies and mouse modeling will provide important insight to understanding genetic factors that contribute to differential susceptibility to air pollutants

Trichoderma-plant-pathogen interactions: advances in genetics of biological control.

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Mukherjee, Mala; Mukherjee, Prasun K; Horwitz, Benjamin A; Zachow, Christin; Berg, Gabriele; Zeilinger, Susanne

2012-12-01

Trichoderma spp. are widely used in agriculture as biofungicides. Induction of plant defense and mycoparasitism (killing of one fungus by another) are considered to be the most important mechanisms of Trichoderma-mediated biological control. Understanding these mechanisms at the molecular level would help in developing strains with superior biocontrol properties. In this article, we review our current understanding of the genetics of interactions of Trichoderma with plants and plant pathogens.

Genetic predispositions and parental bonding interact to shape adults’ physiological responses to social distress

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Esposito, Gianluca; Truzzi, Anna; Setoh, Peipei; Putnick, Diane L.; Shinohara, Kazuyuki; Bornstein, Marc H.

2018-01-01

Parental bonding and oxytocin receptor (OXTR) gene genotype each influences social abilities in adulthood. Here, we hypothesized an interaction between the two – environmental experience (parental bonding history) and genetic factors (OXTR gene genotype) – in shaping adults’ social sensitivity (physiological response to distress). We assessed heart rate and peripheral temperature (tip of the nose) in 42 male adults during presentation of distress vocalizations (distress cries belonging to female human infants and adults as well as bonobo). The two physiological responses index, respectively, state of arousal and readiness to action. Participants’ parental bonding in childhood was assessed through the self-report Parental Bonding Instrument. To assess participants’ genetic predispositions, buccal mucosa cell samples were collected, and region rs2254298 of the oxytocin receptor gene was analyzed: previous OXTR gene findings point to associations between the G allele and better sociality (protective factor) and the A allele and poorer sociality (risk factor). We found a gene * environment interaction for susceptibility to social distress: Participants with a genetic risk factor (A carriers) with a history of high paternal overprotection showed higher heart rate increase than those without this risk factor (G/G genotype) to social distress. Also, a significant effect of the interaction between paternal care and genotype on nose temperature changes was found. This susceptibility appears to represent an indirect pathway through which genes and experiences interact to shape mature social sensitivity in males. PMID:27343933

Interactions between Gut Microbiota, Host Genetics and Diet Modulate the Predisposition to Obesity and Metabolic Syndrome.

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Ussar, Siegfried; Griffin, Nicholas W; Bezy, Olivier; Fujisaka, Shiho; Vienberg, Sara; Softic, Samir; Deng, Luxue; Bry, Lynn; Gordon, Jeffrey I; Kahn, C Ronald

2015-09-01

Obesity, diabetes, and metabolic syndrome result from complex interactions between genetic and environmental factors, including the gut microbiota. To dissect these interactions, we utilized three commonly used inbred strains of mice-obesity/diabetes-prone C57Bl/6J mice, obesity/diabetes-resistant 129S1/SvImJ from Jackson Laboratory, and obesity-prone but diabetes-resistant 129S6/SvEvTac from Taconic-plus three derivative lines generated by breeding these strains in a new, common environment. Analysis of metabolic parameters and gut microbiota in all strains and their environmentally normalized derivatives revealed strong interactions between microbiota, diet, breeding site, and metabolic phenotype. Strain-dependent and strain-independent correlations were found between specific microbiota and phenotypes, some of which could be transferred to germ-free recipient animals by fecal transplantation. Environmental reprogramming of microbiota resulted in 129S6/SvEvTac becoming obesity resistant. Thus, development of obesity/metabolic syndrome is the result of interactions between gut microbiota, host genetics, and diet. In permissive genetic backgrounds, environmental reprograming of microbiota can ameliorate development of metabolic syndrome. Copyright © 2015 Elsevier Inc. All rights reserved.

Ion-solid interaction at low energies: principles and application of quantitative ISS

International Nuclear Information System (INIS)

Niehus, H.; Spitzl, R.

1991-01-01

Quantitative surface analysis with low-energy (500-5000 eV) ion scattering spectroscopy is known to be difficult, most often because of strong charge transfer and multiple scattering effects occurring during ion-surface interaction. In order to avoid neutralization problems, either alkali primary ions or noble gas ions in combination with the detection of all scattered particles was applied. Multiple scattering occurs predominantly at forward scattering and might confound the analysis. Backward scattering (i.e. 180 o impact collision ion scattering) bypasses strongly the multiple scattering complication and has been used successfully for the analysis of a number of surface structures for metals, semiconductors and binary alloys. A simple triangulation concept gives access to mass-selective qualitative surface crystallography. Quantitative surface structures were determined by comparison with computer simulations. (author)

Highly multiplexed and quantitative cell-surface protein profiling using genetically barcoded antibodies.

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Pollock, Samuel B; Hu, Amy; Mou, Yun; Martinko, Alexander J; Julien, Olivier; Hornsby, Michael; Ploder, Lynda; Adams, Jarrett J; Geng, Huimin; Müschen, Markus; Sidhu, Sachdev S; Moffat, Jason; Wells, James A

2018-03-13

Human cells express thousands of different surface proteins that can be used for cell classification, or to distinguish healthy and disease conditions. A method capable of profiling a substantial fraction of the surface proteome simultaneously and inexpensively would enable more accurate and complete classification of cell states. We present a highly multiplexed and quantitative surface proteomic method using genetically barcoded antibodies called phage-antibody next-generation sequencing (PhaNGS). Using 144 preselected antibodies displayed on filamentous phage (Fab-phage) against 44 receptor targets, we assess changes in B cell surface proteins after the development of drug resistance in a patient with acute lymphoblastic leukemia (ALL) and in adaptation to oncogene expression in a Myc-inducible Burkitt lymphoma model. We further show PhaNGS can be applied at the single-cell level. Our results reveal that a common set of proteins including FLT3, NCR3LG1, and ROR1 dominate the response to similar oncogenic perturbations in B cells. Linking high-affinity, selective, genetically encoded binders to NGS enables direct and highly multiplexed protein detection, comparable to RNA-sequencing for mRNA. PhaNGS has the potential to profile a substantial fraction of the surface proteome simultaneously and inexpensively to enable more accurate and complete classification of cell states. Copyright © 2018 the Author(s). Published by PNAS.

Cancer genetics education in a low- to middle-income country: evaluation of an interactive workshop for clinicians in Kenya.

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Jessica A Hill

Full Text Available Clinical genetic testing is becoming an integral part of medical care for inherited disorders. While genetic testing and counseling are readily available in high-income countries, in low- and middle-income countries like Kenya genetic testing is limited and genetic counseling is virtually non-existent. Genetic testing is likely to become widespread in Kenya within the next decade, yet there has not been a concomitant increase in genetic counseling resources. To address this gap, we designed an interactive workshop for clinicians in Kenya focused on the genetics of the childhood eye cancer retinoblastoma. The objectives were to increase retinoblastoma genetics knowledge, build genetic counseling skills and increase confidence in those skills.The workshop was conducted at the 2013 Kenyan National Retinoblastoma Strategy meeting. It included a retinoblastoma genetics presentation, small group discussion of case studies and genetic counseling role-play. Knowledge was assessed by standardized test, and genetic counseling skills and confidence by questionnaire.Knowledge increased significantly post-workshop, driven by increased knowledge of retinoblastoma causative genetics. One-year post-workshop, participant knowledge had returned to baseline, indicating that knowledge retention requires more frequent reinforcement. Participants reported feeling more confident discussing genetics with patients, and had integrated more genetic counseling into patient interactions.A comprehensive retinoblastoma genetics workshop can increase the knowledge and skills necessary for effective retinoblastoma genetic counseling.

Cancer genetics education in a low- to middle-income country: evaluation of an interactive workshop for clinicians in Kenya.

Science.gov (United States)

Hill, Jessica A; Lee, Su Yeon; Njambi, Lucy; Corson, Timothy W; Dimaras, Helen

2015-01-01

Clinical genetic testing is becoming an integral part of medical care for inherited disorders. While genetic testing and counseling are readily available in high-income countries, in low- and middle-income countries like Kenya genetic testing is limited and genetic counseling is virtually non-existent. Genetic testing is likely to become widespread in Kenya within the next decade, yet there has not been a concomitant increase in genetic counseling resources. To address this gap, we designed an interactive workshop for clinicians in Kenya focused on the genetics of the childhood eye cancer retinoblastoma. The objectives were to increase retinoblastoma genetics knowledge, build genetic counseling skills and increase confidence in those skills. The workshop was conducted at the 2013 Kenyan National Retinoblastoma Strategy meeting. It included a retinoblastoma genetics presentation, small group discussion of case studies and genetic counseling role-play. Knowledge was assessed by standardized test, and genetic counseling skills and confidence by questionnaire. Knowledge increased significantly post-workshop, driven by increased knowledge of retinoblastoma causative genetics. One-year post-workshop, participant knowledge had returned to baseline, indicating that knowledge retention requires more frequent reinforcement. Participants reported feeling more confident discussing genetics with patients, and had integrated more genetic counseling into patient interactions. A comprehensive retinoblastoma genetics workshop can increase the knowledge and skills necessary for effective retinoblastoma genetic counseling.

Quantitative trait loci and candidate genes underlying genotype by environment interaction in the response of Arabidopsis thaliana to drought

NARCIS (Netherlands)

El-Soda, M.; Kruijer, Willem; Malosetti, M.; Koornneef, M.; Aarts, M.G.M.

2015-01-01

Drought stress was imposed on two sets of Arabidopsis thaliana genotypes grown in sand under short-day conditions and analysed for several shoot and root growth traits. The response to drought was assessed for quantitative trait locus (QTL) mapping in a genetically diverse set of Arabidopsis

Quantitative genetic analysis of retinal degeneration in the blind cavefish Astyanax mexicanus.

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Kelly E O'Quin

Full Text Available The retina is the light-sensitive tissue of the eye that facilitates vision. Mutations within genes affecting eye development and retinal function cause a host of degenerative visual diseases, including retinitis pigmentosa and anophthalmia/microphthalmia. The characin fish Astyanax mexicanus includes both eyed (surface fish and eyeless (cavefish morphs that initially develop eyes with normal retina; however, early in development, the eyes of cavefish degenerate. Since both surface and cave morphs are members of the same species, they serve as excellent evolutionary mutant models with which to identify genes causing retinal degeneration. In this study, we crossed the eyed and eyeless forms of A. mexicanus and quantified the thickness of individual retinal layers among 115 F(2 hybrid progeny. We used next generation sequencing (RAD-seq and microsatellite mapping to construct a dense genetic map of the Astyanax genome, scan for quantitative trait loci (QTL affecting retinal thickness, and identify candidate genes within these QTL regions. The map we constructed for Astyanax includes nearly 700 markers assembled into 25 linkage groups. Based on our scans with this map, we identified four QTL, one each associated with the thickness of the ganglion, inner nuclear, outer plexiform, and outer nuclear layers of the retina. For all but one QTL, cavefish alleles resulted in a clear reduction in the thickness of the affected layer. Comparative mapping of genetic markers within each QTL revealed that each QTL corresponds to an approximately 35 Mb region of the zebrafish genome. Within each region, we identified several candidate genes associated with the function of each affected retinal layer. Our study is the first to examine Astyanax retinal degeneration in the context of QTL mapping. The regions we identify serve as a starting point for future studies on the genetics of retinal degeneration and eye disease using the evolutionary mutant model Astyanax.

On the road to quantitative genetic/genomic analyses of root growth and development components underlying root architecture

International Nuclear Information System (INIS)

Draye, X.; Dorlodot, S. de; Lavigne, T.

2006-01-01

The quantitative genetic and functional genomic analyses of root development, growth and plasticity will be instrumental in revealing the major regulatory pathways of root architecture. Such knowledge, combined with in-depth consideration of root physiology (e.g. uptake, exsudation), form (space-time dynamics of soil exploration) and ecology (including root environment), will settle the bases for designing root ideotypes for specific environments, for low-input agriculture or for successful agricultural production with minimal impact on the environment. This report summarizes root research initiated in our lab between 2000 and 2004 in the following areas: quantitative analysis of root branching in bananas, high throughput characterisation of root morphology, image analysis, QTL mapping of detailed features of root architecture in rice, and attempts to settle a Crop Root Research Consortium. (author)

Interactive Genetic Algorithm - An Adaptive and Interactive Decision Support Framework for Design of Optimal Groundwater Monitoring Plans

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Babbar-Sebens, M.; Minsker, B. S.

2006-12-01

that met the DM's preference criteria, therefore allowing the expert to select among several strong candidate designs depending on her/his LTM budget, c) two of the methodologies - Case-Based Micro Interactive Genetic Algorithm (CBMIGA) and Interactive Genetic Algorithm with Mixed Initiative Interaction (IGAMII) - were also able to assist in controlling human fatigue and adapt to the DM's learning process.

Quantitative analysis of tip-sample interaction in non-contact scanning force spectroscopy

International Nuclear Information System (INIS)

Palacios-Lidon, Elisa; Colchero, Jaime

2006-01-01

Quantitative characterization of tip-sample interaction in scanning force microscopy is fundamental for optimum image acquisition as well as data interpretation. In this work we discuss how to characterize the electrostatic and van der Waals contribution to tip-sample interaction in non-contact scanning force microscopy precisely. The spectroscopic technique presented is based on the simultaneous measurement of cantilever deflection, oscillation amplitude and frequency shift as a function of tip-sample voltage and tip-sample distance as well as on advanced data processing. Data are acquired at a fixed lateral position as interaction images, with the bias voltage as fast scan, and tip-sample distance as slow scan. Due to the quadratic dependence of the electrostatic interaction with tip-sample voltage the van der Waals force can be separated from the electrostatic force. Using appropriate data processing, the van der Waals interaction, the capacitance and the contact potential can be determined as a function of tip-sample distance. The measurement of resonance frequency shift yields very high signal to noise ratio and the absolute calibration of the measured quantities, while the acquisition of cantilever deflection allows the determination of the tip-sample distance

Genetic vulnerability interacts with parenting and early care education to predict increasing externalizing behavior.

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Lipscomb, Shannon T; Laurent, Heidemarie; Neiderhiser, Jenae M; Shaw, Daniel S; Natsuaki, Misaki N; Reiss, David; Leve, Leslie D

2014-01-01

The current study examined interactions among genetic influences and children's early environments on the development of externalizing behaviors from 18 months to 6 years of age. Participants included 233 families linked through adoption (birth parents and adoptive families). Genetic influences were assessed by birth parent temperamental regulation. Early environments included both family (overreactive parenting) and out-of-home factors (center-based Early Care and Education; ECE). Overreactive parenting predicted more child externalizing behaviors. Attending center-based ECE was associated with increasing externalizing behaviors only for children with genetic liability for dysregulation. Additionally, children who were at risk for externalizing behaviors due to both genetic variability and exposure to center-based ECE were more sensitive to the effects of overreactive parenting on externalizing behavior than other children.

Integrative Analysis of Subcellular Quantitative Proteomics Studies Reveals Functional Cytoskeleton Membrane-Lipid Raft Interactions in Cancer.

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Shah, Anup D; Inder, Kerry L; Shah, Alok K; Cristino, Alexandre S; McKie, Arthur B; Gabra, Hani; Davis, Melissa J; Hill, Michelle M

2016-10-07

Lipid rafts are dynamic membrane microdomains that orchestrate molecular interactions and are implicated in cancer development. To understand the functions of lipid rafts in cancer, we performed an integrated analysis of quantitative lipid raft proteomics data sets modeling progression in breast cancer, melanoma, and renal cell carcinoma. This analysis revealed that cancer development is associated with increased membrane raft-cytoskeleton interactions, with ∼40% of elevated lipid raft proteins being cytoskeletal components. Previous studies suggest a potential functional role for the raft-cytoskeleton in the action of the putative tumor suppressors PTRF/Cavin-1 and Merlin. To extend the observation, we examined lipid raft proteome modulation by an unrelated tumor suppressor opioid binding protein cell-adhesion molecule (OPCML) in ovarian cancer SKOV3 cells. In agreement with the other model systems, quantitative proteomics revealed that 39% of OPCML-depleted lipid raft proteins are cytoskeletal components, with microfilaments and intermediate filaments specifically down-regulated. Furthermore, protein-protein interaction network and simulation analysis showed significantly higher interactions among cancer raft proteins compared with general human raft proteins. Collectively, these results suggest increased cytoskeleton-mediated stabilization of lipid raft domains with greater molecular interactions as a common, functional, and reversible feature of cancer cells.

Genetic Parameters and Combining Ability Effects of Parents for Seed Yield and other Quantitative Traits in Black Gram [Vigna mungo (L. Hepper

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Supriyo CHAKRABORTY

2010-06-01

Full Text Available Line x tester analysis was carried out in black gram [Vigna mungo (L. Hepper], an edible legume, to estimate the gca (general combining ability effects of parents (3 lines and 3 testers and the SCA (specific combining ability effects of 9 crosses for seed yield and other eleven quantitative traits. Though additive and nonadditive gene actions governed the expression of quantitative traits, the magnitude of nonadditive gene action was higher than that of additive gene action for each quantitative trait. Two parents viz. UG157 and DPU915 were good general combiners. Two crosses namely PDB 88-31/DPU 915 and PLU 277/KAU7 had high per se performance along with positive significant SCA effect for seed yield/plant. The degree of dominance revealed overdominance for all the traits except clusters/plant with partial dominance. The predictability ratio also revealed the predominant role of nonadditive gene action in the genetic control of quantitative traits. Narrow sense heritability was also low for each trait. Recurrent selection or biparental mating followed by selection which can exploit both additive and nonadditive gene actions would be of interest for yield improvement in black gram. Due to presence of high magnitude of nonadditive gene action, heterosis breeding could also be attempted to develop low cost hybrid variety using genetic male sterility system in black gram.

Genetic and Quantitative Trait Locus Analysis for Bio-Oil Compounds after Fast Pyrolysis in Maize Cobs.

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Brandon Jeffrey

Full Text Available Fast pyrolysis has been identified as one of the biorenewable conversion platforms that could be a part of an alternative energy future, but it has not yet received the same attention as cellulosic ethanol in the analysis of genetic inheritance within potential feedstocks such as maize. Ten bio-oil compounds were measured via pyrolysis/gas chromatography-mass spectrometry (Py/GC-MS in maize cobs. 184 recombinant inbred lines (RILs of the intermated B73 x Mo17 (IBM Syn4 population were analyzed in two environments, using 1339 markers, for quantitative trait locus (QTL mapping. QTL mapping was performed using composite interval mapping with significance thresholds established by 1000 permutations at α = 0.05. 50 QTL were found in total across those ten traits with R2 values ranging from 1.7 to 5.8%, indicating a complex quantitative inheritance of these traits.

Genome-wide association mapping identifies the genetic basis of discrete and quantitative variation in sexual weaponry in a wild sheep population.

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Johnston, Susan E; McEwan, John C; Pickering, Natalie K; Kijas, James W; Beraldi, Dario; Pilkington, Jill G; Pemberton, Josephine M; Slate, Jon

2011-06-01

Understanding the genetic architecture of phenotypic variation in natural populations is a fundamental goal of evolutionary genetics. Wild Soay sheep (Ovis aries) have an inherited polymorphism for horn morphology in both sexes, controlled by a single autosomal locus, Horns. The majority of males have large normal horns, but a small number have vestigial, deformed horns, known as scurs; females have either normal horns, scurs or no horns (polled). Given that scurred males and polled females have reduced fitness within each sex, it is counterintuitive that the polymorphism persists within the population. Therefore, identifying the genetic basis of horn type will provide a vital foundation for understanding why the different morphs are maintained in the face of natural selection. We conducted a genome-wide association study using ∼36000 single nucleotide polymorphisms (SNPs) and determined the main candidate for Horns as RXFP2, an autosomal gene with a known involvement in determining primary sex characters in humans and mice. Evidence from additional SNPs in and around RXFP2 supports a new model of horn-type inheritance in Soay sheep, and for the first time, sheep with the same horn phenotype but different underlying genotypes can be identified. In addition, RXFP2 was shown to be an additive quantitative trait locus (QTL) for horn size in normal-horned males, accounting for up to 76% of additive genetic variation in this trait. This finding contrasts markedly from genome-wide association studies of quantitative traits in humans and some model species, where it is often observed that mapped loci only explain a modest proportion of the overall genetic variation. © 2011 Blackwell Publishing Ltd.

Evolving ideas about genetics underlying insect virulence to plant resistance in rice-brown planthopper interactions.

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Kobayashi, Tetsuya

2016-01-01

Many plant-parasite interactions that include major plant resistance genes have subsequently been shown to exhibit features of gene-for-gene interactions between plant Resistance genes and parasite Avirulence genes. The brown planthopper (BPH) Nilaparvata lugens is an important pest of rice (Oryza sativa). Historically, major Resistance genes have played an important role in agriculture. As is common in gene-for-gene interactions, evolution of BPH virulence compromises the effectiveness of singly-deployed resistance genes. It is therefore surprising that laboratory studies of BPH have supported the conclusion that virulence is conferred by changes in many genes rather than a change in a single gene, as is proposed by the gene-for-gene model. Here we review the behaviour, physiology and genetics of the BPH in the context of host plant resistance. A problem for genetic understanding has been the use of various insect populations that differ in frequencies of virulent genotypes. We show that the previously proposed polygenic inheritance of BPH virulence can be explained by the heterogeneity of parental populations. Genetic mapping of Avirulence genes indicates that virulence is a monogenic trait. These evolving concepts, which have brought the gene-for-gene model back into the picture, are accelerating our understanding of rice-BPH interactions at the molecular level. Copyright © 2015 Elsevier Ltd. All rights reserved.

Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer

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Newcomb, Polly A.; Campbell, Peter T.; Baron, John A.; Berndt, Sonja I.; Bezieau, Stephane; Brenner, Hermann; Casey, Graham; Chan, Andrew T.; Chang-Claude, Jenny; Du, Mengmeng; Figueiredo, Jane C.; Gallinger, Steven; Giovannucci, Edward L.; Haile, Robert W.; Harrison, Tabitha A.; Hayes, Richard B.; Hoffmeister, Michael; Hopper, John L.; Hudson, Thomas J.; Jeon, Jihyoun; Jenkins, Mark A.; Küry, Sébastien; Le Marchand, Loic; Lin, Yi; Lindor, Noralane M.; Nishihara, Reiko; Ogino, Shuji; Potter, John D.; Rudolph, Anja; Schoen, Robert E.; Seminara, Daniela; Slattery, Martha L.; Thibodeau, Stephen N.; Thornquist, Mark; Toth, Reka; Wallace, Robert; White, Emily; Jiao, Shuo; Lemire, Mathieu; Hsu, Li; Peters, Ulrike

2016-01-01

Genome-wide association studies (GWAS) have identified many genetic susceptibility loci for colorectal cancer (CRC). However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10−8; permuted p-value 3.51x10-8) region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74–0.91]; P = 2.1×10−4) and TT genotypes (OR,0.62 [95% CI, 0.51–0.75]; P = 1.3×10−6) but not associated among those with the CC genotype (p = 0.059). No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk. PMID:27723779

SLiM 2: Flexible, Interactive Forward Genetic Simulations.

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Haller, Benjamin C; Messer, Philipp W

2017-01-01

Modern population genomic datasets hold immense promise for revealing the evolutionary processes operating in natural populations, but a crucial prerequisite for this goal is the ability to model realistic evolutionary scenarios and predict their expected patterns in genomic data. To that end, we present SLiM 2: an evolutionary simulation framework that combines a powerful, fast engine for forward population genetic simulations with the capability of modeling a wide variety of complex evolutionary scenarios. SLiM achieves this flexibility through scriptability, which provides control over most aspects of the simulated evolutionary scenarios with a simple R-like scripting language called Eidos. An example SLiM simulation is presented to illustrate the power of this approach. SLiM 2 also includes a graphical user interface for simulation construction, interactive runtime control, and dynamic visualization of simulation output, facilitating easy and fast model development with quick prototyping and visual debugging. We conclude with a performance comparison between SLiM and two other popular forward genetic simulation packages. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Beyond Punnett Squares: Student Word Association and Explanations of Phenotypic Variation through an Integrative Quantitative Genetics Unit Investigating Anthocyanin Inheritance and Expression in "Brassica rapa" Fast Plants

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Batzli, Janet M.; Smith, Amber R.; Williams, Paul H.; McGee, Seth A.; Dosa, Katalin; Pfammatter, Jesse

2014-01-01

Genetics instruction in introductory biology is often confined to Mendelian genetics and avoids the complexities of variation in quantitative traits. Given the driving question "What determines variation in phenotype (Pv)? (Pv=Genotypic variation Gv + environmental variation Ev)," we developed a 4-wk unit for an inquiry-based laboratory…

pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms.

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Molnos, Sophie; Baumbach, Clemens; Wahl, Simone; Müller-Nurasyid, Martina; Strauch, Konstantin; Wang-Sattler, Rui; Waldenberger, Melanie; Meitinger, Thomas; Adamski, Jerzy; Kastenmüller, Gabi; Suhre, Karsten; Peters, Annette; Grallert, Harald; Theis, Fabian J; Gieger, Christian

2017-09-29

Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different "omics" layers. Existing tools only consider single-nucleotide polymorphism (SNP)-SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables. We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different "omics" layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels. The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/ .

Factor analysis in the Genetics of Asthma International Network family study identifies five major quantitative asthma phenotypes

NARCIS (Netherlands)

Pillai, S. G.; Tang, Y.; van den Oord, E.; Klotsman, M.; Barnes, K.; Carlsen, K.; Gerritsen, J.; Lenney, W.; Silverman, M.; Sly, P.; Sundy, J.; Tsanakas, J.; von Berg, A.; Whyte, M.; Ortega, H. G.; Anderson, W. H.; Helms, P. J.

Background Asthma is a clinically heterogeneous disease caused by a complex interaction between genetic susceptibility and diverse environmental factors. In common with other complex diseases the lack of a standardized scheme to evaluate the phenotypic variability poses challenges in identifying the

The genetic basis of local adaptation for pathogenic fungi in agricultural ecosystems.

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Croll, Daniel; McDonald, Bruce A

2017-04-01

Local adaptation plays a key role in the evolutionary trajectory of host-pathogen interactions. However, the genetic architecture of local adaptation in host-pathogen systems is poorly understood. Fungal plant pathogens in agricultural ecosystems provide highly tractable models to quantify phenotypes and map traits to corresponding genomic loci. The outcome of crop-pathogen interactions is thought to be governed largely by gene-for-gene interactions. However, recent studies showed that virulence can be governed by quantitative trait loci and that many abiotic factors contribute to the outcome of the interaction. After introducing concepts of local adaptation and presenting examples from wild plant pathosystems, we focus this review on a major pathogen of wheat, Zymoseptoria tritici, to show how a multitude of traits can affect local adaptation. Zymoseptoria tritici adapted to different thermal environments across its distribution range, indicating that thermal adaptation may limit effective dispersal to different climates. The application of fungicides led to the rapid evolution of multiple, independent resistant populations. The degree of colony melanization showed strong pleiotropic effects with other traits, including trade-offs with colony growth rates and fungicide sensitivity. The success of the pathogen on its host can be assessed quantitatively by counting pathogen reproductive structures and measuring host damage based on necrotic lesions. Interestingly, these two traits can be weakly correlated and depend both on host and pathogen genotypes. Quantitative trait mapping studies showed that the genetic architecture of locally adapted traits varies from single loci with large effects to many loci with small individual effects. We discuss how local adaptation could hinder or accelerate the development of epidemics in agricultural ecosystems. © 2016 John Wiley & Sons Ltd.

Aging and a genetic KIBRA polymorphism interactively affect feedback- and observation-based probabilistic classification learning.

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Schuck, Nicolas W; Petok, Jessica R; Meeter, Martijn; Schjeide, Brit-Maren M; Schröder, Julia; Bertram, Lars; Gluck, Mark A; Li, Shu-Chen

2018-01-01

Probabilistic category learning involves complex interactions between the hippocampus and striatum that may depend on whether acquisition occurs via feedback or observation. Little is known about how healthy aging affects these processes. We tested whether age-related behavioral differences in probabilistic category learning from feedback or observation depend on a genetic factor known to influence individual differences in hippocampal function, the KIBRA gene (single nucleotide polymorphism rs17070145). Results showed comparable age-related performance impairments in observational as well as feedback-based learning. Moreover, genetic analyses indicated an age-related interactive effect of KIBRA on learning: among older adults, the beneficial T-allele was positively associated with learning from feedback, but negatively with learning from observation. In younger adults, no effects of KIBRA were found. Our results add behavioral genetic evidence to emerging data showing age-related differences in how neural resources relate to memory functions, namely that hippocampal and striatal contributions to probabilistic category learning may vary with age. Our findings highlight the effects genetic factors can have on differential age-related decline of different memory functions. Copyright © 2017 Elsevier Inc. All rights reserved.

Variants of SCARB1 and VDR Involved in Complex Genetic Interactions May Be Implicated in the Genetic Susceptibility to Clear Cell Renal Cell Carcinoma

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Ewelina Pośpiech

2015-01-01

Full Text Available The current data are still inconclusive in terms of a genetic component involved in the susceptibility to renal cell carcinoma. Our aim was to evaluate 40 selected candidate polymorphisms for potential association with clear cell renal cell carcinoma (ccRCC based on independent group of 167 patients and 200 healthy controls. The obtained data were searched for independent effects of particular polymorphisms as well as haplotypes and genetic interactions. Association testing implied position rs4765623 in the SCARB1 gene (OR=1.688, 95% CI: 1.104–2.582, P=0.016 and a haplotype in VDR comprising positions rs739837, rs731236, rs7975232, and rs1544410 (P=0.012 to be the risk factors in the studied population. The study detected several epistatic effects contributing to the genetic susceptibility to ccRCC. Variation in GNAS1 was implicated in a strong synergistic interaction with BIRC5. This effect was part of a model suggested by multifactor dimensionality reduction method including also a synergy between GNAS1 and SCARB1 (P=0.036. Significance of GNAS1-SCARB1 interaction was further confirmed by logistic regression (P=0.041, which also indicated involvement of SCARB1 in additional interaction with EPAS1 (P=0.008 as well as revealing interactions between GNAS1 and EPAS1 (P=0.016, GNAS1 and MC1R (P=0.031, GNAS1 and VDR (P=0.032, and MC1R and VDR (P=0.035.

Information transmission in genetic regulatory networks: a review

International Nuclear Information System (INIS)

Tkacik, Gasper; Walczak, Aleksandra M

2011-01-01

Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'. (topical review)

Development and validation of an event-specific quantitative PCR method for genetically modified maize MIR162.

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Takabatake, Reona; Masubuchi, Tomoko; Futo, Satoshi; Minegishi, Yasutaka; Noguchi, Akio; Kondo, Kazunari; Teshima, Reiko; Kurashima, Takeyo; Mano, Junichi; Kitta, Kazumi

2014-01-01

A novel real-time PCR-based analytical method was developed for the event-specific quantification of a genetically modified (GM) maize event, MIR162. We first prepared a standard plasmid for MIR162 quantification. The conversion factor (Cf) required to calculate the genetically modified organism (GMO) amount was empirically determined for two real-time PCR instruments, the Applied Biosystems 7900HT (ABI7900) and the Applied Biosystems 7500 (ABI7500) for which the determined Cf values were 0.697 and 0.635, respectively. To validate the developed method, a blind test was carried out in an interlaboratory study. The trueness and precision were evaluated as the bias and reproducibility of relative standard deviation (RSDr). The determined biases were less than 25% and the RSDr values were less than 20% at all evaluated concentrations. These results suggested that the limit of quantitation of the method was 0.5%, and that the developed method would thus be suitable for practical analyses for the detection and quantification of MIR162.

Genetic disparity and relationship among quantitatively inherited yield related traits in diallel crosses of upland cotton

International Nuclear Information System (INIS)

Bibi, M.; Khan, N.U.; Mohammad, F.; Gul, R.; Idrees, M.; Sayal, O.U.; Khakwani, A.A.; Khan, I.A.

2011-01-01

In quantitative genetics, development of high yielding genotypes from parental cultivars of same ancestry is some what confusing as compared to genetically diverse parents. However, sufficient recombinations through allelic variations in mating of closely-related populations result in superior agronomic performance. Development of improved cotton genotypes is one of the prime objectives of any cotton breeding programmes. Genetic divergence and yield potential of parental cotton genotypes versus their diallel hybrids, relationship of yield with various morpho-yield traits and their heritability were studied in 8 X 8 F/sub 1/ diallel hybrids and their parental cultivars in Gossypium hirsutum L. during 2008-09 at Khyber Pakhtunkhwa Agricultural University, Peshawar, Pakistan. Highly significant (p less than or equal to 0.01) differences were observed among parental genotypes and F/sub 1/ populations for all the traits. Results revealed that F/sub 1/ hybrids i.e., CIM-506 X CIM-554, CIM-473 X CIM-554, CIM-446 X CIM-496 and CIM-446 X CIM-554 produced significantly higher number of sympodia, bolls per populations showed incredible performance for plant height, locules per boll and seeds plant and seed cotton yield. Some F/sub 1/ per locule. Seed cotton yield manifested positive association with morpho-yield traits which also accounted for greater genetic variations to yield being dependent trait. Heritabilities (broad sense) were moderate to high in magnitude for all populations with larger genetic potential, positive relationship between yield and yield traits. Results revealed that F1 contributing traits and moderate to high heritability can guide intensive selection and improvement per se in segregating populations. (author)

Interactions between environmental factors and maternal-fetal genetic variations: strategies to elucidate risks of preterm birth.

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Pereyra, Silvana; Bertoni, Bernardo; Sapiro, Rossana

2016-07-01

Preterm birth (PTB) is a complex disease in which medical, social, cultural, and hereditary factors contribute to the pathogenesis of this adverse event. Interactions between genes and environmental factors may complicate our understanding of the relative influence of both effects on PTB. To overcome this, we combined data obtained from a cohort of newborns and their mothers with multiplex analysis of inflammatory-related genes and several environmental risk factors of PTB to describe the environmental-genetic influence on PTB. The study aimed to investigate the association between maternal and fetal genetic variations in genes related to the inflammation pathway with PTB and to assess the interaction between environmental factors with these variations. We conducted a case-control study at the Pereira Rossell Hospital Center, Montevideo, Uruguay. The study included 143 mother-offspring dyads who delivered at preterm (gestational ageenvironmental variables. The genes analyzed were: Toll-like receptor 4 (TLR4), Interleukin 6 (IL6), Interleukin 1 beta (IL1B) and Interleukin 12 receptor beta (IL12RB). We detected a significant interaction between IL1B rs16944 polymorphism in maternal samples and IL6 rs1800795 polymorphism in newborns, emphasizing the role of the interaction of maternal and fetal genomes in PTB. In addition, smoke exposure and premature rupture of membranes (PROM) were significantly different between the premature group and controls. IL1B and IL6 polymorphisms in mothers were significantly associated with PTB when controlling for smoke exposure. TLR4 polymorphism and PROM were significantly associated with PTB when controlling for PROM, but only in the case of severe PTB. Interactions between maternal and fetal genomes may influence the timing of birth. By incorporating environmental data, we revealed genetic associations with PTB, a finding not found when we analyzed genetic data alone. Our results stress the importance of studying the effect of

Genetic and environmental factors interact to influence anxiety.

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Gross, Cornelius; Hen, René

2004-01-01

Both genetic and environmental factors influence normal anxiety traits as well as anxiety disorders. In addition it is becoming increasingly clear that these factors interact to produce specific anxiety-related behaviors. For example, in humans and in monkeys mutations in the gene encoding for the serotonin transporter result in increased anxiety in adult life when combined with a stressful environment during development. Another recent example comes from twin studies suggesting that a small hippocampus can be a predisposing condition that renders individuals susceptible to post traumatic stress disorder. Such examples illustrate how specific mutations leading to abnormal brain development may increase vulnerability to environmental insults which may in turn lead to specific anxiety disorders.

Resolving the Complex Genetic Basis of Phenotypic Variation and Variability of Cellular Growth.

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Ziv, Naomi; Shuster, Bentley M; Siegal, Mark L; Gresham, David

2017-07-01

In all organisms, the majority of traits vary continuously between individuals. Explaining the genetic basis of quantitative trait variation requires comprehensively accounting for genetic and nongenetic factors as well as their interactions. The growth of microbial cells can be characterized by a lag duration, an exponential growth phase, and a stationary phase. Parameters that characterize these growth phases can vary among genotypes (phenotypic variation), environmental conditions (phenotypic plasticity), and among isogenic cells in a given environment (phenotypic variability). We used a high-throughput microscopy assay to map genetic loci determining variation in lag duration and exponential growth rate in growth rate-limiting and nonlimiting glucose concentrations, using segregants from a cross of two natural isolates of the budding yeast, Saccharomyces cerevisiae We find that some quantitative trait loci (QTL) are common between traits and environments whereas some are unique, exhibiting gene-by-environment interactions. Furthermore, whereas variation in the central tendency of growth rate or lag duration is explained by many additive loci, differences in phenotypic variability are primarily the result of genetic interactions. We used bulk segregant mapping to increase QTL resolution by performing whole-genome sequencing of complex mixtures of an advanced intercross mapping population grown in selective conditions using glucose-limited chemostats. We find that sequence variation in the high-affinity glucose transporter HXT7 contributes to variation in growth rate and lag duration. Allele replacements of the entire locus, as well as of a single polymorphic amino acid, reveal that the effect of variation in HXT7 depends on genetic, and allelic, background. Amplifications of HXT7 are frequently selected in experimental evolution in glucose-limited environments, but we find that HXT7 amplifications result in antagonistic pleiotropy that is absent in naturally

Can genetics help psychometrics? Improving dimensionality assessment through genetic factor modeling.

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Franić, Sanja; Dolan, Conor V; Borsboom, Denny; Hudziak, James J; van Beijsterveldt, Catherina E M; Boomsma, Dorret I

2013-09-01

In the present article, we discuss the role that quantitative genetic methodology may play in assessing and understanding the dimensionality of psychological (psychometric) instruments. Specifically, we study the relationship between the observed covariance structures, on the one hand, and the underlying genetic and environmental influences giving rise to such structures, on the other. We note that this relationship may be such that it hampers obtaining a clear estimate of dimensionality using standard tools for dimensionality assessment alone. One situation in which dimensionality assessment may be impeded is that in which genetic and environmental influences, of which the observed covariance structure is a function, differ from each other in structure and dimensionality. We demonstrate that in such situations settling dimensionality issues may be problematic, and propose using quantitative genetic modeling to uncover the (possibly different) dimensionalities of the underlying genetic and environmental structures. We illustrate using simulations and an empirical example on childhood internalizing problems.

Genetic Parameters and Combining Ability Effects of Parents for Seed Yield and other Quantitative Traits in Black Gram [Vigna mungo (L. Hepper

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Supriyo CHAKRABORTY

2010-06-01

Full Text Available Line x tester analysis was carried out in black gram [Vigna mungo (L. Hepper], an edible legume, to estimate the gca (general combining ability effects of parents (3 lines and 3 testers and the SCA (specific combining ability effects of 9 crosses for seed yield and other eleven quantitative traits. Though additive and nonadditive gene actions governed the expression of quantitative traits, the magnitude of nonadditive gene action was higher than that of additive gene action for each quantitative trait. Two parents viz. �UG157� and �DPU915� were good general combiners. Two crosses namely �PDB 88-31�/�DPU 915� and �PLU 277�/�KAU7� had high per se performance along with positive significant SCA effect for seed yield/plant. The degree of dominance revealed overdominance for all the traits except clusters/plant with partial dominance. The predictability ratio also revealed the predominant role of nonadditive gene action in the genetic control of quantitative traits. Narrow sense heritability was also low for each trait. Recurrent selection or biparental mating followed by selection which can exploit both additive and nonadditive gene actions would be of interest for yield improvement in black gram. Due to presence of high magnitude of nonadditive gene action, heterosis breeding could also be attempted to develop low cost hybrid variety using genetic male sterility system in black gram.

Pooled-matrix protein interaction screens using Barcode Fusion Genetics.

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Yachie, Nozomu; Petsalaki, Evangelia; Mellor, Joseph C; Weile, Jochen; Jacob, Yves; Verby, Marta; Ozturk, Sedide B; Li, Siyang; Cote, Atina G; Mosca, Roberto; Knapp, Jennifer J; Ko, Minjeong; Yu, Analyn; Gebbia, Marinella; Sahni, Nidhi; Yi, Song; Tyagi, Tanya; Sheykhkarimli, Dayag; Roth, Jonathan F; Wong, Cassandra; Musa, Louai; Snider, Jamie; Liu, Yi-Chun; Yu, Haiyuan; Braun, Pascal; Stagljar, Igor; Hao, Tong; Calderwood, Michael A; Pelletier, Laurence; Aloy, Patrick; Hill, David E; Vidal, Marc; Roth, Frederick P

2016-04-22

High-throughput binary protein interaction mapping is continuing to extend our understanding of cellular function and disease mechanisms. However, we remain one or two orders of magnitude away from a complete interaction map for humans and other major model organisms. Completion will require screening at substantially larger scales with many complementary assays, requiring further efficiency gains in proteome-scale interaction mapping. Here, we report Barcode Fusion Genetics-Yeast Two-Hybrid (BFG-Y2H), by which a full matrix of protein pairs can be screened in a single multiplexed strain pool. BFG-Y2H uses Cre recombination to fuse DNA barcodes from distinct plasmids, generating chimeric protein-pair barcodes that can be quantified via next-generation sequencing. We applied BFG-Y2H to four different matrices ranging in scale from ~25 K to 2.5 M protein pairs. The results show that BFG-Y2H increases the efficiency of protein matrix screening, with quality that is on par with state-of-the-art Y2H methods. © 2016 The Authors. Published under the terms of the CC BY 4.0 license.

Using a Specific RNA-Protein Interaction To Quench the Fluorescent RNA Spinach.

Science.gov (United States)

Roszyk, Laura; Kollenda, Sebastian; Hennig, Sven

2017-12-15

RNAs are involved in interaction networks with other biomolecules and are crucial for proper cell function. Yet their biochemical analysis remains challenging. For Förster Resonance Energy Transfer (FRET), a common tool to study such interaction networks, two interacting molecules have to be fluorescently labeled. "Spinach" is a genetically encodable RNA aptamer that starts to fluoresce upon binding of an organic molecule. Therefore, it is a biological fluorophore tag for RNAs. However, spinach has never been used in a FRET assembly before. Here, we describe how spinach is quenched when close to acceptors. We used RNA-DNA hybridization to bring quenchers or red organic dyes in close proximity to spinach. Furthermore, we investigate RNA-protein interactions quantitatively on the example of Pseudomonas aeruginosa phage coat protein 7 (PP7) and its interacting pp7-RNA. We utilize spinach quenching as a fully genetically encodable system even under lysate conditions. Therefore, this work represents a direct method to analyze RNA-protein interactions by quenching the spinach aptamer.

Event History Analysis in Quantitative Genetics

DEFF Research Database (Denmark)

Maia, Rafael Pimentel

Event history analysis is a clas of statistical methods specially designed to analyze time-to-event characteristics, e.g. the time until death. The aim of the thesis was to present adequate multivariate versions of mixed survival models that properly represent the genetic aspects related to a given...

Quantitative characterization of 3D deformations of cell interactions with soft biomaterials

Science.gov (United States)

Franck, Christian

In recent years, the importance of mechanical forces in directing cellular function has been recognized as a significant factor in biological and physiological processes. In fact, these physical forces are now viewed equally as important as biochemical stimuli in controlling cellular response. Not only do these cellular forces, or cell tractions, play an important role in cell migration, they are also significant to many other physiological and pathological processes, both at the tissue and organ level, including wound healing, inflammation, angiogenesis, and embryogenesis. A complete quantification of cell tractions during cell-material interactions can lead to a deeper understanding of the fundamental role these forces play in cell biology. Thus, understanding the function and role of a cell from a mechanical framework can have important implications towards the development of new implant materials and drug treatments. Previous research has contributed significant descriptions of cell-tissue interactions by quantifying cell tractions in two-dimensional environments; however, most physiological processes are three-dimensional in nature. Recent studies have shown morphological differences in cells cultured on two-dimensional substrates versus three-dimensional matrices, and that the intrinsic extracellular matrix interactions and migration behavior are different in three dimensions versus two dimensions. Hence, measurement techniques are needed to investigate cellular behavior in all three dimensions. This thesis presents a full-field imaging technique capable of quantitatively measuring cell traction forces in all three spatial dimensions, and hence addresses the need of a three-dimensional quantitative imaging technique to gain insight into the fundamental role of physical forces in biological processes. The technique combines laser scanning confocal microscopy (LSCM) with digital volume correlation (DVC) to track the motion of fluorescent particles during cell

Event-specific qualitative and quantitative detection of five genetically modified rice events using a single standard reference molecule.

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Kim, Jae-Hwan; Park, Saet-Byul; Roh, Hyo-Jeong; Shin, Min-Ki; Moon, Gui-Im; Hong, Jin-Hwan; Kim, Hae-Yeong

2017-07-01

One novel standard reference plasmid, namely pUC-RICE5, was constructed as a positive control and calibrator for event-specific qualitative and quantitative detection of genetically modified (GM) rice (Bt63, Kemingdao1, Kefeng6, Kefeng8, and LLRice62). pUC-RICE5 contained fragments of a rice-specific endogenous reference gene (sucrose phosphate synthase) as well as the five GM rice events. An existing qualitative PCR assay approach was modified using pUC-RICE5 to create a quantitative method with limits of detection correlating to approximately 1-10 copies of rice haploid genomes. In this quantitative PCR assay, the square regression coefficients ranged from 0.993 to 1.000. The standard deviation and relative standard deviation values for repeatability ranged from 0.02 to 0.22 and 0.10% to 0.67%, respectively. The Ministry of Food and Drug Safety (Korea) validated the method and the results suggest it could be used routinely to identify five GM rice events. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development and interlaboratory validation of quantitative polymerase chain reaction method for screening analysis of genetically modified soybeans.

Science.gov (United States)

Takabatake, Reona; Onishi, Mari; Koiwa, Tomohiro; Futo, Satoshi; Minegishi, Yasutaka; Akiyama, Hiroshi; Teshima, Reiko; Kurashima, Takeyo; Mano, Junichi; Furui, Satoshi; Kitta, Kazumi

2013-01-01

A novel real-time polymerase chain reaction (PCR)-based quantitative screening method was developed for three genetically modified soybeans: RRS, A2704-12, and MON89788. The 35S promoter (P35S) of cauliflower mosaic virus is introduced into RRS and A2704-12 but not MON89788. We then designed a screening method comprised of the combination of the quantification of P35S and the event-specific quantification of MON89788. The conversion factor (Cf) required to convert the amount of a genetically modified organism (GMO) from a copy number ratio to a weight ratio was determined experimentally. The trueness and precision were evaluated as the bias and reproducibility of relative standard deviation (RSDR), respectively. The determined RSDR values for the method were less than 25% for both targets. We consider that the developed method would be suitable for the simple detection and approximate quantification of GMO.

Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting.

Science.gov (United States)

Zhao, Wei; Ware, Erin B; He, Zihuai; Kardia, Sharon L R; Faul, Jessica D; Smith, Jennifer A

2017-09-29

Obesity, which develops over time, is one of the leading causes of chronic diseases such as cardiovascular disease. However, hundreds of BMI (body mass index)-associated genetic loci identified through large-scale genome-wide association studies (GWAS) only explain about 2.7% of BMI variation. Most common human traits are believed to be influenced by both genetic and environmental factors. Past studies suggest a variety of environmental features that are associated with obesity, including socioeconomic status and psychosocial factors. This study combines both gene/regions and environmental factors to explore whether social/psychosocial factors (childhood and adult socioeconomic status, social support, anger, chronic burden, stressful life events, and depressive symptoms) modify the effect of sets of genetic variants on BMI in European American and African American participants in the Health and Retirement Study (HRS). In order to incorporate longitudinal phenotype data collected in the HRS and investigate entire sets of single nucleotide polymorphisms (SNPs) within gene/region simultaneously, we applied a novel set-based test for gene-environment interaction in longitudinal studies (LGEWIS). Childhood socioeconomic status (parental education) was found to modify the genetic effect in the gene/region around SNP rs9540493 on BMI in European Americans in the HRS. The most significant SNP (rs9540488) by childhood socioeconomic status interaction within the rs9540493 gene/region was suggestively replicated in the Multi-Ethnic Study of Atherosclerosis (MESA) ( p = 0.07).

Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting

Directory of Open Access Journals (Sweden)

Wei Zhao

2017-09-01

Full Text Available Obesity, which develops over time, is one of the leading causes of chronic diseases such as cardiovascular disease. However, hundreds of BMI (body mass index-associated genetic loci identified through large-scale genome-wide association studies (GWAS only explain about 2.7% of BMI variation. Most common human traits are believed to be influenced by both genetic and environmental factors. Past studies suggest a variety of environmental features that are associated with obesity, including socioeconomic status and psychosocial factors. This study combines both gene/regions and environmental factors to explore whether social/psychosocial factors (childhood and adult socioeconomic status, social support, anger, chronic burden, stressful life events, and depressive symptoms modify the effect of sets of genetic variants on BMI in European American and African American participants in the Health and Retirement Study (HRS. In order to incorporate longitudinal phenotype data collected in the HRS and investigate entire sets of single nucleotide polymorphisms (SNPs within gene/region simultaneously, we applied a novel set-based test for gene-environment interaction in longitudinal studies (LGEWIS. Childhood socioeconomic status (parental education was found to modify the genetic effect in the gene/region around SNP rs9540493 on BMI in European Americans in the HRS. The most significant SNP (rs9540488 by childhood socioeconomic status interaction within the rs9540493 gene/region was suggestively replicated in the Multi-Ethnic Study of Atherosclerosis (MESA (p = 0.07.

Genetic effects at pleiotropic loci are context-dependent with consequences for the maintenance of genetic variation in populations.

Directory of Open Access Journals (Sweden)

Heather A Lawson

2011-09-01

Full Text Available Context-dependent genetic effects, including genotype-by-environment and genotype-by-sex interactions, are a potential mechanism by which genetic variation of complex traits is maintained in populations. Pleiotropic genetic effects are also thought to play an important role in evolution, reflecting functional and developmental relationships among traits. We examine context-dependent genetic effects at pleiotropic loci associated with normal variation in multiple metabolic syndrome (MetS components (obesity, dyslipidemia, and diabetes-related traits. MetS prevalence is increasing in Western societies and, while environmental in origin, presents substantial variation in individual response. We identify 23 pleiotropic MetS quantitative trait loci (QTL in an F(16 advanced intercross between the LG/J and SM/J inbred mouse strains (Wustl:LG,SM-G16; n = 1002. Half of each family was fed a high-fat diet and half fed a low-fat diet; and additive, dominance, and parent-of-origin imprinting genotypic effects were examined in animals partitioned into sex, diet, and sex-by-diet cohorts. We examine the context-dependency of the underlying additive, dominance, and imprinting genetic effects of the traits associated with these pleiotropic QTL. Further, we examine sequence polymorphisms (SNPs between LG/J and SM/J as well as differential expression of positional candidate genes in these regions. We show that genetic associations are different in different sex, diet, and sex-by-diet settings. We also show that over- or underdominance and ecological cross-over interactions for single phenotypes may not be common, however multidimensional synthetic phenotypes at loci with pleiotropic effects can produce situations that favor the maintenance of genetic variation in populations. Our findings have important implications for evolution and the notion of personalized medicine.

An information-gain approach to detecting three-way epistatic interactions in genetic association studies

DEFF Research Database (Denmark)

Hu, Ting; Chen, Yuanzhu; Kiralis, Jeff W

2013-01-01

Background Epistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and phenotypic variations. Quantifying epistasis......-way epistasis. Methods Such a measure is based on information gain, and is able to separate all lower order effects from pure three-way epistasis. Results Our method was verified on synthetic data and applied to real data from a candidate-gene study of tuberculosis in a West African population....... In the tuberculosis data, we found a statistically significant pure three-way epistatic interaction effect that was stronger than any lower-order associations. Conclusion Our study provides a methodological basis for detecting and characterizing high-order gene-gene interactions in genetic association studies....

Genetic variation in foundation species governs the dynamics of trophic interactions

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Valencia-Cuevas, Leticia; Mussali-Galante, Patricia; Cano-Santana, Zenón; Pujade-Villar, Juli; Equihua-Martínez, Armando

2018-01-01

Abstract Various studies have demonstrated that the foundation species genetic diversity can have direct effects that extend beyond the individual or population level, affecting the dependent communities. Additionally, these effects may be indirectly extended to higher trophic levels throughout the entire community. Quercus castanea is an oak species with characteristics of foundation species beyond presenting a wide geographical distribution and being a dominant element of Mexican temperate forests. In this study, we analyzed the influence of population (He) and individual (HL) genetic diversity of Q. castanea on its canopy endophagous insect community and associated parasitoids. Specifically, we studied the composition, richness (S) and density of leaf-mining moths (Lepidoptera: Tischeridae, Citheraniidae), gall-forming wasps (Hymenoptera: Cynipidae), and canopy parasitoids of Q. castanea. We sampled 120 trees belonging to six populations (20/site) through the previously recognized gradient of genetic diversity. In total, 22 endophagous insect species belonging to three orders (Hymenoptera, Lepidoptera, and Diptera) and 20 parasitoid species belonging to 13 families were identified. In general, we observed that the individual genetic diversity of the host plant (HL) has a significant positive effect on the S and density of the canopy endophagous insect communities. In contrast, He has a significant negative effect on the S of endophagous insects. Additionally, indirect effects of HL were observed, affecting the S and density of parasitoid insects. Our results suggest that genetic variation in foundation species can be one of the most important factors governing the dynamics of tritrophic interactions that involve oaks, herbivores, and parasitoids. PMID:29492034

The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour.

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Kaiser-Bunbury, Christopher N; Muff, Stefanie; Memmott, Jane; Müller, Christine B; Caflisch, Amedeo

2010-04-01

Species extinctions pose serious threats to the functioning of ecological communities worldwide. We used two qualitative and quantitative pollination networks to simulate extinction patterns following three removal scenarios: random removal and systematic removal of the strongest and weakest interactors. We accounted for pollinator behaviour by including potential links into temporal snapshots (12 consecutive 2-week networks) to reflect mutualists' ability to 'switch' interaction partners (re-wiring). Qualitative data suggested a linear or slower than linear secondary extinction while quantitative data showed sigmoidal decline of plant interaction strength upon removal of the strongest interactor. Temporal snapshots indicated greater stability of re-wired networks over static systems. Tolerance of generalized networks to species extinctions was high in the random removal scenario, with an increase in network stability if species formed new interactions. Anthropogenic disturbance, however, that promote the extinction of the strongest interactors might induce a sudden collapse of pollination networks.

Quantitative relations between interaction parameter, miscibility and function in organic solar cells

KAUST Repository

Ye, Long; Hu, Huawei; Ghasemi, Masoud; Wang, Tonghui; Collins, Brian A; Kim, Joo-Hyun; Jiang, Kui; Carpenter, Joshua H.; Li, Hong; Li, Zhengke; McAfee, Terry; Zhao, Jingbo; Chen, Xiankai; Lai, Joshua Lin Yuk; Ma, Tingxuan; Bredas, Jean-Luc; Yan, He; Ade, Harald

2018-01-01

Although it is known that molecular interactions govern morphology formation and purity of mixed domains of conjugated polymer donors and small-molecule acceptors, and thus largely control the achievable performance of organic solar cells, quantifying interaction-function relations has remained elusive. Here, we first determine the temperature-dependent effective amorphous-amorphous interaction parameter, χaa(T), by mapping out the phase diagram of a model amorphous polymer:fullerene material system. We then establish a quantitative 'constant-kink-saturation' relation between χaa and the fill factor in organic solar cells that is verified in detail in a model system and delineated across numerous high- and low-performing materials systems, including fullerene and non-fullerene acceptors. Our experimental and computational data reveal that a high fill factor is obtained only when χaa is large enough to lead to strong phase separation. Our work outlines a basis for using various miscibility tests and future simulation methods that will significantly reduce or eliminate trial-and-error approaches to material synthesis and device fabrication of functional semiconducting blends and organic blends in general.

Quantitative relations between interaction parameter, miscibility and function in organic solar cells

KAUST Repository

Ye, Long

2018-02-02

Although it is known that molecular interactions govern morphology formation and purity of mixed domains of conjugated polymer donors and small-molecule acceptors, and thus largely control the achievable performance of organic solar cells, quantifying interaction-function relations has remained elusive. Here, we first determine the temperature-dependent effective amorphous-amorphous interaction parameter, χaa(T), by mapping out the phase diagram of a model amorphous polymer:fullerene material system. We then establish a quantitative \\'constant-kink-saturation\\' relation between χaa and the fill factor in organic solar cells that is verified in detail in a model system and delineated across numerous high- and low-performing materials systems, including fullerene and non-fullerene acceptors. Our experimental and computational data reveal that a high fill factor is obtained only when χaa is large enough to lead to strong phase separation. Our work outlines a basis for using various miscibility tests and future simulation methods that will significantly reduce or eliminate trial-and-error approaches to material synthesis and device fabrication of functional semiconducting blends and organic blends in general.

Genetical Genomics for Evolutionary Studies

NARCIS (Netherlands)

Prins, J.C.P.; Smant, G.; Jansen, R.C.

2012-01-01

Genetical genomics combines acquired high-throughput genomic data with genetic analysis. In this chapter, we discuss the application of genetical genomics for evolutionary studies, where new high-throughput molecular technologies are combined with mapping quantitative trait loci (QTL) on the genome

Genetic interactions between neurofibromin and endothelin receptor B in mice.

Directory of Open Access Journals (Sweden)

Mugdha Deo

Full Text Available When mutations in two different genes produce the same mutant phenotype, it suggests that the encoded proteins either interact with each other, or act in parallel to fulfill a similar purpose. Haploinsufficiency of Neurofibromin and over-expression of Endothelin 3 both cause increased numbers of melanocytes to populate the dermis during mouse development, and thus we are interested in how these two signaling pathways might intersect. Neurofibromin is mutated in the human genetic disease, neurofibromatosis type 1, which is characterized by the development of Schwann cell based tumors and skin hyper-pigmentation. Neurofibromin is a GTPase activating protein, while the Endothelin 3 ligand activates Endothelin receptor B, a G protein coupled receptor. In order to study the genetic interactions between endothelin and neurofibromin, we defined the deletion breakpoints of the classical Ednrb piebald lethal allele (Ednrb(s-l and crossed these mice to mice with a loss-of-function mutation in neurofibromin, Dark skin 9 (Dsk9. We found that Neurofibromin haploinsufficiency requires Endothelin receptor B to darken the tail dermis. In contrast, Neurofibromin haploinsufficiency increases the area of the coat that is pigmented in Endothelin receptor B null mice. We also found an oncogenic mutation in the G protein alpha subunit, GNAQ, which couples to Endothelin receptor B, in a uveal melanoma from a patient with neurofibromatosis type 1. Thus, this data suggests that there is a complex relationship between Neurofibromin and Endothelin receptor B.

StrigoQuant: A genetically encoded biosensor for quantifying strigolactone activity and specificity

KAUST Repository

Samodelov, S. L.

2016-11-05

Strigolactones are key regulators of plant development and interaction with symbiotic fungi; however, quantitative tools for strigolactone signaling analysis are lacking. We introduce a genetically encoded hormone biosensor used to analyze strigolactone-mediated processes, including the study of the components involved in the hormone perception/signaling complex and the structural specificity and sensitivity of natural and synthetic strigolactones in Arabidopsis, providing quantitative insights into the stereoselectivity of strigolactone perception. Given the high specificity, sensitivity, dynamic range of activity, modular construction, ease of implementation, and wide applicability, the biosensor StrigoQuant will be useful in unraveling multiple levels of strigolactone metabolic and signaling networks.

Quantitative analysis of the interaction between the envelope protein domains and the core protein of human hepatitis B virus

International Nuclear Information System (INIS)

Choi, Kyoung-Jae; Lim, Chun-Woo; Yoon, Moon-Young; Ahn, Byung-Yoon; Yu, Yeon Gyu

2004-01-01

Interaction between preformed nucleocapsids and viral envelope proteins is critical for the assembly of virus particles in infected cells. The pre-S1 and pre-S2 and cytosolic regions of the human hepatitis B virus envelope protein had been implicated in the interaction with the core protein of nucleocapsids. The binding affinities of specific subdomains of the envelope protein to the core protein were quantitatively measured by both ELISA and BIAcore assay. While a marginal binding was detected with the pre-S1 or pre-S2, the core protein showed high affinities to pre-S with apparent dissociation constants (K D app ) of 7.3 ± 0.9 and 8.2 ± 0.4 μM by ELISA and BIAcore assay, respectively. The circular dichroism analysis suggested that conformational change occurs in pre-S through interaction with core protein. These results substantiate the importance of specific envelope domains in virion assembly, and demonstrate that the interaction between viral proteins can be quantitatively measured in vitro

Genotype-environment interactions affecting preflowering physiological and morphological traits of Brassica rapa grown in two watering regimes

NARCIS (Netherlands)

El-Soda, M.; Boer, M.P.; Bagheri, H.; Hanhart, C.J.; Koornneef, M.; Aarts, M.G.M.

2014-01-01

Plant growth and productivity are greatly affected by drought, which is likely to become more threatening with the predicted global temperature increase. Understanding the genetic architecture of complex quantitative traits and their interaction with water availability may lead to improved crop

Role of the XRCC1 - APE1 interaction in the maintenance of genetic stability

International Nuclear Information System (INIS)

Sossou-Becker, M.

2005-09-01

This thesis is divided in four chapters: the first one concerns the genetic instability, the second one is devoted to the DNA repair, the third one is related to the XRCC1 and the chapter four concerns APE1. Then, are defined the objectives and the results. This work fits into the studies of repair mechanisms. The physical and functional characterisation of the interaction between XRCC1 and APE1 allowed to understand its involvement in the prevention of the genetic instability at the origin of cancer. (N.C.)

Development of a screening method for genetically modified soybean by plasmid-based quantitative competitive polymerase chain reaction.

Science.gov (United States)

Shimizu, Eri; Kato, Hisashi; Nakagawa, Yuki; Kodama, Takashi; Futo, Satoshi; Minegishi, Yasutaka; Watanabe, Takahiro; Akiyama, Hiroshi; Teshima, Reiko; Furui, Satoshi; Hino, Akihiro; Kitta, Kazumi

2008-07-23

A novel type of quantitative competitive polymerase chain reaction (QC-PCR) system for the detection and quantification of the Roundup Ready soybean (RRS) was developed. This system was designed based on the advantage of a fully validated real-time PCR method used for the quantification of RRS in Japan. A plasmid was constructed as a competitor plasmid for the detection and quantification of genetically modified soy, RRS. The plasmid contained the construct-specific sequence of RRS and the taxon-specific sequence of lectin1 (Le1), and both had 21 bp oligonucleotide insertion in the sequences. The plasmid DNA was used as a reference molecule instead of ground seeds, which enabled us to precisely and stably adjust the copy number of targets. The present study demonstrated that the novel plasmid-based QC-PCR method could be a simple and feasible alternative to the real-time PCR method used for the quantification of genetically modified organism contents.

Development of an event-specific hydrolysis probe quantitative real-time polymerase chain reaction assay for Embrapa 5.1 genetically modified common bean (Phaseolus vulgaris).

Science.gov (United States)

Treml, Diana; Venturelli, Gustavo L; Brod, Fábio C A; Faria, Josias C; Arisi, Ana C M

2014-12-10

A genetically modified (GM) common bean event, namely Embrapa 5.1, resistant to the bean golden mosaic virus (BGMV), was approved for commercialization in Brazil. Brazilian regulation for genetically modified organism (GMO) labeling requires that any food containing more than 1% GMO be labeled. The event-specific polymerase chain reaction (PCR) method has been the primary trend for GMO identification and quantitation because of its high specificity based on the flanking sequence. This work reports the development of an event-specific assay, named FGM, for Embrapa 5.1 detection and quantitation by use of SYBR Green or hydrolysis probe. The FGM assay specificity was tested for Embrapa 2.3 event (a noncommercial GM common bean also resistant to BGMV), 46 non-GM common bean varieties, and other crop species including maize, GM maize, soybean, and GM soybean. The FGM assay showed high specificity to detect the Embrapa 5.1 event. Standard curves for the FGM assay presented a mean efficiency of 95% and a limit of detection (LOD) of 100 genome copies in the presence of background DNA. The primers and probe developed are suitable for the detection and quantitation of Embrapa 5.1.

Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

Science.gov (United States)

Yang, Aiyuan; Yan, Chunxia; Zhu, Feng; Zhao, Zhongmeng; Cao, Zhi

2013-01-01

Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds. PMID:23984382

Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

Directory of Open Access Journals (Sweden)

Xuanping Zhang

2013-01-01

Full Text Available Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR, which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds.

Class II HLA interactions modulate genetic risk for multiple sclerosis

Science.gov (United States)

Dilthey, Alexander T; Xifara, Dionysia K; Ban, Maria; Shah, Tejas S; Patsopoulos, Nikolaos A; Alfredsson, Lars; Anderson, Carl A; Attfield, Katherine E; Baranzini, Sergio E; Barrett, Jeffrey; Binder, Thomas M C; Booth, David; Buck, Dorothea; Celius, Elisabeth G; Cotsapas, Chris; D’Alfonso, Sandra; Dendrou, Calliope A; Donnelly, Peter; Dubois, Bénédicte; Fontaine, Bertrand; Fugger, Lars; Goris, An; Gourraud, Pierre-Antoine; Graetz, Christiane; Hemmer, Bernhard; Hillert, Jan; Kockum, Ingrid; Leslie, Stephen; Lill, Christina M; Martinelli-Boneschi, Filippo; Oksenberg, Jorge R; Olsson, Tomas; Oturai, Annette; Saarela, Janna; Søndergaard, Helle Bach; Spurkland, Anne; Taylor, Bruce; Winkelmann, Juliane; Zipp, Frauke; Haines, Jonathan L; Pericak-Vance, Margaret A; Spencer, Chris C A; Stewart, Graeme; Hafler, David A; Ivinson, Adrian J; Harbo, Hanne F; Hauser, Stephen L; De Jager, Philip L; Compston, Alastair; McCauley, Jacob L; Sawcer, Stephen; McVean, Gil

2016-01-01

Association studies have greatly refined the understanding of how variation within the human leukocyte antigen (HLA) genes influences risk of multiple sclerosis. However, the extent to which major effects are modulated by interactions is poorly characterized. We analyzed high-density SNP data on 17,465 cases and 30,385 controls from 11 cohorts of European ancestry, in combination with imputation of classical HLA alleles, to build a high-resolution map of HLA genetic risk and assess the evidence for interactions involving classical HLA alleles. Among new and previously identified class II risk alleles (HLA-DRB1*15:01, HLA-DRB1*13:03, HLA-DRB1*03:01, HLA-DRB1*08:01 and HLA-DQB1*03:02) and class I protective alleles (HLA-A*02:01, HLA-B*44:02, HLA-B*38:01 and HLA-B*55:01), we find evidence for two interactions involving pairs of class II alleles: HLA-DQA1*01:01–HLA-DRB1*15:01 and HLA-DQB1*03:01–HLA-DQB1*03:02. We find no evidence for interactions between classical HLA alleles and non-HLA risk-associated variants and estimate a minimal effect of polygenic epistasis in modulating major risk alleles. PMID:26343388

Interlaboratory validation of quantitative duplex real-time PCR method for screening analysis of genetically modified maize.

Science.gov (United States)

Takabatake, Reona; Koiwa, Tomohiro; Kasahara, Masaki; Takashima, Kaori; Futo, Satoshi; Minegishi, Yasutaka; Akiyama, Hiroshi; Teshima, Reiko; Oguchi, Taichi; Mano, Junichi; Furui, Satoshi; Kitta, Kazumi

2011-01-01

To reduce the cost and time required to routinely perform the genetically modified organism (GMO) test, we developed a duplex quantitative real-time PCR method for a screening analysis simultaneously targeting an event-specific segment for GA21 and Cauliflower Mosaic Virus 35S promoter (P35S) segment [Oguchi et al., J. Food Hyg. Soc. Japan, 50, 117-125 (2009)]. To confirm the validity of the method, an interlaboratory collaborative study was conducted. In the collaborative study, conversion factors (Cfs), which are required to calculate the GMO amount (%), were first determined for two real-time PCR instruments, the ABI PRISM 7900HT and the ABI PRISM 7500. A blind test was then conducted. The limit of quantitation for both GA21 and P35S was estimated to be 0.5% or less. The trueness and precision were evaluated as the bias and reproducibility of the relative standard deviation (RSD(R)). The determined bias and RSD(R) were each less than 25%. We believe the developed method would be useful for the practical screening analysis of GM maize.

Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast

Directory of Open Access Journals (Sweden)

Y. H. Lee

2006-12-01

Full Text Available In this study, optimal parameter estimations are performed for both physical and computational parameters in a mesoscale meteorological model, and their impacts on the quantitative precipitation forecasting (QPF are assessed for a heavy rainfall case occurred at the Korean Peninsula in June 2005. Experiments are carried out using the PSU/NCAR MM5 model and the genetic algorithm (GA for two parameters: the reduction rate of the convective available potential energy in the Kain-Fritsch (KF scheme for cumulus parameterization, and the Asselin filter parameter for numerical stability. The fitness function is defined based on a QPF skill score. It turns out that each optimized parameter significantly improves the QPF skill. Such improvement is maximized when the two optimized parameters are used simultaneously. Our results indicate that optimizations of computational parameters as well as physical parameters and their adequate applications are essential in improving model performance.

Does prenatal valproate interact with a genetic reduction in the serotonin transporter?A rat study on anxiety and cognition

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Bart A Ellenbroek

2016-09-01

Full Text Available There is ample evidence that prenatal exposure to valproate (or valproic acid, VPA enhances the risk of developing Autism Spectrum Disorders (ASD. In line with this, a single injection of VPA induces a multitude of ASD-like symptoms in animals such as rats and mice. However, there is equally strong evidence that genetic factors contribute significantly to the risk of ASD and indeed, like most other psychiatric disorders, ASD is now generally thought to results from an interaction between genetic and environmental factors. Given that VPA significantly impacts on the serotonergic system, and serotonin has strong biochemical and genetic links to ASD, we aimed to investigate the interaction between genetic reduction in the serotonin transporter and prenatal valproate administration. More specifically, we exposed both wildtype (SERT+/+ rats and rats heterozygous for the serotonin transporter deletion (SERT+/- to a single injection of 400 mg/kg VPA at gestational day (GD 12. The offspring, in adulthood, was assessed in four different tests: Elevated Plus Maze and Novelty Suppressed Feeding as measures for anxiety and prepulse inhibition (PPI and latent inhibition as measures for cognition and information processing. The results show that prenatal VPA significantly increased anxiety in both paradigm, reduced PPI and reduced conditioning in the latent inhibition paradigm. However, we failed to find a significant gene – environment interaction. We propose that this may be related to the timing of the VPA injection and suggest that whereas GD12 might be optimal for affecting normal rat, rats with a genetically compromised serotonergic system may be more sensitive to VPA at earlier time points during gestation. Overall our data are the first to investigate gene * environmental interactions in a genetic rat model for ASD suggest that timing may be of crucial importance to the long-term outcome.

Interactome of Obesity: Obesidome : Genetic Obesity, Stress Induced Obesity, Pathogenic Obesity Interaction.

Science.gov (United States)

Geronikolou, Styliani A; Pavlopoulou, Athanasia; Cokkinos, Dennis; Chrousos, George

2017-01-01

Obesity is a chronic disease of increasing prevalence reaching epidemic proportions. Genetic defects as well as epigenetic effects contribute to the obesity phenotype. Investigating gene (e.g. MC4R defects)-environment (behavior, infectious agents, stress) interactions is a relative new field of great research interest. In this study, we have made an effort to create an interactome (henceforth referred to as "obesidome"), where extrinsic stressors response, intrinsic predisposition, immunity response to inflammation and autonomous nervous system implications are integrated. These pathways are presented in one interactome network for the first time. In our study, obesity-related genes/gene products were found to form a complex interactions network.

Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

DEFF Research Database (Denmark)

Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

2014-01-01

A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

DEFF Research Database (Denmark)

Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

2013-01-01

A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

Sensation seeking, peer deviance, and genetic influences on adolescent delinquency: Evidence for person-environment correlation and interaction.

Science.gov (United States)

Mann, Frank D; Patterson, Megan W; Grotzinger, Andrew D; Kretsch, Natalie; Tackett, Jennifer L; Tucker-Drob, Elliot M; Harden, K Paige

2016-07-01

Both sensation seeking and affiliation with deviant peer groups are risk factors for delinquency in adolescence. In this study, we use a sample of adolescent twins (n = 549), 13 to 20 years old (M age = 15.8 years), in order to test the interactive effects of peer deviance and sensation seeking on delinquency in a genetically informative design. Consistent with a socialization effect, affiliation with deviant peers was associated with higher delinquency even after controlling for selection effects using a co-twin-control comparison. At the same time, there was evidence for person-environment correlation; adolescents with genetic dispositions toward higher sensation seeking were more likely to report having deviant peer groups. Genetic influences on sensation seeking substantially overlapped with genetic influences on adolescent delinquency. Finally, the environmentally mediated effect of peer deviance on adolescent delinquency was moderated by individual differences in sensation seeking. Adolescents reporting high levels of sensation seeking were more susceptible to deviant peers, a Person × Environment interaction. These results are consistent with both selection and socialization processes in adolescent peer relationships, and they highlight the role of sensation seeking as an intermediary phenotype for genetic risk for delinquency. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Dissociative conceptual and quantitative problem solving outcomes across interactive engagement and traditional format introductory physics

Directory of Open Access Journals (Sweden)

Mark A. McDaniel

2016-11-01

Full Text Available The existing literature indicates that interactive-engagement (IE based general physics classes improve conceptual learning relative to more traditional lecture-oriented classrooms. Very little research, however, has examined quantitative problem-solving outcomes from IE based relative to traditional lecture-based physics classes. The present study included both pre- and post-course conceptual-learning assessments and a new quantitative physics problem-solving assessment that included three representative conservation of energy problems from a first-semester calculus-based college physics course. Scores for problem translation, plan coherence, solution execution, and evaluation of solution plausibility were extracted for each problem. Over 450 students in three IE-based sections and two traditional lecture sections taught at the same university during the same semester participated. As expected, the IE-based course produced more robust gains on a Force Concept Inventory than did the lecture course. By contrast, when the full sample was considered, gains in quantitative problem solving were significantly greater for lecture than IE-based physics; when students were matched on pre-test scores, there was still no advantage for IE-based physics on gains in quantitative problem solving. Further, the association between performance on the concept inventory and quantitative problem solving was minimal. These results highlight that improved conceptual understanding does not necessarily support improved quantitative physics problem solving, and that the instructional method appears to have less bearing on gains in quantitative problem solving than does the kinds of problems emphasized in the courses and homework and the overlap of these problems to those on the assessment.

Modelling Autistic Features in Mice Using Quantitative Genetic Approaches

NARCIS (Netherlands)

Molenhuis, Remco T; Bruining, Hilgo; Kas, Martien J

2017-01-01

Animal studies provide a unique opportunity to study the consequences of genetic variants at the behavioural level. Human studies have identified hundreds of risk genes for autism spectrum disorder (ASD) that can lead to understanding on how genetic variation contributes to individual differences in

Genetical analysis of the induced variation by gamma radiation in quantitative characters of Caupi [Vigna unguiculata (L.) Walp.

International Nuclear Information System (INIS)

Araujo, J.P.P. de.

1987-10-01

Genetical analysis procedures of the cobalt 60 gamma radiation effects in the induced mutations in quantitative characters of Caupi BR-1 Poty. The following characters were evaluated: day to first flower (FI), number of pods per plant (NVP), pod lenght (CMV), number of suds per pod (NSV), 100 seed wright (PCS), seed yield per plant (PSP) and seed yield per plant estimated by yield components (PSPE). The resistance of irradiated populations to cowpea aphid-borne mosaic virus (CpAMV)was also evaluated. (L.M.J.) [pt

Using multiple PCR and CE with chemiluminescence detection for simultaneous qualitative and quantitative analysis of genetically modified organism.

Science.gov (United States)

Guo, Longhua; Qiu, Bin; Chi, Yuwu; Chen, Guonan

2008-09-01

In this paper, an ultrasensitive CE-CL detection system coupled with a novel double-on-column coaxial flow detection interface was developed for the detection of PCR products. A reliable procedure based on this system had been demonstrated for qualitative and quantitative analysis of genetically modified organism-the detection of Roundup Ready Soy (RRS) samples was presented as an example. The promoter, terminator, function and two reference genes of RRS were amplified with multiplex PCR simultaneously. After that, the multiplex PCR products were labeled with acridinium ester at the 5'-terminal through an amino modification and then analyzed by the proposed CE-CL system. Reproducibility of analysis times and peak heights for the CE-CL analysis were determined to be better than 0.91 and 3.07% (RSD, n=15), respectively, for three consecutive days. It was shown that this method could accurately and qualitatively detect RRS standards and the simulative samples. The evaluation in terms of quantitative analysis of RRS provided by this new method was confirmed by comparing our assay results with those of the standard real-time quantitative PCR (RT-QPCR) using SYBR Green I dyes. The results showed a good coherence between the two methods. This approach demonstrated the possibility for accurate qualitative and quantitative detection of GM plants in a single run.

Brief Communication: Quantitative- and molecular-genetic differentiation in humans and chimpanzees: implications for the evolutionary processes underlying cranial diversification.

Science.gov (United States)

Weaver, Timothy D

2014-08-01

Estimates of the amount of genetic differentiation in humans among major geographic regions (e.g., Eastern Asia vs. Europe) from quantitative-genetic analyses of cranial measurements closely match those from classical- and molecular-genetic markers. Typically, among-region differences account for ∼10% of the total variation. This correspondence is generally interpreted as evidence for the importance of neutral evolutionary processes (e.g., genetic drift) in generating among-region differences in human cranial form, but it was initially surprising because human cranial diversity was frequently assumed to show a strong signature of natural selection. Is the human degree of similarity of cranial and DNA-sequence estimates of among-region genetic differentiation unusual? How do comparisons with other taxa illuminate the evolutionary processes underlying cranial diversification? Chimpanzees provide a useful starting point for placing the human results in a broader comparative context, because common chimpanzees (Pan troglodytes) and bonobos (Pan paniscus) are the extant species most closely related to humans. To address these questions, I used 27 cranial measurements collected on a sample of 861 humans and 263 chimpanzees to estimate the amount of genetic differentiation between pairs of groups (between regions for humans and between species or subspecies for chimpanzees). Consistent with previous results, the human cranial estimates are quite similar to published DNA-sequence estimates. In contrast, the chimpanzee cranial estimates are much smaller than published DNA-sequence estimates. It appears that cranial differentiation has been limited in chimpanzees relative to humans. © 2014 Wiley Periodicals, Inc.

Dietary Magnesium and Genetic Interactions in Diabetes and Related Risk Factors: A Brief Overview of Current Knowledge

Science.gov (United States)

Hruby, Adela; McKeown, Nicola M.; Song, Yiqing; Djoussé, Luc

2013-01-01

Nutritional genomics has exploded in the last decade, yielding insights—both nutrigenomic and nutrigenetic—into the physiology of dietary interactions and our genes. Among these are insights into the regulation of magnesium transport and homeostasis and mechanisms underlying magnesium’s role in insulin and glucose handling. Recent observational evidence has attempted to examine some promising research avenues on interaction between genetics and dietary magnesium in relation to diabetes and diabetes risk factors. This brief review summarizes the recent evidence on dietary magnesium’s role in diabetes and related traits in the presence of underlying genetic risk, and discusses future potential research directions. PMID:24322525

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

Directory of Open Access Journals (Sweden)

Wang Kai

2011-05-01

Full Text Available Abstract Background 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. Findings 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. Conclusions 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/.

Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.

Science.gov (United States)

Roetker, Nicholas S; Page, C David; Yonker, James A; Chang, Vicky; Roan, Carol L; Herd, Pamela; Hauser, Taissa S; Hauser, Robert M; Atwood, Craig S

2013-10-01

We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors-13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors-18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic-environmental-sociobehavioral interactions in depressive symptoms.

Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study

NARCIS (Netherlands)

Langenberg, C.; Sharp, S.; Forouhi, N.G.; Feskens, E.J.M.

2011-01-01

Aims/Hypothesis: Studying gene-lifestyle interaction may help to identify lifestyle factors that modify genetic susceptibility and uncover genetic loci exerting important subgroup effects. Adequately powered studies with prospective, unbiased, standardised assessment of key behavioural factors for

Genetic Pathways to Insomnia

OpenAIRE

Mackenzie J. Lind; Philip R. Gehrman

2016-01-01

This review summarizes current research on the genetics of insomnia, as genetic contributions are thought to be important for insomnia etiology. We begin by providing an overview of genetic methods (both quantitative and measured gene), followed by a discussion of the insomnia genetics literature with regard to each of the following common methodologies: twin and family studies, candidate gene studies, and genome-wide association studies (GWAS). Next, we summarize the most recent gene identif...

A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

Directory of Open Access Journals (Sweden)

Han Kyungsook

2010-06-01

Full Text Available Abstract Background Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. Results In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI. First, a high-coverage and high-precision functional gene network (FGN is constructed by integrating protein-protein interaction (PPI, protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM, on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%. Noticeably, the SSL method is more efficient than SVM, especially for

Molecular signature of epistatic selection: interrogating genetic interactions in the sex-ratio meiotic drive of Drosophila simulans.

Science.gov (United States)

Chevin, Luis-Miguel; Bastide, Héloïse; Montchamp-Moreau, Catherine; Hospital, Frédéric

2009-06-01

Fine scale analyses of signatures of selection allow assessing quantitative aspects of a species' evolutionary genetic history, such as the strength of selection on genes. When several selected loci lie in the same genomic region, their epistatic interactions may also be investigated. Here, we study how the neutral polymorphism pattern was shaped by two close recombining loci that cause 'sex-ratio' meiotic drive in Drosophila simulans, as an example of strong selection with potentially strong epistasis. We compare the polymorphism data observed in a natural population with the results of forward stochastic simulations under several contexts of epistasis between the candidate loci for the drive. We compute the likelihood of different possible scenarios, in order to determine which configuration is most consistent with the data. Our results highlight that fine scale analyses of well-chosen candidate genomic regions provide information-rich data that can be used to investigate the genotype-phenotype-fitness map, which can hardly be studied in genome-wide analyses. We also emphasize that initial conditions and time of observation (here, time after the interruption of a partial selective sweep) are crucial parameters in the interpretation of real data, while these are often overlooked in theoretical studies.

Fisher's fundamental theorem of inclusive fitness and the change in fitness due to natural selection when conspecifics interact

NARCIS (Netherlands)

Bijma, P.

2010-01-01

Competition and cooperation is fundamental to evolution by natural selection, both in animals and plants. Here, I investigate the consequences of such interactions for response in fitness due to natural selection. I provide quantitative genetic expressions for heritable variance and response in

Genetic itemization of exotic sugarcane clones of the basis of quantitative and qualitative parameters

International Nuclear Information System (INIS)

Seema, N.; Khan, M. T.; Khan, I. A.; Yasmeen, S.

2017-01-01

Sugarcane varietal development program in Pakistan primarily depends on evaluation of imported genotypes because of the unfavorable climatic conditions for sugarcane flowering and hybridization in the country. Performance of 41 exotic sugarcane clones was assessed in this study on the basis of seven quantitative (plant height, number of tillers, internode length, number of internode, cane girth, cane yield, and weight per stool) and six qualitative (sucrose %, brix %, CCS %, fiber %, sugar recovery % and sugar yield) attributes. Sugarcane clones comprised of fifteen genotypes from Canal Point (USA), eight from Homma (USA), and eighteen from Brazil. The clones exhibited statistically significant differences for tillers per plant, weight per stool, plant height, cane yield, brix%, sucrose%, fiber%, sugar recovery and sugar yield. Highest cane yield of 51.66 t/ha was observed for Canal Point clone CPNIA-240 while the lowest yield of 26.66 t/ha was recorded in Homma clone HoNIA-795. The highest sugar recovery (10.83 and 10.81) was exhibited by the clones SPNIA-396 and SPNIA-8 whereas the lowest (4.00) was observed in clone SPNIA-05. Moreover, maximum sugar yield was recorded in clone SPNIA-8 (5.37 tha-1) and minimum was observed in clone SPNIA-05 (0.91). Ward's linkage cluster analysis of the exotic clones placed the genotypes into six major groups in dendrogram. Genotypes appeared in the clusters irrespective of their geographical location. Cluster II, IV and V showed excellent qualitative, combination of quantitative and qualitative, and quantitative characters respectively. Clones from different clusters demonstrate genetic variations and thus can be subjected to selection and hybridization for further improvement. The accessions demonstrating excellent cane and sugar yield can serve as potential candidates for varietal development program in Pakistan. (author)

Beyond Punnett Squares: Student Word Association and Explanations of Phenotypic Variation through an Integrative Quantitative Genetics Unit Investigating Anthocyanin Inheritance and Expression in Brassica rapa Fast Plants

Science.gov (United States)

Smith, Amber R.; Williams, Paul H.; McGee, Seth A.; Dósa, Katalin; Pfammatter, Jesse

2014-01-01

Genetics instruction in introductory biology is often confined to Mendelian genetics and avoids the complexities of variation in quantitative traits. Given the driving question “What determines variation in phenotype (Pv)? (Pv=Genotypic variation Gv + environmental variation Ev),” we developed a 4-wk unit for an inquiry-based laboratory course focused on the inheritance and expression of a quantitative trait in varying environments. We utilized Brassica rapa Fast Plants as a model organism to study variation in the phenotype anthocyanin pigment intensity. As an initial curriculum assessment, we used free word association to examine students’ cognitive structures before and after the unit and explanations in students’ final research posters with particular focus on variation (Pv = Gv + Ev). Comparison of pre- and postunit word frequency revealed a shift in words and a pattern of co-occurring concepts indicative of change in cognitive structure, with particular focus on “variation” as a proposed threshold concept and primary goal for students’ explanations. Given review of 53 posters, we found ∼50% of students capable of intermediate to high-level explanations combining both Gv and Ev influence on expression of anthocyanin intensity (Pv). While far from “plug and play,” this conceptually rich, inquiry-based unit holds promise for effective integration of quantitative and Mendelian genetics. PMID:25185225

Study books on ADHD genetics: balanced or biased?

Science.gov (United States)

Te Meerman, Sanne; Batstra, Laura; Hoekstra, Rink; Grietens, Hans

2017-06-01

Academic study books are essential assets for disseminating knowledge about ADHD to future healthcare professionals. This study examined if they are balanced with regard to genetics. We selected and analyzed study books (N=43) used in (pre) master's programmes at 10 universities in the Netherlands. Because the mere behaviourally informed quantitative genetics give a much higher effect size of the genetic involvement in ADHD, it is important that study books contrast these findings with molecular genetics' outcomes. The latter studies use real genetic data, and their low effect sizes expose the potential weaknesses of quantitative genetics, like underestimating the involvement of the environment. Only a quarter of books mention both effect sizes and contrast these findings, while another quarter does not discuss any effect size. Most importantly, however, roughly half of the books in our sample mention only the effect sizes from quantitative genetic studies without addressing the low explained variance of molecular genetic studies. This may confuse readers by suggesting that the weakly associated genes support the quite spectacular, but potentially flawed estimates of twin, family and adoption studies, while they actually contradict them.

Quantitative Genetics Identifies Cryptic Genetic Variation Involved in the Paternal Regulation of Seed Development.

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Nuno D Pires

2016-01-01

Full Text Available Embryonic development requires a correct balancing of maternal and paternal genetic information. This balance is mediated by genomic imprinting, an epigenetic mechanism that leads to parent-of-origin-dependent gene expression. The parental conflict (or kinship theory proposes that imprinting can evolve due to a conflict between maternal and paternal alleles over resource allocation during seed development. One assumption of this theory is that paternal alleles can regulate seed growth; however, paternal effects on seed size are often very low or non-existent. We demonstrate that there is a pool of cryptic genetic variation in the paternal control of Arabidopsis thaliana seed development. Such cryptic variation can be exposed in seeds that maternally inherit a medea mutation, suggesting that MEA acts as a maternal buffer of paternal effects. Genetic mapping using recombinant inbred lines, and a novel method for the mapping of parent-of-origin effects using whole-genome sequencing of segregant bulks, indicate that there are at least six loci with small, paternal effects on seed development. Together, our analyses reveal the existence of a pool of hidden genetic variation on the paternal control of seed development that is likely shaped by parental conflict.

Daf-2, Daf-16 and Daf-23: Genetically Interacting Genes Controlling Dauer Formation in Caenorhabditis Elegans

OpenAIRE

Gottlieb, S.; Ruvkun, G.

1994-01-01

Under conditions of high population density and low food, Caenorhabditis elegans forms an alternative third larval stage, called the dauer stage, which is resistant to desiccation and harsh environments. Genetic analysis of some dauer constitutive (Daf-c) and dauer defective (Daf-d) mutants has revealed a complex pathway that is likely to function in particular neurons and/or responding tissues. Here we analyze the genetic interactions between three genes which comprise a branch of the dauer ...

iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data

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Heinrich Julian

2012-05-01

Full Text Available Abstract In the search for single-nucleotide polymorphisms which influence the observable phenotype, genome wide association studies have become an important technique for the identification of associations between genotype and phenotype of a diverse set of sequence-based data. We present a methodology for the visual assessment of single-nucleotide polymorphisms using interactive hierarchical aggregation techniques combined with methods known from traditional sequence browsers and cluster heatmaps. Our tool, the interactive Hierarchical Aggregation Table (iHAT, facilitates the visualization of multiple sequence alignments, associated metadata, and hierarchical clusterings. Different color maps and aggregation strategies as well as filtering options support the user in finding correlations between sequences and metadata. Similar to other visualizations such as parallel coordinates or heatmaps, iHAT relies on the human pattern-recognition ability for spotting patterns that might indicate correlation or anticorrelation. We demonstrate iHAT using artificial and real-world datasets for DNA and protein association studies as well as expression Quantitative Trait Locus data.

Combining quantitative trait loci analysis with physiological models to predict genotype-specific transpiration rates.

Science.gov (United States)

Reuning, Gretchen A; Bauerle, William L; Mullen, Jack L; McKay, John K

2015-04-01

Transpiration is controlled by evaporative demand and stomatal conductance (gs ), and there can be substantial genetic variation in gs . A key parameter in empirical models of transpiration is minimum stomatal conductance (g0 ), a trait that can be measured and has a large effect on gs and transpiration. In Arabidopsis thaliana, g0 exhibits both environmental and genetic variation, and quantitative trait loci (QTL) have been mapped. We used this information to create a genetically parameterized empirical model to predict transpiration of genotypes. For the parental lines, this worked well. However, in a recombinant inbred population, the predictions proved less accurate. When based only upon their genotype at a single g0 QTL, genotypes were less distinct than our model predicted. Follow-up experiments indicated that both genotype by environment interaction and a polygenic inheritance complicate the application of genetic effects into physiological models. The use of ecophysiological or 'crop' models for predicting transpiration of novel genetic lines will benefit from incorporating further knowledge of the genetic control and degree of independence of core traits/parameters underlying gs variation. © 2014 John Wiley & Sons Ltd.

The McGill Interactive Pediatric OncoGenetic Guidelines: An approach to identifying pediatric oncology patients most likely to benefit from a genetic evaluation.

Science.gov (United States)

Goudie, Catherine; Coltin, Hallie; Witkowski, Leora; Mourad, Stephanie; Malkin, David; Foulkes, William D

2017-08-01

Identifying cancer predisposition syndromes in children with tumors is crucial, yet few clinical guidelines exist to identify children at high risk of having germline mutations. The McGill Interactive Pediatric OncoGenetic Guidelines project aims to create a validated pediatric guideline in the form of a smartphone/tablet application using algorithms to process clinical data and help determine whether to refer a child for genetic assessment. This paper discusses the initial stages of the project, focusing on its overall structure, the methodology underpinning the algorithms, and the upcoming algorithm validation process. © 2017 Wiley Periodicals, Inc.

Estimation of indirect genetic effects in group-housed mink (Neovison vison) should account for systematic interactions either due to kin or sex

DEFF Research Database (Denmark)

Alemu, Setegn Worku; Berg, Peer; Janss, Luc

2016-01-01

interactions in group-housed mink. Furthermore, we investigated whether systematic non-genetic interactions between kin or individuals of the same sex influence the estimates of genetic parameters. As a second objective, we clarify the relationship between estimates of the traditional IGE model and a family...

Screening for interaction effects in gene expression data.

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Peter J Castaldi

Full Text Available Expression quantitative trait (eQTL studies are a powerful tool for identifying genetic variants that affect levels of messenger RNA. Since gene expression is controlled by a complex network of gene-regulating factors, one way to identify these factors is to search for interaction effects between genetic variants and mRNA levels of transcription factors (TFs and their respective target genes. However, identification of interaction effects in gene expression data pose a variety of methodological challenges, and it has become clear that such analyses should be conducted and interpreted with caution. Investigating the validity and interpretability of several interaction tests when screening for eQTL SNPs whose effect on the target gene expression is modified by the expression level of a transcription factor, we characterized two important methodological issues. First, we stress the scale-dependency of interaction effects and highlight that commonly applied transformation of gene expression data can induce or remove interactions, making interpretation of results more challenging. We then demonstrate that, in the setting of moderate to strong interaction effects on the order of what may be reasonably expected for eQTL studies, standard interaction screening can be biased due to heteroscedasticity induced by true interactions. Using simulation and real data analysis, we outline a set of reasonable minimum conditions and sample size requirements for reliable detection of variant-by-environment and variant-by-TF interactions using the heteroscedasticity consistent covariance-based approach.

The Interaction among Microbiota, Immunity, and Genetic and Dietary Factors Is the Condicio Sine Qua Non Celiac Disease Can Develop

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D. Pagliari

2015-01-01

Full Text Available Celiac disease (CD is an immune-mediated enteropathy, triggered by dietary wheat gluten and similar proteins of barley and rye in genetically susceptible individuals. This is a complex disorder involving both environmental and immune-genetic factors. The major genetic risk factor for CD is determined by HLA-DQ genes. Dysfunction of the innate and adaptive immune systems can conceivably cause impairment of mucosal barrier function and development of localized or systemic inflammatory and autoimmune processes. Exposure to gluten is the main environmental trigger responsible for the signs and symptoms of the disease, but exposure to gluten does not fully explain the manifestation of CD. Thus, both genetic determination and environmental exposure to gluten are necessary for the full manifestation of CD; neither of them is sufficient alone. Epidemiological and clinical data suggest that other environmental factors, including infections, alterations in the intestinal microbiota composition, and early feeding practices, might also play a role in disease development. Thus, this interaction is the condicio sine qua non celiac disease can develop. The breakdown of the interaction among microbiota, innate immunity, and genetic and dietary factors leads to disruption of homeostasis and inflammation; and tissue damage occurs. Focusing attention on this interaction and its breakdown may allow a better understanding of the CD pathogenesis and lead to novel translational avenues for preventing and treating this widespread disease.

Plasticity effect of rider-horse interaction on genetic evaluations for Show Jumping discipline in sport horses.

Science.gov (United States)

Bartolomé, E; Menéndez-Buxadera, A; Molina, A; Valera, M

2018-04-01

To obtain a sport horse that excels in the highest levels of competition, breeders must take into account certain genetic and environmental factors that could influence the sport horse's performance, such as the rider-horse interaction (RHI). The main aim of this study was to describe this interaction in a genetic model by modelling it in relation to the horse's age. A total of 31,129 sport results from Spanish Sport Horses were used from a total of 1,101 animals evaluated, and these were grouped in three age levels and had been ridden by 606 different riders. Only riders who had ridden more than one horse (and vice-versa) were considered for the analyses. Five linear models with different random effects were analysed according to the covariates, the homogeneity/heterogeneity of the RHI and the relevant residual random effects. The model of best fit was then selected for the genetic evaluation of the animal. In general, models including the RHI effect (M2, M4 and M5) fitted better than the other models, and the best fit was obtained for M4 (with heterogeneous residual variance). The genetic variance increased constantly with age, whereas heritability showed a response on three intervals. This study revealed the varied evolution of the RHI with age, showing the different "plastic abilities" of this relationship. © 2018 Blackwell Verlag GmbH.

How to measure genetic heterogeneity

International Nuclear Information System (INIS)

Yamada, Ryo

2009-01-01

Genetic information of organisms is coded as a string of four letters, A, T, G and C, a sequence in macromolecules called deoxyribonucleic acid (DNA). DNA sequence offers blueprint of organisms and its heterogeneity determines identity and variation of species. The quantitation of this genetic heterogeneity is fundamental to understand biology. We compared previously-reported three measures, covariance matrix expression of list of loci (pair-wise r 2 ), the most popular index in genetics, and its multi-dimensional form, Ψ, and entropy-based index, ε. Thereafter we proposed two methods so that we could handle the diplotypic heterogeneity and quantitate the conditions where the number of DNA sequence samples is much smaller than the number of possible variants.

Impact of US Brown Swiss genetics on milk quality from low-input herds in Switzerland: interactions with grazing intake and pasture type.

Science.gov (United States)

Stergiadis, S; Bieber, A; Franceschin, E; Isensee, A; Eyre, M D; Maurer, V; Chatzidimitriou, E; Cozzi, G; Bapst, B; Stewart, G; Gordon, A; Butler, G

2015-05-15

This study investigated the effect of, and interactions between, contrasting crossbreed genetics (US Brown Swiss [BS] × Improved Braunvieh [BV] × Original Braunvieh [OB]) and feeding regimes (especially grazing intake and pasture type) on milk fatty acid (FA) profiles. Concentrations of total polyunsaturated FAs, total omega-3 FAs and trans palmitoleic, vaccenic, α-linolenic, eicosapentaenoic and docosapentaenoic acids were higher in cows with a low proportion of BS genetics. Highest concentrations of the nutritionally desirable FAs, trans palmitoleic, vaccenic and eicosapentaenoic acids were found for cows with a low proportion of BS genetics (0-24% and/or 25-49%) on high grazing intake (75-100% of dry matter intake) diets. Multivariate analysis indicated that the proportion of OB genetics is a positive driver for nutritionally desirable monounsaturated and polyunsaturated FAs while BS genetics proportion was positive driver for total and undesirable individual saturated FAs. Significant genetics × feeding regime interactions were also detected for a range of FAs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Easy calculations of lod scores and genetic risks on small computers.

Science.gov (United States)

Lathrop, G M; Lalouel, J M

1984-01-01

A computer program that calculates lod scores and genetic risks for a wide variety of both qualitative and quantitative genetic traits is discussed. An illustration is given of the joint use of a genetic marker, affection status, and quantitative information in counseling situations regarding Duchenne muscular dystrophy. PMID:6585139

A family-based joint test for mean and variance heterogeneity for quantitative traits.

Science.gov (United States)

Cao, Ying; Maxwell, Taylor J; Wei, Peng

2015-01-01

Traditional quantitative trait locus (QTL) analysis focuses on identifying loci associated with mean heterogeneity. Recent research has discovered loci associated with phenotype variance heterogeneity (vQTL), which is important in studying genetic association with complex traits, especially for identifying gene-gene and gene-environment interactions. While several tests have been proposed to detect vQTL for unrelated individuals, there are no tests for related individuals, commonly seen in family-based genetic studies. Here we introduce a likelihood ratio test (LRT) for identifying mean and variance heterogeneity simultaneously or for either effect alone, adjusting for covariates and family relatedness using a linear mixed effect model approach. The LRT test statistic for normally distributed quantitative traits approximately follows χ(2)-distributions. To correct for inflated Type I error for non-normally distributed quantitative traits, we propose a parametric bootstrap-based LRT that removes the best linear unbiased prediction (BLUP) of family random effect. Simulation studies show that our family-based test controls Type I error and has good power, while Type I error inflation is observed when family relatedness is ignored. We demonstrate the utility and efficiency gains of the proposed method using data from the Framingham Heart Study to detect loci associated with body mass index (BMI) variability. © 2014 John Wiley & Sons Ltd/University College London.

MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis.

Science.gov (United States)

Lee, Dong-Yup; Yun, Hongsoek; Park, Sunwon; Lee, Sang Yup

2003-11-01

MetaFluxNet is a program package for managing information on the metabolic reaction network and for quantitatively analyzing metabolic fluxes in an interactive and customized way. It allows users to interpret and examine metabolic behavior in response to genetic and/or environmental modifications. As a result, quantitative in silico simulations of metabolic pathways can be carried out to understand the metabolic status and to design the metabolic engineering strategies. The main features of the program include a well-developed model construction environment, user-friendly interface for metabolic flux analysis (MFA), comparative MFA of strains having different genotypes under various environmental conditions, and automated pathway layout creation. http://mbel.kaist.ac.kr/ A manual for MetaFluxNet is available as PDF file.

Interactive effects of genetic polymorphisms and childhood adversity on brain morphologic changes in depression.

Science.gov (United States)

Kim, Yong-Ku; Ham, Byung-Joo; Han, Kyu-Man

2018-03-10

The etiology of depression is characterized by the interplay of genetic and environmental factors and brain structural alteration. Childhood adversity is a major contributing factor in the development of depression. Interactions between childhood adversity and candidate genes for depression could affect brain morphology via the modulation of neurotrophic factors, serotonergic neurotransmission, or the hypothalamus-pituitary-adrenal (HPA) axis, and this pathway may explain the subsequent onset of depression. Childhood adversity is associated with structural changes in the hippocampus, amygdala, anterior cingulate cortex (ACC), and prefrontal cortex (PFC), as well as white matter tracts such as the corpus callosum, cingulum, and uncinate fasciculus. Childhood adversity showed an interaction with the brain-derived neurotrophic factor (BDNF) gene Val66Met polymorphism, serotonin transporter-linked promoter region (5-HTTLPR), and FK506 binding protein 51 (FKBP5) gene rs1360780 in brain morphologic changes in patients with depression and in a non-clinical population. Individuals with the Met allele of BDNF Val66Met and a history of childhood adversity had reduced volume in the hippocampus and its subfields, amygdala, and PFC and thinner rostral ACC in a study of depressed patients and healthy controls. The S allele of 5-HTTLPR combined with exposure to childhood adversity or a poorer parenting environment was associated with a smaller hippocampal volume and subsequent onset of depression. The FKBP5 gene rs160780 had a significant interaction with childhood adversity in the white matter integrity of brain regions involved in emotion processing. This review identified that imaging genetic studies on childhood adversity may deepen our understanding on the neurobiological background of depression by scrutinizing complicated pathways of genetic factors, early psychosocial environments, and the accompanying morphologic changes in emotion-processing neural circuitry. Copyright

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

A multidirectional non-cell autonomous control and a genetic interaction restricting tobacco etch virus susceptibility in Arabidopsis.

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Suresh Gopalan

2007-10-01

Full Text Available Viruses constitute a major class of pathogens that infect a variety of hosts. Understanding the intricacies of signaling during host-virus interactions should aid in designing disease prevention strategies and in understanding mechanistic aspects of host and pathogen signaling machinery.An Arabidopsis mutant, B149, impaired in susceptibility to Tobacco etch virus (TEV, a positive strand RNA virus of picoRNA family, was identified using a high-throughput genetic screen and a counterselection scheme. The defects include initiation of infection foci, rate of cell-to-cell movement and long distance movement.The defect in infectivity is conferred by a recessive locus. Molecular genetic analysis and complementation analysis with three alleles of a previously published mutant lsp1 (loss of susceptibility to potyviruses indicate a genetic interaction conferring haploinsufficiency between the B149 locus and certain alleles of lsp1 resulting in impaired host susceptibility. The pattern of restriction of TEV foci on leaves at or near the boundaries of certain cell types and leaf boundaries suggest dysregulation of a multidirectional non-cell autonomous regulatory mechanism. Understanding the nature of this multidirectional signal and the molecular genetic mechanism conferring it should potentially reveal a novel arsenal in the cellular machinery.

Dissecting Genetic Network of Fruit Branch Traits in Upland Cotton by Association Mapping Using SSR Markers.

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Yongjun Mei

Full Text Available Genetic architecture of branch traits has large influences on the morphological structure, photosynthetic capacity, planting density, and yield of Upland cotton (Gossypium hirsutum L.. This research aims to reveal the genetic effects of six branch traits, including bottom fruit branch node number (BFBNN, bottom fruit branch length (BFBL, middle fruit branch node number (MFBNN, middle fruit branch length (MFBL, upper fruit branch node number (UFBNN, and upper fruit branch length (UFBL. Association mapping was conducted for these traits of 39 lines and their 178 F1 hybrids in three environments. There were 20 highly significant Quantitative Trait SSRs (QTSs detected by mixed linear model approach analyzing a full genetic model with genetic effects of additive, dominance, epistasis and their environment interaction. The phenotypic variation explained by genetic effects ranged from 32.64 ~ 91.61%, suggesting these branch traits largely influenced by genetic factors.

Maternal-by-environment but not genotype-by-environment interactions in a fish without parental care.

Science.gov (United States)

Vega-Trejo, Regina; Head, Megan L; Jennions, Michael D; Kruuk, Loeske E B

2018-01-01

The impact of environmental conditions on the expression of genetic variance and on maternal effects variance remains an important question in evolutionary quantitative genetics. We investigate here the effects of early environment on variation in seven adult life history, morphological, and secondary sexual traits (including sperm characteristics) in a viviparous poeciliid fish, the mosquitofish Gambusia holbrooki. Specifically, we manipulated food availability during early development and then assessed additive genetic and maternal effects contributions to the overall phenotypic variance in adults. We found higher heritability for female than male traits, but maternal effects variance for traits in both sexes. An interaction between maternal effects variance and rearing environment affected two adult traits (female age at maturity and male size at maturity), but there was no evidence of trade-offs in maternal effects across environments. Our results illustrate (i) the potential for pre-natal maternal effects to interact with offspring environment during development, potentially affecting traits through to adulthood and (ii) that genotype-by-environment interactions might be overestimated if maternal-by-environment interactions are not accounted for, similar to heritability being overestimated if maternal effects are ignored. We also discuss the potential for dominance genetic variance to contribute to the estimate of maternal effects variance.

Identification of New Genetic Susceptibility Loci for Breast Cancer Through Consideration of Gene-Environment Interactions

Science.gov (United States)

Schoeps, Anja; Rudolph, Anja; Seibold, Petra; Dunning, Alison M.; Milne, Roger L.; Bojesen, Stig E.; Swerdlow, Anthony; Andrulis, Irene; Brenner, Hermann; Behrens, Sabine; Orr, Nicholas; Jones, Michael; Ashworth, Alan; Li, Jingmei; Cramp, Helen; Connley, Dan; Czene, Kamila; Darabi, Hatef; Chanock, Stephen J.; Lissowska, Jolanta; Figueroa, Jonine D.; Knight, Julia; Glendon, Gord; Mulligan, Anna M.; Dumont, Martine; Severi, Gianluca; Baglietto, Laura; Olson, Janet; Vachon, Celine; Purrington, Kristen; Moisse, Matthieu; Neven, Patrick; Wildiers, Hans; Spurdle, Amanda; Kosma, Veli-Matti; Kataja, Vesa; Hartikainen, Jaana M.; Hamann, Ute; Ko, Yon-Dschun; Dieffenbach, Aida K.; Arndt, Volker; Stegmaier, Christa; Malats, Núria; Arias Perez, JoséI.; Benítez, Javier; Flyger, Henrik; Nordestgaard, Børge G.; Truong, Théresè; Cordina-Duverger, Emilie; Menegaux, Florence; Silva, Isabel dos Santos; Fletcher, Olivia; Johnson, Nichola; Häberle, Lothar; Beckmann, Matthias W.; Ekici, Arif B.; Braaf, Linde; Atsma, Femke; van den Broek, Alexandra J.; Makalic, Enes; Schmidt, Daniel F.; Southey, Melissa C.; Cox, Angela; Simard, Jacques; Giles, Graham G.; Lambrechts, Diether; Mannermaa, Arto; Brauch, Hiltrud; Guénel, Pascal; Peto, Julian; Fasching, Peter A.; Hopper, John; Flesch-Janys, Dieter; Couch, Fergus; Chenevix-Trench, Georgia; Pharoah, Paul D. P.; Garcia-Closas, Montserrat; Schmidt, Marjanka K.; Hall, Per; Easton, Douglas F.; Chang-Claude, Jenny

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 34,475 cases and 34,786 controls of European ancestry from up to 23 studies in the Breast Cancer Association Consortium were included. Overall, 71,527 single nucleotide polymorphisms (SNPs), enriched for association with breast cancer, were tested for interaction with 10 environmental risk factors using three recently proposed hybrid methods and a joint test of association and interaction. Analyses were adjusted for age, study, population stratification, and confounding factors as applicable. Three SNPs in two independent loci showed statistically significant association: SNPs rs10483028 and rs2242714 in perfect linkage disequilibrium on chromosome 21 and rs12197388 in ARID1B on chromosome 6. While rs12197388 was identified using the joint test with parity and with age at menarche (P-values = 3 × 10−07), the variants on chromosome 21 q22.12, which showed interaction with adult body mass index (BMI) in 8,891 postmenopausal women, were identified by all methods applied. SNP rs10483028 was associated with breast cancer in women with a BMI below 25 kg/m2 (OR = 1.26, 95% CI 1.15–1.38) but not in women with a BMI of 30 kg/m2 or higher (OR = 0.89, 95% CI 0.72–1.11, P for interaction = 3.2 × 10−05). Our findings confirm comparable power of the recent methods for detecting G × E interaction and the utility of using G × E interaction analyses to identify new susceptibility loci. PMID:24248812

Genetic mapping of quantitative trait loci (QTLs) with effects on ...

African Journals Online (AJOL)

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2008-02-05

Feb 5, 2008 ... This paper reports on the development of a genetic ... RILs along with the two parental lines were evaluated in the screen- .... A genetic linkage map of cowpea showing the QTLs (in green) that is associated with the resistance.

Caenorhabditis elegans ABCRNAi transporters interact genetically with rde-2 and mut-7.

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Sundaram, Prema; Han, Wang; Cohen, Nancy; Echalier, Benjamin; Albin, John; Timmons, Lisa

2008-02-01

RNA interference (RNAi) mechanisms are conserved and consist of an interrelated network of activities that not only respond to exogenous dsRNA, but also perform endogenous functions required in the fine tuning of gene expression and in maintaining genome integrity. Not surprisingly, RNAi functions have widespread influences on cellular function and organismal development. Previously, we observed a reduced capacity to mount an RNAi response in nine Caenorhabditis elegans mutants that are defective in ABC transporter genes (ABC(RNAi) mutants). Here, we report an exhaustive study of mutants, collectively defective in 49 different ABC transporter genes, that allowed for the categorization of one additional transporter into the ABC(RNAi) gene class. Genetic complementation tests reveal functions for ABC(RNAi) transporters in the mut-7/rde-2 branch of the RNAi pathway. These second-site noncomplementation interactions suggest that ABC(RNAi) proteins and MUT-7/RDE-2 function together in parallel pathways and/or as multiprotein complexes. Like mut-7 and rde-2, some ABC(RNAi) mutants display transposon silencing defects. Finally, our analyses reveal a genetic interaction network of ABC(RNAi) gene function with respect to this part of the RNAi pathway. From our results, we speculate that the coordinated activities of ABC(RNAi) transporters, through their effects on endogenous RNAi-related mechanisms, ultimately affect chromosome function and integrity.

Genetic risk for violent behavior and environmental exposure to disadvantage and violent crime: the case for gene-environment interaction.

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Barnes, J C; Jacobs, Bruce A

2013-01-01

Despite mounds of evidence to suggest that neighborhood structural factors predict violent behavior, almost no attention has been given to how these influences work synergistically (i.e., interact) with an individual's genetic propensity toward violent behavior. Indeed, two streams of research have, heretofore, flowed independently of one another. On one hand, criminologists have underscored the importance of neighborhood context in the etiology of violence. On the other hand, behavioral geneticists have argued that individual-level genetic propensities are important for understanding violence. The current study seeks to integrate these two compatible frameworks by exploring gene-environment interactions (GxE). Two GxEs were examined and supported by the data (i.e., the National Longitudinal Study of Adolescent Health). Using a scale of genetic risk based on three dopamine genes, the analysis revealed that genetic risk had a greater influence on violent behavior when the individual was also exposed to neighborhood disadvantage or when the individual was exposed to higher violent crime rates. The relevance of these findings for criminological theorizing was considered.

Looping Genomes: Diagnostic Change and the Genetic Makeup of the Autism Population.

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Navon, Daniel; Eyal, Gil

2016-03-01

This article builds on Hacking's framework of "dynamic nominalism" to show how knowledge about biological etiology can interact with the "kinds of people" delineated by diagnostic categories in ways that "loop" or modify both over time. The authors use historical materials to show how "geneticization" played a crucial role in binding together autism as a biosocial community and how evidence from genetics research later made an important contribution to the diagnostic expansion of autism. In the second part of the article, the authors draw on quantitative and qualitative analyses of autism rates over time in several rare conditions that are delineated strictly according to genomic mutations in order to demonstrate that these changes in diagnostic practice helped to both increase autism's prevalence and create its enormous genetic heterogeneity. Thus, a looping process that began with geneticization and involved the social effects of genetics research itself transformed the autism population and its genetic makeup.

Interaction of a genetic risk score with physical activity, physical inactivity, and body mass index in relation to venous thromboembolism risk.

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Kim, Jihye; Kraft, Peter; Hagan, Kaitlin A; Harrington, Laura B; Lindstroem, Sara; Kabrhel, Christopher

2018-06-01

Venous thromboembolism (VTE) is highly heritable. Physical activity, physical inactivity and body mass index (BMI) are also risk factors, but evidence of interaction between genetic and environmental risk factors is limited. Data on 2,134 VTE cases and 3,890 matched controls were obtained from the Nurses' Health Study (NHS), Nurses' Health Study II (NHS II), and Health Professionals Follow-up Study (HPFS). We calculated a weighted genetic risk score (wGRS) using 16 single nucleotide polymorphisms associated with VTE risk in published genome-wide association studies (GWAS). Data on three risk factors, physical activity (metabolic equivalent [MET] hours per week), physical inactivity (sitting hours per week) and BMI, were obtained from biennial questionnaires. VTE cases were incident since cohort inception; controls were matched to cases on age, cohort, and genotype array. Using conditional logistic regression, we assessed joint effects and interaction effects on both additive and multiplicative scales. We also ran models using continuous wGRS stratified by risk-factor categories. We observed a supra-additive interaction between wGRS and BMI. Having both high wGRS and high BMI was associated with a 3.4-fold greater risk of VTE (relative excess risk due to interaction = 0.69, p = 0.046). However, we did not find evidence for a multiplicative interaction with BMI. No interactions were observed for physical activity or inactivity. We found a synergetic effect between a genetic risk score and high BMI on the risk of VTE. Intervention efforts lowering BMI to decrease VTE risk may have particularly large beneficial effects among individuals with high genetic risk. © 2018 WILEY PERIODICALS, INC.

Use of a plant level logic model for quantitative assessment of systems interactions

International Nuclear Information System (INIS)

Chu, B.B.; Rees, D.C.; Kripps, L.P.; Hunt, R.N.; Bradley, M.

1985-01-01

The Electric Power Research Institute (EPRI) has sponsored a research program to investigate methods for identifying systems interactions (SIs) and for the evaluation of their importance. Phase 1 of the EPRI research project focused on the evaluation of methods for identification of SIs. Major results of the Phase 1 activities are the documentation of four different methodologies for identification of potential SIs and development of guidelines for performing an effective plant walkdown in support of an SI analysis. Phase II of the project, currently being performed, is utilizing a plant level logic model of a pressurized water reactor (PWR) to determine the quantitative importance of identified SIs. In Phase II, previously reported events involving interactions between systems were screened and selected on the basis of their relevance to the Baltimore Gas and Electric (BGandE) Calvert Cliffs Nuclear Power Plant design and perceived potential safety significance. Selected events were then incorporated into the BGandE plant level GO logic model. The model is being exercised to calculate the relative importance of these events. Five previously identified event scenarios, extracted from licensee event reports (LERs) are being evaluated during the course of the study. A key feature of the approach being used in Phase II is the use of a logic model in a manner to effectively evaluate the impact of events on the system level and the plant level for the mitigation of transients. Preliminary study results indicate that the developed methodology can be a viable and effective means for determining the quantitative significance of SIs

Genetic by environment interaction for post weaning growth traits in tropical cattle

OpenAIRE

Navès, Michel; Menendez Buxadera, Alberto; Farant, Alain; Mandonnet, Nathalie

2006-01-01

Genetic by environment interactions for post weaning traits were studied in a local breed of cattle, well adapted to tropical conditions. After weaning, 444 beef calves of both sexes were separated within two management systems, either in intensive fattening or at pasture. The traits analysed included weights at standard age, of 365 days (W12), 455 days (W15) and 545 days (W18), and post weaning growth rates from weaning until 15 months (PWG15) or 18 months (PWG18). (Co)varianc...

Genetic and environmental-genetic interaction rules for the myopia based on a family exposed to risk from a myopic environment.

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Wenbo, Li; Congxia, Bai; Hui, Liu

2017-08-30

To quantitatively assess the role of heredity and environmental factors in myopia based on the family with enough exposed to risk from myopic environment for establishment of environmental and genetic index (EGI). A pedigree analysis unit was defined as one child (university student), father, and mother. Information pertaining to visual acuity, experience in participating in the college entrance examination in mainland of China (regarded as a strong environmental risk for myopia), and occupation for pedigree analysis units were obtained. The difference between effect of both genetic and environmental factors (myopia prevalence in children with two myopic parents) and environmental factors (myopia prevalence in children of whom neither parent was myopic) was defined as the EGI. Multiple regression analysis was performed for 114 pedigree using diopters of father, mother, average diopters in parents, maximum and minimum diopters in father and mother as variables. A total of 353 farmers and 162 farmer families were used as a control group. A distinct difference in myopia rate (96.2% versus 57.7%) was observed for children from parents with myopia and parents without myopia (EGI=0.385). The maximum diopter was included to regression equation which was statistically significant. The prevalence of myopia was 9.9% in the farmer. The prevalence in children is similar between the farmer and other families. A new genetic rule that myopia in children was directly related with maximum diopters in father and mother may be suggested. Environmental factors may play a leading role in the formation of myopia. Copyright © 2017 Elsevier B.V. All rights reserved.

The genetic basis of resistance and matching-allele interactions of a host-parasite system: The Daphnia magna-Pasteuria ramosa model.

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Gilberto Bento

2017-02-01

Full Text Available Negative frequency-dependent selection (NFDS is an evolutionary mechanism suggested to govern host-parasite coevolution and the maintenance of genetic diversity at host resistance loci, such as the vertebrate MHC and R-genes in plants. Matching-allele interactions of hosts and parasites that prevent the emergence of host and parasite genotypes that are universally resistant and infective are a genetic mechanism predicted to underpin NFDS. The underlying genetics of matching-allele interactions are unknown even in host-parasite systems with empirical support for coevolution by NFDS, as is the case for the planktonic crustacean Daphnia magna and the bacterial pathogen Pasteuria ramosa. We fine-map one locus associated with D. magna resistance to P. ramosa and genetically characterize two haplotypes of the Pasteuria resistance (PR- locus using de novo genome and transcriptome sequencing. Sequence comparison of PR-locus haplotypes finds dramatic structural polymorphisms between PR-locus haplotypes including a large portion of each haplotype being composed of non-homologous sequences resulting in haplotypes differing in size by 66 kb. The high divergence of PR-locus haplotypes suggest a history of multiple, diverse and repeated instances of structural mutation events and restricted recombination. Annotation of the haplotypes reveals striking differences in gene content. In particular, a group of glycosyltransferase genes that is present in the susceptible but absent in the resistant haplotype. Moreover, in natural populations, we find that the PR-locus polymorphism is associated with variation in resistance to different P. ramosa genotypes, pointing to the PR-locus polymorphism as being responsible for the matching-allele interactions that have been previously described for this system. Our results conclusively identify a genetic basis for the matching-allele interaction observed in a coevolving host-parasite system and provide a first insight into

The genetic basis of resistance and matching-allele interactions of a host-parasite system: The Daphnia magna-Pasteuria ramosa model.

Science.gov (United States)

Bento, Gilberto; Routtu, Jarkko; Fields, Peter D; Bourgeois, Yann; Du Pasquier, Louis; Ebert, Dieter

2017-02-01

Negative frequency-dependent selection (NFDS) is an evolutionary mechanism suggested to govern host-parasite coevolution and the maintenance of genetic diversity at host resistance loci, such as the vertebrate MHC and R-genes in plants. Matching-allele interactions of hosts and parasites that prevent the emergence of host and parasite genotypes that are universally resistant and infective are a genetic mechanism predicted to underpin NFDS. The underlying genetics of matching-allele interactions are unknown even in host-parasite systems with empirical support for coevolution by NFDS, as is the case for the planktonic crustacean Daphnia magna and the bacterial pathogen Pasteuria ramosa. We fine-map one locus associated with D. magna resistance to P. ramosa and genetically characterize two haplotypes of the Pasteuria resistance (PR-) locus using de novo genome and transcriptome sequencing. Sequence comparison of PR-locus haplotypes finds dramatic structural polymorphisms between PR-locus haplotypes including a large portion of each haplotype being composed of non-homologous sequences resulting in haplotypes differing in size by 66 kb. The high divergence of PR-locus haplotypes suggest a history of multiple, diverse and repeated instances of structural mutation events and restricted recombination. Annotation of the haplotypes reveals striking differences in gene content. In particular, a group of glycosyltransferase genes that is present in the susceptible but absent in the resistant haplotype. Moreover, in natural populations, we find that the PR-locus polymorphism is associated with variation in resistance to different P. ramosa genotypes, pointing to the PR-locus polymorphism as being responsible for the matching-allele interactions that have been previously described for this system. Our results conclusively identify a genetic basis for the matching-allele interaction observed in a coevolving host-parasite system and provide a first insight into its molecular basis.

Genetic Architecture and heritability of some Quantitative Characters,oil content and fatty.acid composition in safflower

International Nuclear Information System (INIS)

Ragab, A.I.; Fried, W.

1992-01-01

The nature of gene action for some quantitative, oil content and quality characters in safflower was studied in an F 1 diallel set involving 4 parents. Both additive and dominance genetic variance were important for most traits. The magnitude of non additive gene action was higher than of additive genetic variance for all traits, except for first branch height, palmitic and stearic acids. The distribution of positive and negative alleles in the parental populations was a symmetrical for all traits except for 100-seed weight. Most of dominant genes had positive effects in plant height, oil content and oleic acid. Dominance degree was over dominance for all traits except for flowering date and first branch height which showed partial dominance. The narrow sense heritability was 75%, 82%, and 89% for stearic acid, flowering date and first branch height, whears 50 to 67% were found for seed yield/plant, plant height, and oil content. Values of less than 50% were determined for other traits. The V r-W r graphical analysis showed partial dominance for flowering date, first branch height, no. of capitula/plant, palmitic and linoleic acids. Complete dominance for 100-seed weight and over dominance for plant height, seed yield, oil content and oleic acid. 2 fig., 2 tab

Genetic and Physiological Characterization of Two Clusters of Quantitative Trait Loci Associated With Seed Dormancy and Plant Height in Rice

OpenAIRE

Ye, Heng; Beighley, Donn H.; Feng, Jiuhuan; Gu, Xing-You

2013-01-01

Seed dormancy and plant height have been well-studied in plant genetics, but their relatedness and shared regulatory mechanisms in natural variants remain unclear. The introgression of chromosomal segments from weedy into cultivated rice (Oryza sativa) prompted the detection of two clusters (qSD1-2/qPH1 and qSD7-2/qPH7) of quantitative trait loci both associated with seed dormancy and plant height. Together, these two clusters accounted for >96% of the variances for plant height and ~71% of t...

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

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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

Research on interactive genetic-geological models to evaluate favourability for undiscovered uranium resources

International Nuclear Information System (INIS)

Finch, W.I.; Granger, H.C.; Lupe, R.; McCammon, R.B.

1980-01-01

Current methods of evaluating favourability for undiscovered uranium resources are unduly subjective, quite possibly inconsistent and, as a consequence, of questionable reliability. This research is aimed at reducing the subjectivity and increasing the reliability by designing an improved method that depends largely on geological data and their statistical frequency of occurrence. This progress report outlines a genetic approach to modelling the geological factors that controlled uranium mineralization in order to evaluate the favourability for the occurrence of undiscovered uranium deposits of the type modelled. A genetic model is constructed from all the factors that describe the processes, in chronological sequence, that formed uranium deposits thought to have a common origin. The field and laboratory evidence for the processes constitute a geologic-occurrence base that parallels the chronological sequence of events. The genetic model and the geologic-occurrence base are portrayed as two columns of an interactive matrix called the ''genetic-geologic model''. For each column, eight chronological stages are used to describe the overall formation of the uranium deposits. These stages consist of (1) precursor processes; (2) host-rock formation; (3) preparation of host-rock; (4) uranium-source development; (5) transport of uranium; (6) primary uranium deposition; (7) post-deposition modification; and (8) preservation. To apply the genetic-geological model to evaluate favourability, a question is posed that determines the presence or absence of each attribute listed under the geologic-occurrence base. By building a logic circuit of the attributes according to either their essential or non-essential nature, the resultant match between a well-documented control area and the test area may be determined. The degree of match is a measure of favourability for uranium occurrence as hypothesized in the genetic model

The Interaction of Selective Attention and Cognitive Development on Achievement in Nigerian Secondary School Genetics

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Okoye, Namdi N. S.

2009-01-01

The study tried to examine the interaction between two independent variables of selective attention and cognitive development on Achievement in Genetics at the Secondary School level. In looking at the problem of this study three null hypotheses were generated for testing at 0.05 level of significance. Factorial Analysis of Variance design with…

Hypothesis: Genetic and epigenetic risk factors interact to modulate vulnerability and resilience to FASD

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Elif eTunc-Ozcan

2014-08-01

Full Text Available Fetal alcohol spectrum disorder (FASD presents a collection of symptoms representing physiological and behavioral phenotypes caused by maternal alcohol consumption. Symptom severity is modified by genetic differences in fetal susceptibility and resistance as well as maternal genetic factors such as maternal alcohol sensitivity. Animal models demonstrate that both maternal and paternal genetics contribute to the variation in the fetus’ vulnerability to alcohol exposure. Maternal and paternal genetics define the variations in these phenotypes even without the effect of alcohol in utero, as most of these traits are polygenic, non-Mendelian, in their inheritance. In addition, the epigenetic alterations that instigate the alcohol induced neurodevelopmental deficits can interact with the polygenic inheritance of respective traits. Here, based on specific examples, we present the hypothesis that the principles of non-Mendelian inheritance, or ‘exceptions’ to Mendelian genetics, can be the driving force behind the severity of the prenatal alcohol-exposed individual’s symptomology. One such exception is when maternal alleles lead to an altered intrauterine hormonal environment and, therefore, produce variations in the long-term consequences on the development of the alcohol-exposed fetus. Another exception is when epigenetic regulation of allele-specific gene expression generates disequilibrium between the maternal versus paternal genetic contributions, and thereby, modifies the effect of prenatal alcohol exposure on the fetus. We propose that these situations in which one parent has an exaggerated influence over the offspring’s vulnerability to prenatal alcohol are major contributing mechanisms responsible for the variations in the symptomology of FASD in the exposed generation and beyond.

Fabrication of type I collagen microcarrier using a microfluidic 3D T-junction device and its application for the quantitative analysis of cell-ECM interactions.

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Yoon, Junghyo; Kim, Jaehoon; Jeong, Hyo Eun; Sudo, Ryo; Park, Myung-Jin; Chung, Seok

2016-08-26

We presented a new quantitative analysis for cell and extracellular matrix (ECM) interactions, using cell-coated ECM hydrogel microbeads (hydrobeads) made of type I collagen. The hydrobeads can carry cells as three-dimensional spheroidal forms with an ECM inside, facilitating a direct interaction between the cells and ECM. The cells on hydrobeads do not have a hypoxic core, which opens the possibility for using as a cell microcarrier for bottom-up tissue reconstitution. This technique can utilize various types of cells, even MDA-MB-231 cells, which have weak cell-cell interactions and do not form spheroids in conventional spheroid culture methods. Morphological indices of the cell-coated hydrobead visually present cell-ECM interactions in a quantitative manner.

The genetic architecture of fitness in a seed beetle: assessing the potential for indirect genetic benefits of female choice

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Maklakov AA

2008-10-01

Full Text Available Abstract Background Quantifying the amount of standing genetic variation in fitness represents an empirical challenge. Unfortunately, the shortage of detailed studies of the genetic architecture of fitness has hampered progress in several domains of evolutionary biology. One such area is the study of sexual selection. In particular, the evolution of adaptive female choice by indirect genetic benefits relies on the presence of genetic variation for fitness. Female choice by genetic benefits fall broadly into good genes (additive models and compatibility (non-additive models where the strength of selection is dictated by the genetic architecture of fitness. To characterize the genetic architecture of fitness, we employed a quantitative genetic design (the diallel cross in a population of the seed beetle Callosobruchus maculatus, which is known to exhibit post-copulatory female choice. From reciprocal crosses of inbred lines, we assayed egg production, egg-to-adult survival, and lifetime offspring production of the outbred F1 daughters (F1 productivity. Results We used the bio model to estimate six components of genetic and environmental variance in fitness. We found sizeable additive and non-additive genetic variance in F1 productivity, but lower genetic variance in egg-to-adult survival, which was strongly influenced by maternal and paternal effects. Conclusion Our results show that, in order to gain a relevant understanding of the genetic architecture of fitness, measures of offspring fitness should be inclusive and should include quantifications of offspring reproductive success. We note that our estimate of additive genetic variance in F1 productivity (CVA = 14% is sufficient to generate indirect selection on female choice. However, our results also show that the major determinant of offspring fitness is the genetic interaction between parental genomes, as indicated by large amounts of non-additive genetic variance (dominance and/or epistasis

Counselor-counselee interaction in reproductive genetic counseling: Does a pregnancy in the counselee make a difference?

NARCIS (Netherlands)

Aalfs, Cora M.; Oort, Frans J.; de Haes, Hanneke C. J. M.; Leschot, Nico J.; Smets, Ellen M. A.

2006-01-01

OBJECTIVE: To investigate the influence of a pregnancy and other counselee characteristics on several aspects of counselor-counselee interaction during the initial clinical genetic consultation. METHODS: The consultations, of a group of pregnant women (n = 82) and of a control group of non-pregnant

Developmental and genetic modulation of arsenic biotransformation: A gene by environment interaction?

International Nuclear Information System (INIS)

Meza, Mercedes; Gandolfi, A. Jay; Klimecki, Walter T.

2007-01-01

The complexity of arsenic toxicology has confounded the identification of specific pathways of disease causation. One focal point of arsenic research is aimed at fully characterizing arsenic biotransformation in humans, a process that appears to be quite variable, producing a mixture of several arsenic species with greatly differing toxic potencies. In an effort to characterize genetic determinants of variability in arsenic biotransformation, a genetic association study of 135 subjects in western Sonora, Mexico was performed by testing 23 polymorphic sites in three arsenic biotransformation candidate genes. One gene, arsenic 3 methyltransferase (AS3MT), was strongly associated with the ratio of urinary dimethylarsinic acid to monomethylarsonic acid (D/M) in children (7-11 years) but not in adults (18-79 years). Subsequent analyses revealed that the high D/M values associated with variant AS3MT alleles were primarily due to lower levels of monomethylarsonic acid as percent of total urinary arsenic (%MMA5). In light of several reports of arsenic-induced disease being associated with relatively high %MMA5 levels, these findings raise the possibility that variant AS3MT individuals may suffer less risk from arsenic exposure than non-variant individuals. These analyses also provide evidence that, in this population, regardless of AS3MT variant status, children tend to have lower %MMA5 values than adults, suggesting that the global developmental regulation of arsenic biotransformation may interact with genetic variants in metabolic genes to result in novel genetic effects such as those in this report

Real-Time PCR-Based Quantitation Method for the Genetically Modified Soybean Line GTS 40-3-2.

Science.gov (United States)

Kitta, Kazumi; Takabatake, Reona; Mano, Junichi

2016-01-01

This chapter describes a real-time PCR-based method for quantitation of the relative amount of genetically modified (GM) soybean line GTS 40-3-2 [Roundup Ready(®) soybean (RRS)] contained in a batch. The method targets a taxon-specific soybean gene (lectin gene, Le1) and the specific DNA construct junction region between the Petunia hybrida chloroplast transit peptide sequence and the Agrobacterium 5-enolpyruvylshikimate-3-phosphate synthase gene (epsps) sequence present in GTS 40-3-2. The method employs plasmid pMulSL2 as a reference material in order to quantify the relative amount of GTS 40-3-2 in soybean samples using a conversion factor (Cf) equal to the ratio of the RRS-specific DNA to the taxon-specific DNA in representative genuine GTS 40-3-2 seeds.

Interactive effect of genetic susceptibility with height, body mass index, and hormone replacement therapy on the risk of breast cancer

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Harlid Sophia

2012-06-01

Full Text Available Abstract Background Breast cancer today has many established risk factors, both genetic and environmental, but these risk factors by themselves explain only part of the total cancer incidence. We have investigated potential interactions between certain known genetic and phenotypic risk factors, specifically nine single nucleotide polymorphisms (SNPs and height, body mass index (BMI and hormone replacement therapy (HRT. Methods We analyzed samples from three different study populations: two prospectively followed Swedish cohorts and one Icelandic case–control study. Totally 2884 invasive breast cancer cases and 4508 controls were analysed in the study. Genotypes were determined using Mass spectrometry-Maldi-TOF and phenotypic variables were derived from measurements and/or questionnaires. Odds Ratios and 95% confidence intervals were calculated using unconditional logistic regression with the inclusion of an interaction term in the logistic regression model. Results One SNP (rs851987 in ESR1 tended to interact with height, with an increasingly protective effect of the major allele in taller women (p = 0.007 and rs13281615 (on 8q24 tended to confer risk only in non users of HRT (p-for interaction = 0.03. There were no significant interactions after correction for multiple testing. Conclusions We conclude that much larger sample sets would be necessary to demonstrate interactions between low-risk genetic polymorphisms and the phenotypic variables height, BMI and HRT on the risk for breast cancer. However the present hypothesis-generating study has identified tendencies that would be of interest to evaluate for gene-environment interactions in independent materials.

Exploitation of genetic interaction network topology for the prediction of epistatic behavior

KAUST Repository

Alanis Lobato, Gregorio

2013-10-01

Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

Exploitation of genetic interaction network topology for the prediction of epistatic behavior

KAUST Repository

Alanis Lobato, Gregorio; Cannistraci, Carlo; Ravasi, Timothy

2013-01-01

Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

Interactions between Gut Microbiota, Host Genetics and Diet Modulate the Predisposition to Obesity and Metabolic Syndrome

OpenAIRE

Ussar, Siegfried; Griffin, Nicholas W.; Bezy, Olivier; Fujisaka, Shiho; Vienberg, Sara; Softic, Samir; Deng, Luxue; Bry, Lynn; Gordon, Jeffrey I.; Kahn, C. Ronald

2015-01-01

Obesity, diabetes and metabolic syndrome result from complex interactions between genetic and environmental factors, including the gut microbiota. To dissect these interactions, we utilized three commonly-used inbred strains of mice – obesity/diabetes-prone C57Bl/6J mice, obesity/diabetes-resistant 129S1/SvImJ, from Jackson Laboratory and obesity-prone, but diabetes resistant 129S6/SvEvTac from Taconic - plus three derivative lines generated by breeding these strains in a new, common environm...

Alterations of social interaction through genetic and environmental manipulation of the 22q11.2 gene Sept5 in the mouse brain.

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Harper, Kathryn M; Hiramoto, Takeshi; Tanigaki, Kenji; Kang, Gina; Suzuki, Go; Trimble, William; Hiroi, Noboru

2012-08-01

Social behavior dysfunction is a symptomatic element of schizophrenia and autism spectrum disorder (ASD). Although altered activities in numerous brain regions are associated with defective social cognition and perception, the causative relationship between these altered activities and social cognition and perception-and their genetic underpinnings-are not known in humans. To address these issues, we took advantage of the link between hemizygous deletion of human chromosome 22q11.2 and high rates of social behavior dysfunction, schizophrenia and ASD. We genetically manipulated Sept5, a 22q11.2 gene, and evaluated its role in social interaction in mice. Sept5 deficiency, against a high degree of homogeneity in a congenic genetic background, selectively impaired active affiliative social interaction in mice. Conversely, virally guided overexpression of Sept5 in the hippocampus or, to a lesser extent, the amygdala elevated levels of active affiliative social interaction in C57BL/6J mice. Congenic knockout mice and mice overexpressing Sept5 in the hippocampus or amygdala were indistinguishable from control mice in novelty and olfactory responses, anxiety or motor activity. Moreover, post-weaning individual housing, an environmental condition designed to reduce stress in male mice, selectively raised levels of Sept5 protein in the amygdala and increased active affiliative social interaction in C57BL/6J mice. These findings identify this 22q11.2 gene in the hippocampus and amygdala as a determinant of social interaction and suggest that defective social interaction seen in 22q11.2-associated schizophrenia and ASD can be genetically and environmentally modified by altering this 22q11.2 gene.

A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

Science.gov (United States)

Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed

2013-01-01

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.

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

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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.

Genetic adaptability of inheritance of resistance to biotic and abiotic ...

African Journals Online (AJOL)

Several studies that attempt to identify the genetic basis of quantitative traits ignore the presence of epistatic effects and theirs role in plant genetic adaptability. Epistasis has been detected in the inheritance of many quantitative traits on crop. Moreover, generation means analysis of several traits assessed in diverse ...

Analysis of genetic and genotype X environment interaction effects for agronomic traits of rice (oryza sativa l.) in salt tolerance

International Nuclear Information System (INIS)

Zhou, H.K.; Hayat, Y.; Fang, L.J.; Guo, R.F.; He, J.M.; Xu, H.M.

2010-01-01

A diallel cross experiment of 4 rice (Oryza sativa L.) female and 6 male varieties was conducted to study the genetic effects and their interaction with salt-stress condition of 7 agronomic traits in normal and salt-stressed planting conditions. The panicle length (PL), effective number of panicles per plant (ENP), plumped number of grains per panicles (PNG), total number of grains per panicles (TNG), 1000-grain weight (W), seed setting ratio (SSR) and grain weight per plant (PGW), were investigated. A genetic model including additive effect, dominance effect and their interaction effects with environment (ADE) was employed for analysis of data. It was observed that significant (p<0.05) additive effects, dominance effects, additive X environment interaction effects and dominance X environment interaction effects exist for most of the agronomic traits of rice. In addition, significant (p<0.05) narrow sense heritabilities of ENP, PNG, TNG, W and PGW were found, indicating that the genetic performance of these traits are greatly affected by salt stress condition. A significant (p<0.05) negative correlations in the additive effects and additive X environment interaction effects detected between ENP and PNG suggesting that selection on increasing of ENP can reduce PNG. In addition, there exist a highly significant (p<0.01) positive dominance correlation among the dominance effects of the ENP, PNG and TNG, which shows that it is possible to breed salt-tolerant rice variety by coordinating large panicle and multi-panicle in utilization of heterosis. (author)

Island Rule, quantitative genetics and brain-body size evolution in Homo floresiensis.

Science.gov (United States)

Diniz-Filho, José Alexandre Felizola; Raia, Pasquale

2017-06-28

Colonization of islands often activate a complex chain of adaptive events that, over a relatively short evolutionary time, may drive strong shifts in body size, a pattern known as the Island Rule. It is arguably difficult to perform a direct analysis of the natural selection forces behind such a change in body size. Here, we used quantitative evolutionary genetic models, coupled with simulations and pattern-oriented modelling, to analyse the evolution of brain and body size in Homo floresiensis , a diminutive hominin species that appeared around 700 kya and survived up to relatively recent times (60-90 kya) on Flores Island, Indonesia. The hypothesis of neutral evolution was rejected in 97% of the simulations, and estimated selection gradients are within the range found in living natural populations. We showed that insularity may have triggered slightly different evolutionary trajectories for body and brain size, which means explaining the exceedingly small cranial volume of H. floresiensis requires additional selective forces acting on brain size alone. Our analyses also support previous conclusions that H. floresiensis may be most likely derived from an early Indonesian H. erectus , which is coherent with currently accepted biogeographical scenario for Homo expansion out of Africa. © 2017 The Author(s).

Quantitative genetic analysis of life-history traits of Caenorhabditis elegans in stressful environments

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Shorto Alison

2008-01-01

Full Text Available Abstract Background Organisms live in environments that vary. For life-history traits that vary across environments, fitness will be maximised when the phenotype is appropriately matched to the environmental conditions. For the free-living nematode Caenorhabditis elegans, we have investigated how two major life-history traits, (i the development of environmentally resistant dauer larvae and (ii reproduction, respond to environmental stress (high population density and low food availability, and how these traits vary between lines and the genetic basis of this variation. Results We found that lines of C. elegans vary in their phenotypic plasticity of dauer larva development, i.e. there is variation in the likelihood of developing into a dauer larva for the same environmental change. There was also variation in how lifetime fecundity and the rate of reproduction changed under conditions of environmental stress. These traits were related, such that lines that are highly plastic for dauer larva development also maintain a high population growth rate when stressed. We identified quantitative trait loci (QTL on two chromosomes that control the dauer larva development and population size phenotypes. The QTLs affecting the dauer larva development and population size phenotypes on chromosome II are closely linked, but are genetically separable. This chromosome II QTL controlling dauer larva development does not encompass any loci previously identified to control dauer larva development. This chromosome II region contains many predicted 7-transmembrane receptors. Such proteins are often involved in information transduction, which is clearly relevant to the control of dauer larva development. Conclusion C. elegans alters both its larval development and adult reproductive strategy in response to environmental stress. Together the phenotypic and genotypic data suggest that these two major life-history traits are co-ordinated responses to environmental stress

Quantitative trait loci associated with anthracnose resistance in sorghum

Science.gov (United States)

With an aim to develop a durable resistance to the fungal disease anthracnose, two unique genetic sources of resistance were selected to create genetic mapping populations to identify regions of the sorghum genome that encode anthracnose resistance. A series of quantitative trait loci were identifi...

IQ and schizophrenia in a Swedish national sample: their causal relationship and the interaction of IQ with genetic risk.

Science.gov (United States)

Kendler, Kenneth S; Ohlsson, Henrik; Sundquist, Jan; Sundquist, Kristina

2015-03-01

The authors sought to clarify the relationship between IQ and subsequent risk for schizophrenia. IQ was assessed at ages 18-20 in 1,204,983 Swedish males born between 1951 and 1975. Schizophrenia was assessed by hospital diagnosis through 2010. Cox proportional hazards models were used to investigate future risk for schizophrenia in individuals as a function of their IQ score, and then stratified models using pairs of relatives were used to adjust for familial cluster. Finally, regression models were used to examine the interaction between IQ and genetic liability on risk for schizophrenia. IQ had a monotonic relationship with schizophrenia risk across the IQ range, with a mean increase in risk of 3.8% per 1-point decrease in IQ; this association was stronger in the lower than the higher IQ range. Co-relative control analyses showed a similar association between IQ and schizophrenia in the general population and in cousin, half-sibling, and full-sibling pairs. A robust interaction was seen between genetic liability to schizophrenia and IQ in predicting schizophrenia risk. Genetic susceptibility for schizophrenia had a much stronger impact on risk of illness for those with low than high intelligence. The IQ-genetic liability interaction arose largely from IQ differences between close relatives. IQ assessed in late adolescence is a robust risk factor for subsequent onset of schizophrenia. This association is not the result of a declining IQ associated with insidious onset. In this large, representative sample, we found no evidence for a link between genius and schizophrenia. Co-relative control analyses showed that the association between lower IQ and schizophrenia is not the result of shared familial risk factors and may be causal. The strongest effect was seen with IQ differences within families. High intelligence substantially attenuates the impact of genetic liability on the risk for schizophrenia.

Telomerase RNA Component (TERC) genetic variants interact with the mediterranean diet modifying the inflammatory status and its relationship with aging: CORDIOPREV study

Science.gov (United States)

Background: Leukocyte telomere length (LTL) attrition has been associated with age-related diseases. Telomerase RNA Component (TERC) genetic variants have been associated with LTL; whereas fatty acids (FAs) can interact with genetic factors and influence in aging. We explore whether variability at t...

Exploring Genetic Suppression Interactions on a Global Scale

OpenAIRE

van Leeuwen, Jolanda; Pons, Carles; Mellor, Joseph C.; Yamaguchi, Takafumi N.; Friesen, Helena; Koschwanez, John; Ušaj, Mojca Mattiazzi; Pechlaner, Maria; Takar, Mehmet; Ušaj, Matej; VanderSluis, Benjamin; Andrusiak, Kerry; Bansal, Pritpal; Baryshnikova, Anastasia; Boone, Claire

2016-01-01

Genetic suppression occurs when the phenotypic defects caused by a mutation in a particular gene are rescued by a mutation in a second gene. To explore the principles of genetic suppression, we examined both literature-curated and unbiased experimental data, involving systematic genetic mapping and whole-genome sequencing, to generate a large-scale suppression network among yeast genes. Most suppression pairs identified novel relationships among functionally related genes, providing new insig...

Genetic divergence among pumpkin landraces

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Rebeca Lourenço de Oliveira

2016-04-01

Full Text Available Estimating the genetic variability in germplasm collections is important not only for conserving genetic resources, but also for plant breeding purposes. However, generating a large number of different categories data (qualitative and quantitative often complicate the analysis and results interpretation, resulting in an incomplete distinction of accessions. This study reports the characterization and evaluation of 14 pumpkin (Cucurbita moschata accessions collected from farms in the northern region of Rio de Janeiro state. Genetic diversity among accessions was also estimated using qualitative and quantitative variables considering joint analysis. The plants were grown under field conditions in a randomized block design with three replications and six plants per plot. Eight qualitative traits (leaf size; seed shape; seed color; color of the fruit pulp; hollow; fruit shape; skin color, and fruit skin texture and eight quantitative traits (fruit weight; fruit length; fruit diameter; soluble solids, 100 seed weight, and wall thickness measured in the middle and in the lower stem were evaluated. The data were analyzed considering the Gower distance, and cluster analysis was performed using unweighted pair group method with arithmetic mean (UPGMA. Variability among accessions was observed considering morphoagronomic data. The Gower distance together with UPGMA cluster allowed for good discrimination between accessions in the groups, demonstrating that the simultaneous analysis of qualitative and quantitative data is feasible and may increase the understanding of the variation among accessions.

Interactive knowledge discovery from marketing questionnarie using simulated breeding and inductive learning methods

Energy Technology Data Exchange (ETDEWEB)

Terano, Takao [Univ. of Tsukuba, Tokyo (Japan); Ishino, Yoko [Univ. of Tokyo (Japan)

1996-12-31

This paper describes a novel method to acquire efficient decision rules from questionnaire data using both simulated breeding and inductive learning techniques. The basic ideas of the method are that simulated breeding is used to get the effective features from the questionnaire data and that inductive learning is used to acquire simple decision rules from the data. The simulated breeding is one of the Genetic Algorithm (GA) based techniques to subjectively or interactively evaluate the qualities of offspring generated by genetic operations. In this paper, we show a basic interactive version of the method and two variations: the one with semi-automated GA phases and the one with the relatively evaluation phase via the Analytic Hierarchy Process (AHP). The proposed method has been qualitatively and quantitatively validated by a case study on consumer product questionnaire data.

Quantitative variation in natural populations

International Nuclear Information System (INIS)

Parsons, P.A.

1975-01-01

Quantitative variation is considered in natural populations using Drosophila as the example. A knowledge of such variation enables its rapid exploitation in directional selection experiments as shown for scutellar chaeta number. Where evidence has been obtained, genetic architectures are in qualitative agreement with Mather's concept of balance for traits under stabilizing selection. Additive genetic control is found for acute environmental stresses, but not for less acute stresses as shown by exposure to 60 Co-γ rays. D. simulans probably has a narrower ecological niche than its sibling species D. melanogaster associated with lower genetic heterogeneity. One specific environmental stress to which D. simulans is sensitive in nature is ethyl alcohol as shown by winery data. (U.S.)

Genetics, mental illness, and complex disease: development and distribution of an interactive CD-ROM for genetic counselors. Final report for period 15 August 2000 - 31 December 2002

Energy Technology Data Exchange (ETDEWEB)

McInerney, Joseph D.

2003-03-31

"Genetics and Major Psychiatric Disorders: A Program for Genetic Counselors" provides an introduction to psychiatric genetics, with a focus on the genetics of common complex disease, for genetics professionals. The program is available as a CD-ROM and an online educational resource. The on-line version requires a direct internet connection. Each educational module begins with an interactive case study that raises significant issues addressed in each module. In addition, case studies provided throughout the educational materials support teaching of major concepts. Incorporated throughout the content are expert video clips, video clips from individuals affected by psychiatric illness, and optional "learn more" materials that offer greater depth about a particular topic. The structure of the CD-ROM permits self-navigation, but we have suggested a sequence that allows materials to build upon each other. At any point in the materials, users may pause and look up terms in the glossary or review the DSM-IV criteria for selected psychiatric disorders. A detailed site map is available for those who choose to self navigate through the content.

Quantitative modeling assesses the contribution of bond strengthening, rebinding and force sharing to the avidity of biomolecule interactions.

Directory of Open Access Journals (Sweden)

Valentina Lo Schiavo

Full Text Available Cell adhesion is mediated by numerous membrane receptors. It is desirable to derive the outcome of a cell-surface encounter from the molecular properties of interacting receptors and ligands. However, conventional parameters such as affinity or kinetic constants are often insufficient to account for receptor efficiency. Avidity is a qualitative concept frequently used to describe biomolecule interactions: this includes incompletely defined properties such as the capacity to form multivalent attachments. The aim of this study is to produce a working description of monovalent attachments formed by a model system, then to measure and interpret the behavior of divalent attachments under force. We investigated attachments between antibody-coated microspheres and surfaces coated with sparse monomeric or dimeric ligands. When bonds were subjected to a pulling force, they exhibited both a force-dependent dissociation consistent with Bell's empirical formula and a force- and time-dependent strengthening well described by a single parameter. Divalent attachments were stronger and less dependent on forces than monovalent ones. The proportion of divalent attachments resisting a force of 30 piconewtons for at least 5 s was 3.7 fold higher than that of monovalent attachments. Quantitative modeling showed that this required rebinding, i.e. additional bond formation between surfaces linked by divalent receptors forming only one bond. Further, experimental data were compatible with but did not require stress sharing between bonds within divalent attachments. Thus many ligand-receptor interactions do not behave as single-step reactions in the millisecond to second timescale. Rather, they exhibit progressive stabilization. This explains the high efficiency of multimerized or clustered receptors even when bonds are only subjected to moderate forces. Our approach provides a quantitative way of relating binding avidity to measurable parameters including bond

Quantitative Modeling Assesses the Contribution of Bond Strengthening, Rebinding and Force Sharing to the Avidity of Biomolecule Interactions

Science.gov (United States)

Lo Schiavo, Valentina; Robert, Philippe; Limozin, Laurent; Bongrand, Pierre

2012-01-01

Cell adhesion is mediated by numerous membrane receptors. It is desirable to derive the outcome of a cell-surface encounter from the molecular properties of interacting receptors and ligands. However, conventional parameters such as affinity or kinetic constants are often insufficient to account for receptor efficiency. Avidity is a qualitative concept frequently used to describe biomolecule interactions: this includes incompletely defined properties such as the capacity to form multivalent attachments. The aim of this study is to produce a working description of monovalent attachments formed by a model system, then to measure and interpret the behavior of divalent attachments under force. We investigated attachments between antibody-coated microspheres and surfaces coated with sparse monomeric or dimeric ligands. When bonds were subjected to a pulling force, they exhibited both a force-dependent dissociation consistent with Bell’s empirical formula and a force- and time-dependent strengthening well described by a single parameter. Divalent attachments were stronger and less dependent on forces than monovalent ones. The proportion of divalent attachments resisting a force of 30 piconewtons for at least 5 s was 3.7 fold higher than that of monovalent attachments. Quantitative modeling showed that this required rebinding, i.e. additional bond formation between surfaces linked by divalent receptors forming only one bond. Further, experimental data were compatible with but did not require stress sharing between bonds within divalent attachments. Thus many ligand-receptor interactions do not behave as single-step reactions in the millisecond to second timescale. Rather, they exhibit progressive stabilization. This explains the high efficiency of multimerized or clustered receptors even when bonds are only subjected to moderate forces. Our approach provides a quantitative way of relating binding avidity to measurable parameters including bond maturation, rebinding and

Beyond Punnett squares: Student word association and explanations of phenotypic variation through an integrative quantitative genetics unit investigating anthocyanin inheritance and expression in Brassica rapa Fast plants.

Science.gov (United States)

Batzli, Janet M; Smith, Amber R; Williams, Paul H; McGee, Seth A; Dósa, Katalin; Pfammatter, Jesse

2014-01-01

Genetics instruction in introductory biology is often confined to Mendelian genetics and avoids the complexities of variation in quantitative traits. Given the driving question "What determines variation in phenotype (Pv)? (Pv=Genotypic variation Gv + environmental variation Ev)," we developed a 4-wk unit for an inquiry-based laboratory course focused on the inheritance and expression of a quantitative trait in varying environments. We utilized Brassica rapa Fast Plants as a model organism to study variation in the phenotype anthocyanin pigment intensity. As an initial curriculum assessment, we used free word association to examine students' cognitive structures before and after the unit and explanations in students' final research posters with particular focus on variation (Pv = Gv + Ev). Comparison of pre- and postunit word frequency revealed a shift in words and a pattern of co-occurring concepts indicative of change in cognitive structure, with particular focus on "variation" as a proposed threshold concept and primary goal for students' explanations. Given review of 53 posters, we found ∼50% of students capable of intermediate to high-level explanations combining both Gv and Ev influence on expression of anthocyanin intensity (Pv). While far from "plug and play," this conceptually rich, inquiry-based unit holds promise for effective integration of quantitative and Mendelian genetics. © 2014 J. M. Batzli et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Genetic transformation of an obligate anaerobe, P. gingivalis for FMN-green fluorescent protein expression in studying host-microbe interaction.

Directory of Open Access Journals (Sweden)

Chul Hee Choi

Full Text Available The recent introduction of "oxygen-independent" flavin mononucleotide (FMN-based fluorescent proteins (FbFPs is of major interest to both eukaryotic and prokaryotic microbial biologists. Accordingly, we demonstrate for the first time that an obligate anaerobe, the successful opportunistic pathogen of the oral cavity, Porphyromonas gingivalis, can be genetically engineered for expression of the non-toxic green FbFP. The resulting transformants are functional for studying dynamic bacterial processes in living host cells. The visualization of the transformed P. gingivalis (PgFbFP revealed strong fluorescence that reached a maximum emission at 495 nm as determined by fluorescence microscopy and spectrofluorometry. Human primary gingival epithelial cells (GECs were infected with PgFbFP and the bacterial invasion of host cells was analyzed by a quantitative fluorescence microscopy and antibiotic protection assays. The results showed similar levels of intracellular bacteria for both wild type and PgFbFP strains. In conjunction with organelle specific fluorescent dyes, utilization of the transformed strain provided direct and accurate determination of the live/metabolically active P. gingivalis' trafficking in the GECs over time. Furthermore, the GECs were co-infected with PgFbFP and the ATP-dependent Clp serine protease-deficient mutant (ClpP- to study the differential fates of the two strains within the same host cells. Quantitative co-localization analyses displayed the intracellular PgFbFP significantly associated with the endoplasmic reticulum network, whereas the majority of ClpP- organisms trafficked into the lysosomes. Hence, we have developed a novel and reliable method to characterize live host cell-microbe interactions and demonstrated the adaptability of FMN-green fluorescent protein for studying persistent host infections induced by obligate anaerobic organisms.

Genetic transformation of an obligate anaerobe, P. gingivalis for FMN-green fluorescent protein expression in studying host-microbe interaction.

Science.gov (United States)

Choi, Chul Hee; DeGuzman, Jefferson V; Lamont, Richard J; Yilmaz, Özlem

2011-04-15

The recent introduction of "oxygen-independent" flavin mononucleotide (FMN)-based fluorescent proteins (FbFPs) is of major interest to both eukaryotic and prokaryotic microbial biologists. Accordingly, we demonstrate for the first time that an obligate anaerobe, the successful opportunistic pathogen of the oral cavity, Porphyromonas gingivalis, can be genetically engineered for expression of the non-toxic green FbFP. The resulting transformants are functional for studying dynamic bacterial processes in living host cells. The visualization of the transformed P. gingivalis (PgFbFP) revealed strong fluorescence that reached a maximum emission at 495 nm as determined by fluorescence microscopy and spectrofluorometry. Human primary gingival epithelial cells (GECs) were infected with PgFbFP and the bacterial invasion of host cells was analyzed by a quantitative fluorescence microscopy and antibiotic protection assays. The results showed similar levels of intracellular bacteria for both wild type and PgFbFP strains. In conjunction with organelle specific fluorescent dyes, utilization of the transformed strain provided direct and accurate determination of the live/metabolically active P. gingivalis' trafficking in the GECs over time. Furthermore, the GECs were co-infected with PgFbFP and the ATP-dependent Clp serine protease-deficient mutant (ClpP-) to study the differential fates of the two strains within the same host cells. Quantitative co-localization analyses displayed the intracellular PgFbFP significantly associated with the endoplasmic reticulum network, whereas the majority of ClpP- organisms trafficked into the lysosomes. Hence, we have developed a novel and reliable method to characterize live host cell-microbe interactions and demonstrated the adaptability of FMN-green fluorescent protein for studying persistent host infections induced by obligate anaerobic organisms.

A Genome-Wide mQTL Analysis in Human Adipose Tissue Identifies Genetic Variants Associated with DNA Methylation, Gene Expression and Metabolic Traits

DEFF Research Database (Denmark)

Volkov, Petr; Olsson, Anders H; Gillberg, Linn

2016-01-01

Little is known about the extent to which interactions between genetics and epigenetics may affect the risk of complex metabolic diseases and/or their intermediary phenotypes. We performed a genome-wide DNA methylation quantitative trait locus (mQTL) analysis in human adipose tissue of 119 men, w...... and epigenetic variation in both cis and trans positions influencing gene expression in adipose tissue and in vivo (dys)metabolic traits associated with the development of obesity and diabetes.......Little is known about the extent to which interactions between genetics and epigenetics may affect the risk of complex metabolic diseases and/or their intermediary phenotypes. We performed a genome-wide DNA methylation quantitative trait locus (mQTL) analysis in human adipose tissue of 119 men......, where 592,794 single nucleotide polymorphisms (SNPs) were related to DNA methylation of 477,891 CpG sites, covering 99% of RefSeq genes. SNPs in significant mQTLs were further related to gene expression in adipose tissue and obesity related traits. We found 101,911 SNP-CpG pairs (mQTLs) in cis and 5...

On-Beads Digestion in Conjunction with Data-Dependent Mass Spectrometry: A Shortcut to Quantitative and Dynamic Interaction Proteomics

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Benedetta Turriziani

2014-04-01

Full Text Available With the advent of the “-omics” era, biological research has shifted from functionally analyzing single proteins to understanding how entire protein networks connect and adapt to environmental cues. Frequently, pathological processes are initiated by a malfunctioning protein network rather than a single protein. It is therefore crucial to investigate the regulation of proteins in the context of a pathway first and signaling network second. In this study, we demonstrate that a quantitative interaction proteomic approach, combining immunoprecipitation, in-solution digestion and label-free quantification mass spectrometry, provides data of high accuracy and depth. This protocol is applicable, both to tagged, exogenous and untagged, endogenous proteins. Furthermore, it is fast, reliable and, due to a label-free quantitation approach, allows the comparison of multiple conditions. We further show that we are able to generate data in a medium throughput fashion and that we can quantify dynamic interaction changes in signaling pathways in response to mitogenic stimuli, making our approach a suitable method to generate data for system biology approaches.

International collaborative study of the endogenous reference gene, sucrose phosphate synthase (SPS), used for qualitative and quantitative analysis of genetically modified rice.

Science.gov (United States)

Jiang, Lingxi; Yang, Litao; Zhang, Haibo; Guo, Jinchao; Mazzara, Marco; Van den Eede, Guy; Zhang, Dabing

2009-05-13

One rice ( Oryza sativa ) gene, sucrose phosphate synthase (SPS), has been proven to be a suitable endogenous reference gene for genetically modified (GM) rice detection in a previous study. Herein are the reported results of an international collaborative ring trial for validation of the SPS gene as an endogenous reference gene and its optimized qualitative and quantitative polymerase chain reaction (PCR) systems. A total of 12 genetically modified organism (GMO) detection laboratories from seven countries participated in the ring trial and returned their results. The validated results confirmed the species specificity of the method through testing 10 plant genomic DNAs, low heterogeneity, and a stable single-copy number of the rice SPS gene among 7 indica varieties and 5 japonica varieties. The SPS qualitative PCR assay was validated with a limit of detection (LOD) of 0.1%, which corresponded to about 230 copies of haploid rice genomic DNA, while the limit of quantification (LOQ) for the quantitative PCR system was about 23 copies of haploid rice genomic DNA, with acceptable PCR efficiency and linearity. Furthermore, the bias between the test and true values of eight blind samples ranged from 5.22 to 26.53%. Thus, we believe that the SPS gene is suitable for use as an endogenous reference gene for the identification and quantification of GM rice and its derivates.

Dominant Epistasis Between Two Quantitative Trait Loci Governing Sporulation Efficiency in Yeast Saccharomyces cerevisiae

Science.gov (United States)

Bergman, Juraj; Mitrikeski, Petar T.

2015-01-01

Summary Sporulation efficiency in the yeast Saccharomyces cerevisiae is a well-established model for studying quantitative traits. A variety of genes and nucleotides causing different sporulation efficiencies in laboratory, as well as in wild strains, has already been extensively characterised (mainly by reciprocal hemizygosity analysis and nucleotide exchange methods). We applied a different strategy in order to analyze the variation in sporulation efficiency of laboratory yeast strains. Coupling classical quantitative genetic analysis with simulations of phenotypic distributions (a method we call phenotype modelling) enabled us to obtain a detailed picture of the quantitative trait loci (QTLs) relationships underlying the phenotypic variation of this trait. Using this approach, we were able to uncover a dominant epistatic inheritance of loci governing the phenotype. Moreover, a molecular analysis of known causative quantitative trait genes and nucleotides allowed for the detection of novel alleles, potentially responsible for the observed phenotypic variation. Based on the molecular data, we hypothesise that the observed dominant epistatic relationship could be caused by the interaction of multiple quantitative trait nucleotides distributed across a 60--kb QTL region located on chromosome XIV and the RME1 locus on chromosome VII. Furthermore, we propose a model of molecular pathways which possibly underlie the phenotypic variation of this trait. PMID:27904371

Long-Range Regulatory Polymorphisms Affecting a GABA Receptor Constitute a Quantitative Trait Locus (QTL) for Social Behavior in Caenorhabditis elegans

Science.gov (United States)

Bendesky, Andres; Pitts, Jason; Rockman, Matthew V.; Chen, William C.; Tan, Man-Wah; Kruglyak, Leonid; Bargmann, Cornelia I.

2012-01-01

Aggregation is a social behavior that varies between and within species, providing a model to study the genetic basis of behavioral diversity. In the nematode Caenorhabditis elegans, aggregation is regulated by environmental context and by two neuromodulatory pathways, one dependent on the neuropeptide receptor NPR-1 and one dependent on the TGF-β family protein DAF-7. To gain further insight into the genetic regulation of aggregation, we characterize natural variation underlying behavioral differences between two wild-type C. elegans strains, N2 and CB4856. Using quantitative genetic techniques, including a survey of chromosome substitution strains and QTL analysis of recombinant inbred lines, we identify three new QTLs affecting aggregation in addition to the two known N2 mutations in npr-1 and glb-5. Fine-mapping with near-isogenic lines localized one QTL, accounting for 5%–8% of the behavioral variance between N2 and CB4856, 3′ to the transcript of the GABA neurotransmitter receptor gene exp-1. Quantitative complementation tests demonstrated that this QTL affects exp-1, identifying exp-1 and GABA signaling as new regulators of aggregation. exp-1 interacts genetically with the daf-7 TGF-β pathway, which integrates food availability and population density, and exp-1 mutations affect the level of daf-7 expression. Our results add to growing evidence that genetic variation affecting neurotransmitter receptor genes is a source of natural behavioral variation. PMID:23284308

Long-range regulatory polymorphisms affecting a GABA receptor constitute a quantitative trait locus (QTL for social behavior in Caenorhabditis elegans.

Directory of Open Access Journals (Sweden)

Andres Bendesky

Full Text Available Aggregation is a social behavior that varies between and within species, providing a model to study the genetic basis of behavioral diversity. In the nematode Caenorhabditis elegans, aggregation is regulated by environmental context and by two neuromodulatory pathways, one dependent on the neuropeptide receptor NPR-1 and one dependent on the TGF-β family protein DAF-7. To gain further insight into the genetic regulation of aggregation, we characterize natural variation underlying behavioral differences between two wild-type C. elegans strains, N2 and CB4856. Using quantitative genetic techniques, including a survey of chromosome substitution strains and QTL analysis of recombinant inbred lines, we identify three new QTLs affecting aggregation in addition to the two known N2 mutations in npr-1 and glb-5. Fine-mapping with near-isogenic lines localized one QTL, accounting for 5%-8% of the behavioral variance between N2 and CB4856, 3' to the transcript of the GABA neurotransmitter receptor gene exp-1. Quantitative complementation tests demonstrated that this QTL affects exp-1, identifying exp-1 and GABA signaling as new regulators of aggregation. exp-1 interacts genetically with the daf-7 TGF-β pathway, which integrates food availability and population density, and exp-1 mutations affect the level of daf-7 expression. Our results add to growing evidence that genetic variation affecting neurotransmitter receptor genes is a source of natural behavioral variation.

Genetic and chromosomal effects of ionizing radiation

International Nuclear Information System (INIS)

Anon.

1981-01-01

The genetic and chromosomal effects of ionizing radiations deal with those effects in the descendants of the individuals irradiated. The information base concerning genetic and chromosomal injury to humans from radiation is less adequate than is the information base for cancer and leukemia. As a result, it is not possible to make the kinds of quantitative estimates that have been made for carcinogenesis in previous chapters of this book. The chapter includes a detailed explanation of various types of genetic injuries such as chromosomal diseases, x-linked diseases, autosomal dominant diseases, recessive diseases, and irregularly inherited diseases. Quantitative estimates of mutation rates and incidences are given based on atomic bomb survivors data

Good genes, genetic compatibility and the evolution of polyandry: use of the diallel cross to address competing hypotheses.

Science.gov (United States)

Ivy, T M

2007-03-01

Genetic benefits can enhance the fitness of polyandrous females through the high intrinsic genetic quality of females' mates or through the interaction between female and male genes. I used a full diallel cross, a quantitative genetics design that involves all possible crosses among a set of genetically homogeneous lines, to determine the mechanism through which polyandrous female decorated crickets (Gryllodes sigillatus) obtain genetic benefits. I measured several traits related to fitness and partitioned the phenotypic variance into components representing the contribution of additive genetic variance ('good genes'), nonadditive genetic variance (genetic compatibility), as well as maternal and paternal effects. The results reveal a significant variance attributable to both nonadditive and additive sources in the measured traits, and their influence depended on which trait was considered. The lack of congruence in sources of phenotypic variance among these fitness-related traits suggests that the evolution and maintenance of polyandry are unlikely to have resulted from one selective influence, but rather are the result of the collective effects of a number of factors.

Sir RA Fisher and the Evolution of Genetics

Indian Academy of Sciences (India)

quantitative genetics, a body of work that provides the conceptual underpinnings ... worked extensively with mutations that had large effects on structural ... altering the genetic composition of populations during adaptive ... exercise in ' writing.

Relationship of the Interaction Between Two Quantitative Trait Loci with γ-Globin Expression in β-Thalassemia Intermedia Patients.

Science.gov (United States)

NickAria, Shiva; Haghpanah, Sezaneh; Ramzi, Mani; Karimi, Mehran

2018-05-10

Globin switching is a significant factor on blood hemoglobin (Hb) level but its molecular mechanisms have not yet been identified, however, several quantitative trait loci (QTL) and polymorphisms involved regions on chromosomes 2p, 6q, 8q and X account for variation in the γ-globin expression level. We studied the effect of interaction between a region on intron six of the TOX gene, chromosome 8q (chr8q) and XmnI locus on the γ-globin promoter, chr11p on γ-globin expression in 150 β-thalassemia intermedia (β-TI) patients, evaluated by statistical interaction analysis. Our results showed a significant interaction between one QTL on intron six of the TOX gene (rs9693712) and XmnI locus that effect γ-globin expression. Interchromosomal interaction mediates through transcriptional machanisms to preserve true genome architectural features, chromosomes localization and DNA bending. This interaction can be a part of the unknown molecular mechanism of globin switching and regulation of gene expression.

Interaction of dietary and genetic factors influencing body iron status and risk of type 2 diabetes within the EPIC-InterAct study

NARCIS (Netherlands)

Meidtner, Karina; Podmore, Clara; Kröger, Janine; van der Schouw, Yvonne T; Bendinelli, Benedetta; Agnoli, Claudia; Arriola, Larraitz; Barricarte, Aurelio; Boeing, Heiner; Cross, Amanda J.; Dow, Courtney; Ekblom, Kim; Fagherazzi, Guy; Franks, Paul W.; Gunter, Marc J.; Huerta, José María; Jakszyn, Paula; Jenab, Mazda; Katzke, Verena A.; Key, Timothy J.; Khaw, Kay Tee; Kühn, Tilman; Kyrø, Cecilie; Mancini, Francesca Romana; Melander, Olle; Nilsson, Peter M.; Overvad, Kim; Palli, Domenico; Panico, Salvatore; Quirós, José Ramón; Rodriguez-Barranco, Miguel; Sacerdote, Carlotta; Sluijs, Ivonne; Stepien, Magdalena; Tjonneland, Anne; Tumino, Rosario; Forouhi, Nita G.; Sharp, Stephen J.; Langenberg, Claudia; Schulze, Matthias B.; Riboli, Elio; Wareham, Nicholas J.

2018-01-01

© 2017 by the American Diabetes Association. OBJECTIVE Meat intake has been consistently shown to be positively associated with incident type 2 diabetes. Part of that association may be mediated by body iron status, which is influenced by genetic factors. We aimed to test for interactions of genetic

Weak interactions from 1950-1960: a quantitative bibliometric study of the formation of a field

International Nuclear Information System (INIS)

White, D.H.; Sullivan, D.

1986-01-01

A quantitative technique is illustrated which uses publication statistics from a bibliography of citations in the area of weak interactions to provide a view of trends and patterns in the development of the field during the period from 1950 to 1960. An overview is given of what the physicists working in weak interactions during this period were doing as indicated by an analysis of the subjects of their papers. The dominant problems and concerns are discussed. Focus is then turned to the events surrounding the emergence of the tau/theta particle puzzle, the discovery of parity nonconservation, and the resolution offered by the V-A theory. Displaying the data from the citation index in unusual ways highlights dominant issues of the period, especially the close relationship between theory and experiment in the latter half of the decade. 64 refs., 14 figs

A Comparison of Telephone Genetic Counseling and In-Person Genetic Counseling from the Genetic Counselor's Perspective.

Science.gov (United States)

Burgess, Kelly R; Carmany, Erin P; Trepanier, Angela M

2016-02-01

Growing demand for and limited geographic access to genetic counseling services is increasing the need for alternative service delivery models (SDM) like telephone genetic counseling (TGC). Little research has been done on genetic counselors' perspectives of the practice of TGC. We created an anonymous online survey to assess whether telephone genetic counselors believed the tasks identified in the ABGC (American Board of Genetic Counseling) Practice Analysis were performed similarly or differently in TGC compared to in person genetic counseling (IPGC). If there were differences noted, we sought to determine the nature of the differences and if additional training might be needed to address them. Eighty eight genetic counselors with experience in TGC completed some or all of the survey. Respondents identified differences in 13 (14.8%) of the 88 tasks studied. The tasks identified as most different in TGC were: "establishing rapport through verbal and nonverbal interactions" (60.2%; 50/83 respondents identified the task as different), "recognizing factors affecting the counseling interaction" (47.8%; 32/67), "assessing client/family emotions, support, etc." (40.1%; 27/66) and "educating clients about basic genetic concepts" (35.6%; 26/73). A slight majority (53.8%; 35/65) felt additional training was needed to communicate information without visual aids and more effectively perform psychosocial assessments. In summary, although a majority of genetic counseling tasks are performed similarly between TGC and IPGC, TGC counselors recognize that specific training in the TGC model may be needed to address the key differences.

Genetic risks of ionizing radiation

International Nuclear Information System (INIS)

Sankaranarayanan, K.

1990-01-01

Quantitative genetic risk estimation is made using two methods: the direct method, and the doubling dose (DD) method. The doubling dose currently used is 1 Gy for low LET, low dose, low dose rate irradiation, and is based on mouse data. Tables present the 1988 UNSCEAR estimates of genetic risk using both methods. (L.L.) (Tab.)

Exploring genetic suppression interactions on a global scale.

Science.gov (United States)

van Leeuwen, Jolanda; Pons, Carles; Mellor, Joseph C; Yamaguchi, Takafumi N; Friesen, Helena; Koschwanez, John; Ušaj, Mojca Mattiazzi; Pechlaner, Maria; Takar, Mehmet; Ušaj, Matej; VanderSluis, Benjamin; Andrusiak, Kerry; Bansal, Pritpal; Baryshnikova, Anastasia; Boone, Claire E; Cao, Jessica; Cote, Atina; Gebbia, Marinella; Horecka, Gene; Horecka, Ira; Kuzmin, Elena; Legro, Nicole; Liang, Wendy; van Lieshout, Natascha; McNee, Margaret; San Luis, Bryan-Joseph; Shaeri, Fatemeh; Shuteriqi, Ermira; Sun, Song; Yang, Lu; Youn, Ji-Young; Yuen, Michael; Costanzo, Michael; Gingras, Anne-Claude; Aloy, Patrick; Oostenbrink, Chris; Murray, Andrew; Graham, Todd R; Myers, Chad L; Andrews, Brenda J; Roth, Frederick P; Boone, Charles

2016-11-04

Genetic suppression occurs when the phenotypic defects caused by a mutation in a particular gene are rescued by a mutation in a second gene. To explore the principles of genetic suppression, we examined both literature-curated and unbiased experimental data, involving systematic genetic mapping and whole-genome sequencing, to generate a large-scale suppression network among yeast genes. Most suppression pairs identified novel relationships among functionally related genes, providing new insights into the functional wiring diagram of the cell. In addition to suppressor mutations, we identified frequent secondary mutations,in a subset of genes, that likely cause a delay in the onset of stationary phase, which appears to promote their enrichment within a propagating population. These findings allow us to formulate and quantify general mechanisms of genetic suppression. Copyright © 2016, American Association for the Advancement of Science.

Simplification of genotyping techniques of the ABO blood type experiment and exploration of population genetics.

Science.gov (United States)

Hu, Jian; Zhou, Yi-ren; Ding, Jia-lin; Wang, Zhi-yuan; Liu, Ling; Wang, Ye-kai; Lou, Hui-ling; Qiao, Shou-yi; Wu, Yan-hua

2017-05-20

The ABO blood type is one of the most common and widely used genetic traits in humans. Three glycosyltransferase-encoding gene alleles, I A , I B and i, produce three red blood cell surface antigens, by which the ABO blood type is classified. By using the ABO blood type experiment as an ideal case for genetics teaching, we can easily introduce to the students several genetic concepts, including multiple alleles, gene interaction, single nucleotide polymorphism (SNP) and gene evolution. Herein we have innovated and integrated our ABO blood type genetics experiments. First, in the section of Molecular Genetics, a new method of ABO blood genotyping was established: specific primers based on SNP sites were designed to distinguish three alleles through quantitative real-time PCR. Next, the experimental teaching method of Gene Evolution was innovated in the Population Genetics section: a gene-evolution software was developed to simulate the evolutionary tendency of the ABO genotype encoding alleles under diverse conditions. Our reform aims to extend the contents of genetics experiments, to provide additional teaching approaches, and to improve the learning efficiency of our students eventually.

Partial genetic deletion of neuregulin 1 and adolescent stress interact to alter NMDA receptor binding in the medial prefrontal cortex

Directory of Open Access Journals (Sweden)

Tariq Waseem Chohan

2014-09-01

Full Text Available Schizophrenia is thought to arise due to a complex interaction between genetic and environmental factors during early neurodevelopment. We have recently shown that partial genetic deletion of the schizophrenia susceptibility gene neuregulin 1 (Nrg1 and adolescent stress interact to disturb sensorimotor gating, neuroendocrine activity and dendritic morphology in mice. Both stress and Nrg1 may have converging effects upon N-methyl-D-aspartate receptors (NMDARs which are implicated in the pathogenesis of schizophrenia, sensorimotor gating and dendritic spine plasticity. Using an identical repeated restraint stress paradigm to our previous study, here we determined NMDAR binding across various brain regions in adolescent Nrg1 heterozygous (HET and wild-type (WT mice using [3H] MK-801 autoradiography. Repeated restraint stress increased NMDAR binding in the ventral part of the lateral septum (LSV and the dentate gyrus (DG of the hippocampus irrespective of genotype. Partial genetic deletion of Nrg1 interacted with adolescent stress to promote an altered pattern of NMDAR binding in the infralimbic (IL subregion of the medial prefrontal cortex. In the IL, whilst stress tended to increase NMDAR binding in WT mice, it decreased binding in Nrg1 HET mice. However in the DG, stress selectively increased the expression of NMDAR binding in Nrg1 HET mice but not WT mice. These results demonstrate a Nrg1-stress interaction during adolescence on NMDAR binding in the medial prefrontal cortex.

[Genetics and epigenetics in autism].

Science.gov (United States)

Nakayama, Atsuo; Masaki, Shiego; Aoki, Eiko

2006-11-01

Autism is a behaviorally defined syndrome characterized by impaired social interaction and communication, and restricted, stereotyped interests and behaviors. Several lines of evidence support the contention that genetic factors are a large component to autism etiology. However, in spite of vigorous genetic studies, no single causative or susceptibility gene common in autism has been identified. Thus multiple susceptibility genes in interaction are considered to account for the disorder. Furthermore, environmental risk factors can accelerate the autism development of. Recent advances in understanding the epigenetic regulation may shed light on the interaction among multiple genetic factors and environmental factors.

Unique genetic loci identified for emotional behavior in control and chronic stress conditions.

Directory of Open Access Journals (Sweden)

Kimberly AK Carhuatanta

2014-10-01

Full Text Available An individual’s genetic background affects their emotional behavior and response to stress. Although studies have been conducted to identify genetic predictors for emotional behavior or stress response, it remains unknown how prior stress history alters the interaction between an individual’s genome and their emotional behavior. Therefore, the purpose of this study is to identify chromosomal regions that affect emotional behavior and are sensitive to stress exposure. We utilized the BXD behavioral genetics mouse model to identify chromosomal regions that predict fear learning and emotional behavior following exposure to a control or chronic stress environment. 62 BXD recombinant inbred strains and C57BL/6 and DBA/2 parental strains underwent behavioral testing including a classical fear conditioning paradigm and the elevated plus maze. Distinct quantitative trait loci (QTLs were identified for emotional learning, anxiety and locomotion in control and chronic stress populations. Candidate genes, including those with already known functions in learning and stress were found to reside within the identified QTLs. Our data suggest that chronic stress history reveals novel genetic predictors of emotional behavior.

Neighborhood alcohol outlet density and genetic influences on alcohol use: evidence for gene-environment interaction.

Science.gov (United States)

Slutske, Wendy S; Deutsch, Arielle R; Piasecki, Thomas M

2018-05-07

Genetic influences on alcohol involvement are likely to vary as a function of the 'alcohol environment,' given that exposure to alcohol is a necessary precondition for genetic risk to be expressed. However, few gene-environment interaction studies of alcohol involvement have focused on characteristics of the community-level alcohol environment. The goal of this study was to examine whether living in a community with more alcohol outlets would facilitate the expression of the genetic propensity to drink in a genetically-informed national survey of United States young adults. The participants were 2434 18-26-year-old twin, full-, and half-sibling pairs from Wave III of the National Longitudinal Study of Adolescent to Adult Health. Participants completed in-home interviews in which alcohol use was assessed. Alcohol outlet densities were extracted from state-level liquor license databases aggregated at the census tract level to derive the density of outlets. There was evidence that the estimates of genetic and environmental influences on alcohol use varied as a function of the density of alcohol outlets in the community. For example, the heritability of the frequency of alcohol use for those residing in a neighborhood with ten or more outlets was 74% (95% confidence limits = 55-94%), compared with 16% (95% confidence limits = 0-34%) for those in a neighborhood with zero outlets. This moderating effect of alcohol outlet density was not explained by the state of residence, population density, or neighborhood sociodemographic characteristics. The results suggest that living in a neighborhood with many alcohol outlets may be especially high-risk for those individuals who are genetically predisposed to frequently drink.

Mapping genetic factors controlling potato - cyst nematode interactions

NARCIS (Netherlands)

Rouppe van der Voort, J.N.A.M.

1998-01-01

The thesis describes strategies for genetic mapping of the genomes of the potato cyst nematode and potato. Mapping in cyst nematodes was achieved by AFLP genotyping of single cysts and subsequent segregation analysis in a family of sibling populations. The genetic map of Globodera