Eco-genetic modeling of contemporary life-history evolution.

Science.gov (United States)

Dunlop, Erin S; Heino, Mikko; Dieckmann, Ulf

2009-10-01

We present eco-genetic modeling as a flexible tool for exploring the course and rates of multi-trait life-history evolution in natural populations. We build on existing modeling approaches by combining features that facilitate studying the ecological and evolutionary dynamics of realistically structured populations. In particular, the joint consideration of age and size structure enables the analysis of phenotypically plastic populations with more than a single growth trajectory, and ecological feedback is readily included in the form of density dependence and frequency dependence. Stochasticity and life-history trade-offs can also be implemented. Critically, eco-genetic models permit the incorporation of salient genetic detail such as a population's genetic variances and covariances and the corresponding heritabilities, as well as the probabilistic inheritance and phenotypic expression of quantitative traits. These inclusions are crucial for predicting rates of evolutionary change on both contemporary and longer timescales. An eco-genetic model can be tightly coupled with empirical data and therefore may have considerable practical relevance, in terms of generating testable predictions and evaluating alternative management measures. To illustrate the utility of these models, we present as an example an eco-genetic model used to study harvest-induced evolution of multiple traits in Atlantic cod. The predictions of our model (most notably that harvesting induces a genetic reduction in age and size at maturation, an increase or decrease in growth capacity depending on the minimum-length limit, and an increase in reproductive investment) are corroborated by patterns observed in wild populations. The predicted genetic changes occur together with plastic changes that could phenotypically mask the former. Importantly, our analysis predicts that evolutionary changes show little signs of reversal following a harvest moratorium. This illustrates how predictions offered by

Population genetics models of local ancestry.

Science.gov (United States)

Gravel, Simon

2012-06-01

Migrations have played an important role in shaping the genetic diversity of human populations. Understanding genomic data thus requires careful modeling of historical gene flow. Here we consider the effect of relatively recent population structure and gene flow and interpret genomes of individuals that have ancestry from multiple source populations as mosaics of segments originating from each population. This article describes general and tractable models for local ancestry patterns with a focus on the length distribution of continuous ancestry tracts and the variance in total ancestry proportions among individuals. The models offer improved agreement with Wright-Fisher simulation data when compared to the state-of-the art and can be used to infer time-dependent migration rates from multiple populations. Considering HapMap African-American (ASW) data, we find that a model with two distinct phases of "European" gene flow significantly improves the modeling of both tract lengths and ancestry variances.

[Application of Multiple Genetic Markers in a Case of Determination of Half Sibling].

Science.gov (United States)

Yang, Xue; Shi, Mei-sen; Yuan, Li; Lu, Di

2016-02-01

A case of half sibling was determined with multiple genetic markers, which could be potentially applied for determination of half sibling relationship from same father. Half sibling relationship was detected by 39 autosomal STR genetic markers, 23 Y-chromosomal STR genetic markers and 12 X -chromosomal STR genetic markers among ZHAO -1, ZHAO -2, ZHAO -3, ZHAO -4, and ZHAO-5. According to autosomal STR, Y-STR and X-STR genotyping results, it was determined that ZHAO-4 (alleged half sibling) was unrelated with ZHAO-1 and ZHAO-2; however, ZHAO-3 (alleged half sibling), ZHAO-5 (alleged half sibling) shared same genetic profile with ZHAO-1, and ZHAO-2 from same father. It is reliable to use multiple genetic markers and family gene reconstruction to determine half sibling relationship from same father, but it is difficult to determination by calculating half sibling index with ITO and discriminant functions.

Model selection with multiple regression on distance matrices leads to incorrect inferences.

Directory of Open Access Journals (Sweden)

Ryan P Franckowiak

Full Text Available In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC, its small-sample correction (AICc, and the Bayesian information criterion (BIC to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.

The genetics of multiple sclerosis: review of current and emerging candidates

Directory of Open Access Journals (Sweden)

Muñoz-Culla M

2013-08-01

Full Text Available Maider Muñoz-Culla,1,2 Haritz Irizar,1,2 David Otaegui1,2 1Multiple Sclerosis Unit, Instituto Biodonostia, San Sebastián, Spain; 2Red Española de Esclerosis Múltiple (REEM, Barcelona, Spain Abstract: Multiple sclerosis (MS is a complex disease in which environmental, genetic, and epigenetic factors determine the risk of developing the disease. The human leukocyte antigen region is the strongest susceptibility locus linked to MS, but it does not explain the whole heritability of the disease. To find other non-human leukocyte antigen loci associated with the disease, high-throughput genotyping, sequencing, and gene-expression studies have been performed, producing a valuable quantity of information. An overview of the genomic and expression studies is provided in this review, as well as microRNA-expression studies, highlighting the importance of combining all the layers of information in order to elucidate the causes or pathological mechanisms occurring in the disease. Genetics in MS is a promising field that is presumably going to be very productive in the next decade understanding the cross talk between all the factors contributing to the development of MS. Keywords: multiple sclerosis, genetics, gene expression, microRNA

Behavioral phenotypes in schizophrenic animal models with multiple combinations of genetic and environmental factors.

Science.gov (United States)

Hida, Hirotake; Mouri, Akihiro; Noda, Yukihiro

2013-01-01

Schizophrenia is a multifactorial psychiatric disorder in which both genetic and environmental factors play a role. Genetic [e.g., Disrupted-in-schizophrenia 1 (DISC1), Neuregulin-1 (NRG1)] and environmental factors (e.g., maternal viral infection, obstetric complications, social stress) may act during the developmental period to increase the incidence of schizophrenia. In animal models, interactions between susceptibility genes and the environment can be controlled in ways not possible in humans; therefore, such models are useful for investigating interactions between or within factors in the pathogenesis and pathophysiology of schizophrenia. We provide an overview of schizophrenic animal models investigating interactions between or within factors. First, we reviewed gene-environment interaction animal models, in which schizophrenic candidate gene mutant mice were subjected to perinatal immune activation or adolescent stress. Next, environment-environment interaction animal models, in which mice were subjected to a combination of perinatal immune activation and adolescent administration of drugs, were described. These animal models showed interaction between or within factors; behavioral changes, which were obscured by each factor, were marked by interaction of factors and vice versa. Appropriate behavioral approaches with such models will be invaluable for translational research on novel compounds, and also for providing insight into the pathogenesis and pathophysiology of schizophrenia.

Evaluation of genetically inactivated alpha toxin for protection in multiple mouse models of Staphylococcus aureus infection.

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Rebecca A Brady

Full Text Available Staphylococcus aureus is a major human pathogen and a leading cause of nosocomial and community-acquired infections. Development of a vaccine against this pathogen is an important goal. While S. aureus protective antigens have been identified in the literature, the majority have only been tested in a single animal model of disease. We wished to evaluate the ability of one S. aureus vaccine antigen to protect in multiple mouse models, thus assessing whether protection in one model translates to protection in other models encompassing the full breadth of infections the pathogen can cause. We chose to focus on genetically inactivated alpha toxin mutant HlaH35L. We evaluated the protection afforded by this antigen in three models of infection using the same vaccine dose, regimen, route of immunization, adjuvant, and challenge strain. When mice were immunized with HlaH35L and challenged via a skin and soft tissue infection model, HlaH35L immunization led to a less severe infection and decreased S. aureus levels at the challenge site when compared to controls. Challenge of HlaH35L-immunized mice using a systemic infection model resulted in a limited, but statistically significant decrease in bacterial colonization as compared to that observed with control mice. In contrast, in a prosthetic implant model of chronic biofilm infection, there was no significant difference in bacterial levels when compared to controls. These results demonstrate that vaccines may confer protection against one form of S. aureus disease without conferring protection against other disease presentations and thus underscore a significant challenge in S. aureus vaccine development.

Multiple Genetic Associations with Irish Wolfhound Dilated Cardiomyopathy.

Science.gov (United States)

Simpson, Siobhan; Dunning, Mark D; Brownlie, Serena; Patel, Janika; Godden, Megan; Cobb, Malcolm; Mongan, Nigel P; Rutland, Catrin S

2016-01-01

Cardiac disease is a leading cause of morbidity and mortality in dogs and humans, with dilated cardiomyopathy being a large contributor to this. The Irish Wolfhound (IWH) is one of the most commonly affected breeds and one of the few breeds with genetic loci associated with the disease. Mutations in more than 50 genes are associated with human dilated cardiomyopathy (DCM), yet very few are also associated with canine DCM. Furthermore, none of the identified canine loci explain many cases of the disease and previous work has indicated that genotypes at multiple loci may act together to influence disease development. In this study, loci previously associated with DCM in IWH were tested for associations in a new cohort both individually and in combination. We have identified loci significantly associated with the disease individually, but no genotypes individually or in pairs conferred a significantly greater risk of developing DCM than the population risk. However combining three loci together did result in the identification of a genotype which conferred a greater risk of disease than the overall population risk. This study suggests multiple rather than individual genetic factors, cooperating to influence DCM risk in IWH.

Multiple Genetic Associations with Irish Wolfhound Dilated Cardiomyopathy

Directory of Open Access Journals (Sweden)

Siobhan Simpson

2016-01-01

Full Text Available Cardiac disease is a leading cause of morbidity and mortality in dogs and humans, with dilated cardiomyopathy being a large contributor to this. The Irish Wolfhound (IWH is one of the most commonly affected breeds and one of the few breeds with genetic loci associated with the disease. Mutations in more than 50 genes are associated with human dilated cardiomyopathy (DCM, yet very few are also associated with canine DCM. Furthermore, none of the identified canine loci explain many cases of the disease and previous work has indicated that genotypes at multiple loci may act together to influence disease development. In this study, loci previously associated with DCM in IWH were tested for associations in a new cohort both individually and in combination. We have identified loci significantly associated with the disease individually, but no genotypes individually or in pairs conferred a significantly greater risk of developing DCM than the population risk. However combining three loci together did result in the identification of a genotype which conferred a greater risk of disease than the overall population risk. This study suggests multiple rather than individual genetic factors, cooperating to influence DCM risk in IWH.

Drag reduction of a car model by linear genetic programming control

Science.gov (United States)

Li, Ruiying; Noack, Bernd R.; Cordier, Laurent; Borée, Jacques; Harambat, Fabien

2017-08-01

We investigate open- and closed-loop active control for aerodynamic drag reduction of a car model. Turbulent flow around a blunt-edged Ahmed body is examined at ReH≈ 3× 105 based on body height. The actuation is performed with pulsed jets at all trailing edges (multiple inputs) combined with a Coanda deflection surface. The flow is monitored with 16 pressure sensors distributed at the rear side (multiple outputs). We apply a recently developed model-free control strategy building on genetic programming in Dracopoulos and Kent (Neural Comput Appl 6:214-228, 1997) and Gautier et al. (J Fluid Mech 770:424-441, 2015). The optimized control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback including also time-history information feedback and combinations thereof. Key enabler is linear genetic programming (LGP) as powerful regression technique for optimizing the multiple-input multiple-output control laws. The proposed LGP control can select the best open- or closed-loop control in an unsupervised manner. Approximately 33% base pressure recovery associated with 22% drag reduction is achieved in all considered classes of control laws. Intriguingly, the feedback actuation emulates periodic high-frequency forcing. In addition, the control identified automatically the only sensor which listens to high-frequency flow components with good signal to noise ratio. Our control strategy is, in principle, applicable to all multiple actuators and sensors experiments.

Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

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Avval Zhila Mohajeri

2015-01-01

Full Text Available This paper deals with developing a linear quantitative structure-activity relationship (QSAR model for predicting the RSK inhibition activity of some new compounds. A dataset consisting of 62 pyrazino [1,2-α] indole, diazepino [1,2-α] indole, and imidazole derivatives with known inhibitory activities was used. Multiple linear regressions (MLR technique combined with the stepwise (SW and the genetic algorithm (GA methods as variable selection tools was employed. For more checking stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the results obtained, indicate that the GA-MLR model is superior to the SW-MLR model and that it isapplicable for designing novel RSK inhibitors.

Genomic multiple sequence alignments: refinement using a genetic algorithm

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Lefkowitz Elliot J

2005-08-01

Full Text Available Abstract Background Genomic sequence data cannot be fully appreciated in isolation. Comparative genomics – the practice of comparing genomic sequences from different species – plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a genetic algorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a genetic algorithm. Because a better COFFEE (Consistency based Objective Function For alignmEnt Evaluation score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the genetic algorithm, GenAlignRefine was implemented as a parallel, cluster-based program. Results We tested the GenAlignRefine algorithm by running it on a Linux cluster to refine sequences from a simulation, as well as refine a multiple alignment of 15 Orthopoxvirus genomic sequences approximately 260,000 nucleotides in length that initially had been aligned by Multi-LAGAN. It took approximately 150 minutes for a 40-processor Linux cluster to optimize some 200 fuzzy (poorly aligned regions of the orthopoxvirus alignment. Overall sequence identity increased only

Uncovering the genetic landscape for multiple sleep-wake traits.

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Christopher J Winrow

Full Text Available Despite decades of research in defining sleep-wake properties in mammals, little is known about the nature or identity of genes that regulate sleep, a fundamental behaviour that in humans occupies about one-third of the entire lifespan. While genome-wide association studies in humans and quantitative trait loci (QTL analyses in mice have identified candidate genes for an increasing number of complex traits and genetic diseases, the resources and time-consuming process necessary for obtaining detailed quantitative data have made sleep seemingly intractable to similar large-scale genomic approaches. Here we describe analysis of 20 sleep-wake traits from 269 mice from a genetically segregating population that reveals 52 significant QTL representing a minimum of 20 genomic loci. While many (28 QTL affected a particular sleep-wake trait (e.g., amount of wake across the full 24-hr day, other loci only affected a trait in the light or dark period while some loci had opposite effects on the trait during the light vs. dark. Analysis of a dataset for multiple sleep-wake traits led to previously undetected interactions (including the differential genetic control of number and duration of REM bouts, as well as possible shared genetic regulatory mechanisms for seemingly different unrelated sleep-wake traits (e.g., number of arousals and REM latency. Construction of a Bayesian network for sleep-wake traits and loci led to the identification of sub-networks of linkage not detectable in smaller data sets or limited single-trait analyses. For example, the network analyses revealed a novel chain of causal relationships between the chromosome 17@29cM QTL, total amount of wake, and duration of wake bouts in both light and dark periods that implies a mechanism whereby overall sleep need, mediated by this locus, in turn determines the length of each wake bout. Taken together, the present results reveal a complex genetic landscape underlying multiple sleep-wake traits

An Efficient Stepwise Statistical Test to Identify Multiple Linked Human Genetic Variants Associated with Specific Phenotypic Traits.

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Iksoo Huh

Full Text Available Recent advances in genotyping methodologies have allowed genome-wide association studies (GWAS to accurately identify genetic variants that associate with common or pathological complex traits. Although most GWAS have focused on associations with single genetic variants, joint identification of multiple genetic variants, and how they interact, is essential for understanding the genetic architecture of complex phenotypic traits. Here, we propose an efficient stepwise method based on the Cochran-Mantel-Haenszel test (for stratified categorical data to identify causal joint multiple genetic variants in GWAS. This method combines the CMH statistic with a stepwise procedure to detect multiple genetic variants associated with specific categorical traits, using a series of associated I × J contingency tables and a null hypothesis of no phenotype association. Through a new stratification scheme based on the sum of minor allele count criteria, we make the method more feasible for GWAS data having sample sizes of several thousands. We also examine the properties of the proposed stepwise method via simulation studies, and show that the stepwise CMH test performs better than other existing methods (e.g., logistic regression and detection of associations by Markov blanket for identifying multiple genetic variants. Finally, we apply the proposed approach to two genomic sequencing datasets to detect linked genetic variants associated with bipolar disorder and obesity, respectively.

Graphical models for genetic analyses

DEFF Research Database (Denmark)

Lauritzen, Steffen Lilholt; Sheehan, Nuala A.

2003-01-01

This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separate areas...... of graphical models and genetics. The potential of graphical models is explored and illustrated through a number of example applications where the genetic element is substantial or dominating....

Structural model analysis of multiple quantitative traits.

Directory of Open Access Journals (Sweden)

Renhua Li

2006-07-01

Full Text Available We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate the technique using body composition and bone density data from mouse intercross populations. Using these techniques we are able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research. The identification of causal networks sheds light on the nature of genetic heterogeneity and pleiotropy in complex genetic systems.

Impact of genetic risk loci for multiple sclerosis on expression of proximal genes in patients

KAUST Repository

James, Tojo; Lindé n, Magdalena; Morikawa, Hiromasa; Fernandes, Sunjay Jude; Ruhrmann, Sabrina; Huss, Mikael; Brandi, Maya; Piehl, Fredrik; Jagodic, Maja; Tegner, Jesper; Khademi, Mohsen; Olsson, Tomas; Gomez-Cabrero, David; Kockum, Ingrid

2018-01-01

Despite advancements in genetic studies, it is difficult to understand and characterize the functional relevance of disease-associated genetic variants, especially in the context of a complex multifactorial disease such as Multiple Sclerosis (MS

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

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Magali Michaut

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

Comparing ESC and iPSC?Based Models for Human Genetic Disorders

OpenAIRE

Halevy, Tomer; Urbach, Achia

2014-01-01

Traditionally, human disorders were studied using animal models or somatic cells taken from patients. Such studies enabled the analysis of the molecular mechanisms of numerous disorders, and led to the discovery of new treatments. Yet, these systems are limited or even irrelevant in modeling multiple genetic diseases. The isolation of human embryonic stem cells (ESCs) from diseased blastocysts, the derivation of induced pluripotent stem cells (iPSCs) from patients’ somatic cells, and the ne...

Multiple-trait genetic evaluation using genomic matrix

African Journals Online (AJOL)

Jane

2011-07-06

Jul 6, 2011 ... relationships was estimated through computer simulation and was compared with the accuracy of ... programs, detect animals with superior genetic and select ... genomic matrices in the mixed model equations of BLUP.

[Analysis of genetic models and gene effects on main agronomy characters in rapeseed].

Science.gov (United States)

Li, J; Qiu, J; Tang, Z; Shen, L

1992-01-01

According to four different genetic models, the genetic patterns of 8 agronomy traits were analysed by using the data of 24 generations which included positive and negative cross of 81008 x Tower, both of the varieties are of good quality. The results showed that none of 8 characters could fit in with additive-dominance models. Epistasis was found in all of these characters, and it has significant effect on generation means. Seed weight/plant and some other main yield characters are controlled by duplicate interaction genes. The interaction between triple genes or multiple genes needs to be utilized in yield heterosis.

Volatile terpenoids: multiple functions, biosynthesis, modulation and manipulation by genetic engineering.

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Abbas, Farhat; Ke, Yanguo; Yu, Rangcai; Yue, Yuechong; Amanullah, Sikandar; Jahangir, Muhammad Muzammil; Fan, Yanping

2017-11-01

Terpenoids play several physiological and ecological functions in plant life through direct and indirect plant defenses and also in human society because of their enormous applications in the pharmaceutical, food and cosmetics industries. Through the aid of genetic engineering its role can by magnified to broad spectrum by improving genetic ability of crop plants, enhancing the aroma quality of fruits and flowers and the production of pharmaceutical terpenoids contents in medicinal plants. Terpenoids are structurally diverse and the most abundant plant secondary metabolites, playing an important role in plant life through direct and indirect plant defenses, by attracting pollinators and through different interactions between the plants and their environment. Terpenoids are also significant because of their enormous applications in the pharmaceutical, food and cosmetics industries. Due to their broad distribution and functional versatility, efforts are being made to decode the biosynthetic pathways and comprehend the regulatory mechanisms of terpenoids. This review summarizes the recent advances in biosynthetic pathways, including the spatiotemporal, transcriptional and post-transcriptional regulatory mechanisms. Moreover, we discuss the multiple functions of the terpene synthase genes (TPS), their interaction with the surrounding environment and the use of genetic engineering for terpenoid production in model plants. Here, we also provide an overview of the significance of terpenoid metabolic engineering in crop protection, plant reproduction and plant metabolic engineering approaches for pharmaceutical terpenoids production and future scenarios in agriculture, which call for sustainable production platforms by improving different plant traits.

Genetic diversity and population structure of Lantana camara in India indicates multiple introductions and gene flow.

Science.gov (United States)

Ray, A; Quader, S

2014-05-01

Lantana camara is a highly invasive plant, which has spread over 60 countries and island groups of Asia, Africa and Australia. In India, it was introduced in the early nineteenth century, since when it has expanded and gradually established itself in almost every available ecosystem. We investigated the genetic diversity and population structure of this plant in India in order to understand its introduction, subsequent range expansion and gene flow. A total of 179 individuals were sequenced at three chloroplast loci and 218 individuals were genotyped for six nuclear microsatellites. Both chloroplasts (nine haplotypes) and microsatellites (83 alleles) showed high genetic diversity. Besides, each type of marker confirmed the presence of private polymorphism. We uncovered low to medium population structure in both markers, and found a faint signal of isolation by distance with microsatellites. Bayesian clustering analyses revealed multiple divergent genetic clusters. Taken together, these findings (i.e. high genetic diversity with private alleles and multiple genetic clusters) suggest that Lantana was introduced multiple times and gradually underwent spatial expansion with recurrent gene flow. © 2013 German Botanical Society and The Royal Botanical Society of the Netherlands.

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.

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops.

Science.gov (United States)

Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi

2016-01-01

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops.

Directory of Open Access Journals (Sweden)

Shiori Yabe

Full Text Available Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS, which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the

Optimization of Multiple Traveling Salesman Problem Based on Simulated Annealing Genetic Algorithm

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Xu Mingji

2017-01-01

Full Text Available It is very effective to solve the multi variable optimization problem by using hierarchical genetic algorithm. This thesis analyzes both advantages and disadvantages of hierarchical genetic algorithm and puts forward an improved simulated annealing genetic algorithm. The new algorithm is applied to solve the multiple traveling salesman problem, which can improve the performance of the solution. First, it improves the design of chromosomes hierarchical structure in terms of redundant hierarchical algorithm, and it suggests a suffix design of chromosomes; Second, concerning to some premature problems of genetic algorithm, it proposes a self-identify crossover operator and mutation; Third, when it comes to the problem of weak ability of local search of genetic algorithm, it stretches the fitness by mixing genetic algorithm with simulated annealing algorithm. Forth, it emulates the problems of N traveling salesmen and M cities so as to verify its feasibility. The simulation and calculation shows that this improved algorithm can be quickly converged to a best global solution, which means the algorithm is encouraging in practical uses.

Genetic and functional analyses of SHANK2 mutations suggest a multiple hit model of autism spectrum disorders.

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Claire S Leblond

2012-02-01

Full Text Available Autism spectrum disorders (ASD are a heterogeneous group of neurodevelopmental disorders with a complex inheritance pattern. While many rare variants in synaptic proteins have been identified in patients with ASD, little is known about their effects at the synapse and their interactions with other genetic variations. Here, following the discovery of two de novo SHANK2 deletions by the Autism Genome Project, we identified a novel 421 kb de novo SHANK2 deletion in a patient with autism. We then sequenced SHANK2 in 455 patients with ASD and 431 controls and integrated these results with those reported by Berkel et al. 2010 (n = 396 patients and n = 659 controls. We observed a significant enrichment of variants affecting conserved amino acids in 29 of 851 (3.4% patients and in 16 of 1,090 (1.5% controls (P = 0.004, OR = 2.37, 95% CI = 1.23-4.70. In neuronal cell cultures, the variants identified in patients were associated with a reduced synaptic density at dendrites compared to the variants only detected in controls (P = 0.0013. Interestingly, the three patients with de novo SHANK2 deletions also carried inherited CNVs at 15q11-q13 previously associated with neuropsychiatric disorders. In two cases, the nicotinic receptor CHRNA7 was duplicated and in one case the synaptic translation repressor CYFIP1 was deleted. These results strengthen the role of synaptic gene dysfunction in ASD but also highlight the presence of putative modifier genes, which is in keeping with the "multiple hit model" for ASD. A better knowledge of these genetic interactions will be necessary to understand the complex inheritance pattern of ASD.

Genetic parameters for litter size in Black Slavonian pigs

Energy Technology Data Exchange (ETDEWEB)

Skorput, D.; Gorjanc, G.; Dikic, M.; Lujovic, Z.

2014-06-01

The objective of this study was to estimate genetic parameters for litter size of Black Slavonian pigs using the repeatability, multiple trait, and random regression models, and to consider the possibility to increase litter size in Black Slavonian pigs by selection. A total of 4,733 litter records from the first to the sixth parity from sows that farrowed between January 1998 and December 2010 were included in the analysis. Individual record consisted of the following variables: breeding organisation (eight regions), parity (1-6), service boar, and farrowing season (monthyear interaction). Estimation of all the covariance components with three different models was based on the residual maximum likelihood method. Estimate of additive genetic variance and heritability for number of piglets born alive with repeatability model was 0.23 and 0.10, respectively. Estimates of additive genetic variance with multiple trait and random regression model were in a wider range from 0.05 to 0.65 across parities, and heritabilities were estimated in the range between 0.03 and 0.26. Estimates of phenotypic and additive genetic correlations were much smoother with random regression model in comparison with multiple trait model. Due to unexpected changes of variances along trajectory obtained with multiple trait and random regression model, the best option for genetic evaluation of litter size for now could be the use of repeatability model. With increasing number of data with proper data structure alternative modelling of litter size of Black Slavonian pig using multiple trait and random regression model could be taken into consideration. (Author)

Genetic parameters for litter size in Black Slavonian pigs

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Dubravko Skorput

2014-02-01

Full Text Available The objective of this study was to estimate genetic parameters for litter size of Black Slavonian pigs using the repeatability, multiple trait, and random regression models, and to consider the possibility to increase litter size in Black Slavonian pigs by selection. A total of 4733 litter records from the first to the sixth parity from sows that farrowed between January 1998 and December 2010 were included in the analysis. Individual record consisted of the following variables: breeding organisation (eight regions, parity (1-6, service boar, and farrowing season (month-year interaction. Estimation of all the covariance components with three different models was based on the residual maximum likelihood method. Estimate of additive genetic variance and heritability for number of piglets born alive with repeatability model was 0.23 and 0.10, respectively. Estimates of additive genetic variance with multiple trait and random regression model were in a wider range from 0.05 to 0.65 across parities, and heritabilities were estimated in the range between 0.03 and 0.26. Estimates of phenotypic and additive genetic correlations were much smoother with random regression model in comparison with multiple trait model. Due to unexpected changes of variances along trajectory obtained with multiple trait and random regression model, the best option for genetic evaluation of litter size for now could be the use of repeatability model. With increasing number of data with proper data structure alternative modelling of litter size of Black Slavonian pig using multiple trait and random regression model could be taken into consideration.

Genetic structuring of northern myotis (Myotis septentrionalis) at multiple spatial scales

Science.gov (United States)

Johnson, Joshua B.; Roberts, James H.; King, Timothy L.; Edwards, John W.; Ford, W. Mark; Ray, David A.

2014-01-01

Although groups of bats may be genetically distinguishable at large spatial scales, the effects of forest disturbances, particularly permanent land use conversions on fine-scale population structure and gene flow of summer aggregations of philopatric bat species are less clear. We genotyped and analyzed variation at 10 nuclear DNA microsatellite markers in 182 individuals of the forest-dwelling northern myotis (Myotis septentrionalis) at multiple spatial scales, from within first-order watersheds scaling up to larger regional areas in West Virginia and New York. Our results indicate that groups of northern myotis were genetically indistinguishable at any spatial scale we considered, and the collective population maintained high genetic diversity. It is likely that the ability to migrate, exploit small forest patches, and use networks of mating sites located throughout the Appalachian Mountains, Interior Highlands, and elsewhere in the hibernation range have allowed northern myotis to maintain high genetic diversity and gene flow regardless of forest disturbances at local and regional spatial scales. A consequence of maintaining high gene flow might be the potential to minimize genetic founder effects following population declines caused currently by the enzootic White-nose Syndrome.

Behavior genetic modeling of human fertility

DEFF Research Database (Denmark)

Rodgers, J L; Kohler, H P; Kyvik, K O

2001-01-01

Behavior genetic designs and analysis can be used to address issues of central importance to demography. We use this methodology to document genetic influence on human fertility. Our data come from Danish twin pairs born from 1953 to 1959, measured on age at first attempt to get pregnant (First......Try) and number of children (NumCh). Behavior genetic models were fitted using structural equation modeling and DF analysis. A consistent medium-level additive genetic influence was found for NumCh, equal across genders; a stronger genetic influence was identified for FirstTry, greater for females than for males....... A bivariate analysis indicated significant shared genetic variance between NumCh and FirstTry....

Restless legs syndrome in Czech patients with multiple sclerosis: An epidemiological and genetic study

Czech Academy of Sciences Publication Activity Database

Vávrová, J.; Kemlink, D.; Šonka, K.; Havrdová, E.; Horáková, D.; Pardini, Barbara; Müller-Myhsok, B.; Winkelmann, J.

2012-01-01

Roč. 13, č. 7 (2012), s. 848-851 ISSN 1389-9457 R&D Projects: GA MZd NR8563 Grant - others:GA ČR(CZ) GD309/08/H079; GA MZd(CZ) NT12141 Institutional research plan: CEZ:AV0Z50390512 Keywords : Secondary restless legs syndrome * Multiple sclerosis * Genetic association study Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.487, year: 2012

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

Science.gov (United States)

Ortuño, Francisco M; Valenzuela, Olga; Rojas, Fernando; Pomares, Hector; Florido, Javier P; Urquiza, Jose M; Rojas, Ignacio

2013-09-01

Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

The genetic basis of alcoholism: multiple phenotypes, many genes, complex networks

Science.gov (United States)

2012-01-01

Alcoholism is a significant public health problem. A picture of the genetic architecture underlying alcohol-related phenotypes is emerging from genome-wide association studies and work on genetically tractable model organisms. PMID:22348705

Complete restoration of multiple dystrophin isoforms in genetically corrected Duchenne muscular dystrophy patient–derived cardiomyocytes

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Susi Zatti

2014-01-01

Full Text Available Duchenne muscular dystrophy (DMD–associated cardiac diseases are emerging as a major cause of morbidity and mortality in DMD patients, and many therapies for treatment of skeletal muscle failed to improve cardiac function. The reprogramming of patients' somatic cells into pluripotent stem cells, combined with technologies for correcting the genetic defect, possesses great potential for the development of new treatments for genetic diseases. In this study, we obtained human cardiomyocytes from DMD patient–derived, induced pluripotent stem cells genetically corrected with a human artificial chromosome carrying the whole dystrophin genomic sequence. Stimulation by cytokines was combined with cell culturing on hydrogel with physiological stiffness, allowing an adhesion-dependent maturation and a proper dystrophin expression. The obtained cardiomyocytes showed remarkable sarcomeric organization of cardiac troponin T and α-actinin, expressed cardiac-specific markers, and displayed electrically induced calcium transients lasting less than 1 second. We demonstrated that the human artificial chromosome carrying the whole dystrophin genomic sequence is stably maintained throughout the cardiac differentiation process and that multiple promoters of the dystrophin gene are properly activated, driving expression of different isoforms. These dystrophic cardiomyocytes can be a valuable source for in vitro modeling of DMD-associated cardiac disease. Furthermore, the derivation of genetically corrected, patient-specific cardiomyocytes represents a step toward the development of innovative cell and gene therapy approaches for DMD.

Multiple-trait estimates of genetic parameters for metabolic disease traits, fertility disorders, and their predictors in Canadian Holsteins.

Science.gov (United States)

Jamrozik, J; Koeck, A; Kistemaker, G J; Miglior, F

2016-03-01

Producer-recorded health data for metabolic disease traits and fertility disorders on 35,575 Canadian Holstein cows were jointly analyzed with selected indicator traits. Metabolic diseases included clinical ketosis (KET) and displaced abomasum (DA); fertility disorders were metritis (MET) and retained placenta (RP); and disease indicators were fat-to-protein ratio, milk β-hydroxybutyrate, and body condition score (BCS) in the first lactation. Traits in first and later (up to fifth) lactations were treated as correlated in the multiple-trait (13 traits in total) animal linear model. Bayesian methods with Gibbs sampling were implemented for the analysis. Estimates of heritability for disease incidence were low, up to 0.06 for DA in first lactation. Among disease traits, the environmental herd-year variance constituted 4% of the total variance for KET and less for other traits. First- and later-lactation disease traits were genetically correlated (from 0.66 to 0.72) across all traits, indicating different genetic backgrounds for first and later lactations. Genetic correlations between KET and DA were relatively strong and positive (up to 0.79) in both first- and later-lactation cows. Genetic correlations between fertility disorders were slightly lower. Metritis was strongly genetically correlated with both metabolic disease traits in the first lactation only. All other genetic correlations between metabolic and fertility diseases were statistically nonsignificant. First-lactation KET and MET were strongly positively correlated with later-lactation performance for these traits due to the environmental herd-year effect. Indicator traits were moderately genetically correlated (from 0.30 to 0.63 in absolute values) with both metabolic disease traits in the first lactation. Smaller and mostly nonsignificant genetic correlations were among indicators and metabolic diseases in later lactations. The only significant genetic correlations between indicators and fertility

Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.

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Yiming Hu

2017-06-01

Full Text Available Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

Preimplantation genetic diagnosis for a patient with multiple endocrine neoplasia type 1: case report.

Science.gov (United States)

Lima, Aline Dt; Alves, Vanessa R; Rocha, Andressa R; Martinhago, Ana C; Martinhago, Ciro; Donadio, Nilka; Dzik, Artur; Cavagna, Mario; Gebrim, Luiz H

2018-03-01

Preimplantation genetic diagnosis was carried out for embryonic analysis in a patient with multiple endocrine neoplasia type 1 (MEN1). This is a rare autosomal-dominant cancer syndrome and the patients with MEN1 are characterized by the occurrence of tumors in multiple endocrine tissues, associated with germline and somatic inactivating mutations in the MEN1 gene. This case report documents a successful preimplantation genetic diagnosis (PGD) involving a couple at-risk for MEN1 syndrome, with a birth of a healthy infant. The couple underwent a cycle of controlled ovarian stimulation and intracytoplasmic sperm injection (ICSI). Embryos were biopsied at the blastocyst stage and cryopreserved; we used PCR-based DNA analysis for PGD testing. Only one of the five embryos analyzed for MEN1 syndrome was unaffected. This embryo was thawed and transferred following endometrial preparation. After positive βHCG test; clinical pregnancy was confirmed by ultrasound, and a healthy infant was born. PGD for single gene disorders has been an emerging therapeutic tool for couples who are at risk of passing a genetic disease on to their offspring.

Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure

Science.gov (United States)

Cheng, Chun-Tian; Zhao, Ming-Yan; Chau, K. W.; Wu, Xin-Yu

2006-01-01

Genetic Algorithm (GA) is globally oriented in searching and thus useful in optimizing multiobjective problems, especially where the objective functions are ill-defined. Conceptual rainfall-runoff models that aim at predicting streamflow from the knowledge of precipitation over a catchment have become a basic tool for flood forecasting. The parameter calibration of a conceptual model usually involves the multiple criteria for judging the performances of observed data. However, it is often difficult to derive all objective functions for the parameter calibration problem of a conceptual model. Thus, a new method to the multiple criteria parameter calibration problem, which combines GA with TOPSIS (technique for order performance by similarity to ideal solution) for Xinanjiang model, is presented. This study is an immediate further development of authors' previous research (Cheng, C.T., Ou, C.P., Chau, K.W., 2002. Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration. Journal of Hydrology, 268, 72-86), whose obvious disadvantages are to split the whole procedure into two parts and to become difficult to integrally grasp the best behaviors of model during the calibration procedure. The current method integrates the two parts of Xinanjiang rainfall-runoff model calibration together, simplifying the procedures of model calibration and validation and easily demonstrated the intrinsic phenomenon of observed data in integrity. Comparison of results with two-step procedure shows that the current methodology gives similar results to the previous method, is also feasible and robust, but simpler and easier to apply in practice.

Modeling misidentification errors that result from use of genetic tags in capture-recapture studies

Science.gov (United States)

Yoshizaki, J.; Brownie, C.; Pollock, K.H.; Link, W.A.

2011-01-01

Misidentification of animals is potentially important when naturally existing features (natural tags) such as DNA fingerprints (genetic tags) are used to identify individual animals. For example, when misidentification leads to multiple identities being assigned to an animal, traditional estimators tend to overestimate population size. Accounting for misidentification in capture-recapture models requires detailed understanding of the mechanism. Using genetic tags as an example, we outline a framework for modeling the effect of misidentification in closed population studies when individual identification is based on natural tags that are consistent over time (non-evolving natural tags). We first assume a single sample is obtained per animal for each capture event, and then generalize to the case where multiple samples (such as hair or scat samples) are collected per animal per capture occasion. We introduce methods for estimating population size and, using a simulation study, we show that our new estimators perform well for cases with moderately high capture probabilities or high misidentification rates. In contrast, conventional estimators can seriously overestimate population size when errors due to misidentification are ignored. ?? 2009 Springer Science+Business Media, LLC.

A multiobjective non-dominated sorting genetic algorithm (NSGA-II for the Multiple Traveling Salesman Problem

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Rubén Iván Bolaños

2015-06-01

Full Text Available This paper considers a multi-objective version of the Multiple Traveling Salesman Problem (MOmTSP. In particular, two objectives are considered: the minimization of the total traveled distance and the balance of the working times of the traveling salesmen. The problem is formulated as an integer multi-objective optimization model. A non-dominated sorting genetic algorithm (NSGA-II is proposed to solve the MOmTSP. The solution scheme allows one to find a set of ordered solutions in Pareto fronts by considering the concept of dominance. Tests on real world instances and instances adapted from the literature show the effectiveness of the proposed algorithm.

Helicobacter pylori genetic diversification in the Mongolian gerbil model.

Science.gov (United States)

Beckett, Amber C; Loh, John T; Chopra, Abha; Leary, Shay; Lin, Aung Soe; McDonnell, Wyatt J; Dixon, Beverly R E A; Noto, Jennifer M; Israel, Dawn A; Peek, Richard M; Mallal, Simon; Algood, Holly M Scott; Cover, Timothy L

2018-01-01

Helicobacter pylori requires genetic agility to infect new hosts and establish long-term colonization of changing gastric environments. In this study, we analyzed H. pylori genetic adaptation in the Mongolian gerbil model. This model is of particular interest because H. pylori -infected gerbils develop a high level of gastric inflammation and often develop gastric adenocarcinoma or gastric ulceration. We analyzed the whole genome sequences of H. pylori strains cultured from experimentally infected gerbils, in comparison to the genome sequence of the input strain. The mean annualized single nucleotide polymorphism (SNP) rate per site was 1.5e -5 , which is similar to the rates detected previously in H. pylori- infected humans. Many of the mutations occurred within or upstream of genes associated with iron-related functions ( fur , tonB1 , fecA2 , fecA3 , and frpB3 ) or encoding outer membrane proteins ( alpA, oipA, fecA2, fecA3, frpB3 and cagY ). Most of the SNPs within coding regions (86%) were non-synonymous mutations. Several deletion or insertion mutations led to disruption of open reading frames, suggesting that the corresponding gene products are not required or are deleterious during chronic H. pylori colonization of the gerbil stomach. Five variants (three SNPs and two deletions) were detected in isolates from multiple animals, which suggests that these mutations conferred a selective advantage. One of the mutations (FurR88H) detected in isolates from multiple animals was previously shown to confer increased resistance to oxidative stress, and we now show that this SNP also confers a survival advantage when H. pylori is co-cultured with neutrophils. Collectively, these analyses allow the identification of mutations that are positively selected during H. pylori colonization of the gerbil model.

Genetic variants are major determinants of CSF antibody levels in multiple sclerosis

DEFF Research Database (Denmark)

Goris, An; Pauwels, Ine; Gustavsen, Marte W

2015-01-01

Immunological hallmarks of multiple sclerosis include the production of antibodies in the central nervous system, expressed as presence of oligoclonal bands and/or an increased immunoglobulin G index-the level of immunoglobulin G in the cerebrospinal fluid compared to serum. However, the underlying...... differences between oligoclonal band-positive and -negative patients with multiple sclerosis and reasons for variability in immunoglobulin G index are not known. To identify genetic factors influencing the variation in the antibody levels in the cerebrospinal fluid in multiple sclerosis, we have performed...... a genome-wide association screen in patients collected from nine countries for two traits, presence or absence of oligoclonal bands (n = 3026) and immunoglobulin G index levels (n = 938), followed by a replication in 3891 additional patients. We replicate previously suggested association signals...

Non-linear nuclear engineering models as genetic programming application

International Nuclear Information System (INIS)

Domingos, Roberto P.; Schirru, Roberto; Martinez, Aquilino S.

1997-01-01

This work presents a Genetic Programming paradigm and a nuclear application. A field of Artificial Intelligence, based on the concepts of Species Evolution and Natural Selection, can be understood as a self-programming process where the computer is the main agent responsible for the discovery of a program able to solve a given problem. In the present case, the problem was to find a mathematical expression in symbolic form, able to express the existent relation between equivalent ratio of a fuel cell, the enrichment of fuel elements and the multiplication factor. Such expression would avoid repeatedly reactor physics codes execution for core optimization. The results were compared with those obtained by different techniques such as Neural Networks and Linear Multiple Regression. Genetic Programming has shown to present a performance as good as, and under some features superior to Neural Network and Linear Multiple Regression. (author). 10 refs., 8 figs., 1 tabs

A Hybrid Genetic Algorithm for the Multiple Crossdocks Problem

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Zhaowei Miao

2012-01-01

Full Text Available We study a multiple crossdocks problem with supplier and customer time windows, where any violation of time windows will incur a penalty cost and the flows through the crossdock are constrained by fixed transportation schedules and crossdock capacities. We prove this problem to be NP-hard in the strong sense and therefore focus on developing efficient heuristics. Based on the problem structure, we propose a hybrid genetic algorithm (HGA integrating greedy technique and variable neighborhood search method to solve the problem. Extensive experiments under different scenarios were conducted, and results show that HGA outperforms CPLEX solver, providing solutions in realistic timescales.

Low genetic diversity despite multiple introductions of the invasive plant species Impatiens glandulifera in Europe.

Science.gov (United States)

Hagenblad, Jenny; Hülskötter, Jennifer; Acharya, Kamal Prasad; Brunet, Jörg; Chabrerie, Olivier; Cousins, Sara A O; Dar, Pervaiz A; Diekmann, Martin; De Frenne, Pieter; Hermy, Martin; Jamoneau, Aurélien; Kolb, Annette; Lemke, Isgard; Plue, Jan; Reshi, Zafar A; Graae, Bente Jessen

2015-08-20

Invasive species can be a major threat to native biodiversity and the number of invasive plant species is increasing across the globe. Population genetic studies of invasive species can provide key insights into their invasion history and ensuing evolution, but also for their control. Here we genetically characterise populations of Impatiens glandulifera, an invasive plant in Europe that can have a major impact on native plant communities. We compared populations from the species' native range in Kashmir, India, to those in its invaded range, along a latitudinal gradient in Europe. For comparison, the results from 39 other studies of genetic diversity in invasive species were collated. Our results suggest that I. glandulifera was established in the wild in Europe at least twice, from an area outside of our Kashmir study area. Our results further revealed that the genetic diversity in invasive populations of I. glandulifera is unusually low compared to native populations, in particular when compared to other invasive species. Genetic drift rather than mutation seems to have played a role in differentiating populations in Europe. We find evidence of limitations to local gene flow after introduction to Europe, but somewhat less restrictions in the native range. I. glandulifera populations with significant inbreeding were only found in the species' native range and invasive species in general showed no increase in inbreeding upon leaving their native ranges. In Europe we detect cases of migration between distantly located populations. Human activities therefore seem to, at least partially, have facilitated not only introductions, but also further spread of I. glandulifera across Europe. Although multiple introductions will facilitate the retention of genetic diversity in invasive ranges, widespread invasive species can remain genetically relatively invariant also after multiple introductions. Phenotypic plasticity may therefore be an important component of the

Population genetics of the Eastern Hellbender (Cryptobranchus alleganiensis alleganiensis across multiple spatial scales.

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Shem D Unger

Full Text Available Conservation genetics is a powerful tool to assess the population structure of species and provides a framework for informing management of freshwater ecosystems. As lotic habitats become fragmented, the need to assess gene flow for species of conservation management becomes a priority. The eastern hellbender (Cryptobranchus alleganiensis alleganiensis is a large, fully aquatic paedamorphic salamander. Many populations are experiencing declines throughout their geographic range, yet the genetic ramifications of these declines are currently unknown. To this end, we examined levels of genetic variation and genetic structure at both range-wide and drainage (hierarchical scales. We collected 1,203 individuals from 77 rivers throughout nine states from June 2007 to August 2011. Levels of genetic diversity were relatively high among all sampling locations. We detected significant genetic structure across populations (Fst values ranged from 0.001 between rivers within a single watershed to 0.218 between states. We identified two genetically differentiated groups at the range-wide scale: 1 the Ohio River drainage and 2 the Tennessee River drainage. An analysis of molecular variance (AMOVA based on landscape-scale sampling of basins within the Tennessee River drainage revealed the majority of genetic variation (∼94-98% occurs within rivers. Eastern hellbenders show a strong pattern of isolation by stream distance (IBSD at the drainage level. Understanding levels of genetic variation and differentiation at multiple spatial and biological scales will enable natural resource managers to make more informed decisions and plan effective conservation strategies for cryptic, lotic species.

Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem

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Yu Zhou

2017-01-01

Full Text Available The train-set circulation plan problem (TCPP belongs to the rolling stock scheduling (RSS problem and is similar to the aircraft routing problem (ARP in airline operations and the vehicle routing problem (VRP in the logistics field. However, TCPP involves additional complexity due to the maintenance constraint of train-sets: train-sets must conduct maintenance tasks after running for a certain time and distance. The TCPP is nondeterministic polynomial hard (NP-hard. There is no available algorithm that can obtain the optimal global solution, and many factors such as the utilization mode and the maintenance mode impact the solution of the TCPP. This paper proposes a train-set circulation optimization model to minimize the total connection time and maintenance costs and describes the design of an efficient multiple-population genetic algorithm (MPGA to solve this model. A realistic high-speed railway (HSR case is selected to verify our model and algorithm, and, then, a comparison of different algorithms is carried out. Furthermore, a new maintenance mode is proposed, and related implementation requirements are discussed.

Genetic coding and gene expression - new Quadruplet genetic coding model

Science.gov (United States)

Shankar Singh, Rama

2012-07-01

Successful demonstration of human genome project has opened the door not only for developing personalized medicine and cure for genetic diseases, but it may also answer the complex and difficult question of the origin of life. It may lead to making 21st century, a century of Biological Sciences as well. Based on the central dogma of Biology, genetic codons in conjunction with tRNA play a key role in translating the RNA bases forming sequence of amino acids leading to a synthesized protein. This is the most critical step in synthesizing the right protein needed for personalized medicine and curing genetic diseases. So far, only triplet codons involving three bases of RNA, transcribed from DNA bases, have been used. Since this approach has several inconsistencies and limitations, even the promise of personalized medicine has not been realized. The new Quadruplet genetic coding model proposed and developed here involves all four RNA bases which in conjunction with tRNA will synthesize the right protein. The transcription and translation process used will be the same, but the Quadruplet codons will help overcome most of the inconsistencies and limitations of the triplet codes. Details of this new Quadruplet genetic coding model and its subsequent potential applications including relevance to the origin of life will be presented.

Context trees for privacy-preserving modeling of genetic data

NARCIS (Netherlands)

Kusters, C.J.; Ignatenko, T.

2016-01-01

In this work, we use context trees for privacypreserving modeling of genetic sequences. The resulting estimated models are applied for functional comparison of genetic sequences in a privacy preserving way. Here we define privacy as uncertainty about the genetic source sequence given its model and

Signal Timing Optimization for Corridors with Multiple Highway-Rail Grade Crossings Using Genetic Algorithm

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Yifeng Chen

2018-01-01

Full Text Available Safety and efficiency are two critical issues at highway-rail grade crossings (HRGCs and their nearby intersections. Standard traffic signal optimization programs are not designed to work on roadway networks that contain multiple HRGCs, because their underlying assumption is that the roadway traffic is in a steady-state. During a train event, steady-state conditions do not occur. This is particularly true for corridors that experience high train traffic (e.g., over 2 trains per hour. In this situation, the non-steady-state conditions predominate. This paper develops a simulation-based methodology for optimizing traffic signal timing plan on corridors of this kind. The primary goal is to maximize safety, and the secondary goal is to minimize delay. A Genetic Algorithm (GA was used as the optimization approach in the proposed methodology. A new transition preemption strategy for dual tracks (TPS_DT and a train arrival prediction model were integrated in the proposed methodology. An urban road network with multiple HRGCs in Lincoln, NE, was used as the study network. The microsimulation model VISSIM was used for evaluation purposes and was calibrated to local traffic conditions. A sensitivity analysis with different train traffic scenarios was conducted. It was concluded that the methodology can significantly improve both the safety and efficiency of traffic corridors with HRGCs.

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.

Science.gov (United States)

Ziyatdinov, Andrey; Vázquez-Santiago, Miquel; Brunel, Helena; Martinez-Perez, Angel; Aschard, Hugues; Soria, Jose Manuel

2018-02-27

Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .

Latent spatial models and sampling design for landscape genetics

Science.gov (United States)

Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.

2016-01-01

We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.

Genetic variants are major determinants of CSF antibody levels in multiple sclerosis.

Science.gov (United States)

Goris, An; Pauwels, Ine; Gustavsen, Marte W; van Son, Brechtje; Hilven, Kelly; Bos, Steffan D; Celius, Elisabeth Gulowsen; Berg-Hansen, Pål; Aarseth, Jan; Myhr, Kjell-Morten; D'Alfonso, Sandra; Barizzone, Nadia; Leone, Maurizio A; Martinelli Boneschi, Filippo; Sorosina, Melissa; Liberatore, Giuseppe; Kockum, Ingrid; Olsson, Tomas; Hillert, Jan; Alfredsson, Lars; Bedri, Sahl Khalid; Hemmer, Bernhard; Buck, Dorothea; Berthele, Achim; Knier, Benjamin; Biberacher, Viola; van Pesch, Vincent; Sindic, Christian; Bang Oturai, Annette; Søndergaard, Helle Bach; Sellebjerg, Finn; Jensen, Poul Erik H; Comabella, Manuel; Montalban, Xavier; Pérez-Boza, Jennifer; Malhotra, Sunny; Lechner-Scott, Jeannette; Broadley, Simon; Slee, Mark; Taylor, Bruce; Kermode, Allan G; Gourraud, Pierre-Antoine; Sawcer, Stephen J; Andreassen, Bettina Kullle; Dubois, Bénédicte; Harbo, Hanne F

2015-03-01

Immunological hallmarks of multiple sclerosis include the production of antibodies in the central nervous system, expressed as presence of oligoclonal bands and/or an increased immunoglobulin G index-the level of immunoglobulin G in the cerebrospinal fluid compared to serum. However, the underlying differences between oligoclonal band-positive and -negative patients with multiple sclerosis and reasons for variability in immunoglobulin G index are not known. To identify genetic factors influencing the variation in the antibody levels in the cerebrospinal fluid in multiple sclerosis, we have performed a genome-wide association screen in patients collected from nine countries for two traits, presence or absence of oligoclonal bands (n = 3026) and immunoglobulin G index levels (n = 938), followed by a replication in 3891 additional patients. We replicate previously suggested association signals for oligoclonal band status in the major histocompatibility complex region for the rs9271640*A-rs6457617*G haplotype, correlated with HLA-DRB1*1501, and rs34083746*G, correlated with HLA-DQA1*0301 (P comparing two haplotypes = 8.88 × 10(-16)). Furthermore, we identify a novel association signal of rs9807334, near the ELAC1/SMAD4 genes, for oligoclonal band status (P = 8.45 × 10(-7)). The previously reported association of the immunoglobulin heavy chain locus with immunoglobulin G index reaches strong evidence for association in this data set (P = 3.79 × 10(-37)). We identify two novel associations in the major histocompatibility complex region with immunoglobulin G index: the rs9271640*A-rs6457617*G haplotype (P = 1.59 × 10(-22)), shared with oligoclonal band status, and an additional independent effect of rs6457617*G (P = 3.68 × 10(-6)). Variants identified in this study account for up to 2-fold differences in the odds of being oligoclonal band positive and 7.75% of the variation in immunoglobulin G index. Both traits are associated with clinical features of disease such

Genetic variants are major determinants of CSF antibody levels in multiple sclerosis

Science.gov (United States)

Pauwels, Ine; Gustavsen, Marte W.; van Son, Brechtje; Hilven, Kelly; Bos, Steffan D.; Celius, Elisabeth Gulowsen; Berg-Hansen, Pål; Aarseth, Jan; Myhr, Kjell-Morten; D’Alfonso, Sandra; Barizzone, Nadia; Leone, Maurizio A.; Martinelli Boneschi, Filippo; Sorosina, Melissa; Liberatore, Giuseppe; Kockum, Ingrid; Olsson, Tomas; Hillert, Jan; Alfredsson, Lars; Bedri, Sahl Khalid; Hemmer, Bernhard; Buck, Dorothea; Berthele, Achim; Knier, Benjamin; Biberacher, Viola; van Pesch, Vincent; Sindic, Christian; Bang Oturai, Annette; Søndergaard, Helle Bach; Sellebjerg, Finn; Jensen, Poul Erik H.; Comabella, Manuel; Montalban, Xavier; Pérez-Boza, Jennifer; Malhotra, Sunny; Lechner-Scott, Jeannette; Broadley, Simon; Slee, Mark; Taylor, Bruce; Kermode, Allan G.; Gourraud, Pierre-Antoine; Sawcer, Stephen J.; Andreassen, Bettina Kullle; Dubois, Bénédicte; Harbo, Hanne F.

2015-01-01

Immunological hallmarks of multiple sclerosis include the production of antibodies in the central nervous system, expressed as presence of oligoclonal bands and/or an increased immunoglobulin G index—the level of immunoglobulin G in the cerebrospinal fluid compared to serum. However, the underlying differences between oligoclonal band-positive and -negative patients with multiple sclerosis and reasons for variability in immunoglobulin G index are not known. To identify genetic factors influencing the variation in the antibody levels in the cerebrospinal fluid in multiple sclerosis, we have performed a genome-wide association screen in patients collected from nine countries for two traits, presence or absence of oligoclonal bands (n = 3026) and immunoglobulin G index levels (n = 938), followed by a replication in 3891 additional patients. We replicate previously suggested association signals for oligoclonal band status in the major histocompatibility complex region for the rs9271640*A-rs6457617*G haplotype, correlated with HLA-DRB1*1501, and rs34083746*G, correlated with HLA-DQA1*0301 (P comparing two haplotypes = 8.88 × 10−16). Furthermore, we identify a novel association signal of rs9807334, near the ELAC1/SMAD4 genes, for oligoclonal band status (P = 8.45 × 10−7). The previously reported association of the immunoglobulin heavy chain locus with immunoglobulin G index reaches strong evidence for association in this data set (P = 3.79 × 10−37). We identify two novel associations in the major histocompatibility complex region with immunoglobulin G index: the rs9271640*A-rs6457617*G haplotype (P = 1.59 × 10−22), shared with oligoclonal band status, and an additional independent effect of rs6457617*G (P = 3.68 × 10−6). Variants identified in this study account for up to 2-fold differences in the odds of being oligoclonal band positive and 7.75% of the variation in immunoglobulin G index. Both traits are associated with clinical features of disease such

Noise in Genetic Toggle Switch Models

Directory of Open Access Journals (Sweden)

Andrecut M.

2006-06-01

Full Text Available In this paper we study the intrinsic noise effect on the switching behavior of a simple genetic circuit corresponding to the genetic toggle switch model. The numerical results obtained from a noisy mean-field model are compared to those obtained from the stochastic Gillespie simulation of the corresponding system of chemical reactions. Our results show that by using a two step reaction approach for modeling the transcription and translation processes one can make the system to lock in one of the steady states for exponentially long times.

Behavior genetics: Bees as model

International Nuclear Information System (INIS)

Nates Parra, Guiomar

2011-01-01

The honeybee Apis mellifera (Apidae) is a model widely used in behavior because of its elaborate social life requiring coordinate actions among the members of the society. Within a colony, division of labor, the performance of tasks by different individuals, follows genetically determined physiological changes that go along with aging. Modern advances in tools of molecular biology and genomics, as well as the sequentiation of A. mellifera genome, have enabled a better understanding of honeybee behavior, in particular social behavior. Numerous studies show that aspects of worker behavior are genetically determined, including defensive, hygienic, reproductive and foraging behavior. For example, genetic diversity is associated with specialization to collect water, nectar and pollen. Also, control of worker reproduction is associated with genetic differences. In this paper, I review the methods and the main results from the study of the genetic and genomic basis of some behaviors in bees.

Phylogeographic and population genetic analyses reveal multiple species of Boa and independent origins of insular dwarfism.

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Card, Daren C; Schield, Drew R; Adams, Richard H; Corbin, Andrew B; Perry, Blair W; Andrew, Audra L; Pasquesi, Giulia I M; Smith, Eric N; Jezkova, Tereza; Boback, Scott M; Booth, Warren; Castoe, Todd A

2016-09-01

Boa is a Neotropical genus of snakes historically recognized as monotypic despite its expansive distribution. The distinct morphological traits and color patterns exhibited by these snakes, together with the wide diversity of ecosystems they inhabit, collectively suggest that the genus may represent multiple species. Morphological variation within Boa also includes instances of dwarfism observed in multiple offshore island populations. Despite this substantial diversity, the systematics of the genus Boa has received little attention until very recently. In this study we examined the genetic structure and phylogenetic relationships of Boa populations using mitochondrial sequences and genome-wide SNP data obtained from RADseq. We analyzed these data at multiple geographic scales using a combination of phylogenetic inference (including coalescent-based species delimitation) and population genetic analyses. We identified extensive population structure across the range of the genus Boa and multiple lines of evidence for three widely-distributed clades roughly corresponding with the three primary land masses of the Western Hemisphere. We also find both mitochondrial and nuclear support for independent origins and parallel evolution of dwarfism on offshore island clusters in Belize and Cayos Cochinos Menor, Honduras. Copyright © 2016 Elsevier Inc. All rights reserved.

Analysis of the genetic variation in Mycobacterium tuberculosis strains by multiple genome alignments

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Morales Juan

2008-11-01

Full Text Available Abstract Background The recent determination of the complete nucleotide sequence of several Mycobacterium tuberculosis (MTB genomes allows the use of comparative genomics as a tool for dissecting the nature and consequence of genetic variability within this species. The multiple alignment of the genomes of clinical strains (CDC1551, F11, Haarlem and C, along with the genomes of laboratory strains (H37Rv and H37Ra, provides new insights on the mechanisms of adaptation of this bacterium to the human host. Findings The genetic variation found in six M. tuberculosis strains does not involve significant genomic rearrangements. Most of the variation results from deletion and transposition events preferentially associated with insertion sequences and genes of the PE/PPE family but not with genes implicated in virulence. Using a Perl-based software islandsanalyser, which creates a representation of the genetic variation in the genome, we identified differences in the patterns of distribution and frequency of the polymorphisms across the genome. The identification of genes displaying strain-specific polymorphisms and the extrapolation of the number of strain-specific polymorphisms to an unlimited number of genomes indicates that the different strains contain a limited number of unique polymorphisms. Conclusion The comparison of multiple genomes demonstrates that the M. tuberculosis genome is currently undergoing an active process of gene decay, analogous to the adaptation process of obligate bacterial symbionts. This observation opens new perspectives into the evolution and the understanding of the pathogenesis of this bacterium.

Genetic susceptibility factors for multiple chemical sensitivity revisited

DEFF Research Database (Denmark)

Berg, Nikolaj Drimer; Rasmussen, Henrik Berg; Linneberg, Allan

2010-01-01

of this study was to investigate genetic susceptibility factors for MCS and self-reported chemical sensitivity in a population sample. Ninety six MCS patients and 1,207 controls from a general population divided into four severity groups of chemical sensitivity were genotyped for variants in the genes encoding......Multiple chemical sensitivity (MCS) is characterised by adverse effects due to exposure to low levels of chemical substances. Various genes, especially genes of importance to the metabolism of xenobiotic compounds, have been associated with MCS, but findings are inconsistent. The purpose...... significant (OR=1.2, p=0.28). Fast arylamine N-acetyltransferase 2 metaboliser status was associated with severity of chemical sensitivity only in the most severely affected group in the population sample (OR=3.1, p=0.04). The cholecystokinin 2 receptor allele with 21 CT repeats was associated with MCS when...

Natural selection affects multiple aspects of genetic variation at putatively neutral sites across the human genome.

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Lohmueller, Kirk E; Albrechtsen, Anders; Li, Yingrui; Kim, Su Yeon; Korneliussen, Thorfinn; Vinckenbosch, Nicolas; Tian, Geng; Huerta-Sanchez, Emilia; Feder, Alison F; Grarup, Niels; Jørgensen, Torben; Jiang, Tao; Witte, Daniel R; Sandbæk, Annelli; Hellmann, Ines; Lauritzen, Torsten; Hansen, Torben; Pedersen, Oluf; Wang, Jun; Nielsen, Rasmus

2011-10-01

A major question in evolutionary biology is how natural selection has shaped patterns of genetic variation across the human genome. Previous work has documented a reduction in genetic diversity in regions of the genome with low recombination rates. However, it is unclear whether other summaries of genetic variation, like allele frequencies, are also correlated with recombination rate and whether these correlations can be explained solely by negative selection against deleterious mutations or whether positive selection acting on favorable alleles is also required. Here we attempt to address these questions by analyzing three different genome-wide resequencing datasets from European individuals. We document several significant correlations between different genomic features. In particular, we find that average minor allele frequency and diversity are reduced in regions of low recombination and that human diversity, human-chimp divergence, and average minor allele frequency are reduced near genes. Population genetic simulations show that either positive natural selection acting on favorable mutations or negative natural selection acting against deleterious mutations can explain these correlations. However, models with strong positive selection on nonsynonymous mutations and little negative selection predict a stronger negative correlation between neutral diversity and nonsynonymous divergence than observed in the actual data, supporting the importance of negative, rather than positive, selection throughout the genome. Further, we show that the widespread presence of weakly deleterious alleles, rather than a small number of strongly positively selected mutations, is responsible for the correlation between neutral genetic diversity and recombination rate. This work suggests that natural selection has affected multiple aspects of linked neutral variation throughout the human genome and that positive selection is not required to explain these observations.

Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

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Lane, Peter C. R.; Gobet, Fernand

2013-03-01

Creating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the `speciated non-dominated sorting genetic algorithm' for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data.

Multiple Indicator Stationary Time Series Models.

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Sivo, Stephen A.

2001-01-01

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

Genetic Analysis of Elevated Mastitis Risk Based on Mastitis Indicator Data

DEFF Research Database (Denmark)

Sørensen, Lars Peter; Løvendahl, Peter

Whole-genome sequences and multiple trait phenotypes from large numbers of individuals will soon be available. Well established statistical modeling approaches enable the genetic analyses of complex trait phenotypes while accounting for a variety of additive and non-additive genetic mechanisms....... These modeling approaches have proven to be highly useful to determine population genetic parameters as well as prediction of genetic risk or value. We present statistical modelling approaches that use prior biological information for evaluating the collective action of sets of genetic variants. We have applied...

Cisplatin resistance: a cellular self-defense mechanism resulting from multiple epigenetic and genetic changes.

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Shen, Ding-Wu; Pouliot, Lynn M; Hall, Matthew D; Gottesman, Michael M

2012-07-01

Cisplatin is one of the most effective broad-spectrum anticancer drugs. Its effectiveness seems to be due to the unique properties of cisplatin, which enters cells via multiple pathways and forms multiple different DNA-platinum adducts while initiating a cellular self-defense system by activating or silencing a variety of different genes, resulting in dramatic epigenetic and/or genetic alternations. As a result, the development of cisplatin resistance in human cancer cells in vivo and in vitro by necessity stems from bewilderingly complex genetic and epigenetic changes in gene expression and alterations in protein localization. Extensive published evidence has demonstrated that pleiotropic alterations are frequently detected during development of resistance to this toxic metal compound. Changes occur in almost every mechanism supporting cell survival, including cell growth-promoting pathways, apoptosis, developmental pathways, DNA damage repair, and endocytosis. In general, dozens of genes are affected in cisplatin-resistant cells, including pathways involved in copper metabolism as well as transcription pathways that alter the cytoskeleton, change cell surface presentation of proteins, and regulate epithelial-to-mesenchymal transition. Decreased accumulation is one of the most common features resulting in cisplatin resistance. This seems to be a consequence of numerous epigenetic and genetic changes leading to the loss of cell-surface binding sites and/or transporters for cisplatin, and decreased fluid phase endocytosis.

The genetics of multiple sclerosis: review of current and emerging candidates

Science.gov (United States)

Muñoz-Culla, Maider; Irizar, Haritz; Otaegui, David

2013-01-01

Multiple sclerosis (MS) is a complex disease in which environmental, genetic, and epigenetic factors determine the risk of developing the disease. The human leukocyte antigen region is the strongest susceptibility locus linked to MS, but it does not explain the whole heritability of the disease. To find other non-human leukocyte antigen loci associated with the disease, high-throughput genotyping, sequencing, and gene-expression studies have been performed, producing a valuable quantity of information. An overview of the genomic and expression studies is provided in this review, as well as microRNA-expression studies, highlighting the importance of combining all the layers of information in order to elucidate the causes or pathological mechanisms occurring in the disease. Genetics in MS is a promising field that is presumably going to be very productive in the next decade understanding the cross talk between all the factors contributing to the development of MS. PMID:24019748

Progranulin genetic polymorphisms influence progression of disability and relapse recovery in multiple sclerosis.

Science.gov (United States)

Vercellino, Marco; Fenoglio, Chiara; Galimberti, Daniela; Mattioda, Alessandra; Chiavazza, Carlotta; Binello, Eleonora; Pinessi, Lorenzo; Giobbe, Dario; Scarpini, Elio; Cavalla, Paola

2016-07-01

Progranulin (GRN) is a multifunctional protein involved in inflammation and repair, and also a neurotrophic factor critical for neuronal survival. Progranulin is strongly expressed in multiple sclerosis (MS) brains by macrophages and microglia. In this study we evaluated GRN genetic variability in 400 MS patients, in correlation with clinical variables such as disease severity and relapse recovery. We also evaluated serum progranulin levels in the different groups of GRN variants carriers. We found that incomplete recovery after a relapse is correlated with an increased frequency of the rs9897526 A allele (odds ratio (OR) 4.367, p = 0.005). A more severe disease course (Multiple Sclerosis Severity Score > 5) is correlated with an increased frequency of the rs9897526 A allele (OR 1.886, p = 0.002) and of the rs5848 T allele (OR 1.580, p = 0.019). Carriers of the variants associated with a more severe disease course (rs9897526 A, rs5848 T) have significantly lower levels of circulating progranulin (80.5 ± 9.1 ng/mL vs. 165.7 ng/mL, p = 0.01). GRN genetic polymorphisms likely influence disease course and relapse recovery in MS. © The Author(s), 2015.

The intergenerational multiple deficit model and the case of dyslexia

Directory of Open Access Journals (Sweden)

Elsje evan Bergen

2014-06-01

Full Text Available Which children go on to develop dyslexia? Since dyslexia has a multifactorial aetiology, this question can be restated as: What are the factors that put children at high risk for developing dyslexia? It is argued that a useful theoretical framework to address this question is Pennington’s (2006 multiple deficit model (MDM. This model replaces models that attribute dyslexia to a single underlying cause. Subsequently, the generalist genes hypothesis for learning (disabilities (Plomin & Kovas, 2005 is described and integrated with the MDM. Finally, findings are presented from a longitudinal study with children at family risk for dyslexia. Such studies can contribute to testing and specifying the MDM. In this study, risk factors at both the child and family level were investigated. This led to the proposed intergenerational MDM, in which both parents confer liability via intertwined genetic and environmental pathways. Future scientific directions are discussed to investigate parent-offspring resemblance and transmission patterns, which will shed new light on disorder aetiology.

Genetic classes and genetic categories : Protecting genetic groups through data protection law

NARCIS (Netherlands)

Hallinan, Dara; de Hert, Paul; Taylor, L.; Floridi, L.; van der Sloot, B.

2017-01-01

Each person shares genetic code with others. Thus, one individual’s genome can reveal information about other individuals. When multiple individuals share aspects of genetic architecture, they form a ‘genetic group’. From a social and legal perspective, two types of genetic group exist: Those which

Introduction to genetic algorithms as a modeling tool

International Nuclear Information System (INIS)

Wildberger, A.M.; Hickok, K.A.

1990-01-01

Genetic algorithms are search and classification techniques modeled on natural adaptive systems. This is an introduction to their use as a modeling tool with emphasis on prospects for their application in the power industry. It is intended to provide enough background information for its audience to begin to follow technical developments in genetic algorithms and to recognize those which might impact on electric power engineering. Beginning with a discussion of genetic algorithms and their origin as a model of biological adaptation, their advantages and disadvantages are described in comparison with other modeling tools such as simulation and neural networks in order to provide guidance in selecting appropriate applications. In particular, their use is described for improving expert systems from actual data and they are suggested as an aid in building mathematical models. Using the Thermal Performance Advisor as an example, it is suggested how genetic algorithms might be used to make a conventional expert system and mathematical model of a power plant adapt automatically to changes in the plant's characteristics

Comparing estimates of genetic variance across different relationship models.

Science.gov (United States)

Legarra, Andres

2016-02-01

Use of relationships between individuals to estimate genetic variances and heritabilities via mixed models is standard practice in human, plant and livestock genetics. Different models or information for relationships may give different estimates of genetic variances. However, comparing these estimates across different relationship models is not straightforward as the implied base populations differ between relationship models. In this work, I present a method to compare estimates of variance components across different relationship models. I suggest referring genetic variances obtained using different relationship models to the same reference population, usually a set of individuals in the population. Expected genetic variance of this population is the estimated variance component from the mixed model times a statistic, Dk, which is the average self-relationship minus the average (self- and across-) relationship. For most typical models of relationships, Dk is close to 1. However, this is not true for very deep pedigrees, for identity-by-state relationships, or for non-parametric kernels, which tend to overestimate the genetic variance and the heritability. Using mice data, I show that heritabilities from identity-by-state and kernel-based relationships are overestimated. Weighting these estimates by Dk scales them to a base comparable to genomic or pedigree relationships, avoiding wrong comparisons, for instance, "missing heritabilities". Copyright © 2015 Elsevier Inc. All rights reserved.

Oligoclonal band status in Scandinavian multiple sclerosis patients is associated with specific genetic risk alleles

DEFF Research Database (Denmark)

Mero, Inger-Lise; Gustavsen, Marte W; Sæther, Hanne S

2013-01-01

The presence of oligoclonal bands (OCB) in cerebrospinal fluid (CSF) is a typical finding in multiple sclerosis (MS). We applied data from Norwegian, Swedish and Danish (i.e. Scandinavian) MS patients from a genome-wide association study (GWAS) to search for genetic differences in MS relating...

Analysis of genetic effects of nuclear-cytoplasmic interaction on quantitative traits: genetic model for diploid plants.

Science.gov (United States)

Han, Lide; Yang, Jian; Zhu, Jun

2007-06-01

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction (NCI) as well as their genotype by environment (GE) interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction (AUP) method. Data on four quantitative traits (boll number, lint percentage, fiber length, and micronaire) in Upland cotton (Gossypium hirsutum L.) were analyzed as a worked example to show the effectiveness of the model.

Genetic and infectious profiles of Japanese multiple sclerosis patients.

Directory of Open Access Journals (Sweden)

Satoshi Yoshimura

Full Text Available BACKGROUND: Nationwide surveys conducted in Japan over the past thirty years have revealed a four-fold increase in the estimated number of multiple sclerosis (MS patients, a decrease in the age at onset, and successive increases in patients with conventional MS, which shows an involvement of multiple sites in the central nervous system, including the cerebrum and cerebellum. We aimed to clarify whether genetic and infectious backgrounds correlate to distinct disease phenotypes of MS in Japanese patients. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed HLA-DRB1 and -DPB1 alleles, and IgG antibodies specific for Helicobacter pylori, Chlamydia pneumoniae, varicella zoster virus, and Epstein-Barr virus nuclear antigen (EBNA in 145 MS patients and 367 healthy controls (HCs. Frequencies of DRB1*0405 and DPB1*0301 were significantly higher, and DRB1*0901 and DPB1*0401 significantly lower, in MS patients as compared with HCs. MS patients with DRB1*0405 had a significantly earlier age of onset and lower Progression Index than patients without this allele. The proportion and absolute number of patients with DRB1*0405 successively increased with advancing year of birth. In MS patients without DRB1*0405, the frequency of the DRB1*1501 allele was significantly higher, while the DRB1*0901 allele was significantly lower, compared with HCs. Furthermore, DRB1*0405-negative MS patients were significantly more likely to be positive for EBNA antibodies compared with HCs. CONCLUSIONS: Our study suggests that MS patients harboring DRB1*0405, a genetic risk factor for MS in the Japanese population, have a younger age at onset and a relatively benign disease course, while DRB1*0405-negative MS patients have features similar to Western-type MS in terms of association with Epstein-Barr virus infection and DRB1*1501. The recent increase of MS in young Japanese people may be caused, in part, by an increase in DRB1*0405-positive MS patients.

Nonlinear Modeling and Identification of an Aluminum Honeycomb Panel with Multiple Bolts

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Yongpeng Chu

2016-01-01

Full Text Available This paper focuses on the nonlinear dynamics modeling and parameter identification of an Aluminum Honeycomb Panel (AHP with multiple bolted joints. Finite element method using eight-node solid elements is exploited to model the panel and the bolted connection interface as a homogeneous, isotropic plate and as a thin layer of nonlinear elastic-plastic material, respectively. The material properties of a thin layer are defined by a bilinear elastic plastic model, which can describe the energy dissipation and softening phenomena in the bolted joints under nonlinear states. Experimental tests at low and high excitation levels are performed to reveal the dynamic characteristics of the bolted structure. In particular, the linear material parameters of the panel are identified via experimental tests at low excitation levels, whereas the nonlinear material parameters of the thin layer are updated by using the genetic algorithm to minimize the residual error between the measured and the simulation data at a high excitation level. It is demonstrated by comparing the frequency responses of the updated FEM and the experimental system that the thin layer of bilinear elastic-plastic material is very effective for modeling the nonlinear joint interface of the assembled structure with multiple bolts.

Short communication: Genetic lag represents commercial herd genetic merit more accurately than the 4-path selection model.

Science.gov (United States)

Dechow, C D; Rogers, G W

2018-05-01

Expectation of genetic merit in commercial dairy herds is routinely estimated using a 4-path genetic selection model that was derived for a closed population, but commercial herds using artificial insemination sires are not closed. The 4-path model also predicts a higher rate of genetic progress in elite herds that provide artificial insemination sires than in commercial herds that use such sires, which counters other theoretical assumptions and observations of realized genetic responses. The aim of this work is to clarify whether genetic merit in commercial herds is more accurately reflected under the assumptions of the 4-path genetic response formula or by a genetic lag formula. We demonstrate by tracing the transmission of genetic merit from parents to offspring that the rate of genetic progress in commercial dairy farms is expected to be the same as that in the genetic nucleus. The lag in genetic merit between the nucleus and commercial farms is a function of sire and dam generation interval, the rate of genetic progress in elite artificial insemination herds, and genetic merit of sires and dams. To predict how strategies such as the use of young versus daughter-proven sires, culling heifers following genomic testing, or selective use of sexed semen will alter genetic merit in commercial herds, genetic merit expectations for commercial herds should be modeled using genetic lag expectations. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Genetic and non-genetic animal models for autism spectrum disorders (ASD).

Science.gov (United States)

Ergaz, Zivanit; Weinstein-Fudim, Liza; Ornoy, Asher

2016-09-01

Autism spectrum disorder (ASD) is associated, in addition to complex genetic factors, with a variety of prenatal, perinatal and postnatal etiologies. We discuss the known animal models, mostly in mice and rats, of ASD that helps us to understand the etiology, pathogenesis and treatment of human ASD. We describe only models where behavioral testing has shown autistic like behaviors. Some genetic models mimic known human syndromes like fragile X where ASD is part of the clinical picture, and others are without defined human syndromes. Among the environmentally induced ASD models in rodents, the most common model is the one induced by valproic acid (VPA) either prenatally or early postnatally. VPA induces autism-like behaviors following single exposure during different phases of brain development, implying that the mechanism of action is via a general biological mechanism like epigenetic changes. Maternal infection and inflammation are also associated with ASD in man and animal models. Copyright © 2016 Elsevier Inc. All rights reserved.

Correlations in multiple production on nuclei and Glauber model of multiple scattering

International Nuclear Information System (INIS)

Zoller, V.R.; Nikolaev, N.N.

1982-01-01

Critical analysis of possibility for describing correlation phenomena during multiple production on nuclei within the framework of the Glauber multiple seattering model generalized for particle production processes with Capella, Krziwinski and Shabelsky has been performed. It was mainly concluded that the suggested generalization of the Glauber model gives dependences on Ng(Np) (where Ng-the number of ''grey'' tracess, and Np-the number of protons flying out of nucleus) and, eventually, on #betta# (where #betta#-the number of intranuclear interactions) contradicting experience. Independent of choice of relation between #betta# and Ng(Np) in the model the rapidity corrletor Rsub(eta) is overstated in the central region and understated in the region of nucleus fragmentation. In mean multiplicities these two contradictions of experience are disguised with random compensation and agreement with experience in Nsub(S) (function of Ng) cannot be an argument in favour of the model. It is concluded that eiconal model doesn't permit to quantitatively describe correlation phenomena during the multiple production on nuclei

Genetic analysis of the isolated Faroe Islands reveals SORCS3 as a potential multiple sclerosis risk gene

DEFF Research Database (Denmark)

Binzer, Stefanie; Stenager, Egon; Binzer, Michael

2016-01-01

BACKGROUND: In search of the missing heritability in multiple sclerosis (MS), additional approaches adding to the genetic discoveries of large genome-wide association studies are warranted. OBJECTIVE: The objective of this research paper is to search for rare genetic MS risk variants...... in the genetically homogenous population of the isolated Faroe Islands. METHODS: Twenty-nine Faroese MS cases and 28 controls were genotyped with the HumanOmniExpressExome-chip. The individuals make up 1596 pair-combinations in which we searched for identical-by-descent shared segments using the PLINK...... of neurotrophin factors and involvement in glutamate homeostasis. Although additional work is needed to scrutinise the genetic effect of the SORCS3-covering haplotype, this study suggests that SORCS3 may also be important in MS pathogenesis....

Multiple genetic variants associated with primary biliary cirrhosis in a Han Chinese population.

Science.gov (United States)

Dong, Ming; Li, Jinxin; Tang, Ruqi; Zhu, Ping; Qiu, Fang; Wang, Chan; Qiu, Jie; Wang, Lan; Dai, Yaping; Xu, Ping; Gao, Yueqiu; Han, Chongxu; Wang, Yongzhong; Wu, Jian; Wu, Xudong; Zhang, Kui; Dai, Na; Sun, Weihao; Zhou, Jianpo; Hu, Zhigang; Liu, Lei; Jiang, Yuzhang; Nie, Jinshan; Zhao, Yi; Gong, Yuhua; Tian, Ye; Ji, Hualiang; Jiao, Zhijun; Jiang, Po; Shi, Xingjuan; Jawed, Rohil; Zhang, Yu; Huang, Qinghai; Li, Enling; Wei, Yiran; Xie, Wei; Zhao, Weifeng; Liu, Xiang; Zhu, Xiang; Qiu, Hong; He, Gengsheng; Chen, Weichang; Seldin, Michael F; Gershwin, M Eric; Liu, Xiangdong; Ma, Xiong

2015-06-01

Multiple genome-wide association studies of primary biliary cirrhosis (PBC) in both European and Japanese ancestries have shown significant associations of many genetic loci contributing to the susceptibility to PBC. Major differences in susceptibility loci between these two population groups were observed. In this study, we examined whether the most significant loci observed in either European and/or Japanese cohorts are associated with PBC in a Han Chinese population. In 1070 PBC patients and 1198 controls, we observed highly significant associations at CD80 (rs2293370, P = 2.67 × 10(-8)) and TNFSF15 (rs4979462, P = 3.86 × 10(-8)) and significant associations at 17q12-21 (rs9303277), PDGFB (rs715505), NF-κB1 (rs7665090), IL12RB2 (rs11209050), and STAT4 (rs7574865; all corrected P values rs7574865) was strongly associated after additional control samples were analyzed. Our study is the first large-scale genetic analysis in a Han Chinese PBC cohort. These results do not only reflect that Han Chinese PBC patients share common genetic susceptibility genes with both their Japanese and European counterparts but also suggest a distinctly different genetic susceptibility profile.

Genetic algorithms and genetic programming for multiscale modeling: Applications in materials science and chemistry and advances in scalability

Science.gov (United States)

Sastry, Kumara Narasimha

2007-03-01

Effective and efficient rnultiscale modeling is essential to advance both the science and synthesis in a, wide array of fields such as physics, chemistry, materials science; biology, biotechnology and pharmacology. This study investigates the efficacy and potential of rising genetic algorithms for rnultiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo 1-x) alloy show that GP predicts all barriers with 0.1% error from calculations for less than 3% of active configurations, independent of type of potentials used to obtain the learning set of barriers via molecular dynamics. The resulting method enables 2--9 orders-of-magnitude increase in real-time dynamics simulations taking 4--7 orders-of-magnitude less CPU time. The second case study presents the application of multiobjective genetic algorithms (MOGAs) in multiscaling quantum chemistry simulations. Specifically, MOGAs are used to bridge high-level quantum chemistry and semiempirical methods to provide accurate representation of complex molecular excited-state and ground-state behavior. Results on ethylene and benzene---two common

Unleashing the power of human genetic variation knowledge: New Zealand stakeholder perspectives.

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Gu, Yulong; Warren, James Roy; Day, Karen Jean

2011-01-01

This study aimed to characterize the challenges in using genetic information in health care and to identify opportunities for improvement. Taking a grounded theory approach, semistructured interviews were conducted with 48 participants to collect multiple stakeholder perspectives on genetic services in New Zealand. Three themes emerged from the data: (1) four service delivery models were identified in operation, including both those expected models involving genetic counselors and variations that do not route through the formal genetic service program; (2) multiple barriers to sharing and using genetic information were perceived, including technological, organizational, institutional, legal, ethical, and social issues; and (3) impediments to wider use of genetic testing technology, including variable understanding of genetic test utilities among clinicians and the limited capacity of clinical genetic services. Targeting these problems, information technologies and knowledge management tools have the potential to support key tasks in genetic services delivery, improve knowledge processes, and enhance knowledge networks. Because of the effect of issues in genetic information and knowledge management, the potential of human genetic variation knowledge to enhance health care delivery has been put on a "leash."

Comparing ESC and iPSC—Based Models for Human Genetic Disorders

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Tomer Halevy

2014-10-01

Full Text Available Traditionally, human disorders were studied using animal models or somatic cells taken from patients. Such studies enabled the analysis of the molecular mechanisms of numerous disorders, and led to the discovery of new treatments. Yet, these systems are limited or even irrelevant in modeling multiple genetic diseases. The isolation of human embryonic stem cells (ESCs from diseased blastocysts, the derivation of induced pluripotent stem cells (iPSCs from patients’ somatic cells, and the new technologies for genome editing of pluripotent stem cells have opened a new window of opportunities in the field of disease modeling, and enabled studying diseases that couldn’t be modeled in the past. Importantly, despite the high similarity between ESCs and iPSCs, there are several fundamental differences between these cells, which have important implications regarding disease modeling. In this review we compare ESC-based models to iPSC-based models, and highlight the advantages and disadvantages of each system. We further suggest a roadmap for how to choose the optimal strategy to model each specific disorder.

Comparing ESC and iPSC-Based Models for Human Genetic Disorders.

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Halevy, Tomer; Urbach, Achia

2014-10-24

Traditionally, human disorders were studied using animal models or somatic cells taken from patients. Such studies enabled the analysis of the molecular mechanisms of numerous disorders, and led to the discovery of new treatments. Yet, these systems are limited or even irrelevant in modeling multiple genetic diseases. The isolation of human embryonic stem cells (ESCs) from diseased blastocysts, the derivation of induced pluripotent stem cells (iPSCs) from patients' somatic cells, and the new technologies for genome editing of pluripotent stem cells have opened a new window of opportunities in the field of disease modeling, and enabled studying diseases that couldn't be modeled in the past. Importantly, despite the high similarity between ESCs and iPSCs, there are several fundamental differences between these cells, which have important implications regarding disease modeling. In this review we compare ESC-based models to iPSC-based models, and highlight the advantages and disadvantages of each system. We further suggest a roadmap for how to choose the optimal strategy to model each specific disorder.

Optimal planning approaches with multiple impulses for rendezvous based on hybrid genetic algorithm and control method

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JingRui Zhang

2015-03-01

Full Text Available In this article, we focus on safe and effective completion of a rendezvous and docking task by looking at planning approaches and control with fuel-optimal rendezvous for a target spacecraft running on a near-circular reference orbit. A variety of existent practical path constraints are considered, including the constraints of field of view, impulses, and passive safety. A rendezvous approach is calculated by using a hybrid genetic algorithm with those constraints. Furthermore, a control method of trajectory tracking is adopted to overcome the external disturbances. Based on Clohessy–Wiltshire equations, we first construct the mathematical model of optimal planning approaches of multiple impulses with path constraints. Second, we introduce the principle of hybrid genetic algorithm with both stronger global searching ability and local searching ability. We additionally explain the application of this algorithm in the problem of trajectory planning. Then, we give three-impulse simulation examples to acquire an optimal rendezvous trajectory with the path constraints presented in this article. The effectiveness and applicability of the tracking control method are verified with the optimal trajectory above as control objective through the numerical simulation.

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.

Development and amplification of multiple co-dominant genetic markers from single spores of arbuscular mycorrhizal fungi by nested multiplex PCR

DEFF Research Database (Denmark)

Holtgrewe-Stukenbrock, Eva; Rosendahl, Søren

2005-01-01

Multiple co-dominant genetic markers from single spores of the arbuscular mycorrhizal (AM) fungi Glomus mosseae, Glomus caledonium, and Glomus geosporum were amplified by nested multiplex PCR using a combination of primers for simultaneous amplification of five loci in one PCR. Subsequently, each...... marker was amplified separately in nested PCR using specific primers. Polymorphic loci within the three putative single copy genes GmFOX2, GmTOR2, and GmGIN1 were characterized by sequencing and single strand conformation polymorphisms (SSCP). Primers specific for the LSU rDNA D2 region were included...... are homokaryotic. All isolates of G. mosseae had unique genotypes. The amplification of multiple co-dominant genetic markers from single spores by the nested multiplex PCR approach provides an important tool for future studies of AM fungi population genetics and evolution....

Evolutionary genetics: the Drosophila model

Indian Academy of Sciences (India)

Unknown

Evolutionary genetics straddles the two fundamental processes of life, ... of the genus Drosophila have been used extensively as model systems in experimental ... issue will prove interesting, informative and thought-provoking for both estab-.

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...

[The emphases and basic procedures of genetic counseling in psychotherapeutic model].

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Zhang, Yuan-Zhi; Zhong, Nanbert

2006-11-01

The emphases and basic procedures of genetic counseling are all different with those in old models. In the psychotherapeutic model, genetic counseling will not only focus on counselees' genetic disorders and birth defects, but also their psychological problems. "Client-centered therapy" termed by Carl Rogers plays an important role in genetic counseling process. The basic procedures of psychotherapeutic model of genetic counseling include 7 steps: initial contact, introduction, agendas, inquiry of family history, presenting information, closing the session and follow-up.

Multiple exostotic hypochondroplasia: Syndrome of combined hypochondroplasia and multiple exostoses

International Nuclear Information System (INIS)

Dominguez, R.; Young, L.W.; Girdany, B.R.; Steele, M.W.

1984-01-01

This is a report of a family with major focus on the daughter who had short stature. The mother had hypochondroplasia and the father had multiple exostoses. The daughter's skeletal roentgenograms show features of both hypochondroplasia and multiple exostoses. The roentgenographic, clinical and genetic aspects of these skeletal dysplasias are reviewed and hypochrondroplasia is contrasted with achondroplasia. The genetic and counseling implications of the association of hypochondroplasia and multiple exostoses are discussed. (orig.)

Multiple exostotic hypochondroplasia: Syndrome of combined hypochondroplasia and multiple exostoses

Energy Technology Data Exchange (ETDEWEB)

Dominguez, R.; Young, L.W.; Girdany, B.R.; Steele, M.W.

1984-07-01

This is a report of a family with major focus on the daughter who had short stature. The mother had hypochondroplasia and the father had multiple exostoses. The daughter's skeletal roentgenograms show features of both hypochondroplasia and multiple exostoses. The roentgenographic, clinical and genetic aspects of these skeletal dysplasias are reviewed and hypochrondroplasia is contrasted with achondroplasia. The genetic and counseling implications of the association of hypochondroplasia and multiple exostoses are discussed.

Testing for Nonuniform Differential Item Functioning with Multiple Indicator Multiple Cause Models

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Woods, Carol M.; Grimm, Kevin J.

2011-01-01

In extant literature, multiple indicator multiple cause (MIMIC) models have been presented for identifying items that display uniform differential item functioning (DIF) only, not nonuniform DIF. This article addresses, for apparently the first time, the use of MIMIC models for testing both uniform and nonuniform DIF with categorical indicators. A…

The five-factor model of personality and borderline personality disorder: a genetic analysis of comorbidity.

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Distel, Marijn A; Trull, Timothy J; Willemsen, Gonneke; Vink, Jacqueline M; Derom, Catherine A; Lynskey, Michael; Martin, Nicholas G; Boomsma, Dorret I

2009-12-15

Recently, the nature of personality disorders and their relationship with normal personality traits has received extensive attention. The five-factor model (FFM) of personality, consisting of the personality traits neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness, is one of the proposed models to conceptualize personality disorders as maladaptive variants of continuously distributed personality traits. The present study examined the phenotypic and genetic association between borderline personality and FFM personality traits. Data were available for 4403 monozygotic twins, 4425 dizygotic twins, and 1661 siblings from 6140 Dutch, Belgian, and Australian families. Broad-sense heritability estimates for neuroticism, agreeableness, conscientiousness, extraversion, openness to experience, and borderline personality were 43%, 36%, 43%, 47%, 54%, and 45%, respectively. Phenotypic correlations between borderline personality and the FFM personality traits ranged from .06 for openness to experience to .68 for neuroticism. Multiple regression analyses showed that a combination of high neuroticism and low agreeableness best predicted borderline personality. Multivariate genetic analyses showed the genetic factors that influence individual differences in neuroticism, agreeableness, conscientiousness, and extraversion account for all genetic liability to borderline personality. Unique environmental effects on borderline personality, however, were not completely shared with those for the FFM traits (33% is unique to borderline personality). Borderline personality shares all genetic variation with neuroticism, agreeableness, conscientiousness, and extraversion. The unique environmental influences specific to borderline personality may cause individuals with a specific pattern of personality traits to cross a threshold and develop borderline personality.

An approximate multitrait model for genetic evaluation in dairy cattle with a robust estimation of genetic trends (Open Access publication

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Madsen Per

2007-07-01

Full Text Available Abstract In a stochastic simulation study of a dairy cattle population three multitrait models for estimation of genetic parameters and prediction of breeding values were compared. The first model was an approximate multitrait model using a two-step procedure. The first step was a single trait model for all traits. The solutions for fixed effects from these analyses were subtracted from the phenotypes. A multitrait model only containing an overall mean, an additive genetic and a residual term was applied on these preadjusted data. The second model was similar to the first model, but the multitrait model also contained a year effect. The third model was a full multitrait model. Genetic trends for total merit and for the individual traits in the breeding goal were compared for the three scenarios to rank the models. The full multitrait model gave the highest genetic response, but was not significantly better than the approximate multitrait model including a year effect. The inclusion of a year effect into the second step of the approximate multitrait model significantly improved the genetic trend for total merit. In this study, estimation of genetic parameters for breeding value estimation using models corresponding to the ones used for prediction of breeding values increased the accuracy on the breeding values and thereby the genetic progress.

A major and stable QTL associated with seed weight in soybean across multiple environments and genetic backgrounds.

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Kato, Shin; Sayama, Takashi; Fujii, Kenichiro; Yumoto, Setsuzo; Kono, Yuhi; Hwang, Tae-Young; Kikuchi, Akio; Takada, Yoshitake; Tanaka, Yu; Shiraiwa, Tatsuhiko; Ishimoto, Masao

2014-06-01

We detected a QTL for single seed weight in soybean that was stable across multiple environments and genetic backgrounds with the use of two recombinant inbred line populations. Single seed weight (SSW) in soybean is a key determinant of both seed yield and the quality of soy food products, and it exhibits wide variation. SSW is under genetic control, but the molecular mechanisms of such control remain unclear. We have now investigated quantitative trait loci (QTLs) for SSW in soybean and have identified such a QTL that is stable across multiple environments and genetic backgrounds. Two populations of 225 and 250 recombinant inbred lines were developed from crosses between Japanese and US cultivars of soybean that differ in SSW by a factor of ~2, and these populations were grown in at least three different environments. A whole-genome panel comprising 304 simple sequence repeat (SSR) loci was applied to mapping in each population. We identified 15 significant QTLs for SSW dispersed among 11 chromosomes in the two populations. One QTL located between Sat_284 and Sat_292 on chromosome 17 was detected (3.6 soybean.

Population genetics of Setaria viridis, a new model system.

Science.gov (United States)

Huang, Pu; Feldman, Maximilian; Schroder, Stephan; Bahri, Bochra A; Diao, Xianmin; Zhi, Hui; Estep, Matt; Baxter, Ivan; Devos, Katrien M; Kellogg, Elizabeth A

2014-10-01

An extensive survey of the standing genetic variation in natural populations is among the priority steps in developing a species into a model system. In recent years, green foxtail (Setaria viridis), along with its domesticated form foxtail millet (S. italica), has rapidly become a promising new model system for C4 grasses and bioenergy crops, due to its rapid life cycle, large amount of seed production and small diploid genome, among other characters. However, remarkably little is known about the genetic diversity in natural populations of this species. In this study, we survey the genetic diversity of a worldwide sample of more than 200 S. viridis accessions, using the genotyping-by-sequencing technique. Two distinct genetic groups in S. viridis and a third group resembling S. italica were identified, with considerable admixture among the three groups. We find the genetic variation of North American S. viridis correlates with both geography and climate and is representative of the total genetic diversity in this species. This pattern may reflect several introduction/dispersal events of S. viridis into North America. We also modelled demographic history and show signal of recent population decline in one subgroup. Finally, we show linkage disequilibrium decay is rapid (<45 kb) in our total sample and slow in genetic subgroups. These results together provide an in-depth understanding of the pattern of genetic diversity of this new model species on a broad geographic scale. They also provide key guidelines for on-going and future work including germplasm preservation, local adaptation, crossing designs and genomewide association studies. © 2014 John Wiley & Sons Ltd.

Genetic evidence of multiple loci in dystocia - difficult labour

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Westgren Magnus

2010-06-01

Full Text Available Abstract Background Dystocia, difficult labour, is a common but also complex problem during childbirth. It can be attributed to either weak contractions of the uterus, a large infant, reduced capacity of the pelvis or combinations of these. Previous studies have indicated that there is a genetic component in the susceptibility of experiencing dystocia. The purpose of this study was to identify susceptibility genes in dystocia. Methods A total of 104 women in 47 families were included where at least two sisters had undergone caesarean section at a gestational length of 286 days or more at their first delivery. Study of medical records and a telephone interview was performed to identify subjects with dystocia. Whole-genome scanning using Affymetrix genotyping-arrays and non-parametric linkage (NPL analysis was made in 39 women exhibiting the phenotype of dystocia from 19 families. In 68 women re-sequencing was performed of candidate genes showing suggestive linkage: oxytocin (OXT on chromosome 20 and oxytocin-receptor (OXTR on chromosome 3. Results We found a trend towards linkage with suggestive NPL-score (3.15 on chromosome 12p12. Suggestive linkage peaks were observed on chromosomes 3, 4, 6, 10, 20. Re-sequencing of OXT and OXTR did not reveal any causal variants. Conclusions Dystocia is likely to have a genetic component with variations in multiple genes affecting the patient outcome. We found 6 loci that could be re-evaluated in larger patient cohorts.

Genetic evidence of multiple loci in dystocia - difficult labour

Science.gov (United States)

2010-01-01

Background Dystocia, difficult labour, is a common but also complex problem during childbirth. It can be attributed to either weak contractions of the uterus, a large infant, reduced capacity of the pelvis or combinations of these. Previous studies have indicated that there is a genetic component in the susceptibility of experiencing dystocia. The purpose of this study was to identify susceptibility genes in dystocia. Methods A total of 104 women in 47 families were included where at least two sisters had undergone caesarean section at a gestational length of 286 days or more at their first delivery. Study of medical records and a telephone interview was performed to identify subjects with dystocia. Whole-genome scanning using Affymetrix genotyping-arrays and non-parametric linkage (NPL) analysis was made in 39 women exhibiting the phenotype of dystocia from 19 families. In 68 women re-sequencing was performed of candidate genes showing suggestive linkage: oxytocin (OXT) on chromosome 20 and oxytocin-receptor (OXTR) on chromosome 3. Results We found a trend towards linkage with suggestive NPL-score (3.15) on chromosome 12p12. Suggestive linkage peaks were observed on chromosomes 3, 4, 6, 10, 20. Re-sequencing of OXT and OXTR did not reveal any causal variants. Conclusions Dystocia is likely to have a genetic component with variations in multiple genes affecting the patient outcome. We found 6 loci that could be re-evaluated in larger patient cohorts. PMID:20587075

Invited review: Genetic and genomic mouse models for livestock research

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D. Arends

2018-02-01

Full Text Available Knowledge about the function and functioning of single or multiple interacting genes is of the utmost significance for understanding the organism as a whole and for accurate livestock improvement through genomic selection. This includes, but is not limited to, understanding the ontogenetic and environmentally driven regulation of gene action contributing to simple and complex traits. Genetically modified mice, in which the functions of single genes are annotated; mice with reduced genetic complexity; and simplified structured populations are tools to gain fundamental knowledge of inheritance patterns and whole system genetics and genomics. In this review, we briefly describe existing mouse resources and discuss their value for fundamental and applied research in livestock.

Core neuropathological abnormalities in progranulin-deficient mice are penetrant on multiple genetic backgrounds.

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Petkau, T L; Hill, A; Leavitt, B R

2016-02-19

Loss-of-function mutations in the progranulin gene (GRN) are a common cause of familial frontotemporal lobar degeneration (FTLD). A high degree of heterogeneity in the age-of-onset, duration of disease, and clinical presentation of FTLD, even among families carrying the same GRN mutation, suggests that additional modifying genes may be important to pathogenesis. Progranulin-knockout mice display subtle behavioral abnormalities and progressive neuropathological changes, as well as altered dendritic morphology and synaptic deficits in the hippocampus. In this study we evaluated multiple neuropathological endpoints in aged progranulin knockout mice and their wild-type littermates on two different genetic backgrounds: C57Bl/6 and 129/SvImJ. We find that in most brain regions, both strains are susceptible to progranulin-mediated neuropathological phenotypes, including astrogliosis, microgliosis, and highly accelerated deposition of the aging pigment lipofuscin. Neuroinflammation due to progranulin deficiency is exaggerated in the B6 strain and present, but less pronounced, in the 129 strain. Differences between the strains in hippocampal neuron counts and neuronal morphology suggest a complex role for progranulin in the hippocampus. We conclude that core progranulin-mediated neurodegenerative phenotypes are penetrant on multiple inbred mouse strains, but that genetic background modulates progranulin's role in neuroinflammation and hippocampal biology. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Study on validation method for femur finite element model under multiple loading conditions

Science.gov (United States)

Guan, Fengjiao; Zhang, Guanjun; Liu, Jie; Wang, Shujing; Luo, Xu

2018-03-01

Acquisition of accurate and reliable constitutive parameters related to bio-tissue materials was beneficial to improve biological fidelity of a Finite Element (FE) model and predict impact damages more effectively. In this paper, a femur FE model was established under multiple loading conditions with diverse impact positions. Then, based on sequential response surface method and genetic algorithms, the material parameters identification was transformed to a multi-response optimization problem. Finally, the simulation results successfully coincided with force-displacement curves obtained by numerous experiments. Thus, computational accuracy and efficiency of the entire inverse calculation process were enhanced. This method was able to effectively reduce the computation time in the inverse process of material parameters. Meanwhile, the material parameters obtained by the proposed method achieved higher accuracy.

Genetic and Functional Analyses of SHANK2 Mutations Suggest a Multiple Hit Model of Autism Spectrum Disorders

Science.gov (United States)

Leblond, Claire S.; Heinrich, Jutta; Delorme, Richard; Proepper, Christian; Betancur, Catalina; Huguet, Guillaume; Konyukh, Marina; Chaste, Pauline; Ey, Elodie; Rastam, Maria; Anckarsäter, Henrik; Nygren, Gudrun; Gillberg, I. Carina; Melke, Jonas; Toro, Roberto; Regnault, Beatrice; Fauchereau, Fabien; Mercati, Oriane; Lemière, Nathalie; Skuse, David; Poot, Martin; Holt, Richard; Monaco, Anthony P.; Järvelä, Irma; Kantojärvi, Katri; Vanhala, Raija; Curran, Sarah; Collier, David A.; Bolton, Patrick; Chiocchetti, Andreas; Klauck, Sabine M.; Poustka, Fritz; Freitag, Christine M.; Waltes, Regina; Kopp, Marnie; Duketis, Eftichia; Bacchelli, Elena; Minopoli, Fiorella; Ruta, Liliana; Battaglia, Agatino; Mazzone, Luigi; Maestrini, Elena; Sequeira, Ana F.; Oliveira, Barbara; Vicente, Astrid; Oliveira, Guiomar; Pinto, Dalila; Scherer, Stephen W.; Zelenika, Diana; Delepine, Marc; Lathrop, Mark; Bonneau, Dominique; Guinchat, Vincent; Devillard, Françoise; Assouline, Brigitte; Mouren, Marie-Christine; Leboyer, Marion; Gillberg, Christopher; Boeckers, Tobias M.; Bourgeron, Thomas

2012-01-01

Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders with a complex inheritance pattern. While many rare variants in synaptic proteins have been identified in patients with ASD, little is known about their effects at the synapse and their interactions with other genetic variations. Here, following the discovery of two de novo SHANK2 deletions by the Autism Genome Project, we identified a novel 421 kb de novo SHANK2 deletion in a patient with autism. We then sequenced SHANK2 in 455 patients with ASD and 431 controls and integrated these results with those reported by Berkel et al. 2010 (n = 396 patients and n = 659 controls). We observed a significant enrichment of variants affecting conserved amino acids in 29 of 851 (3.4%) patients and in 16 of 1,090 (1.5%) controls (P = 0.004, OR = 2.37, 95% CI = 1.23–4.70). In neuronal cell cultures, the variants identified in patients were associated with a reduced synaptic density at dendrites compared to the variants only detected in controls (P = 0.0013). Interestingly, the three patients with de novo SHANK2 deletions also carried inherited CNVs at 15q11–q13 previously associated with neuropsychiatric disorders. In two cases, the nicotinic receptor CHRNA7 was duplicated and in one case the synaptic translation repressor CYFIP1 was deleted. These results strengthen the role of synaptic gene dysfunction in ASD but also highlight the presence of putative modifier genes, which is in keeping with the “multiple hit model” for ASD. A better knowledge of these genetic interactions will be necessary to understand the complex inheritance pattern of ASD. PMID:22346768

Shaping asteroid models using genetic evolution (SAGE)

Science.gov (United States)

Bartczak, P.; Dudziński, G.

2018-02-01

In this work, we present SAGE (shaping asteroid models using genetic evolution), an asteroid modelling algorithm based solely on photometric lightcurve data. It produces non-convex shapes, orientations of the rotation axes and rotational periods of asteroids. The main concept behind a genetic evolution algorithm is to produce random populations of shapes and spin-axis orientations by mutating a seed shape and iterating the process until it converges to a stable global minimum. We tested SAGE on five artificial shapes. We also modelled asteroids 433 Eros and 9 Metis, since ground truth observations for them exist, allowing us to validate the models. We compared the derived shape of Eros with the NEAR Shoemaker model and that of Metis with adaptive optics and stellar occultation observations since other models from various inversion methods were available for Metis.

Numeral eddy current sensor modelling based on genetic neural network

International Nuclear Information System (INIS)

Yu Along

2008-01-01

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method

Genetic demographic networks: Mathematical model and applications.

Science.gov (United States)

Kimmel, Marek; Wojdyła, Tomasz

2016-10-01

Recent improvement in the quality of genetic data obtained from extinct human populations and their ancestors encourages searching for answers to basic questions regarding human population history. The most common and successful are model-based approaches, in which genetic data are compared to the data obtained from the assumed demography model. Using such approach, it is possible to either validate or adjust assumed demography. Model fit to data can be obtained based on reverse-time coalescent simulations or forward-time simulations. In this paper we introduce a computational method based on mathematical equation that allows obtaining joint distributions of pairs of individuals under a specified demography model, each of them characterized by a genetic variant at a chosen locus. The two individuals are randomly sampled from either the same or two different populations. The model assumes three types of demographic events (split, merge and migration). Populations evolve according to the time-continuous Moran model with drift and Markov-process mutation. This latter process is described by the Lyapunov-type equation introduced by O'Brien and generalized in our previous works. Application of this equation constitutes an original contribution. In the result section of the paper we present sample applications of our model to both simulated and literature-based demographies. Among other we include a study of the Slavs-Balts-Finns genetic relationship, in which we model split and migrations between the Balts and Slavs. We also include another example that involves the migration rates between farmers and hunters-gatherers, based on modern and ancient DNA samples. This latter process was previously studied using coalescent simulations. Our results are in general agreement with the previous method, which provides validation of our approach. Although our model is not an alternative to simulation methods in the practical sense, it provides an algorithm to compute pairwise

Genetic variants of the alpha-synuclein gene SNCA are associated with multiple system atrophy.

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Ammar Al-Chalabi

Full Text Available BACKGROUND: Multiple system atrophy (MSA is a progressive neurodegenerative disorder characterized by parkinsonism, cerebellar ataxia and autonomic dysfunction. Pathogenic mechanisms remain obscure but the neuropathological hallmark is the presence of alpha-synuclein-immunoreactive glial cytoplasmic inclusions. Genetic variants of the alpha-synuclein gene, SNCA, are thus strong candidates for genetic association with MSA. One follow-up to a genome-wide association of Parkinson's disease has identified association of a SNP in SNCA with MSA. METHODOLOGY/FINDINGS: We evaluated 32 SNPs in the SNCA gene in a European population of 239 cases and 617 controls recruited as part of the Neuroprotection and Natural History in Parkinson Plus Syndromes (NNIPPS study. We used 161 independently collected samples for replication. Two SNCA SNPs showed association with MSA: rs3822086 (P = 0.0044, and rs3775444 (P = 0.012, although only the first survived correction for multiple testing. In the MSA-C subgroup the association strengthened despite more than halving the number of cases: rs3822086 P = 0.0024, OR 2.153, (95% CI 1.3-3.6; rs3775444 P = 0.0017, OR 4.386 (95% CI 1.6-11.7. A 7-SNP haplotype incorporating three SNPs either side of rs3822086 strengthened the association with MSA-C further (best haplotype, P = 8.7 x 10(-4. The association with rs3822086 was replicated in the independent samples (P = 0.035. CONCLUSIONS/SIGNIFICANCE: We report a genetic association between MSA and alpha-synuclein which has replicated in independent samples. The strongest association is with the cerebellar subtype of MSA. TRIAL REGISTRATION: ClinicalTrials.gov NCT00211224.

Hierarchical linear modeling of longitudinal pedigree data for genetic association analysis

DEFF Research Database (Denmark)

Tan, Qihua; B Hjelmborg, Jacob V; Thomassen, Mads

2014-01-01

-effect models to explicitly model the genetic relationship. These have proved to be an efficient way of dealing with sample clustering in pedigree data. Although current algorithms implemented in popular statistical packages are useful for adjusting relatedness in the mixed modeling of genetic effects...... associated with blood pressure with estimated inflation factors of 0.99, suggesting that our modeling of random effects efficiently handles the genetic relatedness in pedigrees. Application to simulated data captures important variants specified in the simulation. Our results show that the method is useful......Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees, which could affect statistical assessment of the genetic effects. Approaches have been proposed to integrate kinship correlation into the mixed...

Parametric optimization of multiple quality characteristics in laser cutting of Inconel-718 by using hybrid approach of multiple regression analysis and genetic algorithm

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Shrivastava, Prashant Kumar; Pandey, Arun Kumar

2018-06-01

Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.

Directional genetic selection by pulp mill effluent on multiple natural populations of three-spined stickleback (Gasterosteus aculeatus).

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Lind, Emma E; Grahn, Mats

2011-05-01

Contamination can cause a rapid environmental change which may require populations to respond with evolutionary changes. To evaluate the effects of pulp mill effluents on population genetics, we sampled three-spined sticklebacks (Gasterosteus aculeatus) near four pulp mills and four adjacent reference sites and analyzed Amplified Fragment Length Polymorphism (AFLP) to compare genetic variability. A fine scale genetic structure was detected and samples from polluted sites separated from reference sites in multidimensional scaling plots (Pselection. When removing 13 F(ST)-outlier loci, significant at the Pselective agent on natural populations of G. aculeatus, causing a convergence in genotype composition change at multiple sites in an open environment. © The Author(s) 2011. This article is published with open access at Springerlink.com

Genetic screens in Caenorhabditis elegans models for neurodegenerative diseases

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Alvarenga Fernandes Sin, Olga; Michels, Helen; Nollen, Ellen A. A.

2014-01-01

Caenorhabditis elegans comprises unique features that make it an attractive model organism in diverse fields of biology. Genetic screens are powerful to identify genes and C. elegans can be customized to forward or reverse genetic screens and to establish gene function. These genetic screens can be

Improvement in genetic evaluation of female fertility in dairy cattle using multiple-trait models including milk production traits

DEFF Research Database (Denmark)

Sun, C; Madsen, P; Lund, M S

2010-01-01

This study investigated the improvement in genetic evaluation of fertility traits by using production traits as secondary traits (MILK = 305-d milk yield, FAT = 305-d fat yield, and PROT = 305-d protein yield). Data including 471,742 records from first lactations of Denmark Holstein cows, covering...... the years of inseminations during first lactations from 1995 to 2004, were analyzed. Six fertility traits (i.e., interval in days from calving to first insemination, calving interval, days open, interval in days from first to last insemination, numbers of inseminations per conception, and nonreturn rate...... stability and predictive ability than single-trait models for all the fertility traits, except for nonreturn rate within 56 d after first service. The stability and predictive ability for the model including MILK or PROT were similar to the model including all 3 milk production traits and better than...

Poisson versus threshold models for genetic analysis of clinical mastitis in US Holsteins.

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Vazquez, A I; Weigel, K A; Gianola, D; Bates, D M; Perez-Cabal, M A; Rosa, G J M; Chang, Y M

2009-10-01

Typically, clinical mastitis is coded as the presence or absence of disease in a given lactation, and records are analyzed with either linear models or binary threshold models. Because the presence of mastitis may include cows with multiple episodes, there is a loss of information when counts are treated as binary responses. Poisson models are appropriated for random variables measured as the number of events, and although these models are used extensively in studying the epidemiology of mastitis, they have rarely been used for studying the genetic aspects of mastitis. Ordinal threshold models are pertinent for ordered categorical responses; although one can hypothesize that the number of clinical mastitis episodes per animal reflects a continuous underlying increase in mastitis susceptibility, these models have rarely been used in genetic analysis of mastitis. The objective of this study was to compare probit, Poisson, and ordinal threshold models for the genetic evaluation of US Holstein sires for clinical mastitis. Mastitis was measured as a binary trait or as the number of mastitis cases. Data from 44,908 first-parity cows recorded in on-farm herd management software were gathered, edited, and processed for the present study. The cows were daughters of 1,861 sires, distributed over 94 herds. Predictive ability was assessed via a 5-fold cross-validation using 2 loss functions: mean squared error of prediction (MSEP) as the end point and a cost difference function. The heritability estimates were 0.061 for mastitis measured as a binary trait in the probit model and 0.085 and 0.132 for the number of mastitis cases in the ordinal threshold and Poisson models, respectively; because of scale differences, only the probit and ordinal threshold models are directly comparable. Among healthy animals, MSEP was smallest for the probit model, and the cost function was smallest for the ordinal threshold model. Among diseased animals, MSEP and the cost function were smallest

Using Workflow Modeling to Identify Areas to Improve Genetic Test Processes in the University of Maryland Translational Pharmacogenomics Project.

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Cutting, Elizabeth M; Overby, Casey L; Banchero, Meghan; Pollin, Toni; Kelemen, Mark; Shuldiner, Alan R; Beitelshees, Amber L

Delivering genetic test results to clinicians is a complex process. It involves many actors and multiple steps, requiring all of these to work together in order to create an optimal course of treatment for the patient. We used information gained from focus groups in order to illustrate the current process of delivering genetic test results to clinicians. We propose a business process model and notation (BPMN) representation of this process for a Translational Pharmacogenomics Project being implemented at the University of Maryland Medical Center, so that personalized medicine program implementers can identify areas to improve genetic testing processes. We found that the current process could be improved to reduce input errors, better inform and notify clinicians about the implications of certain genetic tests, and make results more easily understood. We demonstrate our use of BPMN to improve this important clinical process for CYP2C19 genetic testing in patients undergoing invasive treatment of coronary heart disease.

Animal models for human genetic diseases | Sharif | African Journal ...

African Journals Online (AJOL)

The study of human genetic diseases can be greatly aided by animal models because of their similarity to humans in terms of genetics. In addition to understand diverse aspects of basic biology, model organisms are extensively used in applied research in agriculture, industry, and also in medicine, where they are used to ...

Broad Bandwidth or High Fidelity? Evidence from the Structure of Genetic and Environmental Effects on the Facets of the Five Factor Model

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Briley, Daniel A.; Tucker-Drob, Elliot M.

2017-01-01

The Five Factor Model (FFM) of personality is well-established at the phenotypic level, but much less is known about the coherence of the genetic and environmental influences within each personality domain. Univariate behavioral genetic analyses have consistently found the influence of additive genes and nonshared environment on multiple personality facets, but the extent to which genetic and environmental influences on specific facets reflect more general influences on higher order factors is less clear. We applied a multivariate quantitative-genetic approach to scores on the CPI-Big Five facets for 490 monozygotic and 317 dizygotic twins who took part in the National Merit Twin Study. Our results revealed a complex genetic structure for facets composing all five factors, with both domain-general and facet-specific genetic and environmental influences. Models that required common genetic and environmental influences on each facet to occur by way of effects on a higher order trait did not fit as well as models allowing for common genetic and environmental effects to act directly on the facets for three of the Big Five domains. These results add to the growing body of literature indicating that important variation in personality occurs at the facet level which may be overshadowed by aggregating to the trait level. Research at the facet level, rather than the factor level, is likely to have pragmatic advantages in future research on the genetics of personality. PMID:22695681

Population genetic diversity and fitness in multiple environments

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McGreevy Thomas J

2010-07-01

Full Text Available Abstract Background When a large number of alleles are lost from a population, increases in individual homozygosity may reduce individual fitness through inbreeding depression. Modest losses of allelic diversity may also negatively impact long-term population viability by reducing the capacity of populations to adapt to altered environments. However, it is not clear how much genetic diversity within populations may be lost before populations are put at significant risk. Development of tools to evaluate this relationship would be a valuable contribution to conservation biology. To address these issues, we have created an experimental system that uses laboratory populations of an estuarine crustacean, Americamysis bahia with experimentally manipulated levels of genetic diversity. We created replicate cultures with five distinct levels of genetic diversity and monitored them for 16 weeks in both permissive (ambient seawater and stressful conditions (diluted seawater. The relationship between molecular genetic diversity at presumptive neutral loci and population vulnerability was assessed by AFLP analysis. Results Populations with very low genetic diversity demonstrated reduced fitness relative to high diversity populations even under permissive conditions. Population performance decreased in the stressful environment for all levels of genetic diversity relative to performance in the permissive environment. Twenty percent of the lowest diversity populations went extinct before the end of the study in permissive conditions, whereas 73% of the low diversity lines went extinct in the stressful environment. All high genetic diversity populations persisted for the duration of the study, although population sizes and reproduction were reduced under stressful environmental conditions. Levels of fitness varied more among replicate low diversity populations than among replicate populations with high genetic diversity. There was a significant correlation

Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis

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Sawcer, Stephen; Hellenthal, Garrett; Pirinen, Matti; Spencer, Chris C.A.; Patsopoulos, Nikolaos A.; Moutsianas, Loukas; Dilthey, Alexander; Su, Zhan; Freeman, Colin; Hunt, Sarah E.; Edkins, Sarah; Gray, Emma; Booth, David R.; Potter, Simon C.; Goris, An; Band, Gavin; Oturai, Annette Bang; Strange, Amy; Saarela, Janna; Bellenguez, Céline; Fontaine, Bertrand; Gillman, Matthew; Hemmer, Bernhard; Gwilliam, Rhian; Zipp, Frauke; Jayakumar, Alagurevathi; Martin, Roland; Leslie, Stephen; Hawkins, Stanley; Giannoulatou, Eleni; D’alfonso, Sandra; Blackburn, Hannah; Boneschi, Filippo Martinelli; Liddle, Jennifer; Harbo, Hanne F.; Perez, Marc L.; Spurkland, Anne; Waller, Matthew J; Mycko, Marcin P.; Ricketts, Michelle; Comabella, Manuel; Hammond, Naomi; Kockum, Ingrid; McCann, Owen T.; Ban, Maria; Whittaker, Pamela; Kemppinen, Anu; Weston, Paul; Hawkins, Clive; Widaa, Sara; Zajicek, John; Dronov, Serge; Robertson, Neil; Bumpstead, Suzannah J.; Barcellos, Lisa F.; Ravindrarajah, Rathi; Abraham, Roby; Alfredsson, Lars; Ardlie, Kristin; Aubin, Cristin; Baker, Amie; Baker, Katharine; Baranzini, Sergio E.; Bergamaschi, Laura; Bergamaschi, Roberto; Bernstein, Allan; Berthele, Achim; Boggild, Mike; Bradfield, Jonathan P.; Brassat, David; Broadley, Simon A.; Buck, Dorothea; Butzkueven, Helmut; Capra, Ruggero; Carroll, William M.; Cavalla, Paola; Celius, Elisabeth G.; Cepok, Sabine; Chiavacci, Rosetta; Clerget-Darpoux, Françoise; Clysters, Katleen; Comi, Giancarlo; Cossburn, Mark; Cournu-Rebeix, Isabelle; Cox, Mathew B.; Cozen, Wendy; Cree, Bruce A.C.; Cross, Anne H.; Cusi, Daniele; Daly, Mark J.; Davis, Emma; de Bakker, Paul I.W.; Debouverie, Marc; D’hooghe, Marie Beatrice; Dixon, Katherine; Dobosi, Rita; Dubois, Bénédicte; Ellinghaus, David; Elovaara, Irina; Esposito, Federica; Fontenille, Claire; Foote, Simon; Franke, Andre; Galimberti, Daniela; Ghezzi, Angelo; Glessner, Joseph; Gomez, Refujia; Gout, Olivier; Graham, Colin; Grant, Struan F.A.; Guerini, Franca Rosa; Hakonarson, Hakon; Hall, Per; Hamsten, Anders; Hartung, Hans-Peter; Heard, Rob N.; Heath, Simon; Hobart, Jeremy; Hoshi, Muna; Infante-Duarte, Carmen; Ingram, Gillian; Ingram, Wendy; Islam, Talat; Jagodic, Maja; Kabesch, Michael; Kermode, Allan G.; Kilpatrick, Trevor J.; Kim, Cecilia; Klopp, Norman; Koivisto, Keijo; Larsson, Malin; Lathrop, Mark; Lechner-Scott, Jeannette S.; Leone, Maurizio A.; Leppä, Virpi; Liljedahl, Ulrika; Bomfim, Izaura Lima; Lincoln, Robin R.; Link, Jenny; Liu, Jianjun; Lorentzen, Åslaug R.; Lupoli, Sara; Macciardi, Fabio; Mack, Thomas; Marriott, Mark; Martinelli, Vittorio; Mason, Deborah; McCauley, Jacob L.; Mentch, Frank; Mero, Inger-Lise; Mihalova, Tania; Montalban, Xavier; Mottershead, John; Myhr, Kjell-Morten; Naldi, Paola; Ollier, William; Page, Alison; Palotie, Aarno; Pelletier, Jean; Piccio, Laura; Pickersgill, Trevor; Piehl, Fredrik; Pobywajlo, Susan; Quach, Hong L.; Ramsay, Patricia P.; Reunanen, Mauri; Reynolds, Richard; Rioux, John D.; Rodegher, Mariaemma; Roesner, Sabine; Rubio, Justin P.; Rückert, Ina-Maria; Salvetti, Marco; Salvi, Erika; Santaniello, Adam; Schaefer, Catherine A.; Schreiber, Stefan; Schulze, Christian; Scott, Rodney J.; Sellebjerg, Finn; Selmaj, Krzysztof W.; Sexton, David; Shen, Ling; Simms-Acuna, Brigid; Skidmore, Sheila; Sleiman, Patrick M.A.; Smestad, Cathrine; Sørensen, Per Soelberg; Søndergaard, Helle Bach; Stankovich, Jim; Strange, Richard C.; Sulonen, Anna-Maija; Sundqvist, Emilie; Syvänen, Ann-Christine; Taddeo, Francesca; Taylor, Bruce; Blackwell, Jenefer M.; Tienari, Pentti; Bramon, Elvira; Tourbah, Ayman; Brown, Matthew A.; Tronczynska, Ewa; Casas, Juan P.; Tubridy, Niall; Corvin, Aiden; Vickery, Jane; Jankowski, Janusz; Villoslada, Pablo; Markus, Hugh S.; Wang, Kai; Mathew, Christopher G.; Wason, James; Palmer, Colin N.A.; Wichmann, H-Erich; Plomin, Robert; Willoughby, Ernest; Rautanen, Anna; Winkelmann, Juliane; Wittig, Michael; Trembath, Richard C.; Yaouanq, Jacqueline; Viswanathan, Ananth C.; Zhang, Haitao; Wood, Nicholas W.; Zuvich, Rebecca; Deloukas, Panos; Langford, Cordelia; Duncanson, Audrey; Oksenberg, Jorge R.; Pericak-Vance, Margaret A.; Haines, Jonathan L.; Olsson, Tomas; Hillert, Jan; Ivinson, Adrian J.; De Jager, Philip L.; Peltonen, Leena; Stewart, Graeme J.; Hafler, David A.; Hauser, Stephen L.; McVean, Gil; Donnelly, Peter; Compston, Alastair

2011-01-01

Multiple sclerosis (OMIM 126200) is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability.1 Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals;2,3 and systematic attempts to identify linkage in multiplex families have confirmed that variation within the Major Histocompatibility Complex (MHC) exerts the greatest individual effect on risk.4 Modestly powered Genome-Wide Association Studies (GWAS)5-10 have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects play a key role in disease susceptibility.11 Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the Class I region. Immunologically relevant genes are significantly over-represented amongst those mapping close to the identified loci and particularly implicate T helper cell differentiation in the pathogenesis of multiple sclerosis. PMID:21833088

Modeling Rabbit Responses to Single and Multiple Aerosol ...

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Journal Article Survival models are developed here to predict response and time-to-response for mortality in rabbits following exposures to single or multiple aerosol doses of Bacillus anthracis spores. Hazard function models were developed for a multiple dose dataset to predict the probability of death through specifying dose-response functions and the time between exposure and the time-to-death (TTD). Among the models developed, the best-fitting survival model (baseline model) has an exponential dose-response model with a Weibull TTD distribution. Alternative models assessed employ different underlying dose-response functions and use the assumption that, in a multiple dose scenario, earlier doses affect the hazard functions of each subsequent dose. In addition, published mechanistic models are analyzed and compared with models developed in this paper. None of the alternative models that were assessed provided a statistically significant improvement in fit over the baseline model. The general approach utilizes simple empirical data analysis to develop parsimonious models with limited reliance on mechanistic assumptions. The baseline model predicts TTDs consistent with reported results from three independent high-dose rabbit datasets. More accurate survival models depend upon future development of dose-response datasets specifically designed to assess potential multiple dose effects on response and time-to-response. The process used in this paper to dev

A dynamic genetic-hormonal regulatory network model explains multiple cellular behaviors of the root apical meristem of Arabidopsis thaliana.

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García-Gómez, Mónica L; Azpeitia, Eugenio; Álvarez-Buylla, Elena R

2017-04-01

The study of the concerted action of hormones and transcription factors is fundamental to understand cell differentiation and pattern formation during organ development. The root apical meristem of Arabidopsis thaliana is a useful model to address this. It has a stem cell niche near its tip conformed of a quiescent organizer and stem or initial cells around it, then a proliferation domain followed by a transition domain, where cells diminish division rate before transiting to the elongation zone; here, cells grow anisotropically prior to their final differentiation towards the plant base. A minimal model of the gene regulatory network that underlies cell-fate specification and patterning at the root stem cell niche was proposed before. In this study, we update and couple such network with both the auxin and cytokinin hormone signaling pathways to address how they collectively give rise to attractors that correspond to the genetic and hormonal activity profiles that are characteristic of different cell types along A. thaliana root apical meristem. We used a Boolean model of the genetic-hormonal regulatory network to integrate known and predicted regulatory interactions into alternative models. Our analyses show that, after adding some putative missing interactions, the model includes the necessary and sufficient components and regulatory interactions to recover attractors characteristic of the root cell types, including the auxin and cytokinin activity profiles that correlate with different cellular behaviors along the root apical meristem. Furthermore, the model predicts the existence of activity configurations that could correspond to the transition domain. The model also provides a possible explanation for apparently paradoxical cellular behaviors in the root meristem. For example, how auxin may induce and at the same time inhibit WOX5 expression. According to the model proposed here the hormonal regulation of WOX5 might depend on the cell type. Our results

A dynamic genetic-hormonal regulatory network model explains multiple cellular behaviors of the root apical meristem of Arabidopsis thaliana.

Directory of Open Access Journals (Sweden)

Mónica L García-Gómez

2017-04-01

Full Text Available The study of the concerted action of hormones and transcription factors is fundamental to understand cell differentiation and pattern formation during organ development. The root apical meristem of Arabidopsis thaliana is a useful model to address this. It has a stem cell niche near its tip conformed of a quiescent organizer and stem or initial cells around it, then a proliferation domain followed by a transition domain, where cells diminish division rate before transiting to the elongation zone; here, cells grow anisotropically prior to their final differentiation towards the plant base. A minimal model of the gene regulatory network that underlies cell-fate specification and patterning at the root stem cell niche was proposed before. In this study, we update and couple such network with both the auxin and cytokinin hormone signaling pathways to address how they collectively give rise to attractors that correspond to the genetic and hormonal activity profiles that are characteristic of different cell types along A. thaliana root apical meristem. We used a Boolean model of the genetic-hormonal regulatory network to integrate known and predicted regulatory interactions into alternative models. Our analyses show that, after adding some putative missing interactions, the model includes the necessary and sufficient components and regulatory interactions to recover attractors characteristic of the root cell types, including the auxin and cytokinin activity profiles that correlate with different cellular behaviors along the root apical meristem. Furthermore, the model predicts the existence of activity configurations that could correspond to the transition domain. The model also provides a possible explanation for apparently paradoxical cellular behaviors in the root meristem. For example, how auxin may induce and at the same time inhibit WOX5 expression. According to the model proposed here the hormonal regulation of WOX5 might depend on the cell

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

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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

Genetic Factors Associated with Risk and Disability Progression of Multiple Sclerosis in Slovak Population

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Hanysova Sandra

2017-08-01

Full Text Available Objective: The aim of our study was to determine the relation of particular genetic variants in selected genes (GSTM1, GSTT1 null genotypes; rs1695 GSTP1; rs10735781 EVI5 to the risk of multiple sclerosis (MS development and find out the possible association with disease disability progression rate. Material and methods: Our study included 202 MS patients and 174 healthy control volunteers. MS patients were divided according to disability progression rate to three groups - slowly progressing, mid-rate progressing and rapidly progressing. All DNA samples were isolated from venous blood. Genotyping was performed by PCR-RFLP and multiplex PCR. Results: Our analysis showed that GSTT1 null genotype (OR 0.56; 95%CI 0.33 -0.95; p=0.04 and GSTM1, GSTT1 double null genotype (OR 0.32; 95%CI 0.14 - 0.74; p=0.006 are potentially protective in relation to MS. We observed similar result in GSTT1 null genotype in association with mid-rate progression (OR 0.48; 95%CI 0.24 - 0.97; p=0.05. Frequency of GSTM1 and GSTT1 double null genotype is significantly lower in subgroup of MS patients with progression rate defined as slow (OR 0.22; 95%CI 0.05 - 0.98; p=0.05 and middle (OR 0.33; 95%CI 0.11 - 0.99; p=0.045. We did not show any significant association of genetic changes rs1695 in GSTP1 and rs10735781 in EVI5 with MS or rate of disease progression. Conclusions: Genetic basis of multiple sclerosis is still not fully elucidated. Further research may clarify our results and confirm the value of studied factors for clinical practice.

Obesity during childhood and adolescence increases susceptibility to multiple sclerosis after accounting for established genetic and environmental risk factors

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Gianfrancesco, Milena A.; Acuna, Brigid; Shen, Ling; Briggs, Farren B.S.; Quach, Hong; Bellesis, Kalliope H.; Bernstein, Allan; Hedstrom, Anna K.; Kockum, Ingrid; Alfredsson, Lars; Olsson, Tomas; Schaefer, Catherine; Barcellos, Lisa F.

2014-01-01

Objective To investigate the association between obesity and multiple sclerosis (MS) while accounting for established genetic and environmental risk factors. Methods Participants included members of Kaiser Permanente Medical Care Plan, Northern California Region (KPNC) (1,235 MS cases and 697 controls). Logistic regression models were used to estimate odds ratios (ORs) with 95% confidence intervals (95% CI). Body mass index (BMI) or body size was the primary predictor of each model. Both incident and prevalent MS cases were studied. Results In analyses stratified by gender, being overweight at age 10 and 20 were associated with MS in females (prisk of MS for females with a BMI ≥ 30 kg/m2 was observed (OR = 2.15, 95% CI 1.18, 3.92). Significant associations between BMI in 20’s and MS in males were not observed. Multivariate modeling demonstrated that significant associations between BMI or body size with MS in females persisted after adjusting for history of infectious mononucleosis and genetic risk factors, including HLA-DRB1*15:01 and established non-HLA risk alleles. Interpretation Results show that childhood and adolescence obesity confer increased risk of MS in females beyond established heritable and environmental risk factors. Strong evidence for a dose-effect of BMI in 20’s and MS was observed. The magnitude of BMI association with MS is as large as other known MS risk factors. PMID:25263833

Genetic evaluation with major genes and polygenic inheritance when some animals are not genotyped using gene content multiple-trait BLUP.

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Legarra, Andrés; Vitezica, Zulma G

2015-11-17

In pedigreed populations with a major gene segregating for a quantitative trait, it is not clear how to use pedigree, genotype and phenotype information when some individuals are not genotyped. We propose to consider gene content at the major gene as a second trait correlated to the quantitative trait, in a gene content multiple-trait best linear unbiased prediction (GCMTBLUP) method. The genetic covariance between the trait and gene content at the major gene is a function of the substitution effect of the gene. This genetic covariance can be written in a multiple-trait form that accommodates any pattern of missing values for either genotype or phenotype data. Effects of major gene alleles and the genetic covariance between genotype at the major gene and the phenotype can be estimated using standard EM-REML or Gibbs sampling. Prediction of breeding values with genotypes at the major gene can use multiple-trait BLUP software. Major genes with more than two alleles can be considered by including negative covariances between gene contents at each different allele. We simulated two scenarios: a selected and an unselected trait with heritabilities of 0.05 and 0.5, respectively. In both cases, the major gene explained half the genetic variation. Competing methods used imputed gene contents derived by the method of Gengler et al. or by iterative peeling. Imputed gene contents, in contrast to GCMTBLUP, do not consider information on the quantitative trait for genotype prediction. GCMTBLUP gave unbiased estimates of the gene effect, in contrast to the other methods, with less bias and better or equal accuracy of prediction. GCMTBLUP improved estimation of genotypes in non-genotyped individuals, in particular if these individuals had own phenotype records and the trait had a high heritability. Ignoring the major gene in genetic evaluation led to serious biases and decreased prediction accuracy. CGMTBLUP is the best linear predictor of additive genetic merit including

CRISPR/Cas9 : A molecular Swiss army knife for simultaneous introduction of multiple genetic modifications in Saccharomyces cerevisiae

NARCIS (Netherlands)

Mans, R.; Van Rossum, H.M.; Wijsman, M.; Backx, A.; Kuijpers, N.G.A.; van den Broek, M.; Daran-Lapujade, P.A.S.; Pronk, J.T.; Van Maris, A.J.A.; Daran, J.G.

2015-01-01

A variety of techniques for strain engineering in Saccharomyces cerevisiae have recently been developed. However, especially when multiple genetic manipulations are required, strain construction is still a time-consuming process. This study describes new CRISPR/Cas9-based approaches for easy, fast

Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis

DEFF Research Database (Denmark)

Sawcer, Stephen; Hellenthal, Garrett; Pirinen, Matti

2011-01-01

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown...... the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture...... underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working...

A genetic algorithm for multiple relay selection in two-way relaying cognitive radio networks

KAUST Repository

Alsharoa, Ahmad M.

2013-09-01

In this paper, we investigate a multiple relay selection scheme for two-way relaying cognitive radio networks where primary users and secondary users operate on the same frequency band. More specifically, cooperative relays using Amplifyand- Forward (AF) protocol are optimally selected to maximize the sum rate of the secondary users without degrading the Quality of Service (QoS) of the primary users by respecting a tolerated interference threshold. A strong optimization tool based on genetic algorithm is employed to solve our formulated optimization problem where discrete relay power levels are considered. Our simulation results show that the practical heuristic approach achieves almost the same performance of the optimal multiple relay selection scheme either with discrete or continuous power distributions. Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.

QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR).

Science.gov (United States)

Rafiei, Hamid; Khanzadeh, Marziyeh; Mozaffari, Shahla; Bostanifar, Mohammad Hassan; Avval, Zhila Mohajeri; Aalizadeh, Reza; Pourbasheer, Eslam

2016-01-01

Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors . A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the dataset) and a test set (20 % of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) techniques were used as variable selection tools. Multiple linear regression method was then used to linearly correlate the selected descriptors with inhibitory activities. Several validation technique including leave-one-out and leave-group-out cross-validation, Y-randomization method were used to evaluate the internal capability of the derived models. The external prediction ability of the derived models was further analyzed using modified r(2), concordance correlation coefficient values and Golbraikh and Tropsha acceptable model criteria's. Based on the derived results (GA-MLR), some new insights toward molecular structural requirements for obtaining better inhibitory activity were obtained.

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome.

Science.gov (United States)

Wang, Julia; Al-Ouran, Rami; Hu, Yanhui; Kim, Seon-Young; Wan, Ying-Wooi; Wangler, Michael F; Yamamoto, Shinya; Chao, Hsiao-Tuan; Comjean, Aram; Mohr, Stephanie E; Perrimon, Norbert; Liu, Zhandong; Bellen, Hugo J

2017-06-01

One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

ENU mutagenesis to generate genetically modified rat models.

Science.gov (United States)

van Boxtel, Ruben; Gould, Michael N; Cuppen, Edwin; Smits, Bart M G

2010-01-01

The rat is one of the most preferred model organisms in biomedical research and has been extremely useful for linking physiology and pathology to the genome. However, approaches to genetically modify specific genes in the rat germ line remain relatively scarce. To date, the most efficient approach for generating genetically modified rats has been the target-selected N-ethyl-N-nitrosourea (ENU) mutagenesis-based technology. Here, we describe the detailed protocols for ENU mutagenesis and mutant retrieval in the rat model organism.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

Science.gov (United States)

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Dynamics of radioecological and genetic processes in populations of mammalian model species at contamination of ecosystems

International Nuclear Information System (INIS)

Ryabokon', N.I.; Goncharova, R.I.

2008-01-01

A short review of data on the time course of radiobiological and genetic processes in natural populations of mammalian model species inhabiting radiocontaminated ecosystems over many generations is presented here. The described time-courses of biological end-points in these populations do not reflect the time course of the whole-body dose rates, but do the outcome of multiple processes, including the direct response to individual irradiation, the transgeneration transmission and accumulation of induced damages and the development of adaptation. (authors)

Teaching Genetic Counseling Skills: Incorporating a Genetic Counseling Adaptation Continuum Model to Address Psychosocial Complexity.

Science.gov (United States)

Shugar, Andrea

2017-04-01

Genetic counselors are trained health care professionals who effectively integrate both psychosocial counseling and information-giving into their practice. Preparing genetic counseling students for clinical practice is a challenging task, particularly when helping them develop effective and active counseling skills. Resistance to incorporating these skills may stem from decreased confidence, fear of causing harm or a lack of clarity of psycho-social goals. The author reflects on the personal challenges experienced in teaching genetic counselling students to work with psychological and social complexity, and proposes a Genetic Counseling Adaptation Continuum model and methodology to guide students in the use of advanced counseling skills.

THE ALLOMETRIC-AUTOREGRESSIVE MODEL IN GENETIC ...

African Journals Online (AJOL)

The application of an allometric-autoregressive model for the quantification of growth and efficiency of feed utilization for purposes of selection for ... be of value in genetic studies. ... mass) gives a fair indication of the cumulative preweaning.

Genetic and environmental contributions to body mass index: comparative analysis of monozygotic twins, dizygotic twins and same-age unrelated siblings.

Science.gov (United States)

Segal, N L; Feng, R; McGuire, S A; Allison, D B; Miller, S

2009-01-01

Earlier studies have established that a substantial percentage of variance in obesity-related phenotypes is explained by genetic components. However, only one study has used both virtual twins (VTs) and biological twins and was able to simultaneously estimate additive genetic, non-additive genetic, shared environmental and unshared environmental components in body mass index (BMI). Our current goal was to re-estimate four components of variance in BMI, applying a more rigorous model to biological and virtual multiples with additional data. Virtual multiples share the same family environment, offering unique opportunities to estimate common environmental influence on phenotypes that cannot be separated from the non-additive genetic component using only biological multiples. Data included 929 individuals from 164 monozygotic twin pairs, 156 dizygotic twin pairs, five triplet sets, one quadruplet set, 128 VT pairs, two virtual triplet sets and two virtual quadruplet sets. Virtual multiples consist of one biological child (or twins or triplets) plus one same-aged adoptee who are all raised together since infancy. We estimated the additive genetic, non-additive genetic, shared environmental and unshared random components in BMI using a linear mixed model. The analysis was adjusted for age, age(2), age(3), height, height(2), height(3), gender and race. Both non-additive genetic and common environmental contributions were significant in our model (P-valuesrole in BMI and that common environmental factors such as diet or exercise also affect BMI. This conclusion is consistent with our earlier study using a smaller sample and shows the utility of virtual multiples for separating non-additive genetic variance from common environmental variance.

Parallelized Genetic Identification of the Thermal-Electrochemical Model for Lithium-Ion Battery

Directory of Open Access Journals (Sweden)

Liqiang Zhang

2013-01-01

Full Text Available The parameters of a well predicted model can be used as health characteristics for Lithium-ion battery. This article reports a parallelized parameter identification of the thermal-electrochemical model, which significantly reduces the time consumption of parameter identification. Since the P2D model has the most predictability, it is chosen for further research and expanded to the thermal-electrochemical model by coupling thermal effect and temperature-dependent parameters. Then Genetic Algorithm is used for parameter identification, but it takes too much time because of the long time simulation of model. For this reason, a computer cluster is built by surplus computing resource in our laboratory based on Parallel Computing Toolbox and Distributed Computing Server in MATLAB. The performance of two parallelized methods, namely Single Program Multiple Data (SPMD and parallel FOR loop (PARFOR, is investigated and then the parallelized GA identification is proposed. With this method, model simulations running parallelly and the parameter identification could be speeded up more than a dozen times, and the identification result is batter than that from serial GA. This conclusion is validated by model parameter identification of a real LiFePO4 battery.

Animal models for human genetic diseases

African Journals Online (AJOL)

Sharif Sons

The study of human genetic diseases can be greatly aided by animal models because of their similarity .... and gene targeting in embryonic stem cells) has been a powerful tool in .... endonucleases that are designed to make a doublestrand.

Modelling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks

International Nuclear Information System (INIS)

Amanifard, N.; Nariman-Zadeh, N.; Farahani, M.H.; Khalkhali, A.

2008-01-01

Over the past 15 years there have been several research efforts to capture the stall inception nature in axial flow compressors. However previous analytical models could not explain the formation of short-length-scale stall cells. This paper provides a new model based on evolved GMDH neural network for transient evolution of multiple short-length-scale stall cells in an axial compressor. Genetic Algorithms (GAs) are also employed for optimal design of connectivity configuration of such GMDH-type neural networks. In this way, low-pass filter (LPF) pressure trace near the rotor leading edge is modelled with respect to the variation of pressure coefficient, flow rate coefficient, and number of rotor rotations which are defined as inputs

Developing a Model of Advanced Training to Promote Career Advancement for Certified Genetic Counselors: An Investigation of Expanded Skills, Advanced Training Paths, and Professional Opportunities.

Science.gov (United States)

Baty, Bonnie J; Trepanier, Angela; Bennett, Robin L; Davis, Claire; Erby, Lori; Hippman, Catriona; Lerner, Barbara; Matthews, Anne; Myers, Melanie F; Robbins, Carol B; Singletary, Claire N

2016-08-01

There are currently multiple paths through which genetic counselors can acquire advanced knowledge and skills. However, outside of continuing education opportunities, there are few formal training programs designed specifically for the advanced training of genetic counselors. In the genetic counseling profession, there is currently considerable debate about the paths that should be available to attain advanced skills, as well as the skills that might be needed for practice in the future. The Association of Genetic Counseling Program Directors (AGCPD) convened a national committee, the Committee on Advanced Training for Certified Genetic Counselors (CATCGC), to investigate varied paths to post-master's training and career development. The committee began its work by developing three related grids that view career advancement from the viewpoints of the skills needed to advance (skills), ways to obtain these skills (paths), and existing genetic counselor positions that offer career change or advancement (positions). Here we describe previous work related to genetic counselor career advancement, the charge of the CATCGC, our preliminary work in developing a model through which to view genetic counselor advanced training and career advancement opportunities, and our next steps in further developing and disseminating the model.

Geometrical model of multiple production

International Nuclear Information System (INIS)

Chikovani, Z.E.; Jenkovszky, L.L.; Kvaratshelia, T.M.; Struminskij, B.V.

1988-01-01

The relation between geometrical and KNO-scaling and their violation is studied in a geometrical model of multiple production of hadrons. Predictions concerning the behaviour of correlation coefficients at future accelerators are given

Multiplicity Control in Structural Equation Modeling

Science.gov (United States)

Cribbie, Robert A.

2007-01-01

Researchers conducting structural equation modeling analyses rarely, if ever, control for the inflated probability of Type I errors when evaluating the statistical significance of multiple parameters in a model. In this study, the Type I error control, power and true model rates of famsilywise and false discovery rate controlling procedures were…

A rapid and efficient protocol for in vitro multiplication of genetically uniform Stevia rebaudiana (Bertoni).

Science.gov (United States)

Khan, A; Jayanthi, M; Gantasala, Nagavara Prasad; Bhooshan, N; Rao, Uma

2016-07-01

Stevia rebaudiana (Bertoni), commonly called candy leaf or sweet leaf, endemic to South America, is an important medicinal plant. As a source of low calorie natural sweetener 'stevoside', it is used in obesity, diabetes, treatment of heartburn and tooth decay, and also serves as a food supplement. Large scale commercial propagation of S. rebaudiana demands a suitable protocol. Here, we propose an improved protocol for in vitro multiplication of S. rebaudiana from nodal explants. In this protocol, the effect of laboratory grade urea on multiple shoot induction from nodal explants was studied. The nodal explants were initially cultured on Murashige and Skoog (MS) basal media for 2 weeks which facilitated the axillary bud break. Further, culturing of these explants on MS medium fortified with 6 benzyl amninopurine (BAP) (2 mg/L) and Naphthalene acetic acid (NAA) (1 mg/L) with and .without urea (5 mg/L) for a period of 40 days revealed maximum shoot production of 44.56 from a single nodal explant in media supplemented with urea as compared to 22.44 without urea. The differences in the number of shoots produced were significant and these shoots readily rooted in MS media with NAA (4 mg/L). Primary and secondary hardening was successful in these plants. There were no visible morphological abnormalities observed in the micropropagated plantlets. Genetic analysis from random samples also revealed that these plants are genetically uniform. The advantage of the present protocol is that the complete process of multiple shoot induction, rooting and hardening could be completed within a period of 6 months as compared to the existing protocols.

Alternate service delivery models in cancer genetic counseling: a mini-review

Directory of Open Access Journals (Sweden)

Adam Hudson Buchanan

2016-05-01

Full Text Available Demand for cancer genetic counseling has grown rapidly in recent years as germline genomic information has become increasingly incorporated into cancer care and the field has entered the public consciousness through high-profile celebrity publications. Increased demand and existing variability in the availability of trained cancer genetics clinicians place a priority on developing and evaluating alternate service delivery models for genetic counseling. This mini-review summarizes the state of science regarding service delivery models such as telephone counseling, telegenetics and group counseling. Research on comparative effectiveness of these models in traditional individual, in-person genetic counseling has been promising for improving access to care in a manner acceptable to patients. Yet, it has not fully evaluated the short- and long-term patient- and system-level outcomes that will help answer the question of whether these models achieve the same beneficial psychosocial and behavioral outcomes as traditional cancer genetic counseling. We propose a research agenda focused on comparative effectiveness of available service delivery models and how to match models to patients and practice settings. Only through this rigorous research can clinicians and systems find the optimal balance of clinical quality, ready and secure access to care, and financial sustainability. Such research will be integral to achieving the promise of genomic medicine in oncology.

Estimation and interpretation of genetic effects with epistasis using the NOIA model.

Science.gov (United States)

Alvarez-Castro, José M; Carlborg, Orjan; Rönnegård, Lars

2012-01-01

We introduce this communication with a brief outline of the historical landmarks in genetic modeling, especially concerning epistasis. Then, we present methods for the use of genetic modeling in QTL analyses. In particular, we summarize the essential expressions of the natural and orthogonal interactions (NOIA) model of genetic effects. Our motivation for reviewing that theory here is twofold. First, this review presents a digest of the expressions for the application of the NOIA model, which are often mixed with intermediate and additional formulae in the original articles. Second, we make the required theory handy for the reader to relate the genetic concepts to the particular mathematical expressions underlying them. We illustrate those relations by providing graphical interpretations and a diagram summarizing the key features for applying genetic modeling with epistasis in comprehensive QTL analyses. Finally, we briefly review some examples of the application of NOIA to real data and the way it improves the interpretability of the results.

Association analysis of multiple traits by an approach of combining ...

Indian Academy of Sciences (India)

Lili Chen

diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic ... genthaler and Thilly 2007), the combined multivariate and ... Because of using reverse regression model, our.

Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.

Science.gov (United States)

Seaman, Shaun R; Hughes, Rachael A

2018-06-01

Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable.

Genetic engineering in nonhuman primates for human disease modeling.

Science.gov (United States)

Sato, Kenya; Sasaki, Erika

2018-02-01

Nonhuman primate (NHP) experimental models have contributed greatly to human health research by assessing the safety and efficacy of newly developed drugs, due to their physiological and anatomical similarities to humans. To generate NHP disease models, drug-inducible methods, and surgical treatment methods have been employed. Recent developments in genetic and developmental engineering in NHPs offer new options for producing genetically modified disease models. Moreover, in recent years, genome-editing technology has emerged to further promote this trend and the generation of disease model NHPs has entered a new era. In this review, we summarize the generation of conventional disease model NHPs and discuss new solutions to the problem of mosaicism in genome-editing technology.

Obesity during childhood and adolescence increases susceptibility to multiple sclerosis after accounting for established genetic and environmental risk factors.

Science.gov (United States)

Gianfrancesco, Milena A; Acuna, Brigid; Shen, Ling; Briggs, Farren B S; Quach, Hong; Bellesis, Kalliope H; Bernstein, Allan; Hedstrom, Anna K; Kockum, Ingrid; Alfredsson, Lars; Olsson, Tomas; Schaefer, Catherine; Barcellos, Lisa F

2014-01-01

To investigate the association between obesity and multiple sclerosis (MS) while accounting for established genetic and environmental risk factors. Participants included members of Kaiser Permanente Medical Care Plan, Northern California Region (KPNC) (1235 MS cases and 697 controls). Logistic regression models were used to estimate odds ratios (ORs) with 95% confidence intervals (95% CI). Body mass index (BMI) or body size was the primary predictor of each model. Both incident and prevalent MS cases were studied. In analyses stratified by gender, being overweight at ages 10 and 20 were associated with MS in females (pchildhood and adolescence obesity confer increased risk of MS in females beyond established heritable and environmental risk factors. Strong evidence for a dose-effect of BMI in 20s and MS was observed. The magnitude of BMI association with MS is as large as other known MS risk factors. Copyright © 2014 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

Experimental Autoimmune Encephalomyelitis (EAE) as Animal Models of Multiple Sclerosis (MS).

Science.gov (United States)

Glatigny, Simon; Bettelli, Estelle

2018-01-08

Multiple sclerosis (MS) is a multifocal demyelinating disease of the central nervous system (CNS) leading to the progressive destruction of the myelin sheath surrounding axons. It can present with variable clinical and pathological manifestations, which might reflect the involvement of distinct pathogenic processes. Although the mechanisms leading to the development of the disease are not fully understood, numerous evidences indicate that MS is an autoimmune disease, the initiation and progression of which are dependent on an autoimmune response against myelin antigens. In addition, genetic susceptibility and environmental triggers likely contribute to the initiation of the disease. At this time, there is no cure for MS, but several disease-modifying therapies (DMTs) are available to control and slow down disease progression. A good number of these DMTs were identified and tested using animal models of MS referred to as experimental autoimmune encephalomyelitis (EAE). In this review, we will recapitulate the characteristics of EAE models and discuss how they help shed light on MS pathogenesis and help test new treatments for MS patients. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.

Modeling genetic imprinting effects of DNA sequences with multilocus polymorphism data

Directory of Open Access Journals (Sweden)

Staud Roland

2009-08-01

Full Text Available Abstract Single nucleotide polymorphisms (SNPs represent the most widespread type of DNA sequence variation in the human genome and they have recently emerged as valuable genetic markers for revealing the genetic architecture of complex traits in terms of nucleotide combination and sequence. Here, we extend an algorithmic model for the haplotype analysis of SNPs to estimate the effects of genetic imprinting expressed at the DNA sequence level. The model provides a general procedure for identifying the number and types of optimal DNA sequence variants that are expressed differently due to their parental origin. The model is used to analyze a genetic data set collected from a pain genetics project. We find that DNA haplotype GAC from three SNPs, OPRKG36T (with two alleles G and T, OPRKA843G (with alleles A and G, and OPRKC846T (with alleles C and T, at the kappa-opioid receptor, triggers a significant effect on pain sensitivity, but with expression significantly depending on the parent from which it is inherited (p = 0.008. With a tremendous advance in SNP identification and automated screening, the model founded on haplotype discovery and statistical inference may provide a useful tool for genetic analysis of any quantitative trait with complex inheritance.

Sleep and Development in Genetically Tractable Model Organisms.

Science.gov (United States)

Kayser, Matthew S; Biron, David

2016-05-01

Sleep is widely recognized as essential, but without a clear singular function. Inadequate sleep impairs cognition, metabolism, immune function, and many other processes. Work in genetic model systems has greatly expanded our understanding of basic sleep neurobiology as well as introduced new concepts for why we sleep. Among these is an idea with its roots in human work nearly 50 years old: sleep in early life is crucial for normal brain maturation. Nearly all known species that sleep do so more while immature, and this increased sleep coincides with a period of exuberant synaptogenesis and massive neural circuit remodeling. Adequate sleep also appears critical for normal neurodevelopmental progression. This article describes recent findings regarding molecular and circuit mechanisms of sleep, with a focus on development and the insights garnered from models amenable to detailed genetic analyses. Copyright © 2016 by the Genetics Society of America.

Energy sorghum--a genetic model for the design of C4 grass bioenergy crops.

Science.gov (United States)

Mullet, John; Morishige, Daryl; McCormick, Ryan; Truong, Sandra; Hilley, Josie; McKinley, Brian; Anderson, Robert; Olson, Sara N; Rooney, William

2014-07-01

Sorghum is emerging as an excellent genetic model for the design of C4 grass bioenergy crops. Annual energy Sorghum hybrids also serve as a source of biomass for bioenergy production. Elucidation of Sorghum's flowering time gene regulatory network, and identification of complementary alleles for photoperiod sensitivity, enabled large-scale generation of energy Sorghum hybrids for testing and commercial use. Energy Sorghum hybrids with long vegetative growth phases were found to accumulate more than twice as much biomass as grain Sorghum, owing to extended growing seasons, greater light interception, and higher radiation use efficiency. High biomass yield, efficient nitrogen recycling, and preferential accumulation of stem biomass with low nitrogen content contributed to energy Sorghum's elevated nitrogen use efficiency. Sorghum's integrated genetics-genomics-breeding platform, diverse germplasm, and the opportunity for annual testing of new genetic designs in controlled environments and in multiple field locations is aiding fundamental discovery, and accelerating the improvement of biomass yield and optimization of composition for biofuels production. Recent advances in wide hybridization between Sorghum and other C4 grasses could allow the deployment of improved genetic designs of annual energy Sorghums in the form of wide-hybrid perennial crops. The current trajectory of energy Sorghum genetic improvement indicates that it will be possible to sustainably produce biofuels from C4 grass bioenergy crops that are cost competitive with petroleum-based transportation fuels. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

SUMMIT (Serially Unified Multicenter Multiple Sclerosis Investigation): creating a repository of deeply phenotyped contemporary multiple sclerosis cohorts.

Science.gov (United States)

Bove, Riley; Chitnis, Tanuja; Cree, Bruce Ac; Tintoré, Mar; Naegelin, Yvonne; Uitdehaag, Bernard Mj; Kappos, Ludwig; Khoury, Samia J; Montalban, Xavier; Hauser, Stephen L; Weiner, Howard L

2017-08-01

There is a pressing need for robust longitudinal cohort studies in the modern treatment era of multiple sclerosis. Build a multiple sclerosis (MS) cohort repository to capture the variability of disability accumulation, as well as provide the depth of characterization (clinical, radiologic, genetic, biospecimens) required to adequately model and ultimately predict a patient's course. Serially Unified Multicenter Multiple Sclerosis Investigation (SUMMIT) is an international multi-center, prospectively enrolled cohort with over a decade of comprehensive follow-up on more than 1000 patients from two large North American academic MS Centers (Brigham and Women's Hospital (Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB; BWH)) and University of California, San Francisco (Expression/genomics, Proteomics, Imaging, and Clinical (EPIC))). It is bringing online more than 2500 patients from additional international MS Centers (Basel (Universitätsspital Basel (UHB)), VU University Medical Center MS Center Amsterdam (MSCA), Multiple Sclerosis Center of Catalonia-Vall d'Hebron Hospital (Barcelona clinically isolated syndrome (CIS) cohort), and American University of Beirut Medical Center (AUBMC-Multiple Sclerosis Interdisciplinary Research (AMIR)). We provide evidence for harmonization of two of the initial cohorts in terms of the characterization of demographics, disease, and treatment-related variables; demonstrate several proof-of-principle analyses examining genetic and radiologic predictors of disease progression; and discuss the steps involved in expanding SUMMIT into a repository accessible to the broader scientific community.

Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics

Science.gov (United States)

Welch, Stephen M.; White, Jeffrey W.; Thorp, Kelly R.; Bello, Nora M.

2018-01-01

Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP’s) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP’s as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p<10−6919) on the sets of environments used to estimate them (instability), calling in to question the assumption that they represent fundamental genetic traits. The issues of expressivity, equifinality and instability must be addressed before the genetic mapping of GSP’s becomes a robust means to help solve the genotype-to-phenotype problem in crops. PMID:29672629

A simple algorithm to estimate genetic variance in an animal threshold model using Bayesian inference Genetics Selection Evolution 2010, 42:29

DEFF Research Database (Denmark)

Ødegård, Jørgen; Meuwissen, Theo HE; Heringstad, Bjørg

2010-01-01

Background In the genetic analysis of binary traits with one observation per animal, animal threshold models frequently give biased heritability estimates. In some cases, this problem can be circumvented by fitting sire- or sire-dam models. However, these models are not appropriate in cases where...... records exist for the parents). Furthermore, the new algorithm showed much faster Markov chain mixing properties for genetic parameters (similar to the sire-dam model). Conclusions The new algorithm to estimate genetic parameters via Gibbs sampling solves the bias problems typically occurring in animal...... individual records exist on parents. Therefore, the aim of our study was to develop a new Gibbs sampling algorithm for a proper estimation of genetic (co)variance components within an animal threshold model framework. Methods In the proposed algorithm, individuals are classified as either "informative...

Genomic Feature Models

DEFF Research Database (Denmark)

Sørensen, Peter; Edwards, Stefan McKinnon; Rohde, Palle Duun

-additive genetic mechanisms. These modeling approaches have proven to be highly useful to determine population genetic parameters as well as prediction of genetic risk or value. We present a series of statistical modelling approaches that use prior biological information for evaluating the collective action......Whole-genome sequences and multiple trait phenotypes from large numbers of individuals will soon be available in many populations. Well established statistical modeling approaches enable the genetic analyses of complex trait phenotypes while accounting for a variety of additive and non...... regions and gene ontologies) that provide better model fit and increase predictive ability of the statistical model for this trait....

Combined genetic algorithm and multiple linear regression (GA-MLR) optimizer: Application to multi-exponential fluorescence decay surface.

Science.gov (United States)

Fisz, Jacek J

2006-12-07

The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi

Integration of multiple, excess, backup, and expected covering models

OpenAIRE

M S Daskin; K Hogan; C ReVelle

1988-01-01

The concepts of multiple, excess, backup, and expected coverage are defined. Model formulations using these constructs are reviewed and contrasted to illustrate the relationships between them. Several new formulations are presented as is a new derivation of the expected covering model which indicates more clearly the relationship of the model to other multi-state covering models. An expected covering model with multiple time standards is also presented.

Evolving Four Part Harmony Using a Multiple Worlds Model

DEFF Research Database (Denmark)

Scirea, Marco; Brown, Joseph Alexander

2015-01-01

This application of the Multiple Worlds Model examines a collaborative fitness model for generating four part harmonies. In this model we have multiple populations and the fitness of the individuals is based on the ability of a member from each population to work with the members of other...

Genetic diversity and differentiation patterns in Micromeria from the Canary Islands are congruent with multiple colonization dynamics and the establishment of species syngameons.

Science.gov (United States)

Curto, M; Puppo, P; Kratschmer, S; Meimberg, H

2017-08-22

Especially on islands closer to the mainland, such as the Canary Islands, different lineages that originated by multiple colonization events could have merged by hybridization, which then could have promoted radiation events (Herben et al., J Ecol 93: 572-575, 2005; Saunders and Gibson, J Ecol 93: 649-652, 2005; Caujapé-Castells, Jesters, red queens, boomerangs and surfers: a molecular outlook on the diversity of the Canarian endemic flora, 2011). This is an alternative to the scenario where evolution is mostly driven by drift (Silvertown, J Ecol 92: 168-173, 2004; Silvertown et al., J Ecol 93: 653-657, 2005). In the former case hybridization should be reflected in the genetic structure and diversity patterns of island species. In the present work we investigate Micromeria from the Canary Islands by extensively studying their phylogeographic pattern based on 15 microsatellite loci and 945 samples. These results are interpreted according to the hypotheses outlined above. Genetic structure assessment allowed us to genetically differentiate most Micromeria species and supported their current classification. We found that populations on younger islands were significantly more genetically diverse and less differentiated than those on older islands. Moreover, we found that genetic distance on younger islands was in accordance with an isolation-by-distance pattern, while on the older islands this was not the case. We also found evidence of introgression among species and islands. These results are congruent with a scenario of multiple colonizations during the expansion onto new islands. Hybridization contributes to the grouping of multiple lineages into highly diverse populations. Thus, in our case, islands receive several colonization events from different sources, which are combined into sink populations. This mechanism is in accordance with the surfing syngameon hypothesis. Contrary to the surfing syngameon current form, our results may reflect a slightly different

Potential uses of genetic geological modelling to identify new uranium provinces

International Nuclear Information System (INIS)

Finch, W.I.

1982-01-01

Genetic-geological modelling is the placing of the various processes of the development of a uranium province into distinct stages that are ordered chronologically and made part of a matrix with corresponding geologic evidence. The models can be applied to a given region by using one of several methods to determine a numerical favorability rating. Two of the possible methods, geologic decision analysis and an oil-and-gas type of play analysis, are briefly described. Simplified genetic models are given for environments of the quartz-pebble conglomerate, unconformity-related vein, and sandstone types of deposits. Comparison of the genetic models of these three sedimentary-related environments reveals several common attributes that may define a general uranium province environment

Genetic algorithm as a variable selection procedure for the simulation of 13C nuclear magnetic resonance spectra of flavonoid derivatives using multiple linear regression.

Science.gov (United States)

Ghavami, Raoof; Najafi, Amir; Sajadi, Mohammad; Djannaty, Farhad

2008-09-01

In order to accurately simulate (13)C NMR spectra of hydroxy, polyhydroxy and methoxy substituted flavonoid a quantitative structure-property relationship (QSPR) model, relating atom-based calculated descriptors to (13)C NMR chemical shifts (ppm, TMS=0), is developed. A dataset consisting of 50 flavonoid derivatives was employed for the present analysis. A set of 417 topological, geometrical, and electronic descriptors representing various structural characteristics was calculated and separate multilinear QSPR models were developed between each carbon atom of flavonoid and the calculated descriptors. Genetic algorithm (GA) and multiple linear regression analysis (MLRA) were used to select the descriptors and to generate the correlation models. Analysis of the results revealed a correlation coefficient and root mean square error (RMSE) of 0.994 and 2.53ppm, respectively, for the prediction set.

Double-multiple streamtube model for Darrieus in turbines

Science.gov (United States)

Paraschivoiu, I.

1981-01-01

An analytical model is proposed for calculating the rotor performance and aerodynamic blade forces for Darrieus wind turbines with curved blades. The method of analysis uses a multiple-streamtube model, divided into two parts: one modeling the upstream half-cycle of the rotor and the other, the downstream half-cycle. The upwind and downwind components of the induced velocities at each level of the rotor were obtained using the principle of two actuator disks in tandem. Variation of the induced velocities in the two parts of the rotor produces larger forces in the upstream zone and smaller forces in the downstream zone. Comparisons of the overall rotor performance with previous methods and field test data show the important improvement obtained with the present model. The calculations were made using the computer code CARDAA developed at IREQ. The double-multiple streamtube model presented has two major advantages: it requires a much shorter computer time than the three-dimensional vortex model and is more accurate than multiple-streamtube model in predicting the aerodynamic blade loads.

A Drosophila model for toxicogenomics: Genetic variation in susceptibility to heavy metal exposure.

Directory of Open Access Journals (Sweden)

Shanshan Zhou

2017-07-01

Full Text Available The genetic factors that give rise to variation in susceptibility to environmental toxins remain largely unexplored. Studies on genetic variation in susceptibility to environmental toxins are challenging in human populations, due to the variety of clinical symptoms and difficulty in determining which symptoms causally result from toxic exposure; uncontrolled environments, often with exposure to multiple toxicants; and difficulty in relating phenotypic effect size to toxic dose, especially when symptoms become manifest with a substantial time lag. Drosophila melanogaster is a powerful model that enables genome-wide studies for the identification of allelic variants that contribute to variation in susceptibility to environmental toxins, since the genetic background, environmental rearing conditions and toxic exposure can be precisely controlled. Here, we used extreme QTL mapping in an outbred population derived from the D. melanogaster Genetic Reference Panel to identify alleles associated with resistance to lead and/or cadmium, two ubiquitous environmental toxins that present serious health risks. We identified single nucleotide polymorphisms (SNPs associated with variation in resistance to both heavy metals as well as SNPs associated with resistance specific to each of them. The effects of these SNPs were largely sex-specific. We applied mutational and RNAi analyses to 33 candidate genes and functionally validated 28 of them. We constructed networks of candidate genes as blueprints for orthologous networks of human genes. The latter not only provided functional contexts for known human targets of heavy metal toxicity, but also implicated novel candidate susceptibility genes. These studies validate Drosophila as a translational toxicogenomics gene discovery system.

Genetic variations in multiple myeloma II

DEFF Research Database (Denmark)

Vangsted, A.; Klausen, T.W.; Vogel, U.

2012-01-01

Association studies on genetic variation to treatment effect may serve as a predictive marker for effect of treatment and can also uncover biological pathways behind drug effect. Single-nucleotide polymorphisms (SNPs) have been studied in relation to high-dose treatment (HDT), thalidomide- and bo...

Portfolio optimization by using linear programing models based on genetic algorithm

Science.gov (United States)

Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

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/.

Two-level mixed modeling of longitudinal pedigree data for genetic association analysis

DEFF Research Database (Denmark)

Tan, Q.

2013-01-01

of follow-up. Approaches have been proposed to integrate kinship correlation into the mixed effect models to explicitly model the genetic relationship which have been proven as an efficient way for dealing with sample clustering in pedigree data. Although useful for adjusting relatedness in the mixed...... assess the genetic associations with the mean level and the rate of change in a phenotype both with kinship correlation integrated in the mixed effect models. We apply our method to longitudinal pedigree data to estimate the genetic effects on systolic blood pressure measured over time in large pedigrees......Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees which could affect statistical assessment of the genetic effects on both the mean level of the phenotype and its rate of change over the time...

Genetic differentiation across multiple spatial scales of the Red Sea of the corals Stylophora pistillata and Pocillopora verrucosa

KAUST Repository

Monroe, Alison

2015-12-01

Observing populations at different spatial scales gives greater insight into the specific processes driving genetic differentiation and population structure. Here we determined population connectivity across multiple spatial scales in the Red Sea to determine the population structures of two reef building corals Stylophora pistillata and Pocillopora verrucosa. The Red sea is a 2,250 km long body of water with extremely variable latitudinal environmental gradients. Mitochondrial and microsatellite markers were used to determine distinct lineages and to look for genetic differentiation among sampling sites. No distinctive population structure across the latitudinal gradient was discovered within this study suggesting a phenotypic plasticity of both these species to various environments. Stylophora pistillata displayed a heterogeneous distribution of three distinct genetic populations on both a fine and large scale. Fst, Gst, and Dest were all significant (p-value<0.05) and showed moderate genetic differentiation between all sampling sites. However this seems to be byproduct of the heterogeneous distribution, as no distinct genetic population breaks were found. Stylophora pistillata showed greater population structure on a fine scale suggesting genetic selection based on fine scale environmental variations. However, further environmental and oceanographic data is needed to make more inferences on this structure at small spatial scales. This study highlights the deficits of knowledge of both the Red Sea and coral plasticity in regards to local environmental conditions.

A Continuous Correlated Beta Process Model for Genetic Ancestry in Admixed Populations.

Science.gov (United States)

Gompert, Zachariah

2016-01-01

Admixture and recombination create populations and genomes with genetic ancestry from multiple source populations. Analyses of genetic ancestry in admixed populations are relevant for trait and disease mapping, studies of speciation, and conservation efforts. Consequently, many methods have been developed to infer genome-average ancestry and to deconvolute ancestry into continuous local ancestry blocks or tracts within individuals. Current methods for local ancestry inference perform well when admixture occurred recently or hybridization is ongoing, or when admixture occurred in the distant past such that local ancestry blocks have fixed in the admixed population. However, methods to infer local ancestry frequencies in isolated admixed populations still segregating for ancestry do not exist. In the current paper, I develop and test a continuous correlated beta process model to fill this analytical gap. The method explicitly models autocorrelations in ancestry frequencies at the population-level and uses discriminant analysis of SNP windows to take advantage of ancestry blocks within individuals. Analyses of simulated data sets show that the method is generally accurate such that ancestry frequency estimates exhibited low root-mean-square error and were highly correlated with the true values, particularly when large (±10 or ±20) SNP windows were used. Along these lines, the proposed method outperformed post hoc inference of ancestry frequencies from a traditional hidden Markov model (i.e., the linkage model in structure), particularly when admixture occurred more distantly in the past with little on-going gene flow or was followed by natural selection. The reliability and utility of the method was further assessed by analyzing genetic ancestry in an admixed human population (Uyghur) and three populations from a hybrid zone between Mus domesticus and M. musculus. Considerable variation in ancestry frequencies was detected within and among chromosomes in the Uyghur

Genetic parameters and genetic trends in the Chinese × European Tiameslan composite pig line. I. Genetic parameters

Directory of Open Access Journals (Sweden)

Legault Christian

2000-01-01

Full Text Available Abstract Genetic parameters of body weight at 4 (W4 w, 8 (W8 w and 22 (W22 w weeks of age, days from 20 to 100 kg (DT, average backfat thickness at 100 kg (ABT, teat number (TEAT, number of good teats (GTEAT, total number of piglets born (TNB, born alive (NBA and weaned (NW per litter, and birth to weaning survival rate (SURV were estimated in the Chinese × European Tiameslan composite line using restricted maximum likelihood methodology applied to a multiple trait animal model. Performance data from a total of 4 881 males and 4 799 females from 1 341 litters were analysed. Different models were fitted to the data in order to estimate the importance of maternal effects on production traits, as well as genetic correlations between male and female performance. The results showed the existence of significant maternal effects on W4w, W8w and ABT and of variance heterogeneity between sexes for W22w, DT, ABT and GTEAT. Genetic correlations between sexes were 0.79, 0.71 and 0.82, respectively, for W22w, DT and ABT and above 0.90 for the other traits. Heritability estimates were larger than (ABT and TEAT or similar to (other traits average literature values. Some genetic antagonism was evidenced between production traits, particularly W4w, W8w and ABT, and reproductive traits.

Common genetic variants in the 9p21 region and their associations with multiple tumours.

Science.gov (United States)

Gu, F; Pfeiffer, R M; Bhattacharjee, S; Han, S S; Taylor, P R; Berndt, S; Yang, H; Sigurdson, A J; Toro, J; Mirabello, L; Greene, M H; Freedman, N D; Abnet, C C; Dawsey, S M; Hu, N; Qiao, Y-L; Ding, T; Brenner, A V; Garcia-Closas, M; Hayes, R; Brinton, L A; Lissowska, J; Wentzensen, N; Kratz, C; Moore, L E; Ziegler, R G; Chow, W-H; Savage, S A; Burdette, L; Yeager, M; Chanock, S J; Chatterjee, N; Tucker, M A; Goldstein, A M; Yang, X R

2013-04-02

The chromosome 9p21.3 region has been implicated in the pathogenesis of multiple cancers. We systematically examined up to 203 tagging SNPs of 22 genes on 9p21.3 (19.9-32.8 Mb) in eight case-control studies: thyroid cancer, endometrial cancer (EC), renal cell carcinoma, colorectal cancer (CRC), colorectal adenoma (CA), oesophageal squamous cell carcinoma (ESCC), gastric cardia adenocarcinoma and osteosarcoma (OS). We used logistic regression to perform single SNP analyses for each study separately, adjusting for study-specific covariates. We combined SNP results across studies by fixed-effect meta-analyses and a newly developed subset-based statistical approach (ASSET). Gene-based P-values were obtained by the minP method using the Adaptive Rank Truncated Product program. We adjusted for multiple comparisons by Bonferroni correction. Rs3731239 in cyclin-dependent kinase inhibitors 2A (CDKN2A) was significantly associated with ESCC (P=7 × 10(-6)). The CDKN2A-ESCC association was further supported by gene-based analyses (Pgene=0.0001). In the meta-analyses by ASSET, four SNPs (rs3731239 in CDKN2A, rs615552 and rs573687 in CDKN2B and rs564398 in CDKN2BAS) showed significant associations with ESCC and EC (PASSET (P=0.007). Our data indicate that genetic variants in CDKN2A, and possibly nearby genes, may be associated with ESCC and several other tumours, further highlighting the importance of 9p21.3 genetic variants in carcinogenesis.

Model comparisons and genetic and environmental parameter ...

African Journals Online (AJOL)

arc

Model comparisons and genetic and environmental parameter estimates of growth and the ... breeding strategies and for accurate breeding value estimation. The objectives ...... Sci. 23, 72-76. Van Wyk, J.B., Fair, M.D. & Cloete, S.W.P., 2003.

Whole-genome sequencing of monozygotic twins discordant for schizophrenia indicates multiple genetic risk factors for schizophrenia.

Science.gov (United States)

Tang, Jinsong; Fan, Yu; Li, Hong; Xiang, Qun; Zhang, Deng-Feng; Li, Zongchang; He, Ying; Liao, Yanhui; Wang, Ya; He, Fan; Zhang, Fengyu; Shugart, Yin Yao; Liu, Chunyu; Tang, Yanqing; Chan, Raymond C K; Wang, Chuan-Yue; Yao, Yong-Gang; Chen, Xiaogang

2017-06-20

Schizophrenia is a common disorder with a high heritability, but its genetic architecture is still elusive. We implemented whole-genome sequencing (WGS) analysis of 8 families with monozygotic (MZ) twin pairs discordant for schizophrenia to assess potential association of de novo mutations (DNMs) or inherited variants with susceptibility to schizophrenia. Eight non-synonymous DNMs (including one splicing site) were identified and shared by twins, which were either located in previously reported schizophrenia risk genes (p.V24689I mutation in TTN, p.S2506T mutation in GCN1L1, IVS3+1G > T in DOCK1) or had a benign to damaging effect according to in silico prediction analysis. By searching the inherited rare damaging or loss-of-function (LOF) variants and common susceptible alleles from three classes of schizophrenia candidate genes, we were able to distill genetic alterations in several schizophrenia risk genes, including GAD1, PLXNA2, RELN and FEZ1. Four inherited copy number variations (CNVs; including a large deletion at 16p13.11) implicated for schizophrenia were identified in four families, respectively. Most of families carried both missense DNMs and inherited risk variants, which might suggest that DNMs, inherited rare damaging variants and common risk alleles together conferred to schizophrenia susceptibility. Our results support that schizophrenia is caused by a combination of multiple genetic factors, with each DNM/variant showing a relatively small effect size. Copyright © 2017 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. All rights reserved.

Simulating pattern-process relationships to validate landscape genetic models

Science.gov (United States)

A. J. Shirk; S. A. Cushman; E. L. Landguth

2012-01-01

Landscapes may resist gene flow and thereby give rise to a pattern of genetic isolation within a population. The mechanism by which a landscape resists gene flow can be inferred by evaluating the relationship between landscape models and an observed pattern of genetic isolation. This approach risks false inferences because researchers can never feasibly test all...

From animal models to human disease: a genetic approach for personalized medicine in ALS.

Science.gov (United States)

Picher-Martel, Vincent; Valdmanis, Paul N; Gould, Peter V; Julien, Jean-Pierre; Dupré, Nicolas

2016-07-11

Amyotrophic Lateral Sclerosis (ALS) is the most frequent motor neuron disease in adults. Classical ALS is characterized by the death of upper and lower motor neurons leading to progressive paralysis. Approximately 10 % of ALS patients have familial form of the disease. Numerous different gene mutations have been found in familial cases of ALS, such as mutations in superoxide dismutase 1 (SOD1), TAR DNA-binding protein 43 (TDP-43), fused in sarcoma (FUS), C9ORF72, ubiquilin-2 (UBQLN2), optineurin (OPTN) and others. Multiple animal models were generated to mimic the disease and to test future treatments. However, no animal model fully replicates the spectrum of phenotypes in the human disease and it is difficult to assess how a therapeutic effect in disease models can predict efficacy in humans. Importantly, the genetic and phenotypic heterogeneity of ALS leads to a variety of responses to similar treatment regimens. From this has emerged the concept of personalized medicine (PM), which is a medical scheme that combines study of genetic, environmental and clinical diagnostic testing, including biomarkers, to individualized patient care. In this perspective, we used subgroups of specific ALS-linked gene mutations to go through existing animal models and to provide a comprehensive profile of the differences and similarities between animal models of disease and human disease. Finally, we reviewed application of biomarkers and gene therapies relevant in personalized medicine approach. For instance, this includes viral delivering of antisense oligonucleotide and small interfering RNA in SOD1, TDP-43 and C9orf72 mice models. Promising gene therapies raised possibilities for treating differently the major mutations in familial ALS cases.

Multiple model cardinalized probability hypothesis density filter

Science.gov (United States)

Georgescu, Ramona; Willett, Peter

2011-09-01

The Probability Hypothesis Density (PHD) filter propagates the first-moment approximation to the multi-target Bayesian posterior distribution while the Cardinalized PHD (CPHD) filter propagates both the posterior likelihood of (an unlabeled) target state and the posterior probability mass function of the number of targets. Extensions of the PHD filter to the multiple model (MM) framework have been published and were implemented either with a Sequential Monte Carlo or a Gaussian Mixture approach. In this work, we introduce the multiple model version of the more elaborate CPHD filter. We present the derivation of the prediction and update steps of the MMCPHD particularized for the case of two target motion models and proceed to show that in the case of a single model, the new MMCPHD equations reduce to the original CPHD equations.

Coronary artery disease-associated genetic variants and biomarkers of inflammation

DEFF Research Database (Denmark)

Christiansen, Morten Krogh; Larsen, Sanne Bøjet; Nyegaard, Mette

2017-01-01

score was calculated to assess the combined risk associated with all the genetic variants. A multiple linear regression model was used to assess associations between the genetic risk score, single SNPs, and the five inflammatory biomarkers. RESULTS:The minor allele (G) (CAD risk allele) of rs2075650......INTRODUCTION:Genetic constitution and inflammation both contribute to development of coronary artery disease (CAD). Several CAD-associated single-nucleotide polymorphisms (SNPs) have recently been identified, but their functions are largely unknown. We investigated the associations between CAD...

Mean multiplicity in the Regge models with rising cross sections

International Nuclear Information System (INIS)

Chikovani, Z.E.; Kobylisky, N.A.; Martynov, E.S.

1979-01-01

Behaviour of the mean multiplicity and the total cross section σsub(t) of hadron-hadron interactions is considered in the framework of the Regge models at high energies. Generating function was plotted for models of dipole and froissaron, and the mean multiplicity and multiplicity moments were calculated. It is shown that approximately ln 2 S (energy square) in the dipole model, which is in good agreement with the experiment. It is also found that in various Regge models approximately σsub(t)lnS

Could age modify the effect of genetic variants in IL6 and TNF-α genes in multiple myeloma?

Science.gov (United States)

Martino, Alessandro; Buda, Gabriele; Maggini, Valentina; Lapi, Francesco; Lupia, Antonella; Di Bello, Domenica; Orciuolo, Enrico; Galimberti, Sara; Barale, Roberto; Petrini, Mario; Rossi, Anna Maria

2012-05-01

Cytokines play a central role in multiple myeloma (MM) pathogenesis thus genetic variations within cytokines coding genes could influence MM susceptibility and therapy outcome. We investigated the impact of 8 SNPs in these genes in 202 MM cases and 235 controls also evaluating their impact on therapy outcome in a subset of 91 patients. Despite the overall negative findings, we found a significant age-modified effect of IL6 and TNF-α SNPs, on MM risk and therapy outcome, respectively. Therefore, this observation suggests that genetic variation in inflammation-related genes could be an important mediator of the complex interplay between ageing and cancer. Copyright © 2012 Elsevier Ltd. All rights reserved.

Research on Innovating, Applying Multiple Paths Routing Technique Based on Fuzzy Logic and Genetic Algorithm for Routing Messages in Service - Oriented Routing

Directory of Open Access Journals (Sweden)

Nguyen Thanh Long

2015-02-01

Full Text Available MANET (short for Mobile Ad-Hoc Network consists of a set of mobile network nodes, network configuration changes very fast. In content based routing, data is transferred from source node to request nodes is not based on destination addresses. Therefore, it is very flexible and reliable, because source node does not need to know destination nodes. If We can find multiple paths that satisfies bandwidth requirement, split the original message into multiple smaller messages to transmit concurrently on these paths. On destination nodes, combine separated messages into the original message. Hence it can utilize better network resources, causes data transfer rate to be higher, load balancing, failover. Service Oriented Routing is inherited from the model of content based routing (CBR, combined with several advanced techniques such as Multicast, multiple path routing, Genetic algorithm to increase the data rate, and data encryption to ensure information security. Fuzzy logic is a logical field study evaluating the accuracy of the results based on the approximation of the components involved, make decisions based on many factors relative accuracy based on experimental or mathematical proof. This article presents some techniques to support multiple path routing from one network node to a set of nodes with guaranteed quality of service. By using these techniques can decrease the network load, congestion, use network resources efficiently.

PM Synchronous Motor Dynamic Modeling with Genetic Algorithm ...

African Journals Online (AJOL)

Adel

This paper proposes dynamic modeling simulation for ac Surface Permanent Magnet Synchronous ... Simulations are implemented using MATLAB with its genetic algorithm toolbox. .... selection, the process that drives biological evolution.

Fischer 344 and Lewis rat strains as a model of genetic vulnerability to drug addiction

Directory of Open Access Journals (Sweden)

Cristina eCadoni

2016-02-01

Full Text Available Today it is well acknowledged that both nature and nurture play important roles in the genesis of psychopathologies, including drug addiction. Increasing evidence suggests that genetic factors contribute for at least 40-60 % of the variation in liability to drug dependence. Human genetic studies suggest that multiple genes of small effect, rather than single genes, contribute to the genesis of behavioral psychopathologies. Therefore the use of inbred rat strains might provide a valuable tool to identify differences, linked to genotype, important in liability to addiction and related disorders. In this regard, Lewis and Fischer 344 inbred rats have been proposed as a model of genetic vulnerability to drug addiction, given their innate differences in sensitivity to the reinforcing and rewarding effects of drugs of abuse, as well their different responsiveness to stressful stimuli. This review will provide evidence in support of this model for the study of the genetic influence on addiction vulnerability, with particular emphasis to differences in mesolimbic dopamine (DA transmission, rewarding and emotional function. It will be highlighted that Lewis and Fischer 344 rats differ not only in several indices of DA transmission and adaptive changes following repeated drug exposure, but also in hypothalamic-pituitary-adrenal (HPA axis responsiveness, influencing not only the ability of the individual to cope with stressful events, but also interfering with rewarding and motivational processes, given the influence of corticosteroids on dopamine neurons functionality.Further differences between the two strains, as impulsivity or anxiousness, might contribute to their different proneness to addiction, and likely these features might be linked to their different DA neurotransmission plasticity. Although differences in other neurotransmitter systems might deserve further investigations, results from the reviewed studies might open new vistas in

Fischer 344 and Lewis Rat Strains as a Model of Genetic Vulnerability to Drug Addiction.

Science.gov (United States)

Cadoni, Cristina

2016-01-01

Today it is well acknowledged that both nature and nurture play important roles in the genesis of psychopathologies, including drug addiction. Increasing evidence suggests that genetic factors contribute for at least 40-60% of the variation in liability to drug dependence. Human genetic studies suggest that multiple genes of small effect, rather than single genes, contribute to the genesis of behavioral psychopathologies. Therefore, the use of inbred rat strains might provide a valuable tool to identify differences, linked to genotype, important in liability to addiction and related disorders. In this regard, Lewis and Fischer 344 inbred rats have been proposed as a model of genetic vulnerability to drug addiction, given their innate differences in sensitivity to the reinforcing and rewarding effects of drugs of abuse, as well their different responsiveness to stressful stimuli. This review will provide evidence in support of this model for the study of the genetic influence on addiction vulnerability, with particular emphasis on differences in mesolimbic dopamine (DA) transmission, rewarding and emotional function. It will be highlighted that Lewis and Fischer 344 rats differ not only in several indices of DA transmission and adaptive changes following repeated drug exposure, but also in hypothalamic-pituitary-adrenal (HPA) axis responsiveness, influencing not only the ability of the individual to cope with stressful events, but also interfering with rewarding and motivational processes, given the influence of corticosteroids on dopamine neuron functionality. Further differences between the two strains, as impulsivity or anxiousness, might contribute to their different proneness to addiction, and likely these features might be linked to their different DA neurotransmission plasticity. Although differences in other neurotransmitter systems might deserve further investigation, results from the reviewed studies might open new vistas in understanding aberrant

Predictive value of testing for multiple genetic variants in multifactorial diseases: implications for the discourse on ethical, legal and social issues

Directory of Open Access Journals (Sweden)

A. Cecile J.W. Janssens

2006-12-01

Full Text Available Multifactorial diseases such as type 2 diabetes, osteoporosis, and cardiovascular disease are caused by a complex interplay of many genetic and nongenetic factors, each of which conveys a minor increase in the risk of disease. Unraveling the genetic origins of these diseases is expected to lead to individualized medicine, in which the prevention and treatment strategies are personalized on the basis of the results of predictive genetic tests. This great optimism is counterbalanced by concerns about the ethical, legal, and social implications of genomic medicine, such as the protection of privacy and autonomy, stigmatization, discrimination, and the psychological burden of genetic testing. These concerns are translated from genetic testing in monogenic disorders, but this translation may not be appropriate. Multiple genetic testing (genomic profiling has essential differences from genetic testing in monogenic disorders. The differences lie in the lower predictive value of the test results, the pleiotropic effects of susceptibility genes, and the low inheritance of genomic profiles. For these reasons, genomic profiling may be more similar to nongenetic tests than to predictive tests for monogenic diseases. Therefore, ethical, legal, and social issues that apply to predictive genetic testing for monogenic diseases may not be relevant for the prediction of multifactorial disorders in genomic medicine.

The genetic analysis of repeated measures I: Simplex models

NARCIS (Netherlands)

Molenaar, P.C.M.; Boomsma, D.I.

1987-01-01

Extends the simplex model to a model that may be used for the genetic and environmental analysis of covariance (ANCOVA) structures. This "double" simplex structure can be specified as a linear structural relationships model. It is shown that data that give rise to a simplex correlation structure,

Multiple model adaptive control with mixing

Science.gov (United States)

Kuipers, Matthew

Despite the remarkable theoretical accomplishments and successful applications of adaptive control, the field is not sufficiently mature to solve challenging control problems requiring strict performance and safety guarantees. Towards addressing these issues, a novel deterministic multiple-model adaptive control approach called adaptive mixing control is proposed. In this approach, adaptation comes from a high-level system called the supervisor that mixes into feedback a number of candidate controllers, each finely-tuned to a subset of the parameter space. The mixing signal, the supervisor's output, is generated by estimating the unknown parameters and, at every instant of time, calculating the contribution level of each candidate controller based on certainty equivalence. The proposed architecture provides two characteristics relevant to solving stringent, performance-driven applications. First, the full-suite of linear time invariant control tools is available. A disadvantage of conventional adaptive control is its restriction to utilizing only those control laws whose solutions can be feasibly computed in real-time, such as model reference and pole-placement type controllers. Because its candidate controllers are computed off line, the proposed approach suffers no such restriction. Second, the supervisor's output is smooth and does not necessarily depend on explicit a priori knowledge of the disturbance model. These characteristics can lead to improved performance by avoiding the unnecessary switching and chattering behaviors associated with some other multiple adaptive control approaches. The stability and robustness properties of the adaptive scheme are analyzed. It is shown that the mean-square regulation error is of the order of the modeling error. And when the parameter estimate converges to its true value, which is guaranteed if a persistence of excitation condition is satisfied, the adaptive closed-loop system converges exponentially fast to a closed

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

[Future challenges in multiple sclerosis].

Science.gov (United States)

Fernández, Óscar

2014-12-01

Multiple sclerosis occurs in genetically susceptible individuals, in whom an unknown environmental factor triggers an immune response, giving rise to a chronic and disabling autoimmune disease. Currently, significant progress is being made in our knowledge of the frequency and distribution of multiple sclerosis and its risk factors, genetics, pathology, pathogenesis, diagnostic and prognostic markers, and treatment. This has radically changed patients' and clinicians' expectations of multiple sclerosis and has raised hope that there will soon be a way to control the disease. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

Design of Xen Hybrid Multiple Police Model

Science.gov (United States)

Sun, Lei; Lin, Renhao; Zhu, Xianwei

2017-10-01

Virtualization Technology has attracted more and more attention. As a popular open-source virtualization tools, XEN is used more and more frequently. Xsm, XEN security model, has also been widespread concern. The safety status classification has not been established in the XSM, and it uses the virtual machine as a managed object to make Dom0 a unique administrative domain that does not meet the minimum privilege. According to these questions, we design a Hybrid multiple police model named SV_HMPMD that organically integrates multiple single security policy models include DTE,RBAC,BLP. It can fullfill the requirement of confidentiality and integrity for security model and use different particle size to different domain. In order to improve BLP’s practicability, the model introduce multi-level security labels. In order to divide the privilege in detail, we combine DTE with RBAC. In order to oversize privilege, we limit the privilege of domain0.

Genetic demixing and evolution in linear stepping stone models

Science.gov (United States)

Korolev, K. S.; Avlund, Mikkel; Hallatschek, Oskar; Nelson, David R.

2010-04-01

Results for mutation, selection, genetic drift, and migration in a one-dimensional continuous population are reviewed and extended. The population is described by a continuous limit of the stepping stone model, which leads to the stochastic Fisher-Kolmogorov-Petrovsky-Piscounov equation with additional terms describing mutations. Although the stepping stone model was first proposed for population genetics, it is closely related to “voter models” of interest in nonequilibrium statistical mechanics. The stepping stone model can also be regarded as an approximation to the dynamics of a thin layer of actively growing pioneers at the frontier of a colony of micro-organisms undergoing a range expansion on a Petri dish. The population tends to segregate into monoallelic domains. This segregation slows down genetic drift and selection because these two evolutionary forces can only act at the boundaries between the domains; the effects of mutation, however, are not significantly affected by the segregation. Although fixation in the neutral well-mixed (or “zero-dimensional”) model occurs exponentially in time, it occurs only algebraically fast in the one-dimensional model. An unusual sublinear increase is also found in the variance of the spatially averaged allele frequency with time. If selection is weak, selective sweeps occur exponentially fast in both well-mixed and one-dimensional populations, but the time constants are different. The relatively unexplored problem of evolutionary dynamics at the edge of an expanding circular colony is studied as well. Also reviewed are how the observed patterns of genetic diversity can be used for statistical inference and the differences are highlighted between the well-mixed and one-dimensional models. Although the focus is on two alleles or variants, q -allele Potts-like models of gene segregation are considered as well. Most of the analytical results are checked with simulations and could be tested against recent spatial

Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming

Directory of Open Access Journals (Sweden)

Wafa Abdelmalek

2009-01-01

Full Text Available The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting. By using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting's performance measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.

Nuclear reactors project optimization based on neural network and genetic algorithm

International Nuclear Information System (INIS)

Pereira, Claudio M.N.A.; Schirru, Roberto; Martinez, Aquilino S.

1997-01-01

This work presents a prototype of a system for nuclear reactor core design optimization based on genetic algorithms and artificial neural networks. A neural network is modeled and trained in order to predict the flux and the neutron multiplication factor values based in the enrichment, network pitch and cladding thickness, with average error less than 2%. The values predicted by the neural network are used by a genetic algorithm in this heuristic search, guided by an objective function that rewards the high flux values and penalizes multiplication factors far from the required value. Associating the quick prediction - that may substitute the reactor physics calculation code - with the global optimization capacity of the genetic algorithm, it was obtained a quick and effective system for nuclear reactor core design optimization. (author). 11 refs., 8 figs., 3 tabs

Genetic Resources in the “Calabaza Pipiana” Squash (Cucurbita argyrosperma) in Mexico: Genetic Diversity, Genetic Differentiation and Distribution Models

Science.gov (United States)

Sánchez-de la Vega, Guillermo; Castellanos-Morales, Gabriela; Gámez, Niza; Hernández-Rosales, Helena S.; Vázquez-Lobo, Alejandra; Aguirre-Planter, Erika; Jaramillo-Correa, Juan P.; Montes-Hernández, Salvador; Lira-Saade, Rafael; Eguiarte, Luis E.

2018-01-01

Analyses of genetic variation allow understanding the origin, diversification and genetic resources of cultivated plants. Domesticated taxa and their wild relatives are ideal systems for studying genetic processes of plant domestication and their joint is important to evaluate the distribution of their genetic resources. Such is the case of the domesticated subspecies C. argyrosperma ssp. argyrosperma, known in Mexico as calabaza pipiana, and its wild relative C. argyrosperma ssp. sororia. The main aim of this study was to use molecular data (microsatellites) to assess the levels of genetic variation and genetic differentiation within and among populations of domesticated argyrosperma across its distribution in Mexico in comparison to its wild relative, sororia, and to identify environmental suitability in previously proposed centers of domestication. We analyzed nine unlinked nuclear microsatellite loci to assess levels of diversity and distribution of genetic variation within and among populations in 440 individuals from 19 populations of cultivated landraces of argyrosperma and from six wild populations of sororia, in order to conduct a first systematic analysis of their genetic resources. We also used species distribution models (SDMs) for sororia to identify changes in this wild subspecies’ distribution from the Holocene (∼6,000 years ago) to the present, and to assess the presence of suitable environmental conditions in previously proposed domestication sites. Genetic variation was similar among subspecies (HE = 0.428 in sororia, and HE = 0.410 in argyrosperma). Nine argyrosperma populations showed significant levels of inbreeding. Both subspecies are well differentiated, and genetic differentiation (FST) among populations within each subspecies ranged from 0.152 to 0.652. Within argyrosperma we found three genetic groups (Northern Mexico, Yucatan Peninsula, including Michoacan and Veracruz, and Pacific coast plus Durango). We detected low levels of gene

Genetic Resources in the “Calabaza Pipiana” Squash (Cucurbita argyrosperma in Mexico: Genetic Diversity, Genetic Differentiation and Distribution Models

Directory of Open Access Journals (Sweden)

Guillermo Sánchez-de la Vega

2018-03-01

Full Text Available Analyses of genetic variation allow understanding the origin, diversification and genetic resources of cultivated plants. Domesticated taxa and their wild relatives are ideal systems for studying genetic processes of plant domestication and their joint is important to evaluate the distribution of their genetic resources. Such is the case of the domesticated subspecies C. argyrosperma ssp. argyrosperma, known in Mexico as calabaza pipiana, and its wild relative C. argyrosperma ssp. sororia. The main aim of this study was to use molecular data (microsatellites to assess the levels of genetic variation and genetic differentiation within and among populations of domesticated argyrosperma across its distribution in Mexico in comparison to its wild relative, sororia, and to identify environmental suitability in previously proposed centers of domestication. We analyzed nine unlinked nuclear microsatellite loci to assess levels of diversity and distribution of genetic variation within and among populations in 440 individuals from 19 populations of cultivated landraces of argyrosperma and from six wild populations of sororia, in order to conduct a first systematic analysis of their genetic resources. We also used species distribution models (SDMs for sororia to identify changes in this wild subspecies’ distribution from the Holocene (∼6,000 years ago to the present, and to assess the presence of suitable environmental conditions in previously proposed domestication sites. Genetic variation was similar among subspecies (HE = 0.428 in sororia, and HE = 0.410 in argyrosperma. Nine argyrosperma populations showed significant levels of inbreeding. Both subspecies are well differentiated, and genetic differentiation (FST among populations within each subspecies ranged from 0.152 to 0.652. Within argyrosperma we found three genetic groups (Northern Mexico, Yucatan Peninsula, including Michoacan and Veracruz, and Pacific coast plus Durango. We detected low

An animal model of differential genetic risk for methamphetamine intake

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Tamara ePhillips

2015-09-01

Full Text Available The question of whether genetic factors contribute to risk for methamphetamine (MA use and dependence has not been intensively investigated. Compared to human populations, genetic animal models offer the advantages of control over genetic family history and drug exposure. Using selective breeding, we created lines of mice that differ in genetic risk for voluntary MA intake and identified the chromosomal addresses of contributory genes. A quantitative trait locus was identified on chromosome 10 that accounts for more than 50% of the genetic variance in MA intake in the selected mouse lines. In addition, behavioral and physiological screening identified differences corresponding with risk for MA intake that have generated hypotheses that are testable in humans. Heightened sensitivity to aversive and certain physiological effects of MA, such as MA-induced reduction in body temperature, are hallmarks of mice bred for low MA intake. Furthermore, unlike MA-avoiding mice, MA-preferring mice are sensitive to rewarding and reinforcing MA effects, and to MA-induced increases in brain extracellular dopamine levels. Gene expression analyses implicate the importance of a network enriched in transcription factor genes, some of which regulate the mu opioid receptor gene, Oprm1, in risk for MA use. Neuroimmune factors appear to play a role in differential response to MA between the mice bred for high and low intake. In addition, chromosome 10 candidate gene studies provide strong support for a trace amine associated receptor 1 gene, Taar1, polymorphism in risk for MA intake. MA is a trace amine-associated receptor 1 (TAAR1 agonist, and a non-functional Taar1 allele segregates with high MA consumption. Thus, reduced TAAR1 function has the potential to increase risk for MA use. Overall, existing findings support the MA drinking lines as a powerful model for identifying genetic factors involved in determining risk for harmful MA use. Future directions include the

Supersymmetric U(1)' model with multiple dark matters

International Nuclear Information System (INIS)

Hur, Taeil; Lee, Hye-Sung; Nasri, Salah

2008-01-01

We consider a scenario where a supersymmetric model has multiple dark matter particles. Adding a U(1) ' gauge symmetry is a well-motivated extension of the minimal supersymmetric standard model (MSSM). It can cure the problems of the MSSM such as the μ problem or the proton decay problem with high-dimensional lepton number and baryon number violating operators which R parity allows. An extra parity (U parity) may arise as a residual discrete symmetry after U(1) ' gauge symmetry is spontaneously broken. The lightest U-parity particle (LUP) is stable under the new parity becoming a new dark matter candidate. Up to three massive particles can be stable in the presence of the R parity and the U parity. We numerically illustrate that multiple stable particles in our model can satisfy both constraints from the relic density and the direct detection, thus providing a specific scenario where a supersymmetric model has well-motivated multiple dark matters consistent with experimental constraints. The scenario provides new possibilities in the present and upcoming dark matter searches in the direct detection and collider experiments

Multiple Social Networks, Data Models and Measures for

DEFF Research Database (Denmark)

Magnani, Matteo; Rossi, Luca

2017-01-01

Multiple Social Network Analysis is a discipline defining models, measures, methodologies, and algorithms to study multiple social networks together as a single social system. It is particularly valuable when the networks are interconnected, e.g., the same actors are present in more than one...

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.

Is the Experience of Thermal Pain Genetics Dependent?

Directory of Open Access Journals (Sweden)

Emilia Horjales-Araujo

2015-01-01

Full Text Available It is suggested that genetic variations explain a significant portion of the variability in pain perception; therefore, increased understanding of pain-related genetic influences may identify new targets for therapies and treatments. The relative contribution of the different genes to the variance in clinical and experimental pain responses remains unknown. It is suggested that the genetic contributions to pain perception vary across pain modalities. For example, it has been suggested that more than 60% of the variance in cold pressor responses can be explained by genetic factors; in comparison, only 26% of the variance in heat pain responses is explained by these variations. Thus, the selection of pain model might markedly influence the magnitude of the association between the pain phenotype and genetic variability. Thermal pain sensation is complex with multiple molecular and cellular mechanisms operating alone and in combination within the peripheral and central nervous system. It is thus highly probable that the thermal pain experience is affected by genetic variants in one or more of the pathways involved in the thermal pain signaling. This review aims to present and discuss some of the genetic variations that have previously been associated with different experimental thermal pain models.

Multiple Response Regression for Gaussian Mixture Models with Known Labels.

Science.gov (United States)

Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng

2012-12-01

Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.

A Rational Model In Theoretical Genetics

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Karl Javorszky

2008-07-01

Full Text Available This model connects information processing in biological organisms with methods and concepts used in classical, technical information processing. The central concept shows copying and regulatory interaction between a logical sequence consisting of triplets and the amount of constituents of a set. The basic mathematical model of information processing within a biological cell has been worked out. The cell in the model copies its present state into a sequence and reads it off the sequence. The sequence comes in triplets and is not one sequence but appears in two almost identical varieties. We treat consecutive and contemporary assemblies of information carrying media as equally suited to contain information. Methods used so far utilised the consecutive property of media, while in biology one observes the concurrent existence of specific realisations of possibilities. Genetics connects the two approaches by using an interplay between consecutively (sequentially ordered logical markers (the DNA and the state of the set engulfing the DNA. Several mathematical tools have been evolved to assemble an interface between sequentially ordered carriers and the same number of carriers if they arrive contemporaneously. Using linguistic theory and formal logic one concludes that measurement(s on a cell are a (set of logical sentence(s relating to an assembly of n objects with group structures among each other. We linearise and count all possible group relations on a set of n objects and introduce the concept of multidimensional partitions hitherto left undefined. We introduce the concept of a maximally structured set by establishing an upper limit to the information carrying capacity of n objects used commutatively and sequentially at the same time (like genetics does. The copying and re-copying mechanism which is the core matter with genetics appears in the model as differing transmission efficiency coefficients of media if the media are used once sequentially

SDG and qualitative trend based model multiple scale validation

Science.gov (United States)

Gao, Dong; Xu, Xin; Yin, Jianjin; Zhang, Hongyu; Zhang, Beike

2017-09-01

Verification, Validation and Accreditation (VV&A) is key technology of simulation and modelling. For the traditional model validation methods, the completeness is weak; it is carried out in one scale; it depends on human experience. The SDG (Signed Directed Graph) and qualitative trend based multiple scale validation is proposed. First the SDG model is built and qualitative trends are added to the model. And then complete testing scenarios are produced by positive inference. The multiple scale validation is carried out by comparing the testing scenarios with outputs of simulation model in different scales. Finally, the effectiveness is proved by carrying out validation for a reactor model.

A Multiple Items EPQ/EOQ Model for a Vendor and Multiple Buyers System with Considering Continuous and Discrete Demand Simultaneously

Science.gov (United States)

Jonrinaldi; Rahman, T.; Henmaidi; Wirdianto, E.; Zhang, D. Z.

2018-03-01

This paper proposed a mathematical model for multiple items Economic Production and Order Quantity (EPQ/EOQ) with considering continuous and discrete demand simultaneously in a system consisting of a vendor and multiple buyers. This model is used to investigate the optimal production lot size of the vendor and the number of shipments policy of orders to multiple buyers. The model considers the multiple buyers’ holding cost as well as transportation cost, which minimize the total production and inventory costs of the system. The continuous demand from any other customers can be fulfilled anytime by the vendor while the discrete demand from multiple buyers can be fulfilled by the vendor using the multiple delivery policy with a number of shipments of items in the production cycle time. A mathematical model is developed to illustrate the system based on EPQ and EOQ model. Solution procedures are proposed to solve the model using a Mixed Integer Non Linear Programming (MINLP) and algorithm methods. Then, the numerical example is provided to illustrate the system and results are discussed.

Genetics of traffic assignment models for strategic transport planning

NARCIS (Netherlands)

Bliemer, M.C.J.; Raadsen, M.P.H.; Brederode, L.J.N.; Bell, M.G.H.; Wismans, Luc Johannes Josephus; Smith, M.J.

2016-01-01

This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in capability (genes). We argue that all traffic

Genetic optimization of steam multi-turbines system

International Nuclear Information System (INIS)

Olszewski, Pawel

2014-01-01

Optimization analysis of partially loaded cogeneration, multiple-stages steam turbines system was numerically investigated by using own-developed code (C++). The system can be controlled by following variables: fresh steam temperature, pressure, and flow rates through all stages in steam turbines. Five various strategies, four thermodynamics and one economical, which quantify system operation, were defined and discussed as an optimization functions. Mathematical model of steam turbines calculates steam properties according to the formulation proposed by the International Association for the Properties of Water and Steam. Genetic algorithm GENOCOP was implemented as a solving engine for non–linear problem with handling constrains. Using formulated methodology, example solution for partially loaded system, composed of five steam turbines (30 input variables) with different characteristics, was obtained for five strategies. The genetic algorithm found multiple solutions (various input parameters sets) giving similar overall results. In real application it allows for appropriate scheduling of machine operation that would affect equable time load of every system compounds. Also based on these results three strategies where chosen as the most complex: the first thermodynamic law energy and exergy efficiency maximization and total equivalent energy minimization. These strategies can be successfully used in optimization of real cogeneration applications. - Highlights: • Genetic optimization model for a set of five various steam turbines was presented. • Four various thermodynamic optimization strategies were proposed and discussed. • Operational parameters (steam pressure, temperature, flow) influence was examined. • Genetic algorithm generated optimal solutions giving the best estimators values. • It has been found that similar energy effect can be obtained for various inputs

The multiple genetic causes of central hypothyroidism.

Science.gov (United States)

Persani, Luca; Bonomi, Marco

2017-03-01

An insufficient stimulation by thyrotropin (TSH) of an otherwise normal thyroid gland represents the cause of Central Hypothyrodism (CeH). CeH is about 1000-folds rarer than Primary Hypothyroidism and often represents a real challenge for the clinicians, mainly because they cannot rely on adequately sensitive parameters for diagnosis or management, as it occurs with circulating TSH in PH. Therefore, CeH diagnosis can be frequently missed or delayed in patients with a previously unknown pituitary involvement. A series of genetic defects have been described to account for isolated CeH or combined pituitary hormone defects (CPHDs) with variable clinical characteristics and degrees of severity. The recently identified candidate gene IGSF1 appears frequently involved. This review provides an updated illustration of the different genetic defects accounting for CeH. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cross-validation analysis for genetic evaluation models for ranking in endurance horses.

Science.gov (United States)

García-Ballesteros, S; Varona, L; Valera, M; Gutiérrez, J P; Cervantes, I

2018-01-01

Ranking trait was used as a selection criterion for competition horses to estimate racing performance. In the literature the most common approaches to estimate breeding values are the linear or threshold statistical models. However, recent studies have shown that a Thurstonian approach was able to fix the race effect (competitive level of the horses that participate in the same race), thus suggesting a better prediction accuracy of breeding values for ranking trait. The aim of this study was to compare the predictability of linear, threshold and Thurstonian approaches for genetic evaluation of ranking in endurance horses. For this purpose, eight genetic models were used for each approach with different combinations of random effects: rider, rider-horse interaction and environmental permanent effect. All genetic models included gender, age and race as systematic effects. The database that was used contained 4065 ranking records from 966 horses and that for the pedigree contained 8733 animals (47% Arabian horses), with an estimated heritability around 0.10 for the ranking trait. The prediction ability of the models for racing performance was evaluated using a cross-validation approach. The average correlation between real and predicted performances across genetic models was around 0.25 for threshold, 0.58 for linear and 0.60 for Thurstonian approaches. Although no significant differences were found between models within approaches, the best genetic model included: the rider and rider-horse random effects for threshold, only rider and environmental permanent effects for linear approach and all random effects for Thurstonian approach. The absolute correlations of predicted breeding values among models were higher between threshold and Thurstonian: 0.90, 0.91 and 0.88 for all animals, top 20% and top 5% best animals. For rank correlations these figures were 0.85, 0.84 and 0.86. The lower values were those between linear and threshold approaches (0.65, 0.62 and 0.51). In

Multiple commodities in statistical microeconomics: Model and market

Science.gov (United States)

Baaquie, Belal E.; Yu, Miao; Du, Xin

2016-11-01

A statistical generalization of microeconomics has been made in Baaquie (2013). In Baaquie et al. (2015), the market behavior of single commodities was analyzed and it was shown that market data provides strong support for the statistical microeconomic description of commodity prices. The case of multiple commodities is studied and a parsimonious generalization of the single commodity model is made for the multiple commodities case. Market data shows that the generalization can accurately model the simultaneous correlation functions of up to four commodities. To accurately model five or more commodities, further terms have to be included in the model. This study shows that the statistical microeconomics approach is a comprehensive and complete formulation of microeconomics, and which is independent to the mainstream formulation of microeconomics.

Global and 3D spatial assessment of neuroinflammation in rodent models of Multiple Sclerosis.

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Shashank Gupta

Full Text Available Multiple Sclerosis (MS is a progressive autoimmune inflammatory and demyelinating disease of the central nervous system (CNS. T cells play a key role in the progression of neuroinflammation in MS and also in the experimental autoimmune encephalomyelitis (EAE animal models for the disease. A technology for quantitative and 3 dimensional (3D spatial assessment of inflammation in this and other CNS inflammatory conditions is much needed. Here we present a procedure for 3D spatial assessment and global quantification of the development of neuroinflammation based on Optical Projection Tomography (OPT. Applying this approach to the analysis of rodent models of MS, we provide global quantitative data of the major inflammatory component as a function of the clinical course. Our data demonstrates a strong correlation between the development and progression of neuroinflammation and clinical disease in several mouse and a rat model of MS refining the information regarding the spatial dynamics of the inflammatory component in EAE. This method provides a powerful tool to investigate the effect of environmental and genetic forces and for assessing the therapeutic effects of drug therapy in animal models of MS and other neuroinflammatory/neurodegenerative disorders.

AgMIP Training in Multiple Crop Models and Tools

Science.gov (United States)

Boote, Kenneth J.; Porter, Cheryl H.; Hargreaves, John; Hoogenboom, Gerrit; Thornburn, Peter; Mutter, Carolyn

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has the goal of using multiple crop models to evaluate climate impacts on agricultural production and food security in developed and developing countries. There are several major limitations that must be overcome to achieve this goal, including the need to train AgMIP regional research team (RRT) crop modelers to use models other than the ones they are currently familiar with, plus the need to harmonize and interconvert the disparate input file formats used for the various models. Two activities were followed to address these shortcomings among AgMIP RRTs to enable them to use multiple models to evaluate climate impacts on crop production and food security. We designed and conducted courses in which participants trained on two different sets of crop models, with emphasis on the model of least experience. In a second activity, the AgMIP IT group created templates for inputting data on soils, management, weather, and crops into AgMIP harmonized databases, and developed translation tools for converting the harmonized data into files that are ready for multiple crop model simulations. The strategies for creating and conducting the multi-model course and developing entry and translation tools are reviewed in this chapter.

Applicability of genetic algorithms to parameter estimation of economic models

Directory of Open Access Journals (Sweden)

Marcel Ševela

2004-01-01

Full Text Available The paper concentrates on capability of genetic algorithms for parameter estimation of non-linear economic models. In the paper we test the ability of genetic algorithms to estimate of parameters of demand function for durable goods and simultaneously search for parameters of genetic algorithm that lead to maximum effectiveness of the computation algorithm. The genetic algorithms connect deterministic iterative computation methods with stochastic methods. In the genteic aůgorithm approach each possible solution is represented by one individual, those life and lifes of all generations of individuals run under a few parameter of genetic algorithm. Our simulations resulted in optimal mutation rate of 15% of all bits in chromosomes, optimal elitism rate 20%. We can not set the optimal extend of generation, because it proves positive correlation with effectiveness of genetic algorithm in all range under research, but its impact is degreasing. The used genetic algorithm was sensitive to mutation rate at most, than to extend of generation. The sensitivity to elitism rate is not so strong.

A Continuous Correlated Beta Process Model for Genetic Ancestry in Admixed Populations.

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Zachariah Gompert

Full Text Available Admixture and recombination create populations and genomes with genetic ancestry from multiple source populations. Analyses of genetic ancestry in admixed populations are relevant for trait and disease mapping, studies of speciation, and conservation efforts. Consequently, many methods have been developed to infer genome-average ancestry and to deconvolute ancestry into continuous local ancestry blocks or tracts within individuals. Current methods for local ancestry inference perform well when admixture occurred recently or hybridization is ongoing, or when admixture occurred in the distant past such that local ancestry blocks have fixed in the admixed population. However, methods to infer local ancestry frequencies in isolated admixed populations still segregating for ancestry do not exist. In the current paper, I develop and test a continuous correlated beta process model to fill this analytical gap. The method explicitly models autocorrelations in ancestry frequencies at the population-level and uses discriminant analysis of SNP windows to take advantage of ancestry blocks within individuals. Analyses of simulated data sets show that the method is generally accurate such that ancestry frequency estimates exhibited low root-mean-square error and were highly correlated with the true values, particularly when large (±10 or ±20 SNP windows were used. Along these lines, the proposed method outperformed post hoc inference of ancestry frequencies from a traditional hidden Markov model (i.e., the linkage model in structure, particularly when admixture occurred more distantly in the past with little on-going gene flow or was followed by natural selection. The reliability and utility of the method was further assessed by analyzing genetic ancestry in an admixed human population (Uyghur and three populations from a hybrid zone between Mus domesticus and M. musculus. Considerable variation in ancestry frequencies was detected within and among

BPA genetic monitoring - BPA Genetic Monitoring Project

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National Oceanic and Atmospheric Administration, Department of Commerce — Initiated in 1989, this study monitors genetic changes associated with hatchery propagation in multiple Snake River sub-basins for Chinook salmon and steelhead. We...

Molecular evolution of avian reovirus: evidence for genetic diversity and reassortment of the S-class genome segments and multiple cocirculating lineages

International Nuclear Information System (INIS)

Liu, Hung J.; Lee, Long H.; Hsu, Hsiao W.; Kuo, Liam C.; Liao, Ming H.

2003-01-01

Nucleotide sequences of the S-class genome segments of 17 field-isolates and vaccine strains of avian reovirus (ARV) isolated over a 23-year period from different hosts, pathotypes, and geographic locations were examined and analyzed to define phylogenetic profiles and evolutionary mechanism. The S1 genome segment showed noticeably higher divergence than the other S-class genes. The σC-encoding gene has evolved into six distinct lineages. In contrast, the other S-class genes showed less divergence than that of the σC-encoding gene and have evolved into two to three major distinct lineages, respectively. Comparative sequence analysis provided evidence indicating extensive sequence divergence between ARV and other orthoreoviruses. The evolutionary trees of each gene were distinct, suggesting that these genes evolve in an independent manner. Furthermore, variable topologies were the result of frequent genetic reassortment among multiple cocirculating lineages. Results showed genetic diversity correlated more closely with date of isolation and geographic sites than with host species and pathotypes. This is the first evidence demonstrating genetic variability among circulating ARVs through a combination of evolutionary mechanisms involving multiple cocirculating lineages and genetic reassortment. The evolutionary rates and patterns of base substitutions were examined. The evolutionary rate for the σC-encoding gene and σC protein was higher than for the other S-class genes and other family of viruses. With the exception of the σC-encoding gene, which nonsynonymous substitutions predominate over synonymous, the evolutionary process of the other S-class genes can be explained by the neutral theory of molecular evolution. Results revealed that synonymous substitutions predominate over nonsynonymous in the S-class genes, even though genetic diversity and substitution rates vary among the viruses

Effect of genetic polymorphisms on development of gout.

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Urano, Wako; Taniguchi, Atsuo; Inoue, Eisuke; Sekita, Chieko; Ichikawa, Naomi; Koseki, Yumi; Kamatani, Naoyuki; Yamanaka, Hisashi

2013-08-01

To validate the association between genetic polymorphisms and gout in Japanese patients, and to investigate the cumulative effects of multiple genetic factors on the development of gout. Subjects were 153 Japanese male patients with gout and 532 male controls. The genotypes of 11 polymorphisms in the 10 genes that have been indicated to be associated with serum uric acid levels or gout were determined. The cumulative effects of the genetic polymorphisms were investigated using a weighted genotype risk score (wGRS) based on the number of risk alleles and the OR for gout. A model to discriminate between patients with gout and controls was constructed by incorporating the wGRS and clinical factors. C statistics method was applied to evaluate the capability of the model to discriminate gout patients from controls. Seven polymorphisms were shown to be associated with gout. The mean wGRS was significantly higher in patients with gout (15.2 ± 2.01) compared to controls (13.4 ± 2.10; p gout. A prediction model for gout that incorporates genetic and clinical factors may be useful for identifying individuals who are at risk of gout.

Efficient Adoption and Assessment of Multiple Process Improvement Reference Models

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Simona Jeners

2013-06-01

Full Text Available A variety of reference models such as CMMI, COBIT or ITIL support IT organizations to improve their processes. These process improvement reference models (IRMs cover different domains such as IT development, IT Services or IT Governance but also share some similarities. As there are organizations that address multiple domains and need to coordinate their processes in their improvement we present MoSaIC, an approach to support organizations to efficiently adopt and conform to multiple IRMs. Our solution realizes a semantic integration of IRMs based on common meta-models. The resulting IRM integration model enables organizations to efficiently implement and asses multiple IRMs and to benefit from synergy effects.

Characterization of recombination features and the genetic basis in multiple cattle breeds.

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Shen, Botong; Jiang, Jicai; Seroussi, Eyal; Liu, George E; Ma, Li

2018-04-27

Crossover generated by meiotic recombination is a fundamental event that facilitates meiosis and sexual reproduction. Comparative studies have shown wide variation in recombination rate among species, but the characterization of recombination features between cattle breeds has not yet been performed. Cattle populations in North America count millions, and the dairy industry has genotyped millions of individuals with pedigree information that provide a unique opportunity to study breed-level variations in recombination. Based on large pedigrees of Jersey, Ayrshire and Brown Swiss cattle with genotype data, we identified over 3.4 million maternal and paternal crossover events from 161,309 three-generation families. We constructed six breed- and sex-specific genome-wide recombination maps using 58,982 autosomal SNPs for two sexes in the three dairy cattle breeds. A comparative analysis of the six recombination maps revealed similar global recombination patterns between cattle breeds but with significant differences between sexes. We confirmed that male recombination map is 10% longer than the female map in all three cattle breeds, consistent with previously reported results in Holstein cattle. When comparing recombination hotspot regions between cattle breeds, we found that 30% and 10% of the hotspots were shared between breeds in males and females, respectively, with each breed exhibiting some breed-specific hotspots. Finally, our multiple-breed GWAS found that SNPs in eight loci affected recombination rate and that the PRDM9 gene associated with hotspot usage in multiple cattle breeds, indicating a shared genetic basis for recombination across dairy cattle breeds. Collectively, our results generated breed- and sex-specific recombination maps for multiple cattle breeds, provided a comprehensive characterization and comparison of recombination patterns between breeds, and expanded our understanding of the breed-level variations in recombination features within an

Genetic algorithms and experimental discrimination of SUSY models

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Allanach, B.C.; Quevedo, F.; Grellscheid, D.

2004-01-01

We introduce genetic algorithms as a means to estimate the accuracy required to discriminate among different models using experimental observables. We exemplify the technique in the context of the minimal supersymmetric standard model. If supersymmetric particles are discovered, models of supersymmetry breaking will be fit to the observed spectrum and it is beneficial to ask beforehand: what accuracy is required to always allow the discrimination of two particular models and which are the most important masses to observe? Each model predicts a bounded patch in the space of observables once unknown parameters are scanned over. The questions can be answered by minimising a 'distance' measure between the two hypersurfaces. We construct a distance measure that scales like a constant fraction of an observable, since that is how the experimental errors are expected to scale. Genetic algorithms, including concepts such as natural selection, fitness and mutations, provide a solution to the minimisation problem. We illustrate the efficiency of the method by comparing three different classes of string models for which the above questions could not be answered with previous techniques. The required accuracy is in the range accessible to the Large Hadron Collider (LHC) when combined with a future linear collider (LC) facility. The technique presented here can be applied to more general classes of models or observables. (author)

Initial assessment of a model relating intratumoral genetic heterogeneity to radiological morphology

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Noterdaeme, O; Kelly, M; Friend, P; Soonowalla, Z; Steers, G; Brady, M

2010-01-01

Tumour heterogeneity has major implications for tumour development and response to therapy. Tumour heterogeneity results from mutations in the genes responsible for mismatch repair or maintenance of chromosomal stability. Cells with different genetic properties may grow at different rates and exhibit different resistance to therapeutic interventions. To date, there exists no approach to non-invasively assess tumour heterogeneity. Here we present a biologically inspired model of tumour growth, which relates intratumoral genetic heterogeneity to gross morphology visible on radiological images. The model represents the development of a tumour as a set of expanding spheres, each sphere representing a distinct clonal centre, with the sprouting of new spheres corresponding to new clonal centres. Each clonal centre may possess different characteristics relating to genetic composition, growth rate and response to treatment. We present a clinical example for which the model accurately tracks tumour growth and shows the correspondence to genetic variation (as determined by array comparative genomic hybridisation). One clinical implication of our work is that the assessment of heterogeneous tumours using Response Evaluation Criteria In Solid Tumours (RECIST) or volume measurements may not accurately reflect tumour growth, stability or the response to treatment. We believe that this is the first model linking the macro-scale appearance of tumours to their genetic composition. We anticipate that our model will provide a more informative way to assess the response of heterogeneous tumours to treatment, which is of increasing importance with the development of novel targeted anti-cancer treatments. PMID:19690073

Enhanced hexose fermentation by Saccharomyces cerevisiae through integration of stoichiometric modeling and genetic screening.

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Quarterman, Josh; Kim, Soo Rin; Kim, Pan-Jun; Jin, Yong-Su

2015-01-20

In order to determine beneficial gene deletions for ethanol production by the yeast Saccharomyces cerevisiae, we performed an in silico gene deletion experiment based on a genome-scale metabolic model. Genes coding for two oxidative phosphorylation reactions (cytochrome c oxidase and ubiquinol cytochrome c reductase) were identified by the model-based simulation as potential deletion targets for enhancing ethanol production and maintaining acceptable overall growth rate in oxygen-limited conditions. Since the two target enzymes are composed of multiple subunits, we conducted a genetic screening study to evaluate the in silico results and compare the effect of deleting various portions of the respiratory enzyme complexes. Over two-thirds of the knockout mutants identified by the in silico study did exhibit experimental behavior in qualitative agreement with model predictions, but the exceptions illustrate the limitation of using a purely stoichiometric model-based approach. Furthermore, there was a substantial quantitative variation in phenotype among the various respiration-deficient mutants that were screened in this study, and three genes encoding respiratory enzyme subunits were identified as the best knockout targets for improving hexose fermentation in microaerobic conditions. Specifically, deletion of either COX9 or QCR9 resulted in higher ethanol production rates than the parental strain by 37% and 27%, respectively, with slight growth disadvantages. Also, deletion of QCR6 led to improved ethanol production rate by 24% with no growth disadvantage. The beneficial effects of these gene deletions were consistently demonstrated in different strain backgrounds and with four common hexoses. The combination of stoichiometric modeling and genetic screening using a systematic knockout collection was useful for narrowing a large set of gene targets and identifying targets of interest. Copyright © 2014 Elsevier B.V. All rights reserved.

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

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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

Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

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Louis, S.J.; Raines, G.L.

2003-01-01

We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

Different concepts and models of information for family-relevant genetic findings: comparison and ethical analysis.

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Lenk, Christian; Frommeld, Debora

2015-08-01

Genetic predispositions often concern not only individual persons, but also other family members. Advances in the development of genetic tests lead to a growing number of genetic diagnoses in medical practice and to an increasing importance of genetic counseling. In the present article, a number of ethical foundations and preconditions for this issue are discussed. Four different models for the handling of genetic information are presented and analyzed including a discussion of practical implications. The different models' ranges of content reach from a strictly autonomous position over self-governed arrangements in the practice of genetic counseling up to the involvement of official bodies and committees. The different models show a number of elements which seem to be very useful for the handling of genetic data in families from an ethical perspective. In contrast, the limitations of the standard medical attempt regarding confidentiality and personal autonomy in the context of genetic information in the family are described. Finally, recommendations for further ethical research and the development of genetic counseling in families are given.

Preimplantation genetic diagnosis for Duchenne muscular dystrophy by multiple displacement amplification.

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Ren, Zi; Zeng, Hai-tao; Xu, Yan-wen; Zhuang, Guang-lun; Deng, Jie; Zhang, Cheng; Zhou, Can-quan

2009-02-01

To evaluate the use of multiple displacement amplification (MDA) in preimplantation genetic diagnosis (PGD) for female carriers with Duchenne muscular dystrophy (DMD). MDA was used to amplify a whole genome of single cells. Following the setup on single cells, the test was applied in two clinical cases of PGD. One mutant exon, six short tandem repeats (STR) markers within the dystrophin gene, and amelogenin were incorporated into singleplex polymerase chain reaction (PCR) assays on MDA products of single blastomeres. Center for reproductive medicine in First Affiliated Hospital, Sun Yat-sen University, China. Two female carriers with a duplication of exons 3-11 and a deletion of exons 47-50, respectively. The MDA of single cells and fluorescent PCR assays for PGD. The ability to analyze single blastomeres for DMD using MDA. The protocol setup previously allowed for the accurate diagnosis of each embryo. Two clinical cases resulted in a healthy girl, which was the first successful clinical application of MDA in PGD for DMD. We suggest that this protocol is reliable to increase the accuracy of the PGD for DMD.

Assessment of Poisson, logit, and linear models for genetic analysis of clinical mastitis in Norwegian Red cows.

Science.gov (United States)

Vazquez, A I; Gianola, D; Bates, D; Weigel, K A; Heringstad, B

2009-02-01

Clinical mastitis is typically coded as presence/absence during some period of exposure, and records are analyzed with linear or binary data models. Because presence includes cows with multiple episodes, there is loss of information when a count is treated as a binary response. The Poisson model is designed for counting random variables, and although it is used extensively in epidemiology of mastitis, it has rarely been used for studying the genetics of mastitis. Many models have been proposed for genetic analysis of mastitis, but they have not been formally compared. The main goal of this study was to compare linear (Gaussian), Bernoulli (with logit link), and Poisson models for the purpose of genetic evaluation of sires for mastitis in dairy cattle. The response variables were clinical mastitis (CM; 0, 1) and number of CM cases (NCM; 0, 1, 2, ..). Data consisted of records on 36,178 first-lactation daughters of 245 Norwegian Red sires distributed over 5,286 herds. Predictive ability of models was assessed via a 3-fold cross-validation using mean squared error of prediction (MSEP) as the end-point. Between-sire variance estimates for NCM were 0.065 in Poisson and 0.007 in the linear model. For CM the between-sire variance was 0.093 in logit and 0.003 in the linear model. The ratio between herd and sire variances for the models with NCM response was 4.6 and 3.5 for Poisson and linear, respectively, and for model for CM was 3.7 in both logit and linear models. The MSEP for all cows was similar. However, within healthy animals, MSEP was 0.085 (Poisson), 0.090 (linear for NCM), 0.053 (logit), and 0.056 (linear for CM). For mastitic animals the MSEP values were 1.206 (Poisson), 1.185 (linear for NCM response), 1.333 (logit), and 1.319 (linear for CM response). The models for count variables had a better performance when predicting diseased animals and also had a similar performance between them. Logit and linear models for CM had better predictive ability for healthy

[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.

Genetic educational needs and the role of genetics in primary care: a focus group study with multiple perspectives

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van der Vleuten Cees

2011-02-01

Full Text Available Abstract Background Available evidence suggests that improvements in genetics education are needed to prepare primary care providers for the impact of ongoing rapid advances in genomics. Postgraduate (physician training and master (midwifery training programmes in primary care and public health are failing to meet these perceived educational needs. The aim of this study was to explore the role of genetics in primary care (i.e. family medicine and midwifery care and the need for education in this area as perceived by primary care providers, patient advocacy groups and clinical genetics professionals. Methods Forty-four participants took part in three types of focus groups: mono-disciplinary groups of general practitioners and midwives, respectively and multidisciplinary groups composed of a diverse set of experts. The focus group sessions were audio-taped, transcribed verbatim and analysed using content analysis. Recurrent themes were identified. Results Four themes emerged regarding the educational needs and the role of genetics in primary care: (1 genetics knowledge, (2 family history, (3 ethical dilemmas and psychosocial effects in relation to genetics and (4 insight into the organisation and role of clinical genetics services. These themes reflect a shift in the role of genetics in primary care with implications for education. Although all focus group participants acknowledged the importance of genetics education, general practitioners felt this need more urgently than midwives and more strongly emphasized their perceived knowledge deficiencies. Conclusion The responsibilities of primary care providers with regard to genetics require further study. The results of this study will help to develop effective genetics education strategies to improve primary care providers' competencies in this area. More research into the educational priorities in genetics is needed to design courses that are suitable for postgraduate and master programmes for

Maternal Smoking During Pregnancy and Offspring Birth Weight: A Genetically-Informed Approach Comparing Multiple Raters

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Knopik, Valerie S.; Marceau, Kristine; Palmer, Rohan H. C.; Smith, Taylor F.; Heath, Andrew C.

2016-01-01

Maternal smoking during pregnancy (SDP) is a significant public health concern with adverse consequences to the health and well-being of the fetus. There is considerable debate about the best method of assessing SDP, including birth/medical records, timeline follow-back approaches, multiple reporters, and biological verification (e.g., cotinine). This is particularly salient for genetically-informed approaches where it is not always possible or practical to do a prospective study starting during the prenatal period when concurrent biological specimen samples can be collected with ease. In a sample of families (N = 173) specifically selected for sibling pairs discordant for prenatal smoking exposure, we: (1) compare rates of agreement across different types of report—maternal report of SDP, paternal report of maternal SDP, and SDP contained on birth records from the Department of Vital Statistics; (2) examine whether SDP is predictive of birth weight outcomes using our best SDP report as identified via step (1); and (3) use a sibling-comparison approach that controls for genetic and familial influences that siblings share in order to assess the effects of SDP on birth weight. Results show high agreement between reporters and support the utility of retrospective report of SDP. Further, we replicate a causal association between SDP and birth weight, wherein SDP results in reduced birth weight even when accounting for genetic and familial confounding factors via a sibling comparison approach. PMID:26494459

Parallel Genetic Algorithms for calibrating Cellular Automata models: Application to lava flows

International Nuclear Information System (INIS)

D'Ambrosio, D.; Spataro, W.; Di Gregorio, S.; Calabria Univ., Cosenza; Crisci, G.M.; Rongo, R.; Calabria Univ., Cosenza

2005-01-01

Cellular Automata are highly nonlinear dynamical systems which are suitable far simulating natural phenomena whose behaviour may be specified in terms of local interactions. The Cellular Automata model SCIARA, developed far the simulation of lava flows, demonstrated to be able to reproduce the behaviour of Etnean events. However, in order to apply the model far the prediction of future scenarios, a thorough calibrating phase is required. This work presents the application of Genetic Algorithms, general-purpose search algorithms inspired to natural selection and genetics, far the parameters optimisation of the model SCIARA. Difficulties due to the elevated computational time suggested the adoption a Master-Slave Parallel Genetic Algorithm far the calibration of the model with respect to the 2001 Mt. Etna eruption. Results demonstrated the usefulness of the approach, both in terms of computing time and quality of performed simulations

Adaptive Active Noise Suppression Using Multiple Model Switching Strategy

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Quanzhen Huang

2017-01-01

Full Text Available Active noise suppression for applications where the system response varies with time is a difficult problem. The computation burden for the existing control algorithms with online identification is heavy and easy to cause control system instability. A new active noise control algorithm is proposed in this paper by employing multiple model switching strategy for secondary path varying. The computation is significantly reduced. Firstly, a noise control system modeling method is proposed for duct-like applications. Then a multiple model adaptive control algorithm is proposed with a new multiple model switching strategy based on filter-u least mean square (FULMS algorithm. Finally, the proposed algorithm was implemented on Texas Instruments digital signal processor (DSP TMS320F28335 and real time experiments were done to test the proposed algorithm and FULMS algorithm with online identification. Experimental verification tests show that the proposed algorithm is effective with good noise suppression performance.

Genetic Algorithm Based Microscale Vehicle Emissions Modelling

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Sicong Zhu

2015-01-01

Full Text Available There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2 are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.

[The genetics of thrombosis in cancer].

Science.gov (United States)

Soria, José Manuel; López, Sonia

2015-01-01

Venous thromboembolism (VTE) is a multifactorial and complex disease in which the interaction of genetic factors (estimated at 60%) and environmental factors (e.g., the use of oral contraceptives, pregnancy, immobility and cancer) determine the risk of thrombosis for each individual. In particular, the association between thrombosis and cancer is well established. Approximately 20% of patients with cancer develop a thromboembolic event over the course of the natural history of the tumor process, with thrombosis being the second leading cause of death for these patients. One of the greatest challenges currently facing the field of oncology is the identification of patients at high risk of VTE who can benefit from thromboprophylaxis. Currently, there is a VTE risk prediction model for patients with cancer (the Khorana risk score); however, its ability to identify patients at high risk is very low. It is important to note that this score, which is based on five clinical parameters, ignores the genetic variability associated with VTE risk. In this article, we present the preliminary results of the Oncothromb study, whose objective is to develop an individual VTE risk prediction model for patients with cancer who are treated with outpatient chemotherapy. Our model includes the clinical and genetic data on each patient (Thrombo inCode(®) genetic profile). Only by integrating multiple layers of biological information (clinical, plasmatic and genetic) we could obtain models that provide accurate information as to which patients are at high risk of developing a thromboembolic event associated with cancer so as to take appropriate prophylactic measures. Copyright © 2015 Elsevier España, S.L.U. All rights reserved.

Model parameters estimation and sensitivity by genetic algorithms

International Nuclear Information System (INIS)

Marseguerra, Marzio; Zio, Enrico; Podofillini, Luca

2003-01-01

In this paper we illustrate the possibility of extracting qualitative information on the importance of the parameters of a model in the course of a Genetic Algorithms (GAs) optimization procedure for the estimation of such parameters. The Genetic Algorithms' search of the optimal solution is performed according to procedures that resemble those of natural selection and genetics: an initial population of alternative solutions evolves within the search space through the four fundamental operations of parent selection, crossover, replacement, and mutation. During the search, the algorithm examines a large amount of solution points which possibly carries relevant information on the underlying model characteristics. A possible utilization of this information amounts to create and update an archive with the set of best solutions found at each generation and then to analyze the evolution of the statistics of the archive along the successive generations. From this analysis one can retrieve information regarding the speed of convergence and stabilization of the different control (decision) variables of the optimization problem. In this work we analyze the evolution strategy followed by a GA in its search for the optimal solution with the aim of extracting information on the importance of the control (decision) variables of the optimization with respect to the sensitivity of the objective function. The study refers to a GA search for optimal estimates of the effective parameters in a lumped nuclear reactor model of literature. The supporting observation is that, as most optimization procedures do, the GA search evolves towards convergence in such a way to stabilize first the most important parameters of the model and later those which influence little the model outputs. In this sense, besides estimating efficiently the parameters values, the optimization approach also allows us to provide a qualitative ranking of their importance in contributing to the model output. The

[The discussion of the infiltrative model of mathematical knowledge to genetics teaching].

Science.gov (United States)

Liu, Jun; Luo, Pei-Gao

2011-11-01

Genetics, the core course of biological field, is an importance major-basic course in curriculum of many majors related with biology. Due to strong theoretical and practical as well as abstract of genetics, it is too difficult to study on genetics for many students. At the same time, mathematics is one of the basic courses in curriculum of the major related natural science, which has close relationship with the establishment, development and modification of genetics. In this paper, to establish the intrinsic logistic relationship and construct the integral knowledge network and to help students improving the analytic, comprehensive and logistic abilities, we applied some mathematical infiltrative model genetic knowledge in genetics teaching, which could help students more deeply learn and understand genetic knowledge.

Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

Science.gov (United States)

Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

2014-05-01

Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

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

Human genetics of infectious diseases: a unified theory

Science.gov (United States)

Casanova, Jean-Laurent; Abel, Laurent

2007-01-01

Since the early 1950s, the dominant paradigm in the human genetics of infectious diseases postulates that rare monogenic immunodeficiencies confer vulnerability to multiple infectious diseases (one gene, multiple infections), whereas common infections are associated with the polygenic inheritance of multiple susceptibility genes (one infection, multiple genes). Recent studies, since 1996 in particular, have challenged this view. A newly recognised group of primary immunodeficiencies predisposing the individual to a principal or single type of infection is emerging. In parallel, several common infections have been shown to reflect the inheritance of one major susceptibility gene, at least in some populations. This novel causal relationship (one gene, one infection) blurs the distinction between patient-based Mendelian genetics and population-based complex genetics, and provides a unified conceptual frame for exploring the molecular genetic basis of infectious diseases in humans. PMID:17255931

Investigation of the three-dimensional lattice HP protein folding model using a genetic algorithm

Directory of Open Access Journals (Sweden)

Fábio L. Custódio

2004-01-01

Full Text Available An approach to the hydrophobic-polar (HP protein folding model was developed using a genetic algorithm (GA to find the optimal structures on a 3D cubic lattice. A modification was introduced to the scoring system of the original model to improve the model's capacity to generate more natural-like structures. The modification was based on the assumption that it may be preferable for a hydrophobic monomer to have a polar neighbor than to be in direct contact with the polar solvent. The compactness and the segregation criteria were used to compare structures created by the original HP model and by the modified one. An islands' algorithm, a new selection scheme and multiple-points crossover were used to improve the performance of the algorithm. Ten sequences, seven with length 27 and three with length 64 were analyzed. Our results suggest that the modified model has a greater tendency to form globular structures. This might be preferable, since the original HP model does not take into account the positioning of long polar segments. The algorithm was implemented in the form of a program with a graphical user interface that might have a didactical potential in the study of GA and on the understanding of hydrophobic core formation.

Genetic Process Mining: Alignment-based Process Model Mutation

NARCIS (Netherlands)

Eck, van M.L.; Buijs, J.C.A.M.; Dongen, van B.F.; Fournier, F.; Mendling, J.

2015-01-01

The Evolutionary Tree Miner (ETM) is a genetic process discovery algorithm that enables the user to guide the discovery process based on preferences with respect to four process model quality dimensions: replay fitness, precision, generalization and simplicity. Traditionally, the ETM algorithm uses

Genetic search feature selection for affective modeling

DEFF Research Database (Denmark)

Martínez, Héctor P.; Yannakakis, Georgios N.

2010-01-01

Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...

Emerging technologies to create inducible and genetically defined porcine cancer models

Directory of Open Access Journals (Sweden)

Lawrence B Schook

2016-02-01

Full Text Available There is an emerging need for new animal models that address unmet translational cancer research requirements. Transgenic porcine models provide an exceptional opportunity due to their genetic, anatomic and physiological similarities with humans. Due to recent advances in the sequencing of domestic animal genomes and the development of new organism cloning technologies, it is now very feasible to utilize pigs as a malleable species, with similar anatomic and physiological features with humans, in which to develop cancer models. In this review, we discuss genetic modification technologies successfully used to produce porcine biomedical models, in particular the Cre-loxP System as well as major advances and perspectives the CRISPR/Cas9 System. Recent advancements in porcine tumor modeling and genome editing will bring porcine models to the forefront of translational cancer research.

Emerging Technologies to Create Inducible and Genetically Defined Porcine Cancer Models.

Science.gov (United States)

Schook, Lawrence B; Rund, Laurie; Begnini, Karine R; Remião, Mariana H; Seixas, Fabiana K; Collares, Tiago

2016-01-01

There is an emerging need for new animal models that address unmet translational cancer research requirements. Transgenic porcine models provide an exceptional opportunity due to their genetic, anatomic, and physiological similarities with humans. Due to recent advances in the sequencing of domestic animal genomes and the development of new organism cloning technologies, it is now very feasible to utilize pigs as a malleable species, with similar anatomic and physiological features with humans, in which to develop cancer models. In this review, we discuss genetic modification technologies successfully used to produce porcine biomedical models, in particular the Cre-loxP System as well as major advances and perspectives the CRISPR/Cas9 System. Recent advancements in porcine tumor modeling and genome editing will bring porcine models to the forefront of translational cancer research.

Quantitative genetics of Taura syndrome resistance in Pacific (Penaeus vannamei): A cure model approach

DEFF Research Database (Denmark)

Ødegård, Jørgen; Gitterle, Thomas; Madsen, Per

2011-01-01

cure survival model using Gibbs sampling, treating susceptibility and endurance as separate genetic traits. Results: Overall mortality at the end of test was 28%, while 38% of the population was considered susceptible to the disease. The estimated underlying heritability was high for susceptibility (0....... However, genetic evaluation of susceptibility based on the cure model showed clear associations with standard genetic evaluations that ignore the cure fraction for these data. Using the current testing design, genetic variation in observed survival time and absolute survival at the end of test were most...

A test for the parameters of multiple linear regression models ...

African Journals Online (AJOL)

A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...

Genetic hotels for the standard genetic code: evolutionary analysis based upon novel three-dimensional algebraic models.

Science.gov (United States)

José, Marco V; Morgado, Eberto R; Govezensky, Tzipe

2011-07-01

Herein, we rigorously develop novel 3-dimensional algebraic models called Genetic Hotels of the Standard Genetic Code (SGC). We start by considering the primeval RNA genetic code which consists of the 16 codons of type RNY (purine-any base-pyrimidine). Using simple algebraic operations, we show how the RNA code could have evolved toward the current SGC via two different intermediate evolutionary stages called Extended RNA code type I and II. By rotations or translations of the subset RNY, we arrive at the SGC via the former (type I) or via the latter (type II), respectively. Biologically, the Extended RNA code type I, consists of all codons of the type RNY plus codons obtained by considering the RNA code but in the second (NYR type) and third (YRN type) reading frames. The Extended RNA code type II, comprises all codons of the type RNY plus codons that arise from transversions of the RNA code in the first (YNY type) and third (RNR) nucleotide bases. Since the dimensions of remarkable subsets of the Genetic Hotels are not necessarily integer numbers, we also introduce the concept of algebraic fractal dimension. A general decoding function which maps each codon to its corresponding amino acid or the stop signals is also derived. The Phenotypic Hotel of amino acids is also illustrated. The proposed evolutionary paths are discussed in terms of the existing theories of the evolution of the SGC. The adoption of 3-dimensional models of the Genetic and Phenotypic Hotels will facilitate the understanding of the biological properties of the SGC.

Identification of Treatment Targets in a Genetic Mouse Model of Voluntary Methamphetamine Drinking.

Science.gov (United States)

Phillips, T J; Mootz, J R K; Reed, C

2016-01-01

Methamphetamine has powerful stimulant and euphoric effects that are experienced as rewarding and encourage use. Methamphetamine addiction is associated with debilitating illnesses, destroyed relationships, child neglect, violence, and crime; but after many years of research, broadly effective medications have not been identified. Individual differences that may impact not only risk for developing a methamphetamine use disorder but also affect treatment response have not been fully considered. Human studies have identified candidate genes that may be relevant, but lack of control over drug history, the common use or coabuse of multiple addictive drugs, and restrictions on the types of data that can be collected in humans are barriers to progress. To overcome some of these issues, a genetic animal model comprised of lines of mice selectively bred for high and low voluntary methamphetamine intake was developed to identify risk and protective alleles for methamphetamine consumption, and identify therapeutic targets. The mu opioid receptor gene was supported as a target for genes within a top-ranked transcription factor network associated with level of methamphetamine intake. In addition, mice that consume high levels of methamphetamine were found to possess a nonfunctional form of the trace amine-associated receptor 1 (TAAR1). The Taar1 gene is within a mouse chromosome 10 quantitative trait locus for methamphetamine consumption, and TAAR1 function determines sensitivity to aversive effects of methamphetamine that may curb intake. The genes, gene interaction partners, and protein products identified in this genetic mouse model represent treatment target candidates for methamphetamine addiction. © 2016 Elsevier Inc. All rights reserved.

Parametric modeling for damped sinusoids from multiple channels

DEFF Research Database (Denmark)

Zhou, Zhenhua; So, Hing Cheung; Christensen, Mads Græsbøll

2013-01-01

frequencies and damping factors are then computed with the multi-channel weighted linear prediction method. The estimated sinusoidal poles are then matched to each channel according to the extreme value theory of distribution of random fields. Simulations are performed to show the performance advantages......The problem of parametric modeling for noisy damped sinusoidal signals from multiple channels is addressed. Utilizing the shift invariance property of the signal subspace, the number of distinct sinusoidal poles in the multiple channels is first determined. With the estimated number, the distinct...... of the proposed multi-channel sinusoidal modeling methodology compared with existing methods....

Integrative Analysis of Prognosis Data on Multiple Cancer Subtypes

Science.gov (United States)

Liu, Jin; Huang, Jian; Zhang, Yawei; Lan, Qing; Rothman, Nathaniel; Zheng, Tongzhang; Ma, Shuangge

2014-01-01

Summary In cancer research, profiling studies have been extensively conducted, searching for genes/SNPs associated with prognosis. Cancer is diverse. Examining the similarity and difference in the genetic basis of multiple subtypes of the same cancer can lead to a better understanding of their connections and distinctions. Classic meta-analysis methods analyze each subtype separately and then compare analysis results across subtypes. Integrative analysis methods, in contrast, analyze the raw data on multiple subtypes simultaneously and can outperform meta-analysis methods. In this study, prognosis data on multiple subtypes of the same cancer are analyzed. An AFT (accelerated failure time) model is adopted to describe survival. The genetic basis of multiple subtypes is described using the heterogeneity model, which allows a gene/SNP to be associated with prognosis of some subtypes but not others. A compound penalization method is developed to identify genes that contain important SNPs associated with prognosis. The proposed method has an intuitive formulation and is realized using an iterative algorithm. Asymptotic properties are rigorously established. Simulation shows that the proposed method has satisfactory performance and outperforms a penalization-based meta-analysis method and a regularized thresholding method. An NHL (non-Hodgkin lymphoma) prognosis study with SNP measurements is analyzed. Genes associated with the three major subtypes, namely DLBCL, FL, and CLL/SLL, are identified. The proposed method identifies genes that are different from alternatives and have important implications and satisfactory prediction performance. PMID:24766212

A model for diagnosing and explaining multiple disorders.

Science.gov (United States)

Jamieson, P W

1991-08-01

The ability to diagnose multiple interacting disorders and explain them in a coherent causal framework has only partially been achieved in medical expert systems. This paper proposes a causal model for diagnosing and explaining multiple disorders whose key elements are: physician-directed hypotheses generation, object-oriented knowledge representation, and novel explanation heuristics. The heuristics modify and link the explanations to make the physician aware of diagnostic complexities. A computer program incorporating the model currently is in use for diagnosing peripheral nerve and muscle disorders. The program successfully diagnoses and explains interactions between diseases in terms of underlying pathophysiologic concepts. The model offers a new architecture for medical domains where reasoning from first principles is difficult but explanation of disease interactions is crucial for the system's operation.

The genetic legacy of multiple beaver reintroductions in Central Europe.

Science.gov (United States)

Frosch, Christiane; Kraus, Robert H S; Angst, Christof; Allgöwer, Rainer; Michaux, Johan; Teubner, Jana; Nowak, Carsten

2014-01-01

The comeback of the Eurasian beaver (Castor fiber) throughout western and central Europe is considered a major conservation success. Traditionally, several subspecies are recognised by morphology and mitochondrial haplotype, each linked to a relict population. During various reintroduction programs in the 20th century, beavers from multiple source localities were released and now form viable populations. These programs differed in their reintroduction strategies, i.e., using pure subspecies vs. mixed source populations. This inhomogeneity in management actions generated ongoing debates regarding the origin of present beaver populations and appropriate management plans for the future. By sequencing of the mitochondrial control region and microsatellite genotyping of 235 beaver individuals from five selected regions in Germany, Switzerland, Luxembourg, and Belgium we show that beavers from at least four source origins currently form admixed, genetically diverse populations that spread across the study region. While regional occurrences of invasive North American beavers (n = 20) were found, all but one C. fiber bore the mitochondrial haplotype of the autochthonous western Evolutionary Significant Unit (ESU). Considering this, as well as the viability of admixed populations and the fact that the fusion of different lineages is already progressing in all studied regions, we argue that admixture between different beaver source populations should be generally accepted.

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

Estimates of genetic parameters, genetic trends, and inbreeding in a crossbred dairy sheep research flock in the United States.

Science.gov (United States)

Murphy, T W; Berger, Y M; Holman, P W; Baldin, M; Burgett, R L; Thomas, D L

2017-10-01

For the past 2 decades, the Spooner Agriculture Research Station (ARS) of the University of Wisconsin-Madison operated the only dairy sheep research flock in North America. The objectives of the present study were to 1) obtain estimates of genetic parameters for lactation and reproductive traits in dairy ewes, 2) estimate the amount of genetic change in these traits over time, and 3) quantify the level of inbreeding in this flock over the last 20 yr. Multiple-trait repeatability models (MTRM) were used to analyze ewe traits through their first 6 parities. The first MTRM jointly analyzed milk (180-d-adjusted milk yield [180d MY]), fat (180-d-adjusted fat yield [180d FY]), and protein (180-d-adjusted protein yield [180d PY]) yields adjusted to 180 d of lactation; number of lambs born per ewe lambing (NLB); and lactation average test-day somatic cell score (LSCS). A second MTRM analyzed 180d MY, NLB, LSCS, and percentage milk fat (%F) and percentage milk protein (%P). The 3 yield traits were moderately heritable (0.26 to 0.32) and strongly genetically correlated (0.91 to 0.96). Percentage milk fat and %P were highly heritable (0.53 and 0.61, respectively) and moderately genetically correlated (0.61). Milk yield adjusted to 180 d was negatively genetically correlated with %F and %P (-0.31 and -0.34, respectively). Ewe prolificacy was not significantly ( > 0.67) genetically correlated with yield traits, %P, or LSCS but lowly negatively correlated with %F (-0.26). Lactation somatic cell score was unfavorably genetically correlated with yield traits (0.28 to 0.39) but not significantly ( > 0.09) correlated with %F, %P, and NLB. Within-trait multiple-trait models through the first 4 parities revealed that 180d MY, 180d FY, 180d PY, %F, and %P were strongly genetically correlated across parity (0.67 to 1.00). However, the genetic correlations across parity for NLB and LSCS were somewhat lower (0.51 to 0.96). Regressing predicted breeding values for 180d MY, without and with

Medulloblastoma: Molecular Genetics and Animal Models

Directory of Open Access Journals (Sweden)

Corey Raffel

2004-07-01

Full Text Available Medulloblastoma is a primary brain tumor found in the cerebellum of children. The tumor occurs in association with two inherited cancer syndromes: Turcot syndrome and Gorlin syndrome. Insights into the molecular biology of the tumor have come from looking at alterations in the genes altered in these syndromes, PTC and APC, respectively. Murine models of medulloblastoma have been constructed based on these alterations. Additional murine models that, while mimicking the appearance of the human tumor, seem unrelated to the human tumor's molecular alterations have been made. In this review, the clinical picture, origin, molecular biology, murine models of medulloblastoma are discussed. Although a great deal has been discovered about this tumor, the genetic alterations responsible for tumor development in a majority of patients have yet to be described.

Multiple genetic origins of histidine-rich protein 2 gene deletion in Plasmodium falciparum parasites from Peru

Science.gov (United States)

Akinyi, Sheila; Hayden, Tonya; Gamboa, Dionicia; Torres, Katherine; Bendezu, Jorge; Abdallah, Joseph F.; Griffing, Sean M.; Quezada, Wilmer Marquiño; Arrospide, Nancy; De Oliveira, Alexandre Macedo; Lucas, Carmen; Magill, Alan J.; Bacon, David J.; Barnwell, John W.; Udhayakumar, Venkatachalam

2013-01-01

The majority of malaria rapid diagnostic tests (RDTs) detect Plasmodium falciparum histidine-rich protein 2 (PfHRP2), encoded by the pfhrp2 gene. Recently, P. falciparum isolates from Peru were found to lack pfhrp2 leading to false-negative RDT results. We hypothesized that pfhrp2-deleted parasites in Peru derived from a single genetic event. We evaluated the parasite population structure and pfhrp2 haplotype of samples collected between 1998 and 2005 using seven neutral and seven chromosome 8 microsatellite markers, respectively. Five distinct pfhrp2 haplotypes, corresponding to five neutral microsatellite-based clonal lineages, were detected in 1998-2001; pfhrp2 deletions occurred within four haplotypes. In 2003-2005, outcrossing among the parasite lineages resulted in eight population clusters that inherited the five pfhrp2 haplotypes seen previously and a new haplotype; pfhrp2 deletions occurred within four of these haplotypes. These findings indicate that the genetic origin of pfhrp2 deletion in Peru was not a single event, but likely occurred multiple times. PMID:24077522

Predictive value of testing for multiple genetic variants in multifactorial

NARCIS (Netherlands)

A.C.J.W. Janssens (Cécile); M.J. Khoury (Muin Joseph)

2009-01-01

textabstractMultifactorial diseases such as type 2 diabetes, osteoporosis, and cardiovascular disease are caused by a complex interplay of many genetic and nongenetic factors, each of which conveys a minor increase in the risk of disease. Unraveling the genetic origins of these diseases is

Genetic models of absence epilepsy: New concepts and insights

NARCIS (Netherlands)

Luijtelaar, E.L.J.M. van; Coenen, A.M.L.; Schwartzkroin, P.A.

2009-01-01

The discovery, development, and use of genetic rodent models of absence epilepsy have led to a new theory about the origin of absence seizures. A focal zone has been identified in the peri-oral region of the somatosensory cortex in WAG/Rij and GAERS – the two most commonly used models – from which

Dynamic modeling of genetic networks using genetic algorithm and S-system.

Science.gov (United States)

Kikuchi, Shinichi; Tominaga, Daisuke; Arita, Masanori; Takahashi, Katsutoshi; Tomita, Masaru

2003-03-22

The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.

Genetic mouse models relevant to schizophrenia: taking stock and looking forward.

Science.gov (United States)

Harrison, Paul J; Pritchett, David; Stumpenhorst, Katharina; Betts, Jill F; Nissen, Wiebke; Schweimer, Judith; Lane, Tracy; Burnet, Philip W J; Lamsa, Karri P; Sharp, Trevor; Bannerman, David M; Tunbridge, Elizabeth M

2012-03-01

Genetic mouse models relevant to schizophrenia complement, and have to a large extent supplanted, pharmacological and lesion-based rat models. The main attraction is that they potentially have greater construct validity; however, they share the fundamental limitations of all animal models of psychiatric disorder, and must also be viewed in the context of the uncertain and complex genetic architecture of psychosis. Some of the key issues, including the choice of gene to target, the manner of its manipulation, gene-gene and gene-environment interactions, and phenotypic characterization, are briefly considered in this commentary, illustrated by the relevant papers reported in this special issue. Copyright © 2011 Elsevier Ltd. All rights reserved.

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle

Directory of Open Access Journals (Sweden)

C. I. Cho

2016-05-01

Full Text Available The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs, and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK, fat yield (FAT, protein yield (PROT, and solids-not-fat yield (SNF. The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP of the third to fifth order (L3–L5, fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order. The residual variances in the models were either homogeneous (HOM or heterogeneous (15 classes, HET15; 60 classes, HET60. A total of nine models (3 orders of polynomials×3 types of residual variance including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC and/or Schwarz Bayesian information criteria (BIC statistics to identify the model(s of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF and L4-HET15 (FAT, which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first

Genetics Home Reference: multiple epiphyseal dysplasia

Science.gov (United States)

... Hamel BC, Spranger J, Zabel B, Cohn DH, Cole WG, Hecht JT, Superti-Furga A. Recessive multiple ... medicine? What is newborn screening? New Pages Lyme disease Fibromyalgia White-Sutton syndrome All New & Updated Pages ...

Application of Multiple Evaluation Models in Brazil

Directory of Open Access Journals (Sweden)

Rafael Victal Saliba

2008-07-01

Full Text Available Based on two different samples, this article tests the performance of a number of Value Drivers commonly used for evaluating companies by finance practitioners, through simple regression models of cross-section type which estimate the parameters associated to each Value Driver, denominated Market Multiples. We are able to diagnose the behavior of several multiples in the period 1994-2004, with an outlook also on the particularities of the economic activities performed by the sample companies (and their impacts on the performance through a subsequent analysis with segregation of companies in the sample by sectors. Extrapolating simple multiples evaluation standards from analysts of the main financial institutions in Brazil, we find that adjusting the ratio formulation to allow for an intercept does not provide satisfactory results in terms of pricing errors reduction. Results found, in spite of evidencing certain relative and absolute superiority among the multiples, may not be generically representative, given samples limitation.

ENU mutagenesis to generate genetically modified rat models

NARCIS (Netherlands)

van Boxtel, R.; Gould, M.; Cuppen, E.; Smits, B.M.

2010-01-01

The rat is one of the most preferred model organisms in biomedical research and has been extremely useful for linking physiology and pathology to the genome. However, approaches to genetically modify specific genes in the rat germ line remain relatively scarce. To date, the most efficient approach

A review of animal models used to evaluate potential allergenicity of genetically modified organisms (GMOs)

DEFF Research Database (Denmark)

Marsteller, Nathan; Bøgh, Katrine Lindholm; Goodman, Richard E.

2017-01-01

Food safety regulators request prediction of allergenicity for newly expressed proteins in genetically modified (GM) crops and in novel foods. Some have suggested using animal models to assess potential allergenicity. A variety of animal models have been used in research to evaluate sensitisation...... of genetically modified organisms (GMOs).......Food safety regulators request prediction of allergenicity for newly expressed proteins in genetically modified (GM) crops and in novel foods. Some have suggested using animal models to assess potential allergenicity. A variety of animal models have been used in research to evaluate sensitisation...

Applications of Systems Genetics and Biology for Obesity Using Pig Models

DEFF Research Database (Denmark)

Kogelman, Lisette; Kadarmideen, Haja N.

2016-01-01

approach, a branch of systems biology. In this chapter, we will describe the state of the art of genetic studies on human obesity, using pig populations. We will describe the features of using the pig as a model for human obesity and briefly discuss the genetics of obesity, and we will focus on systems...

Defining conservation units in a stocking-induced genetic melting pot: unraveling native and multiple exotic genetic imprints of recent and historical secondary contact in Adriatic grayling.

Science.gov (United States)

Meraner, Andreas; Cornetti, Luca; Gandolfi, Andrea

2014-04-01

The definition of conservation units is crucial for the sustainable management of endangered species, though particularly challenging when recent and past anthropogenic and natural gene flow might have played a role. The conservation of the European grayling, Thymallus thymallus, is particularly complex in its southern distribution area, where the Adriatic evolutionary lineage is endangered by a long history of anthropogenic disturbance, intensive stocking and potentially widespread genetic introgression. We provide mtDNA sequence and microsatellite data of 683 grayling from 30 sites of Adriatic as well as Danubian and Atlantic origin. We apply Bayesian clustering and Approximate Bayesian Computation (ABC) to detect microgeographic population structure and to infer the demographic history of the Adriatic populations, to define appropriate conservation units. Varying frequencies of indigenous genetic signatures of the Adriatic grayling were revealed, spanning from marginal genetic introgression to the collapse of native gene pools. Genetic introgression involved multiple exotic source populations of Danubian and Atlantic origin, thus evidencing the negative impact of few decades of stocking. Within the Adige River system, a contact zone of western Adriatic and eastern Danubian populations was detected, with ABC analyses suggesting a historical anthropogenic origin of eastern Adige populations, most likely founded by medieval translocations. Substantial river-specific population substructure within the Adriatic grayling Evolutionary Significant Unit points to the definition of different conservation units. We finally propose a catalog of management measures, including the legal prohibition of stocking exotic grayling and the use of molecular markers in supportive- and captive-breeding programs.

Achondroplasia and multiple-suture craniosynostosis.

Science.gov (United States)

Albino, Frank P; Wood, Benjamin C; Oluigbo, Chima O; Lee, Angela C; Oh, Albert K; Rogers, Gary F

2015-01-01

Genetic mutations in the fibroblast growth factor receptor 3 gene may lead to achondroplasia or syndromic forms of craniosynostosis. Despite sharing a common genetic basis, craniosynostosis has rarely been described in cases of confirmed achondroplasia. We report an infant with achondroplasia who developed progressive multiple-suture craniosynostosis to discuss the genetic link between these clinical entities and to describe the technical challenges associated with the operative management.

Deterministic integer multiple firing depending on initial state in Wang model

Energy Technology Data Exchange (ETDEWEB)

Xie Yong [Institute of Nonlinear Dynamics, MSSV, Department of Engineering Mechanics, Xi' an Jiaotong University, Xi' an 710049 (China)]. E-mail: yxie@mail.xjtu.edu.cn; Xu Jianxue [Institute of Nonlinear Dynamics, MSSV, Department of Engineering Mechanics, Xi' an Jiaotong University, Xi' an 710049 (China); Jiang Jun [Institute of Nonlinear Dynamics, MSSV, Department of Engineering Mechanics, Xi' an Jiaotong University, Xi' an 710049 (China)

2006-12-15

We investigate numerically dynamical behaviour of the Wang model, which describes the rhythmic activities of thalamic relay neurons. The model neuron exhibits Type I excitability from a global view, but Type II excitability from a local view. There exists a narrow range of bistability, in which a subthreshold oscillation and a suprathreshold firing behaviour coexist. A special firing pattern, integer multiple firing can be found in the certain part of the bistable range. The characteristic feature of such firing pattern is that the histogram of interspike intervals has a multipeaked structure, and the peaks are located at about integer multiples of a basic interspike interval. Since the Wang model is noise-free, the integer multiple firing is a deterministic firing pattern. The existence of bistability leads to the deterministic integer multiple firing depending on the initial state of the model neuron, i.e., the initial values of the state variables.

Deterministic integer multiple firing depending on initial state in Wang model

International Nuclear Information System (INIS)

Xie Yong; Xu Jianxue; Jiang Jun

2006-01-01

We investigate numerically dynamical behaviour of the Wang model, which describes the rhythmic activities of thalamic relay neurons. The model neuron exhibits Type I excitability from a global view, but Type II excitability from a local view. There exists a narrow range of bistability, in which a subthreshold oscillation and a suprathreshold firing behaviour coexist. A special firing pattern, integer multiple firing can be found in the certain part of the bistable range. The characteristic feature of such firing pattern is that the histogram of interspike intervals has a multipeaked structure, and the peaks are located at about integer multiples of a basic interspike interval. Since the Wang model is noise-free, the integer multiple firing is a deterministic firing pattern. The existence of bistability leads to the deterministic integer multiple firing depending on the initial state of the model neuron, i.e., the initial values of the state variables

A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants.

Science.gov (United States)

Goedhart, Paul W; van der Voet, Hilko; Baldacchino, Ferdinando; Arpaia, Salvatore

2014-04-01

Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.

Multiple regression and beyond an introduction to multiple regression and structural equation modeling

CERN Document Server

Keith, Timothy Z

2014-01-01

Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.

Genetic Modeling of Radiation Injury in Prostate Cancer Patients Treated with Radiotherapy

Science.gov (United States)

2017-10-01

AWARD NUMBER: W81XWH-15-1-0681 TITLE: Genetic Modeling of Radiation Injury in Prostate Cancer Patients Treated with Radiotherapy PRINCIPAL...TITLE AND SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-15-1-0681Genetic Modeling of Radiation Injury in Prostate Cancer Patients Treated...effects, urinary morbidity, rectal injury, sexual dysfunction 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF

Equilibrium and non-equilibrium concepts in forest genetic modelling: population- and individually-based approaches

OpenAIRE

Kramer, Koen; van der Werf, D. C.

2010-01-01

The environment is changing and so are forests, in their functioning, in species composition, and in the species’ genetic composition. Many empirical and process-based models exist to support forest management. However, most of these models do not consider the impact of environmental changes and forest management on genetic diversity nor on the rate of adaptation of critical plant processes. How genetic diversity and rates of adaptation depend on management actions is a crucial next step in m...

New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise

DEFF Research Database (Denmark)

Dong, Yiqiu; Zeng, Tieyong

2013-01-01

A new hybrid variational model for recovering blurred images in the presence of multiplicative noise is proposed. Inspired by previous work on multiplicative noise removal, an I-divergence technique is used to build a strictly convex model under a condition that ensures the uniqueness...

Empirical Refinements of a Molecular Genetics Learning Progression: The Molecular Constructs

Science.gov (United States)

Todd, Amber; Kenyon, Lisa

2016-01-01

This article describes revisions to four of the eight constructs of the Duncan molecular genetics learning progression [Duncan, Rogat, & Yarden, (2009)]. As learning progressions remain hypothetical models until validated by multiple rounds of empirical studies, these revisions are an important step toward validating the progression. Our…

Model-based problem solving through symbolic regression via pareto genetic programming

NARCIS (Netherlands)

Vladislavleva, E.

2008-01-01

Pareto genetic programming methodology is extended by additional generic model selection and generation strategies that (1) drive the modeling engine to creation of models of reduced non-linearity and increased generalization capabilities, and (2) improve the effectiveness of the search for robust

Genetic parameters and genetic trends in the Chinese × European Tiameslan composite pig line. II. Genetic trends

Directory of Open Access Journals (Sweden)

Legault Christian

2000-01-01

Full Text Available Abstract The Tiameslan line was created between 1983 and 1985 by mating Meishan × Jiaxing crossbred Chinese boars with sows from the Laconie composite male line. The Tiameslan line has been selected since then on an index combining average backfat thickness (ABT and days from 20 to 100 kg (DT. Direct and correlated responses to 11 years of selection were estimated using BLUP methodology applied to a multiple trait animal model. A total of 11 traits were considered, i.e.: ABT, DT, body weight at 4 (W4w, 8 (W8w and 22 (W22w weeks of age, teat number (TEAT, number of good teats (GTEAT, total number of piglets born (TNB, born alive (NBA and weaned (NW per litter, and birth to weaning survival rate (SURV. Performance data from a total of 4 881 males and 4 799 females from 1 341 litters were analysed. The models included both direct and maternal effects for ABT, W4w and W8w. Male and female performances were considered as different traits for W22w, DT and ABT. Genetic parameters estimated in another paper (Zhang et al., Genet. Sel. Evol. 32 (2000 41-56 were used to perform the analyses. Favourable phenotypic (ΔP and direct genetic trends (ΔGd were obtained for post-weaning growth traits and ABT. Trends for maternal effects were limited. Phenotypic and genetic trends were larger in females than in males for ABT (e.g. ΔGd = -0.48 vs. -0.38 mm/year, were larger in males for W22w (ΔGd = 0.90 vs. 0.58 kg/year and were similar in both sexes for DT (ΔGd = -0.54 vs. -0.55 day/year. Phenotypic and genetic trends were slightly favourable for W4w, W8w, TEAT and GTEAT and close to zero for reproductive traits.

Genetics on the Fly: A Primer on the Drosophila Model System

Science.gov (United States)

Hales, Karen G.; Korey, Christopher A.; Larracuente, Amanda M.; Roberts, David M.

2015-01-01

Fruit flies of the genus Drosophila have been an attractive and effective genetic model organism since Thomas Hunt Morgan and colleagues made seminal discoveries with them a century ago. Work with Drosophila has enabled dramatic advances in cell and developmental biology, neurobiology and behavior, molecular biology, evolutionary and population genetics, and other fields. With more tissue types and observable behaviors than in other short-generation model organisms, and with vast genome data available for many species within the genus, the fly’s tractable complexity will continue to enable exciting opportunities to explore mechanisms of complex developmental programs, behaviors, and broader evolutionary questions. This primer describes the organism’s natural history, the features of sequenced genomes within the genus, the wide range of available genetic tools and online resources, the types of biological questions Drosophila can help address, and historical milestones. PMID:26564900

Comprehensive genetic assessment of the human embryo: can empiric application of microarray comparative genomic hybridization reduce multiple gestation rate by single fresh blastocyst transfer?

Science.gov (United States)

Sills, Eric Scott; Yang, Zhihong; Walsh, David J; Salem, Shala A

2012-09-01

The unacceptable multiple gestation rate currently associated with in vitro fertilization (IVF) would be substantially alleviated if the routine practice of transferring more than one embryo were reconsidered. While transferring a single embryo is an effective method to reduce the clinical problem of multiple gestation, rigid adherence to this approach has been criticized for negatively impacting clinical pregnancy success in IVF. In general, single embryo transfer is viewed cautiously by IVF patients although greater acceptance would result from a more effective embryo selection method. Selection of one embryo for fresh transfer on the basis of chromosomal normalcy should achieve the dual objective of maintaining satisfactory clinical pregnancy rates and minimizing the multiple gestation problem, because embryo aneuploidy is a major contributing factor in implantation failure and miscarriage in IVF. The initial techniques for preimplantation genetic screening unfortunately lacked sufficient sensitivity and did not yield the expected results in IVF. However, newer molecular genetic methods could be incorporated with standard IVF to bring the goal of single embryo transfer within reach. Aiming to make multiple embryo transfers obsolete and unnecessary, and recognizing that array comparative genomic hybridization (aCGH) will typically require an additional 12 h of laboratory time to complete, we propose adopting aCGH for mainstream use in clinical IVF practice. As aCGH technology continues to develop and becomes increasingly available at lower cost, it may soon be considered unusual for IVF laboratories to select a single embryo for fresh transfer without regard to its chromosomal competency. In this report, we provide a rationale supporting aCGH as the preferred methodology to provide a comprehensive genetic assessment of the single embryo before fresh transfer in IVF. The logistics and cost of integrating aCGH with IVF to enable fresh embryo transfer are also

Whole-genome sequencing of monozygotic twins discordant for schizophrenia indicates multiple genetic risk factors for schizophrenia

Institute of Scientific and Technical Information of China (English)

Jinsong Tang; Fan He; Fengyu Zhang; Yin Yao Shugart; Chunyu Liu; Yanqing Tang; Raymond C.K.Chan; Chuan-Yue Wang; Yong-Gang Yao; Xiaogang Chen; Yu Fan; Hong Li; Qun Xiang; Deng-Feng Zhang; Zongchang Li; Ying He; Yanhui Liao; Ya Wang

2017-01-01

Schizophrenia is a common disorder with a high heritability,but its genetic architecture is still elusive.We implemented whole-genome sequencing (WGS) analysis of 8 families with monozygotic (MZ) twin pairs discordant for schizophrenia to assess potential association of de novo mutations (DNMs) or inherited variants with susceptibility to schizophrenia.Eight non-synonymous DNMs (including one splicing site) were identified and shared by twins,which were either located in previously reported schizophrenia risk genes (p.V24689I mutation in TTN,p.S2506T mutation in GCN1L1,IVS3+1G ＞ T in DOCK1) or had a benign to damaging effect according to in silico prediction analysis.By searching the inherited rare damaging or loss-of-function (LOF) variants and common susceptible alleles from three classes of schizophrenia candidate genes,we were able to distill genetic alterations in several schizophrenia risk genes,including GAD1,PLXNA2,RELN and FEZ1.Four inherited copy number variations (CNVs;including a large deletion at 16p13.11) implicated for schizophrenia were identified in four families,respectively.Most of families carried both missense DNMs and inherited risk variants,which might suggest that DNMs,inherited rare damaging variants and common risk alleles together conferred to schizophrenia susceptibility.Our results support that schizophrenia is caused by a combination of multiple genetic factors,with each DNM/variant showing a relatively small effect size.

The effect of level of feeding, genetic merit, body condition score and age on biological parameters of a mammary gland model.

Science.gov (United States)

Bryant, J R; Lopez-Villalobos, N; Holmes, C W; Pryce, J E; Pitman, G D; Davis, S R

2007-03-01

An evolutionary algorithm was applied to a mechanistic model of the mammary gland to find the parameter values that minimised the difference between predicted and actual lactation curves of milk yields in New Zealand Jersey cattle managed at different feeding levels. The effect of feeding level, genetic merit, body condition score at parturition and age on total lactation yields of milk, fat and protein, days in milk, live weight and evolutionary algorithm derived mammary gland parameters was then determined using a multiple regression model. The mechanistic model of the mammary gland was able to fit lactation curves that corresponded to actual lactation curves with a high degree of accuracy. The senescence rate of quiescent (inactive) alveoli was highest at the very low feeding level. The active alveoli population at peak lactation was highest at very low feeding levels, but lower nutritional status at this feeding level prevented high milk yields from being achieved. Genetic merit had a significant linear effect on the active alveoli population at peak and mid to late lactation, with higher values in animals, which had higher breeding values for milk yields. A type of genetic merit × feeding level scaling effect was observed for total yields of milk and fat, and total number of alveoli produced from conception until the end of lactation with the benefits of increases in genetic merit being greater at high feeding levels. A genetic merit × age scaling effect was observed for total lactation protein yields. Initial rates of differentiation of progenitor cells declined with age. Production levels of alveoli from conception to the end of lactation were lowest in 5- to 8-year-old animals; however, in these older animals, quiescent alveoli were reactivated more frequently. The active alveoli population at peak lactation and rates of active alveoli proceeding to quiescence were highest in animals of intermediate body condition scores of 4.0 to 5.0. The results

Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data.

Science.gov (United States)

Gu, Deqing; Jian, Xingxing; Zhang, Cheng; Hua, Qiang

2017-01-01

Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named logical transformation of model (LTM) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.

Genetic introgression and the survival of Florida panther kittens

Science.gov (United States)

Hostetler, Jeffrey A.; Onorato, David P.; Nichols, James D.; Johnson, Warren E.; Roelke, Melody E.; O'Brien, Stephen J.; Jansen, Deborah; Oli, Madan K.

2010-01-01

Estimates of survival for the young of a species are critical for population models. These models can often be improved by determining the effects of management actions and population abundance on this demographic parameter. We used multiple sources of data collected during 1982–2008 and a live-recapture dead-recovery modeling framework to estimate and model survival of Florida panther (Puma concolor coryi) kittens (age 0–1 year). Overall, annual survival of Florida panther kittens was 0.323 ± 0.071 (SE), which was lower than estimates used in previous population models. In 1995, female pumas from Texas (P. c. stanleyana) were released into occupied panther range as part of an intentional introgression program to restore genetic variability. We found that kitten survival generally increased with degree of admixture: F1 admixed and backcrossed to Texas kittens survived better than canonical Florida panther and backcrossed to canonical kittens. Average heterozygosity positively influenced kitten and older panther survival, whereas index of panther abundance negatively influenced kitten survival. Our results provide strong evidence for the positive population-level impact of genetic introgression on Florida panthers. Our approach to integrate data from multiple sources was effective at improving robustness as well as precision of estimates of Florida panther kitten survival, and can be useful in estimating vital rates for other elusive species with sparse data.

Mathematical Modeling of Loop Heat Pipes with Multiple Capillary Pumps and Multiple Condensers. Part 1; Stead State Stimulations

Science.gov (United States)

Hoang, Triem T.; OConnell, Tamara; Ku, Jentung

2004-01-01

Loop Heat Pipes (LHPs) have proven themselves as reliable and robust heat transport devices for spacecraft thermal control systems. So far, the LHPs in earth-orbit satellites perform very well as expected. Conventional LHPs usually consist of a single capillary pump for heat acquisition and a single condenser for heat rejection. Multiple pump/multiple condenser LHPs have shown to function very well in ground testing. Nevertheless, the test results of a dual pump/condenser LHP also revealed that the dual LHP behaved in a complicated manner due to the interaction between the pumps and condensers. Thus it is redundant to say that more research is needed before they are ready for 0-g deployment. One research area that perhaps compels immediate attention is the analytical modeling of LHPs, particularly the transient phenomena. Modeling a single pump/single condenser LHP is difficult enough. Only a handful of computer codes are available for both steady state and transient simulations of conventional LHPs. No previous effort was made to develop an analytical model (or even a complete theory) to predict the operational behavior of the multiple pump/multiple condenser LHP systems. The current research project offered a basic theory of the multiple pump/multiple condenser LHP operation. From it, a computer code was developed to predict the LHP saturation temperature in accordance with the system operating and environmental conditions.

A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk

Directory of Open Access Journals (Sweden)

Lewei Duan

2013-01-01

Full Text Available A variety of methods have been proposed for studying the association of multiple genes thought to be involved in a common pathway for a particular disease. Here, we present an extension of a Bayesian hierarchical modeling strategy that allows for multiple SNPs within each gene, with external prior information at either the SNP or gene level. The model involves variable selection at the SNP level through latent indicator variables and Bayesian shrinkage at the gene level towards a prior mean vector and covariance matrix that depend on external information. The entire model is fitted using Markov chain Monte Carlo methods. Simulation studies show that the approach is capable of recovering many of the truly causal SNPs and genes, depending upon their frequency and size of their effects. The method is applied to data on 504 SNPs in 38 candidate genes involved in DNA damage response in the WECARE study of second breast cancers in relation to radiotherapy exposure.

Predictive performance models and multiple task performance

Science.gov (United States)

Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

1989-01-01

Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

Prediction of warfarin maintenance dose in Han Chinese patients using a mechanistic model based on genetic and non-genetic factors.

Science.gov (United States)

Lu, Yuan; Yang, Jinbo; Zhang, Haiyan; Yang, Jin

2013-07-01

Many attempts have been made to predict the warfarin maintenance dose in patients beginning warfarin therapy using a descriptive model based on multiple linear regression. Here we report the first attempt to develop a comprehensive mechanistic model integrating in vitro-in vivo extrapolation (IVIVE) with a pharmacokinetic-pharmacodynamic model to predict the warfarin maintenance dose in Han Chinese patients. The model incorporates demographic factors [sex, age, body weight (BW)] and the genetic polymorphisms of cytochrome P450 (CYP) 2C9 (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1). Information on the various factors, mean warfarin daily dose and International Normalized Ratio (INR) was available for a cohort of 197 Han Chinese patients. Based on in vitro enzyme kinetic parameters for S-warfarin metabolism, demographic data for Han Chinese and some scaling factors, the S-warfarin clearance (CL) was predicted for patients in the cohort with different CYP2C9 genotypes using IVIVE. The plasma concentration of S-warfarin after a single oral dose was simulated using a one-compartment pharmacokinetic model with first-order absorption and a lag time and was combined with a mechanistic coagulation model to simulate the INR response. The warfarin maintenance dose was then predicted based on the demographic data and genotypes of CYP2C9 and VKORC1 for each patient and using the observed steady-state INR (INRss) as a target value. Finally, sensitivity analysis was carried out to determine which factor(s) affect the warfarin maintenance dose most strongly. The predictive performance of this mechanistic model is not inferior to that of our previous descriptive model. There were significant differences in the mean warfarin daily dose in patients with different CYP2C9 and VKORC1 genotypes. Using IVIVE, the predicted mean CL of S-warfarin for patients with CYP2C9*1/*3 (0.092 l/h, n = 11) was 57 % less than for those with wild-type *1/*1 (0.215 l/h, n

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method.

Science.gov (United States)

Tuta, Jure; Juric, Matjaz B

2018-03-24

This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

Directory of Open Access Journals (Sweden)

Jure Tuta

2018-03-01

Full Text Available This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method, a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.. Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

Genetic testing in congenital heart disease:A clinical approach

Institute of Scientific and Technical Information of China (English)

Marie A Chaix; Gregor Andelfinger; Paul Khairy

2016-01-01

Congenital heart disease(CHD) is the most common type of birth defect. Traditionally, a polygenic model defined by the interaction of multiple genes and environmental factors was hypothesized to account for different forms of CHD. It is now understood that the contribution of genetics to CHD extends beyond a single unified paradigm. For example, monogenic models and chromosomal abnormalities have been associated with various syndromic and non-syndromic forms of CHD. In such instances, genetic investigation and testing may potentially play an important role in clinical care. A family tree with a detailed phenotypic description serves as the initial screening tool to identify potentially inherited defects and to guide further genetic investigation. The selection of a genetic test is contingent upon the particular diagnostic hypothesis generated by clinical examination. Genetic investigation in CHD may carry the potential to improve prognosis by yielding valuable information with regards to personalized medical care, confidence in the clinical diagnosis, and/or targeted patient followup. Moreover, genetic assessment may serve as a tool to predict recurrence risk, define the pattern of inheritance within a family, and evaluate the need for further family screening. In some circumstances, prenatal or preimplantation genetic screening could identify fetuses or embryos at high risk for CHD. Although genetics may appear to constitute a highly specialized sector of cardiology, basic knowledge regarding inheritance patterns, recurrence risks, and available screening and diagnostic tools, including their strengths and limitations, could assist the treating physician in providing sound counsel.

Estimation of genetic parameters related to eggshell strength using random regression models.

Science.gov (United States)

Guo, J; Ma, M; Qu, L; Shen, M; Dou, T; Wang, K

2015-01-01

This study examined the changes in eggshell strength and the genetic parameters related to this trait throughout a hen's laying life using random regression. The data were collected from a crossbred population between 2011 and 2014, where the eggshell strength was determined repeatedly for 2260 hens. Using random regression models (RRMs), several Legendre polynomials were employed to estimate the fixed, direct genetic and permanent environment effects. The residual effects were treated as independently distributed with heterogeneous variance for each test week. The direct genetic variance was included with second-order Legendre polynomials and the permanent environment with third-order Legendre polynomials. The heritability of eggshell strength ranged from 0.26 to 0.43, the repeatability ranged between 0.47 and 0.69, and the estimated genetic correlations between test weeks was high at > 0.67. The first eigenvalue of the genetic covariance matrix accounted for about 97% of the sum of all the eigenvalues. The flexibility and statistical power of RRM suggest that this model could be an effective method to improve eggshell quality and to reduce losses due to cracked eggs in a breeding plan.

Multispecies genetic objectives in spatial conservation planning.

Science.gov (United States)

Nielsen, Erica S; Beger, Maria; Henriques, Romina; Selkoe, Kimberly A; von der Heyden, Sophie

2017-08-01

Growing threats to biodiversity and global alteration of habitats and species distributions make it increasingly necessary to consider evolutionary patterns in conservation decision making. Yet, there is no clear-cut guidance on how genetic features can be incorporated into conservation-planning processes, despite multiple molecular markers and several genetic metrics for each marker type to choose from. Genetic patterns differ between species, but the potential tradeoffs among genetic objectives for multiple species in conservation planning are currently understudied. We compared spatial conservation prioritizations derived from 2 metrics of genetic diversity (nucleotide and haplotype diversity) and 2 metrics of genetic isolation (private haplotypes and local genetic differentiation) in mitochondrial DNA of 5 marine species. We compared outcomes of conservation plans based only on habitat representation with plans based on genetic data and habitat representation. Fewer priority areas were selected for conservation plans based solely on habitat representation than on plans that included habitat and genetic data. All 4 genetic metrics selected approximately similar conservation-priority areas, which is likely a result of prioritizing genetic patterns across a genetically diverse array of species. Largely, our results suggest that multispecies genetic conservation objectives are vital to creating protected-area networks that appropriately preserve community-level evolutionary patterns. © 2016 Society for Conservation Biology.

The evolution of menstruation: A new model for genetic assimilation

Science.gov (United States)

Emera, D.; Romero, R.; Wagner, G.

2012-01-01

Why do humans menstruate while most mammals do not? Here, we present our answer to this long-debated question, arguing that (i) menstruation occurs as a mechanistic consequence of hormone-induced differentiation of the endometrium (referred to as spontaneous decidualization, or SD); (ii) SD evolved because of maternal-fetal conflict; and (iii) SD evolved by genetic assimilation of the decidualization reaction, which is induced by the fetus in non-menstruating species. The idea that menstruation occurs as a consequence of SD has been proposed in the past, but here we present a novel hypothesis on how SD evolved. We argue that decidualization became genetically stabilized in menstruating lineages, allowing females to prepare for pregnancy without any signal from the fetus. We present three models for the evolution of SD by genetic assimilation, based on recent advances in our understanding of the mechanisms of endometrial differentiation and implantation. Testing these models will ultimately shed light on the evolutionary significance of menstruation, as well as on the etiology of human reproductive disorders like endometriosis and recurrent pregnancy loss. PMID:22057551

Application of the genetic algorithm to blume-emery-griffiths model: Test Cases

International Nuclear Information System (INIS)

Erdinc, A.

2004-01-01

The equilibrium properties of the Blume-Emery-Griffiths (BEO) model Hamiltonian with the arbitrary bilinear (1), biquadratic (K) and crystal field interaction (D) are studied using the genetic algorithm technique. Results are compared with lowest approximation of the cluster variation method (CVM), which is identical to the mean field approximation. We found that the genetic algorithm to be very efficient for fast search at the average fraction of the spins, especially in the early stages as the system is far from the equilibrium state. A combination of the genetic algorithm followed by one of the well-tested simulation techniques seems to be an optimal approach. The curvature of the inverse magnetic susceptibility is also presented for the stable state of the BEG model

Modeling the Isentropic Head Value of Centrifugal Gas Compressor using Genetic Programming

Directory of Open Access Journals (Sweden)

Safiyullah Ferozkhan

2016-01-01

Full Text Available Gas compressor performance is vital in oil and gas industry because of the equipment criticality which requires continuous operations. Plant operators often face difficulties in predicting appropriate time for maintenance and would usually rely on time based predictive maintenance intervals as recommended by original equipment manufacturer (OEM. The objective of this work is to develop the computational model to find the isentropic head value using genetic programming. The isentropic head value is calculated from the OEM performance chart. Inlet mass flow rate and speed of the compressor are taken as the input value. The obtained results from the GP computational models show good agreement with experimental and target data with the average prediction error of 1.318%. The genetic programming computational model will assist machinery engineers to quantify performance deterioration of gas compressor and the results from this study will be then utilized to estimate future maintenance requirements based on the historical data. In general, this genetic programming modelling provides a powerful solution for gas compressor operators to realize predictive maintenance approach in their operations.

Evolutionary model with genetics, aging, and knowledge

Science.gov (United States)

Bustillos, Armando Ticona; de Oliveira, Paulo Murilo

2004-02-01

We represent a process of learning by using bit strings, where 1 bits represent the knowledge acquired by individuals. Two ways of learning are considered: individual learning by trial and error, and social learning by copying knowledge from other individuals or from parents in the case of species with parental care. The age-structured bit string allows us to study how knowledge is accumulated during life and its influence over the genetic pool of a population after many generations. We use the Penna model to represent the genetic inheritance of each individual. In order to study how the accumulated knowledge influences the survival process, we include it to help individuals to avoid the various death situations. Modifications in the Verhulst factor do not show any special feature due to its random nature. However, by adding years to life as a function of the accumulated knowledge, we observe an improvement of the survival rates while the genetic fitness of the population becomes worse. In this latter case, knowledge becomes more important in the last years of life where individuals are threatened by diseases. Effects of offspring overprotection and differences between individual and social learning can also be observed. Sexual selection as a function of knowledge shows some effects when fidelity is imposed.

Neurofibromatosis type 1 and multiple sclerosis: Genetically related ...

African Journals Online (AJOL)

Solaf M. Elsayed

2016-10-25

Oct 25, 2016 ... a Genetics Unit, Children's Hospital, Ain Shams University, Egypt b Neurology Department ... Through the past 6 months, she started to develop short term memory loss with intact long term memory. There was no other motor ...

Genetic relationships between detailed reproductive traits and performance traits in Holstein-Friesian dairy cattle.

Science.gov (United States)

Carthy, T R; Ryan, D P; Fitzgerald, A M; Evans, R D; Berry, D P

2016-02-01

The objective of the study was to estimate the genetic relationships between detailed reproductive traits derived from ultrasound examination of the reproductive tract and a range of performance traits in Holstein-Friesian dairy cows. The performance traits investigated included calving performance, milk production, somatic cell score (i.e., logarithm transformation of somatic cell count), carcass traits, and body-related linear type traits. Detailed reproductive traits included (1) resumed cyclicity at the time of examination, (2) multiple ovulations, (3) early ovulation, (4) heat detection, (5) ovarian cystic structures, (6) embryo loss, and (7) uterine score, measured on a 1 (little or no fluid with normal tone) to 4 (large quantity of fluid with a flaccid tone) scale, based on the tone of the uterine wall and the quantity of fluid present in the uterus. (Co)variance components were estimated using a repeatability animal linear mixed model. Genetic merit for greater milk, fat, and protein yield was associated with a reduced ability to resume cyclicity postpartum (genetic correlations ranged from -0.25 to -0.15). Higher genetic merit for milk yield was also associated with a greater genetic susceptibility to multiple ovulations. Genetic predisposition to elevated somatic cell score was associated with a decreased likelihood of cyclicity postpartum (genetic correlation of -0.32) and a greater risk of both multiple ovulations (genetic correlation of 0.25) and embryo loss (genetic correlation of 0.32). Greater body condition score was genetically associated with an increased likelihood of resumption of cyclicity postpartum (genetic correlation of 0.52). Genetically heavier, fatter carcasses with better conformation were also associated with an increased likelihood of resumed cyclicity by the time of examination (genetic correlations ranged from 0.24 to 0.41). Genetically heavier carcasses were associated with an inferior uterine score as well as a greater

Genetic complexity and multiple infections with more Parvovirus species in naturally infected cats

Directory of Open Access Journals (Sweden)

Battilani Mara

2011-03-01

Full Text Available Abstract Parvoviruses of carnivores include three closely related autonomous parvoviruses: canine parvovirus (CPV, feline panleukopenia virus (FPV and mink enteritis virus (MEV. These viruses cause a variety of serious diseases, especially in young patients, since they have a remarkable predilection for replication in rapidly dividing cells. FPV is not the only parvovirus species which infects cats; in addition to MEV, the new variants of canine parvovirus, CPV-2a, 2b and 2c have also penetrated the feline host-range, and they are able to infect and replicate in cats, causing diseases indistinguishable from feline panleukopenia. Furthermore, as cats are susceptible to both CPV-2 and FPV viruses, superinfection and co-infection with multiple parvovirus strains may occur, potentially facilitating recombination and high genetic heterogeneity. In the light of the importance of cats as a potential source of genetic diversity for parvoviruses and, since feline panleukopenia virus has re-emerged as a major cause of mortality in felines, the present study has explored the molecular characteristics of parvovirus strains circulating in cat populations. The most significant findings reported in this study were (a the detection of mixed infection FPV/CPV with the presence of one parvovirus variant which is a true intermediate between FPV/CPV and (b the quasispecies cloud size of one CPV sample variant 2c. In conclusion, this study provides new important results about the evolutionary dynamics of CPV infections in cats, showing that CPV has presumably started a new process of readaptation in feline hosts.

Operationalizing the Reciprocal Engagement Model of Genetic Counseling Practice: a Framework for the Scalable Delivery of Genomic Counseling and Testing.

Science.gov (United States)

Schmidlen, Tara; Sturm, Amy C; Hovick, Shelly; Scheinfeldt, Laura; Scott Roberts, J; Morr, Lindsey; McElroy, Joseph; Toland, Amanda E; Christman, Michael; O'Daniel, Julianne M; Gordon, Erynn S; Bernhardt, Barbara A; Ormond, Kelly E; Sweet, Kevin

2018-02-19

With the advent of widespread genomic testing for diagnostic indications and disease risk assessment, there is increased need to optimize genetic counseling services to support the scalable delivery of precision medicine. Here, we describe how we operationalized the reciprocal engagement model of genetic counseling practice to develop a framework of counseling components and strategies for the delivery of genomic results. This framework was constructed based upon qualitative research with patients receiving genomic counseling following online receipt of potentially actionable complex disease and pharmacogenomics reports. Consultation with a transdisciplinary group of investigators, including practicing genetic counselors, was sought to ensure broad scope and applicability of these strategies for use with any large-scale genomic testing effort. We preserve the provision of pre-test education and informed consent as established in Mendelian/single-gene disease genetic counseling practice. Following receipt of genomic results, patients are afforded the opportunity to tailor the counseling agenda by selecting the specific test results they wish to discuss, specifying questions for discussion, and indicating their preference for counseling modality. The genetic counselor uses these patient preferences to set the genomic counseling session and to personalize result communication and risk reduction recommendations. Tailored visual aids and result summary reports divide areas of risk (genetic variant, family history, lifestyle) for each disease to facilitate discussion of multiple disease risks. Post-counseling, session summary reports are actively routed to both the patient and their physician team to encourage review and follow-up. Given the breadth of genomic information potentially resulting from genomic testing, this framework is put forth as a starting point to meet the need for scalable genetic counseling services in the delivery of precision medicine.

A Genetic Epidemiological Study of Behavioral Traits

NARCIS (Netherlands)

N. Amin (Najaf)

2011-01-01

textabstractHuman behavioural genetics aims to unravel the genetic and environmental contributions to variations in human behaviour. Behaviour is a complex trait, involving multiple genes that are affected by a variety of other factors. Genetic epidemiological research of behaviour goes back to

Genetic counseling and testing for gynecological cancers ...

African Journals Online (AJOL)

undergraduates of universities in Ibadan to genetic counseling and testing (GCT) for ... questionnaire, information on their understanding of GCT, perception of implications, and ... by genetic counseling from suitably trained health-care providers and genetic testing of selected high-risk individuals ..... Multiple sexual partners.

A Genetic Animal Model of Alcoholism for Screening Medications to Treat Addiction

Science.gov (United States)

Bell, Richard L.; Hauser, Sheketha; Rodd, Zachary A.; Liang, Tiebing; Sari, Youssef; McClintick, Jeanette; Rahman, Shafiqur; Engleman, Eric A.

2016-01-01

The purpose of this review is to present up-to-date pharmacological, genetic and behavioral findings from the alcohol-preferring P rat and summarize similar past work. Behaviorally, the focus will be on how the P rat meets criteria put forth for a valid animal model of alcoholism with a highlight on its use as an animal model of polysubstance abuse, including alcohol, nicotine and psychostimulants. Pharmacologically and genetically, the focus will be on the neurotransmitter and neuropeptide systems that have received the most attention: cholinergic, dopaminergic, GABAergic, glutamatergic, serotonergic, noradrenergic, corticotrophin releasing hormone, opioid, and neuropeptide Y. Herein we sought to place the P rat’s behavioral and neurochemical phenotypes, and to some extent its genotype, in the context of the clinical literature. After reviewing the findings thus far, this paper discusses future directions for expanding the use of this genetic animal model of alcoholism to identify molecular targets for treating drug addiction in general. PMID:27055615

Genetic enhancement of macroautophagy in vertebrate models of neurodegenerative diseases.

Science.gov (United States)

Ejlerskov, Patrick; Ashkenazi, Avraham; Rubinsztein, David C

2018-04-03

Most of the neurodegenerative diseases that afflict humans manifest with the intraneuronal accumulation of toxic proteins that are aggregate-prone. Extensive data in cell and neuronal models support the concept that such proteins, like mutant huntingtin or alpha-synuclein, are substrates for macroautophagy (hereafter autophagy). Furthermore, autophagy-inducing compounds lower the levels of such proteins and ameliorate their toxicity in diverse animal models of neurodegenerative diseases. However, most of these compounds also have autophagy-independent effects and it is important to understand if similar benefits are seen with genetic strategies that upregulate autophagy, as this strengthens the validity of this strategy in such diseases. Here we review studies in vertebrate models using genetic manipulations of core autophagy genes and describe how these improve pathology and neurodegeneration, supporting the validity of autophagy upregulation as a target for certain neurodegenerative diseases. Copyright © 2018 Elsevier Inc. All rights reserved.

Multiple Scattering Model for Optical Coherence Tomography with Rytov Approximation

KAUST Repository

Li, Muxingzi

2017-01-01

of speckles due to multiple scatterers within the coherence length, and other random noise. Motivated by the above two challenges, a multiple scattering model based on Rytov approximation and Gaussian beam optics is proposed for the OCT setup. Some previous

Application of hierarchical genetic models to Raven and WAIS subtests: a Dutch twin study.

Science.gov (United States)

Rijsdijk, Frühling V; Vernon, P A; Boomsma, Dorret I

2002-05-01

Hierarchical models of intelligence are highly informative and widely accepted. Application of these models to twin data, however, is sparse. This paper addresses the question of how a genetic hierarchical model fits the Wechsler Adult Intelligence Scale (WAIS) subtests and the Raven Standard Progressive test score, collected in 194 18-year-old Dutch twin pairs. We investigated whether first-order group factors possess genetic and environmental variance independent of the higher-order general factor and whether the hierarchical structure is significant for all sources of variance. A hierarchical model with the 3 Cohen group-factors (verbal comprehension, perceptual organisation and freedom-from-distractibility) and a higher-order g factor showed the best fit to the phenotypic data and to additive genetic influences (A), whereas the unique environmental source of variance (E) could be modeled by a single general factor and specifics. There was no evidence for common environmental influences. The covariation among the WAIS group factors and the covariation between the group factors and the Raven is predominantly influenced by a second-order genetic factor and strongly support the notion of a biological basis of g.

Linear Mixed Models in Statistical Genetics

NARCIS (Netherlands)

R. de Vlaming (Ronald)

2017-01-01

markdownabstractOne of the goals of statistical genetics is to elucidate the genetic architecture of phenotypes (i.e., observable individual characteristics) that are affected by many genetic variants (e.g., single-nucleotide polymorphisms; SNPs). A particular aim is to identify specific SNPs that

One Novel Multiple-Target Plasmid Reference Molecule Targeting Eight Genetically Modified Canola Events for Genetically Modified Canola Detection.

Science.gov (United States)

Li, Zhuqing; Li, Xiang; Wang, Canhua; Song, Guiwen; Pi, Liqun; Zheng, Lan; Zhang, Dabing; Yang, Litao

2017-09-27

Multiple-target plasmid DNA reference materials have been generated and utilized as good substitutes of matrix-based reference materials in the analysis of genetically modified organisms (GMOs). Herein, we report the construction of one multiple-target plasmid reference molecule, pCAN, which harbors eight GM canola event-specific sequences (RF1, RF2, MS1, MS8, Topas 19/2, Oxy235, RT73, and T45) and a partial sequence of the canola endogenous reference gene PEP. The applicability of this plasmid reference material in qualitative and quantitative PCR assays of the eight GM canola events was evaluated, including the analysis of specificity, limit of detection (LOD), limit of quantification (LOQ), and performance of pCAN in the analysis of various canola samples, etc. The LODs are 15 copies for RF2, MS1, and RT73 assays using pCAN as the calibrator and 10 genome copies for the other events. The LOQ in each event-specific real-time PCR assay is 20 copies. In quantitative real-time PCR analysis, the PCR efficiencies of all event-specific and PEP assays are between 91% and 97%, and the squared regression coefficients (R 2 ) are all higher than 0.99. The quantification bias values varied from 0.47% to 20.68% with relative standard deviation (RSD) from 1.06% to 24.61% in the quantification of simulated samples. Furthermore, 10 practical canola samples sampled from imported shipments in the port of Shanghai, China, were analyzed employing pCAN as the calibrator, and the results were comparable with those assays using commercial certified materials as the calibrator. Concluding from these results, we believe that this newly developed pCAN plasmid is one good candidate for being a plasmid DNA reference material in the detection and quantification of the eight GM canola events in routine analysis.

Models for genetic evaluations of claw health traits in Spanish dairy cattle.

Science.gov (United States)

Pérez-Cabal, M A; Charfeddine, N

2015-11-01

Genetic parameters of 7 claw health traits from Spanish dairy cattle were estimated and the predictive ability of linear and ordinal threshold models were compared and assessed. This study included data on interdigital and digital dermatitis (DE), sole ulcer (SU), white line disease (WL), interdigital hyperplasia (IH), interdigital phlegmon (IP), and chronic laminitis (CL) collected between July 2012 and June 2013 from 834 dairy herds visited by 21 trained trimmers. An overall claw disorder (OCD) was also considered an indicator the absence or the presence of at least 1 of the 6 disorders. Claw health traits were scored as categorical traits with 3 degrees of severity (nonaffected, mild, and severe disorder). Genetic parameters were estimated by fitting both a standard linear model and an ordinal threshold animal model. Around 21% of cows had at least 1 claw disorder, and the most frequent disorders were SU, DE, WL, and CL. Heritabilities of claw disorders estimated with a linear model ranged from 0.01 (IP) to 0.05 (OCD), whereas estimates from the ordinal threshold models ranged from 0.06 to 0.39 (for IP and IH, respectively). Repeatabilities of claw health estimated with the linear model varied from 0.03 to 0.18 and estimates with the ordinal threshold model ranged from 0.33 to 0.69. The global trait OCD was correlated with all disorders, except for IH and IP when the linear model was fitted. Two different genetic backgrounds of claw disorders were found. Digital dermatitis showed positive correlations with IH and IP, whereas SU was positively correlated with WL and CL. The predictive ability of the models was assessed using mean squared error and Pearson correlation between the real observation and the corresponding prediction using cross-validation. Regardless of the claw health status, the linear model led to smaller mean squared error. However, differences in predictive ability were found when predicting nonaffected and affected animals. For most traits

Reducing bias in population and landscape genetic inferences: the effects of sampling related individuals and multiple life stages.

Science.gov (United States)

Peterman, William; Brocato, Emily R; Semlitsch, Raymond D; Eggert, Lori S

2016-01-01

In population or landscape genetics studies, an unbiased sampling scheme is essential for generating accurate results, but logistics may lead to deviations from the sample design. Such deviations may come in the form of sampling multiple life stages. Presently, it is largely unknown what effect sampling different life stages can have on population or landscape genetic inference, or how mixing life stages can affect the parameters being measured. Additionally, the removal of siblings from a data set is considered best-practice, but direct comparisons of inferences made with and without siblings are limited. In this study, we sampled embryos, larvae, and adult Ambystoma maculatum from five ponds in Missouri, and analyzed them at 15 microsatellite loci. We calculated allelic richness, heterozygosity and effective population sizes for each life stage at each pond and tested for genetic differentiation (F ST and D C ) and isolation-by-distance (IBD) among ponds. We tested for differences in each of these measures between life stages, and in a pooled population of all life stages. All calculations were done with and without sibling pairs to assess the effect of sibling removal. We also assessed the effect of reducing the number of microsatellites used to make inference. No statistically significant differences were found among ponds or life stages for any of the population genetic measures, but patterns of IBD differed among life stages. There was significant IBD when using adult samples, but tests using embryos, larvae, or a combination of the three life stages were not significant. We found that increasing the ratio of larval or embryo samples in the analysis of genetic distance weakened the IBD relationship, and when using D C , the IBD was no longer significant when larvae and embryos exceeded 60% of the population sample. Further, power to detect an IBD relationship was reduced when fewer microsatellites were used in the analysis.

Reducing bias in population and landscape genetic inferences: the effects of sampling related individuals and multiple life stages

Directory of Open Access Journals (Sweden)

William Peterman

2016-03-01

Full Text Available In population or landscape genetics studies, an unbiased sampling scheme is essential for generating accurate results, but logistics may lead to deviations from the sample design. Such deviations may come in the form of sampling multiple life stages. Presently, it is largely unknown what effect sampling different life stages can have on population or landscape genetic inference, or how mixing life stages can affect the parameters being measured. Additionally, the removal of siblings from a data set is considered best-practice, but direct comparisons of inferences made with and without siblings are limited. In this study, we sampled embryos, larvae, and adult Ambystoma maculatum from five ponds in Missouri, and analyzed them at 15 microsatellite loci. We calculated allelic richness, heterozygosity and effective population sizes for each life stage at each pond and tested for genetic differentiation (FST and DC and isolation-by-distance (IBD among ponds. We tested for differences in each of these measures between life stages, and in a pooled population of all life stages. All calculations were done with and without sibling pairs to assess the effect of sibling removal. We also assessed the effect of reducing the number of microsatellites used to make inference. No statistically significant differences were found among ponds or life stages for any of the population genetic measures, but patterns of IBD differed among life stages. There was significant IBD when using adult samples, but tests using embryos, larvae, or a combination of the three life stages were not significant. We found that increasing the ratio of larval or embryo samples in the analysis of genetic distance weakened the IBD relationship, and when using DC, the IBD was no longer significant when larvae and embryos exceeded 60% of the population sample. Further, power to detect an IBD relationship was reduced when fewer microsatellites were used in the analysis.

Genetic Optimization Algorithm for Metabolic Engineering Revisited

Directory of Open Access Journals (Sweden)

Tobias B. Alter

2018-05-01

Full Text Available To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and the proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i multiple, non-linear engineering objectives; (ii the identification of gene target-sets according to logical gene-protein-reaction associations; (iii minimization of the number of network perturbations; and (iv the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

Setaria viridis as a model system to advance millet genetics and genomics

Directory of Open Access Journals (Sweden)

Pu Huang

2016-11-01

Full Text Available Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools and resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail (Setaria viridis as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica. These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crop.

Setaria viridis as a Model System to Advance Millet Genetics and Genomics.

Science.gov (United States)

Huang, Pu; Shyu, Christine; Coelho, Carla P; Cao, Yingying; Brutnell, Thomas P

2016-01-01

Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools and resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail ( Setaria viridis ) as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica . These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crops.

Setaria viridis as a Model System to Advance Millet Genetics and Genomics

Science.gov (United States)

Huang, Pu; Shyu, Christine; Coelho, Carla P.; Cao, Yingying; Brutnell, Thomas P.

2016-01-01

Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools and resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail (Setaria viridis) as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica. These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crops. PMID:27965689

Recent developments in computer modeling add ecological realism to landscape genetics

Science.gov (United States)

Background / Question / Methods A factor limiting the rate of progress in landscape genetics has been the shortage of spatial models capable of linking life history attributes such as dispersal behavior to complex dynamic landscape features. The recent development of new models...

MODEL PENSKORAN PARTIAL CREDIT PADA BUTIR MULTIPLE TRUE-FALSE BIDANG FISIKA

Directory of Open Access Journals (Sweden)

Wasis Wasis

2013-01-01

Full Text Available Tujuan penelitian ini menghasilkan model penskoran politomus untuk respons butir multiple true-false, sehingga dapat mengestimasi secara lebih akurat kemampuan di bidang fisika. Pengembangan penskoran menggunakan Four-D model dan diuji akurasinya melalui penelitian empiris dan simulasi. Penelitian empiris menggunakan 15 butir multiple true-false yang diambil dari soal UMPTN tahun 1996-2006 dan dikenakan pada 410 mahasiswa baru FMIPA Universitas Negeri Surabaya angkatan tahun 2007. Respons peserta tes diskor dengan tiga model partial credit (PCM I; II; dan III dan secara dikotomus. Hasil penskoran dianalisis dengan program Quest untuk mendapat-kan estimasi tingkat kesukaran butir (δ dan estimasi ke-mampuan peserta (θ untuk menentukan nilai fungsi informasi tes dan kesalahan baku estimasi. Penelitian simulasi mengguna-kan data bangkitan berdasarkan parameter empiris (δ dan θ memakai program statistik SAS dan akurasi estimasinya di-analisis dengan metode root mean squared error (RMSE. Hasil penelitian ini menunjukkan: (i Penskoran PCM dengan pem-bobotan mampu mengestimasi kemampuan lebih akurat di-bandingkan tanpa pembobotan maupun secara dikotomus; (ii Semakin banyak jumlah kategori dalam penskoran partial credit, semakin akurat. Kata kunci: model penskoran partial credit, butir multiple true-false ____________________________________________________________ THE PARTIAL CREDIT SCORING MODEL FOR THE MULTIPLE TRUE-FALSE BUTIRS IN PHYSICS Abstract This study is an attempt to overcome the weaknesses. This study aims to produce a polytomous scoring model for responses to multiple true-false butirs in order to get a more accurate estimation of abilities in physics. It adopts the Four-D model and its accuracy is assessed through empirical and simulation studies. The empirical study employed 15 multiple true-false butirs taken from the New Students Entrance Test of State University the year of 1996–2006. It administered to 410 new students enrolled

A Unifying Model for the Analysis of Phenotypic, Genetic and Geographic Data

DEFF Research Database (Denmark)

Guillot, Gilles; Rena, Sabrina; Ledevin, Ronan

2012-01-01

Recognition of evolutionary units (species, populations) requires integrating several kinds of data such as genetic or phenotypic markers or spatial information, in order to get a comprehensive view concerning the dierentiation of the units. We propose a statistical model with a double original...... advantage: (i) it incorporates information about the spatial distribution of the samples, with the aim to increase inference power and to relate more explicitly observed patterns to geography; and (ii) it allows one to analyze genetic and phenotypic data within a unied model and inference framework, thus...... an intricate case of inter- and intra-species dierentiation based on an original data-set of georeferenced genetic and morphometric markers obtained on Myodes voles from Sweden. A computer program is made available as an extension of the R package Geneland....

Genetic Algorithms for a Parameter Estimation of a Fermentation Process Model: A Comparison

Directory of Open Access Journals (Sweden)

Olympia Roeva

2005-12-01

Full Text Available In this paper the problem of a parameter estimation using genetic algorithms is examined. A case study considering the estimation of 6 parameters of a nonlinear dynamic model of E. coli fermentation is presented as a test problem. The parameter estimation problem is stated as a nonlinear programming problem subject to nonlinear differential-algebraic constraints. This problem is known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based local optimization methods fail to arrive satisfied solutions. To overcome their limitations, the use of different genetic algorithms as stochastic global optimization methods is explored. These algorithms are proved to be very suitable for the optimization of highly non-linear problems with many variables. Genetic algorithms can guarantee global optimality and robustness. These facts make them advantageous in use for parameter identification of fermentation models. A comparison between simple, modified and multi-population genetic algorithms is presented. The best result is obtained using the modified genetic algorithm. The considered algorithms converged very closely to the cost value but the modified algorithm is in times faster than other two.

Males and females contribute unequally to offspring genetic diversity in the polygynandrous mating system of wild boar.

Directory of Open Access Journals (Sweden)

Javier Pérez-González

Full Text Available The maintenance of genetic diversity across generations depends on both the number of reproducing males and females. Variance in reproductive success, multiple paternity and litter size can all affect the relative contributions of male and female parents to genetic variation of progeny. The mating system of the wild boar (Sus scrofa has been described as polygynous, although evidence of multiple paternity in litters has been found. Using 14 microsatellite markers, we evaluated the contribution of males and females to genetic variation in the next generation in independent wild boar populations from the Iberian Peninsula and Hungary. Genetic contributions of males and females were obtained by distinguishing the paternal and maternal genetic component inherited by the progeny. We found that the paternally inherited genetic component of progeny was more diverse than the maternally inherited component. Simulations showed that this finding might be due to a sampling bias. However, after controlling for the bias by fitting both the genetic diversity in the adult population and the number of reproductive individuals in the models, paternally inherited genotypes remained more diverse than those inherited maternally. Our results suggest new insights into how promiscuous mating systems can help maintain genetic variation.

Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

Science.gov (United States)

Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, Hartwig H; Piepho, Hans-Peter; Gordillo, Andres; Wilde, Peer; Bauer, Eva; Schön, Chris-Carolin

2016-11-01

Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S 2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S 2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS  = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.

Amniotic Fluid Stem Cells: A Novel Source for Modeling of Human Genetic Diseases

Directory of Open Access Journals (Sweden)

Ivana Antonucci

2016-04-01

Full Text Available In recent years, great interest has been devoted to the use of Induced Pluripotent Stem cells (iPS for modeling of human genetic diseases, due to the possibility of reprogramming somatic cells of affected patients into pluripotent cells, enabling differentiation into several cell types, and allowing investigations into the molecular mechanisms of the disease. However, the protocol of iPS generation still suffers from technical limitations, showing low efficiency, being expensive and time consuming. Amniotic Fluid Stem cells (AFS represent a potential alternative novel source of stem cells for modeling of human genetic diseases. In fact, by means of prenatal diagnosis, a number of fetuses affected by chromosomal or Mendelian diseases can be identified, and the amniotic fluid collected for genetic testing can be used, after diagnosis, for the isolation, culture and differentiation of AFS cells. This can provide a useful stem cell model for the investigation of the molecular basis of the diagnosed disease without the necessity of producing iPS, since AFS cells show some features of pluripotency and are able to differentiate in cells derived from all three germ layers “in vitro”. In this article, we describe the potential benefits provided by using AFS cells in the modeling of human genetic diseases.

A collaborative scheduling model for the supply-hub with multiple suppliers and multiple manufacturers.

Science.gov (United States)

Li, Guo; Lv, Fei; Guan, Xu

2014-01-01

This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment.

DNA repair and the genetic control of radiosensitivity in yeast

International Nuclear Information System (INIS)

Haynes, R.H.

1975-01-01

The following topics are discussed: advantages of yeasts for easily manipulated model systems for studies on molecular biology of eukaryotes; induction of x-ray-resistant mutants by radiations and chemicals; genetics of uv-sensitive mutants; loci of genes affecting radiosensitivity; gene interactions in multiple mutants; liquid-holding recovery; mitotic and meiotic recombination; and repair of yeast mitochondrial DNA

Genetic mouse models of brain ageing and Alzheimer's disease.

Science.gov (United States)

Bilkei-Gorzo, Andras

2014-05-01

Progression of brain ageing is influenced by a complex interaction of genetic and environmental factors. Analysis of genetically modified animals with uniform genetic backgrounds in a standardised, controlled environment enables the dissection of critical determinants of brain ageing on a molecular level. Human and animal studies suggest that increased load of damaged macromolecules, efficacy of DNA maintenance, mitochondrial activity, and cellular stress defences are critical determinants of brain ageing. Surprisingly, mouse lines with genetic impairment of anti-oxidative capacity generally did not show enhanced cognitive ageing but rather an increased sensitivity to oxidative challenge. Mouse lines with impaired mitochondrial activity had critically short life spans or severe and rapidly progressing neurodegeneration. Strains with impaired clearance in damaged macromolecules or defects in the regulation of cellular stress defences showed alterations in the onset and progression of cognitive decline. Importantly, reduced insulin/insulin-like growth factor signalling generally increased life span but impaired cognitive functions revealing a complex interaction between ageing of the brain and of the body. Brain ageing is accompanied by an increased risk of developing Alzheimer's disease. Transgenic mouse models expressing high levels of mutant human amyloid precursor protein showed a number of symptoms and pathophysiological processes typical for early phase of Alzheimer's disease. Generally, therapeutic strategies effective against Alzheimer's disease in humans were also active in the Tg2576, APP23, APP/PS1 and 5xFAD lines, but a large number of false positive findings were also reported. The 3xtg AD model likely has the highest face and construct validity but further studies are needed. Copyright © 2013 Elsevier Inc. All rights reserved.

Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach

DEFF Research Database (Denmark)

Bothwell, H.; Bisbing, S.; Therkildsen, Nina Overgaard

2013-01-01

It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental...... loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi...... variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major...

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

Science.gov (United States)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Application of genetic algorithm in radio ecological models parameter determination

Energy Technology Data Exchange (ETDEWEB)

Pantelic, G. [Institute of Occupatioanl Health and Radiological Protection ' Dr Dragomir Karajovic' , Belgrade (Serbia)

2006-07-01

The method of genetic algorithms was used to determine the biological half-life of 137 Cs in cow milk after the accident in Chernobyl. Methodologically genetic algorithms are based on the fact that natural processes tend to optimize themselves and therefore this method should be more efficient in providing optimal solutions in the modeling of radio ecological and environmental events. The calculated biological half-life of 137 Cs in milk is (32 {+-} 3) days and transfer coefficient from grass to milk is (0.019 {+-} 0.005). (authors)

Visual detection of multiple genetically modified organisms in a capillary array.

Science.gov (United States)

Shao, Ning; Chen, Jianwei; Hu, Jiaying; Li, Rong; Zhang, Dabing; Guo, Shujuan; Hui, Junhou; Liu, Peng; Yang, Litao; Tao, Sheng-Ce

2017-01-31

There is an urgent need for rapid, low-cost multiplex methodologies for the monitoring of genetically modified organisms (GMOs). Here, we report a C[combining low line]apillary A[combining low line]rray-based L[combining low line]oop-mediated isothermal amplification for M[combining low line]ultiplex visual detection of nucleic acids (CALM) platform for the simple and rapid monitoring of GMOs. In CALM, loop-mediated isothermal amplification (LAMP) primer sets are pre-fixed to the inner surface of capillaries. The surface of the capillary array is hydrophobic while the capillaries are hydrophilic, enabling the simultaneous loading and separation of the LAMP reaction mixtures into each capillary by capillary forces. LAMP reactions in the capillaries are then performed in parallel, and the results are visually detected by illumination with a hand-held UV device. Using CALM, we successfully detected seven frequently used transgenic genes/elements and five plant endogenous reference genes with high specificity and sensitivity. Moreover, we found that measurements of real-world blind samples by CALM are consistent with results obtained by independent real-time PCRs. Thus, with an ability to detect multiple nucleic acids in a single easy-to-operate test, we believe that CALM will become a widely applied technology in GMO monitoring.

Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases.

Directory of Open Access Journals (Sweden)

Hariklia Eleftherohorinou

2009-11-01

Full Text Available Although the introduction of genome-wide association studies (GWAS have greatly increased the number of genes associated with common diseases, only a small proportion of the predicted genetic contribution has so far been elucidated. Studying the cumulative variation of polymorphisms in multiple genes acting in functional pathways may provide a complementary approach to the more common single SNP association approach in understanding genetic determinants of common disease. We developed a novel pathway-based method to assess the combined contribution of multiple genetic variants acting within canonical biological pathways and applied it to data from 14,000 UK individuals with 7 common diseases. We tested inflammatory pathways for association with Crohn's disease (CD, rheumatoid arthritis (RA and type 1 diabetes (T1D with 4 non-inflammatory diseases as controls. Using a variable selection algorithm, we identified variants responsible for the pathway association and evaluated their use for disease prediction using a 10 fold cross-validation framework in order to calculate out-of-sample area under the Receiver Operating Curve (AUC. The generalisability of these predictive models was tested on an independent birth cohort from Northern Finland. Multiple canonical inflammatory pathways showed highly significant associations (p 10(-3-10(-20 with CD, T1D and RA. Variable selection identified on average a set of 205 SNPs (149 genes for T1D, 350 SNPs (189 genes for RA and 493 SNPs (277 genes for CD. The pattern of polymorphisms at these SNPS were found to be highly predictive of T1D (91% AUC and RA (85% AUC, and weakly predictive of CD (60% AUC. The predictive ability of the T1D model (without any parameter refitting had good predictive ability (79% AUC in the Finnish cohort. Our analysis suggests that genetic contribution to common inflammatory diseases operates through multiple genes interacting in functional pathways.

Simulated evolution applied to study the genetic code optimality using a model of codon reassignments.

Science.gov (United States)

Santos, José; Monteagudo, Angel

2011-02-21

As the canonical code is not universal, different theories about its origin and organization have appeared. The optimization or level of adaptation of the canonical genetic code was measured taking into account the harmful consequences resulting from point mutations leading to the replacement of one amino acid for another. There are two basic theories to measure the level of optimization: the statistical approach, which compares the canonical genetic code with many randomly generated alternative ones, and the engineering approach, which compares the canonical code with the best possible alternative. Here we used a genetic algorithm to search for better adapted hypothetical codes and as a method to guess the difficulty in finding such alternative codes, allowing to clearly situate the canonical code in the fitness landscape. This novel proposal of the use of evolutionary computing provides a new perspective in the open debate between the use of the statistical approach, which postulates that the genetic code conserves amino acid properties far better than expected from a random code, and the engineering approach, which tends to indicate that the canonical genetic code is still far from optimal. We used two models of hypothetical codes: one that reflects the known examples of codon reassignment and the model most used in the two approaches which reflects the current genetic code translation table. Although the standard code is far from a possible optimum considering both models, when the more realistic model of the codon reassignments was used, the evolutionary algorithm had more difficulty to overcome the efficiency of the canonical genetic code. Simulated evolution clearly reveals that the canonical genetic code is far from optimal regarding its optimization. Nevertheless, the efficiency of the canonical code increases when mistranslations are taken into account with the two models, as indicated by the fact that the best possible codes show the patterns of the

Simulated evolution applied to study the genetic code optimality using a model of codon reassignments

Directory of Open Access Journals (Sweden)

Monteagudo Ángel

2011-02-01

Full Text Available Abstract Background As the canonical code is not universal, different theories about its origin and organization have appeared. The optimization or level of adaptation of the canonical genetic code was measured taking into account the harmful consequences resulting from point mutations leading to the replacement of one amino acid for another. There are two basic theories to measure the level of optimization: the statistical approach, which compares the canonical genetic code with many randomly generated alternative ones, and the engineering approach, which compares the canonical code with the best possible alternative. Results Here we used a genetic algorithm to search for better adapted hypothetical codes and as a method to guess the difficulty in finding such alternative codes, allowing to clearly situate the canonical code in the fitness landscape. This novel proposal of the use of evolutionary computing provides a new perspective in the open debate between the use of the statistical approach, which postulates that the genetic code conserves amino acid properties far better than expected from a random code, and the engineering approach, which tends to indicate that the canonical genetic code is still far from optimal. We used two models of hypothetical codes: one that reflects the known examples of codon reassignment and the model most used in the two approaches which reflects the current genetic code translation table. Although the standard code is far from a possible optimum considering both models, when the more realistic model of the codon reassignments was used, the evolutionary algorithm had more difficulty to overcome the efficiency of the canonical genetic code. Conclusions Simulated evolution clearly reveals that the canonical genetic code is far from optimal regarding its optimization. Nevertheless, the efficiency of the canonical code increases when mistranslations are taken into account with the two models, as indicated by the

A Model for Understanding the Genetic Basis for Disparity in Prostate Cancer Risk

Science.gov (United States)

2017-10-01

AWARD NUMBER: W81XWH-15-1-0529 TITLE: A Model for Understanding the Genetic Basis for Disparity in Prostate Cancer Risk PRINCIPAL INVESTIGATOR...AND SUBTITLE A Model for Understanding the Genetic Basis for Disparity in Prostate Cancer Risk 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-15-1...STATEMENT Approved for Public Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Prostate cancer is the most commonly diagnosed cancer in

Reduction of bias in neutron multiplicity assay using a weighted point model

Energy Technology Data Exchange (ETDEWEB)

Geist, W. H. (William H.); Krick, M. S. (Merlyn S.); Mayo, D. R. (Douglas R.)

2004-01-01

Accurate assay of most common plutonium samples was the development goal for the nondestructive assay technique of neutron multiplicity counting. Over the past 20 years the technique has been proven for relatively pure oxides and small metal items. Unfortunately, the technique results in large biases when assaying large metal items. Limiting assumptions, such as unifoh multiplication, in the point model used to derive the multiplicity equations causes these biases for large dense items. A weighted point model has been developed to overcome some of the limitations in the standard point model. Weighting factors are detemiined from Monte Carlo calculations using the MCNPX code. Monte Carlo calculations give the dependence of the weighting factors on sample mass and geometry, and simulated assays using Monte Carlo give the theoretical accuracy of the weighted-point-model assay. Measured multiplicity data evaluated with both the standard and weighted point models are compared to reference values to give the experimental accuracy of the assay. Initial results show significant promise for the weighted point model in reducing or eliminating biases in the neutron multiplicity assay of metal items. The negative biases observed in the assay of plutonium metal samples are caused by variations in the neutron multiplication for neutrons originating in various locations in the sample. The bias depends on the mass and shape of the sample and depends on the amount and energy distribution of the ({alpha},n) neutrons in the sample. When the standard point model is used, this variable-multiplication bias overestimates the multiplication and alpha values of the sample, and underestimates the plutonium mass. The weighted point model potentially can provide assay accuracy of {approx}2% (1 {sigma}) for cylindrical plutonium metal samples < 4 kg with {alpha} < 1 without knowing the exact shape of the samples, provided that the ({alpha},n) source is uniformly distributed throughout the

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

A genetic algorithm for optimizing multi-pole Debye models of tissue dielectric properties

International Nuclear Information System (INIS)

Clegg, J; Robinson, M P

2012-01-01

Models of tissue dielectric properties (permittivity and conductivity) enable the interactions of tissues and electromagnetic fields to be simulated, which has many useful applications in microwave imaging, radio propagation, and non-ionizing radiation dosimetry. Parametric formulae are available, based on a multi-pole model of tissue dispersions, but although they give the dielectric properties over a wide frequency range, they do not convert easily to the time domain. An alternative is the multi-pole Debye model which works well in both time and frequency domains. Genetic algorithms are an evolutionary approach to optimization, and we found that this technique was effective at finding the best values of the multi-Debye parameters. Our genetic algorithm optimized these parameters to fit to either a Cole–Cole model or to measured data, and worked well over wide or narrow frequency ranges. Over 10 Hz–10 GHz the best fits for muscle, fat or bone were each found for ten dispersions or poles in the multi-Debye model. The genetic algorithm is a fast and effective method of developing tissue models that compares favourably with alternatives such as the rational polynomial fit. (paper)

Moral enhancement requires multiple virtues.

Science.gov (United States)

Hughes, James J

2015-01-01

Some of the debates around the concept of moral enhancement have focused on whether the improvement of a single trait, such as empathy or intelligence, would be a good in general, or in all circumstances. All virtue theories, however, both secular and religious, have articulated multiple virtues that temper and inform one another in the development of a mature moral character. The project of moral enhancement requires a reengagement with virtue ethics and contemporary moral psychology to develop an empirically grounded model of the virtues and a fuller model of character development. Each of these virtues may be manipulable with electronic, psychopharmaceutical, and genetic interventions. A set of interdependent virtues is proposed, along with some of the research pointing to ways such virtues could be enhanced.

Infinite Multiple Membership Relational Modeling for Complex Networks

DEFF Research Database (Denmark)

Mørup, Morten; Schmidt, Mikkel Nørgaard; Hansen, Lars Kai

Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiplemembership latent feature model for networks. Contrary to existing...... multiplemembership models that scale quadratically in the number of vertices the proposedmodel scales linearly in the number of links admittingmultiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show...

Genetic Evaluation and Ranking of Different Animal Models Using ...

African Journals Online (AJOL)

An animal model utilizes all relationships available in a given data set. Estimates for variance components for additive direct, additive maternal, maternal environmental and direct environmental effects, and their covariances between direct and maternal genetic effects for post weaning growth traits have been obtained with ...

Predictability of Genetic Interactions from Functional Gene Modules

Directory of Open Access Journals (Sweden)

Jonathan H. Young

2017-02-01

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

Producing genetic knowledge and citizenship through the Internet: mothers, pediatric genetics, and cybermedicine.

Science.gov (United States)

Schaffer, Rebecca; Kuczynski, Kristine; Skinner, Debra

2008-01-01

This article analyses data from a longitudinal, ethnographic study conducted in the United States to examine how 100 mothers of children with genetic disorders used the Internet to interpret, produce, and circulate genetic knowledge pertaining to their child's condition. We describe how they came to value their own experiential knowledge, helped shift the boundaries of what counts as authoritative knowledge, and assumed the role of genetic citizen, fighting for specific rights while shouldering and contesting concomitant duties and obligations. This exploration of e-health use contributes to our understanding of the social practices and power relations that cut across online and off-line worlds to co-produce genetic knowledge and genetic citizenship in multiple contexts.

Genetic modelling in schizophrenia according to HLA typing.

Science.gov (United States)

Smeraldi, E; Macciardi, F; Gasperini, M; Orsini, A; Bellodi, L; Fabio, G; Morabito, A

1986-09-01

Studying families of schizophrenic patients, we observed that the risk of developing the overt form of the illness could be enhanced by some factors. Among these various factors we focused our attention on a biological variable, namely the presence or the absence of particular HLA antigens: partitioning our schizophrenic patients according to their HLA structure (i.e. those with HLA-A1 or CRAG-A1 antigens and those with HLA-non-CRAG-A1 antigens, respectively), revealed different illness distribution in the two groups. From a genetic point of view, this finding suggests the presence of heterogeneity in the hypothetical liability system related to schizophrenia and we evaluated the heterogeneity hypothesis by applying alternative genetic models to our data, trying to detect more biologically homogeneous subgroups of the disease.

Genetic testing in congenital heart disease: A clinical approach

Science.gov (United States)

Chaix, Marie A; Andelfinger, Gregor; Khairy, Paul

2016-01-01

Congenital heart disease (CHD) is the most common type of birth defect. Traditionally, a polygenic model defined by the interaction of multiple genes and environmental factors was hypothesized to account for different forms of CHD. It is now understood that the contribution of genetics to CHD extends beyond a single unified paradigm. For example, monogenic models and chromosomal abnormalities have been associated with various syndromic and non-syndromic forms of CHD. In such instances, genetic investigation and testing may potentially play an important role in clinical care. A family tree with a detailed phenotypic description serves as the initial screening tool to identify potentially inherited defects and to guide further genetic investigation. The selection of a genetic test is contingent upon the particular diagnostic hypothesis generated by clinical examination. Genetic investigation in CHD may carry the potential to improve prognosis by yielding valuable information with regards to personalized medical care, confidence in the clinical diagnosis, and/or targeted patient follow-up. Moreover, genetic assessment may serve as a tool to predict recurrence risk, define the pattern of inheritance within a family, and evaluate the need for further family screening. In some circumstances, prenatal or preimplantation genetic screening could identify fetuses or embryos at high risk for CHD. Although genetics may appear to constitute a highly specialized sector of cardiology, basic knowledge regarding inheritance patterns, recurrence risks, and available screening and diagnostic tools, including their strengths and limitations, could assist the treating physician in providing sound counsel. PMID:26981213

Genetic algorithm based optimization of advanced solar cell designs modeled in Silvaco AtlasTM

OpenAIRE

Utsler, James

2006-01-01

A genetic algorithm was used to optimize the power output of multi-junction solar cells. Solar cell operation was modeled using the Silvaco ATLASTM software. The output of the ATLASTM simulation runs served as the input to the genetic algorithm. The genetic algorithm was run as a diffusing computation on a network of eighteen dual processor nodes. Results showed that the genetic algorithm produced better power output optimizations when compared with the results obtained using the hill cli...

Sequential interactions of silver-silica nanocomposite (Ag-SiO2 NC) with cell wall, metabolism and genetic stability of Pseudomonas aeruginosa, a multiple antibiotic-resistant bacterium

Digital Repository Service at National Institute of Oceanography (India)

Anas, A.; Jiya, J.; Rameez, M.J.; Anand, P.B.; Anantharaman, M.R.; Nair, S.

The study was carried out to understand the effect of silver–silica nanocomposite (Ag-SiO sub(2)NC) on the cell wall integrity, metabolism and genetic stability of Pseudomonas aeruginosa, a multiple drug-resistant bacterium. Bacterial sensitivity...

Sequential interactions of silver-silica nanocomposite (Ag-SiO2NC) with cell wall, metabolism and genetic stability of Pseudomonas aeruginosa, a multiple antibiotic-resistant bacterium

Digital Repository Service at National Institute of Oceanography (India)

Anas, A.; Jiya, J.; Rameez, M.J.; Anand, P.B.; Anantharaman, M.R.; Nair, S.

The study was carried out to understand the effect of silver-silica nanocomposite (Ag-SiO sub(2)NC) on the cell wall integrity, metabolism and genetic stability of Pseudomonas aeruginosa, a multiple drug-resistant bacterium Bacterial sensitivity...

Forecasting Shaharchay River Flow in Lake Urmia Basin using Genetic Programming and M5 Model Tree

Directory of Open Access Journals (Sweden)

S. Samadianfard

2017-01-01

Full Text Available Introduction: Precise prediction of river flows is the key factor for proper planning and management of water resources. Thus, obtaining the reliable methods for predicting river flows has great importance in water resource engineering. In the recent years, applications of intelligent methods such as artificial neural networks, fuzzy systems and genetic programming in water science and engineering have been grown extensively. These mentioned methods are able to model nonlinear process of river flows without any need to geometric properties. A huge number of studies have been reported in the field of using intelligent methods in water resource engineering. For example, Noorani and Salehi (23 presented a model for predicting runoff in Lighvan basin using adaptive neuro-fuzzy network and compared the performance of it with neural network and fuzzy inference methods in east Azerbaijan, Iran. Nabizadeh et al. (21 used fuzzy inference system and adaptive neuro-fuzzy inference system in order to predict river flow in Lighvan river. Khalili et al. (13 proposed a BL-ARCH method for prediction of flows in Shaharchay River in Urmia. Khu et al. (16 used genetic programming for runoff prediction in Orgeval catchment in France. Firat and Gungor (11 evaluated the fuzzy-neural model for predicting Mendes river flow in Turkey. The goal of present study is comparing the performance of genetic programming and M5 model trees for prediction of Shaharchay river flow in the basin of Lake Urmia and obtaining a comprehensive insight of their abilities. Materials and Methods: Shaharchay river as a main source of providing drinking water of Urmia city and agricultural needs of surrounding lands and finally one of the main input sources of Lake Urmia is quite important in the region. For obtaining the predetermined goals of present study, average monthly flows of Shaharchay River in Band hydrometric station has been gathered from 1951 to 2011. Then, two third of mentioned

Application of genetic algorithm in radio ecological models parameter determination

International Nuclear Information System (INIS)

Pantelic, G.

2006-01-01

The method of genetic algorithms was used to determine the biological half-life of 137 Cs in cow milk after the accident in Chernobyl. Methodologically genetic algorithms are based on the fact that natural processes tend to optimize themselves and therefore this method should be more efficient in providing optimal solutions in the modeling of radio ecological and environmental events. The calculated biological half-life of 137 Cs in milk is (32 ± 3) days and transfer coefficient from grass to milk is (0.019 ± 0.005). (authors)

TNF receptor 1 genetic risk mirrors outcome of anti-TNF therapy in multiple sclerosis

DEFF Research Database (Denmark)

Gregory, Adam P; Dendrou, Calliope A; Attfield, Kathrine E

2012-01-01

), but not with other autoimmune conditions such as rheumatoid arthritis, psoriasis and Crohn’s disease. By analysing MS GWAS data in conjunction with the 1000 Genomes Project data we provide genetic evidence that strongly implicates this SNP, rs1800693, as the causal variant in the TNFRSF1A region. We further...... make to disease risk has raised questions regarding their medical relevance. Here we have investigated a single nucleotide polymorphism (SNP) in the TNFRSF1A gene, that encodes tumour necrosis factor receptor 1 (TNFR1), which was discovered through GWAS to be associated with multiple sclerosis (MS...... substantiate this through functional studies showing that the MS risk allele directs expression of a novel, soluble form of TNFR1 that can block TNF. Importantly, TNF-blocking drugs can promote onset or exacerbation of MS, but they have proven highly efficacious in the treatment of autoimmune diseases...

Optimization of the Darrieus wind turbines with double-multiple-streamtube model

International Nuclear Information System (INIS)

Paraschivoiu, I.

1985-01-01

This paper discusses a new improvement of the double-multiple-stream tube model by considering the stream tube expansion effects on the Darrieus wind turbine. These effects, allowing a more realistic modeling of the upwind/downwind flow field asymmetries inherent in the Darrieus rotor, were calculated by using CARDAAX computer code. When the dynamic stall is introduced in the double-multiple-stream tube model, the aerodynamic loads and performance show significant changes in the range of low tip-speed ratio

Beyond clinical utility: The multiple values of DTC genetics

Directory of Open Access Journals (Sweden)

Mauro Turrini

2016-03-01

Full Text Available One point of consensus in the otherwise very controversial discussion about the benefits and dangers of DTC genetics in the health domain is the lack of substantial clinical utility. At the same time, both the empirical and conceptual literature indicate that health-related DTC tests can have value and utility outside of the clinic. We argue that a broader and multi-faceted conceptualization of utility and value would enrich the ethical and social discussion of DTC testing in several ways: First, looking at ways in which DTC testing can have personal and social value for users – in the form of entertainment, learning, or a way to relate to others – can help to explain why people still take DTC tests, and will, further down the line, foster a more nuanced understanding of secondary and tertiary uses of DTC test results (which could very well unearth new ethical and regulatory challenges. Second, considering the economic value and broader utility of DTC testing foregrounds wider social and political aspects than have been dominant in the ethical and regulatory debates surrounding DTC genetics so far. These wider political aspects include the profound power asymmetries that characterize the collection and use of personal genetic data in many contexts.

Application of multiple objective models to water resources planning and management

International Nuclear Information System (INIS)

North, R.M.

1993-01-01

Over the past 30 years, we have seen the birth and growth of multiple objective analysis from an idea without tools to one with useful applications. Models have been developed and applications have been researched to address the multiple purposes and objectives inherent in the development and management of water resources. A practical approach to multiple objective modelling incorporates macroeconomic-based policies and expectations in order to optimize the results from both engineering (structural) and management (non-structural) alternatives, while taking into account the economic and environmental trade-offs. (author). 27 refs, 4 figs, 3 tabs

The Mouse Lemur, a Genetic Model Organism for Primate Biology, Behavior, and Health.

Science.gov (United States)

Ezran, Camille; Karanewsky, Caitlin J; Pendleton, Jozeph L; Sholtz, Alex; Krasnow, Maya R; Willick, Jason; Razafindrakoto, Andriamahery; Zohdy, Sarah; Albertelli, Megan A; Krasnow, Mark A

2017-06-01

Systematic genetic studies of a handful of diverse organisms over the past 50 years have transformed our understanding of biology. However, many aspects of primate biology, behavior, and disease are absent or poorly modeled in any of the current genetic model organisms including mice. We surveyed the animal kingdom to find other animals with advantages similar to mice that might better exemplify primate biology, and identified mouse lemurs ( Microcebus spp.) as the outstanding candidate. Mouse lemurs are prosimian primates, roughly half the genetic distance between mice and humans. They are the smallest, fastest developing, and among the most prolific and abundant primates in the world, distributed throughout the island of Madagascar, many in separate breeding populations due to habitat destruction. Their physiology, behavior, and phylogeny have been studied for decades in laboratory colonies in Europe and in field studies in Malagasy rainforests, and a high quality reference genome sequence has recently been completed. To initiate a classical genetic approach, we developed a deep phenotyping protocol and have screened hundreds of laboratory and wild mouse lemurs for interesting phenotypes and begun mapping the underlying mutations, in collaboration with leading mouse lemur biologists. We also seek to establish a mouse lemur gene "knockout" library by sequencing the genomes of thousands of mouse lemurs to identify null alleles in most genes from the large pool of natural genetic variants. As part of this effort, we have begun a citizen science project in which students across Madagascar explore the remarkable biology around their schools, including longitudinal studies of the local mouse lemurs. We hope this work spawns a new model organism and cultivates a deep genetic understanding of primate biology and health. We also hope it establishes a new and ethical method of genetics that bridges biological, behavioral, medical, and conservation disciplines, while

Predicting type 2 diabetes using genetic and environmental risk factors in a multi-ethnic Malaysian cohort.

Science.gov (United States)

Abdullah, N; Abdul Murad, N A; Mohd Haniff, E A; Syafruddin, S E; Attia, J; Oldmeadow, C; Kamaruddin, M A; Abd Jalal, N; Ismail, N; Ishak, M; Jamal, R; Scott, R J; Holliday, E G

2017-08-01

Malaysia has a high and rising prevalence of type 2 diabetes (T2D). While environmental (non-genetic) risk factors for the disease are well established, the role of genetic variations and gene-environment interactions remain understudied in this population. This study aimed to estimate the relative contributions of environmental and genetic risk factors to T2D in Malaysia and also to assess evidence for gene-environment interactions that may explain additional risk variation. This was a case-control study including 1604 Malays, 1654 Chinese and 1728 Indians from the Malaysian Cohort Project. The proportion of T2D risk variance explained by known genetic and environmental factors was assessed by fitting multivariable logistic regression models and evaluating McFadden's pseudo R 2 and the area under the receiver-operating characteristic curve (AUC). Models with and without the genetic risk score (GRS) were compared using the log likelihood ratio Chi-squared test and AUCs. Multiplicative interaction between genetic and environmental risk factors was assessed via logistic regression within and across ancestral groups. Interactions were assessed for the GRS and its 62 constituent variants. The models including environmental risk factors only had pseudo R 2 values of 16.5-28.3% and AUC of 0.75-0.83. Incorporating a genetic score aggregating 62 T2D-associated risk variants significantly increased the model fit (likelihood ratio P-value of 2.50 × 10 -4 -4.83 × 10 -12 ) and increased the pseudo R 2 by about 1-2% and AUC by 1-3%. None of the gene-environment interactions reached significance after multiple testing adjustment, either for the GRS or individual variants. For individual variants, 33 out of 310 tested associations showed nominal statistical significance with 0.001 variation in Malaysian population groups. If gene-environment interactions involving common genetic variants exist, they are likely of small effect, requiring substantially larger samples for

A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers

Directory of Open Access Journals (Sweden)

Guo Li

2014-01-01

Full Text Available This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment.

A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers

Science.gov (United States)

Lv, Fei; Guan, Xu

2014-01-01

This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment. PMID:24892104

Genetic parameters for racing records in trotters using linear and generalized linear models.

Science.gov (United States)

Suontama, M; van der Werf, J H J; Juga, J; Ojala, M

2012-09-01

Heritability and repeatability and genetic and phenotypic correlations were estimated for trotting race records with linear and generalized linear models using 510,519 records on 17,792 Finnhorses and 513,161 records on 25,536 Standardbred trotters. Heritability and repeatability were estimated for single racing time and earnings traits with linear models, and logarithmic scale was used for racing time and fourth-root scale for earnings to correct for nonnormality. Generalized linear models with a gamma distribution were applied for single racing time and with a multinomial distribution for single earnings traits. In addition, genetic parameters for annual earnings were estimated with linear models on the observed and fourth-root scales. Racing success traits of single placings, winnings, breaking stride, and disqualifications were analyzed using generalized linear models with a binomial distribution. Estimates of heritability were greatest for racing time, which ranged from 0.32 to 0.34. Estimates of heritability were low for single earnings with all distributions, ranging from 0.01 to 0.09. Annual earnings were closer to normal distribution than single earnings. Heritability estimates were moderate for annual earnings on the fourth-root scale, 0.19 for Finnhorses and 0.27 for Standardbred trotters. Heritability estimates for binomial racing success variables ranged from 0.04 to 0.12, being greatest for winnings and least for breaking stride. Genetic correlations among racing traits were high, whereas phenotypic correlations were mainly low to moderate, except correlations between racing time and earnings were high. On the basis of a moderate heritability and moderate to high repeatability for racing time and annual earnings, selection of horses for these traits is effective when based on a few repeated records. Because of high genetic correlations, direct selection for racing time and annual earnings would also result in good genetic response in racing success.

A novel variational Bayes multiple locus Z-statistic for genome-wide association studies with Bayesian model averaging

Science.gov (United States)

Logsdon, Benjamin A.; Carty, Cara L.; Reiner, Alexander P.; Dai, James Y.; Kooperberg, Charles

2012-01-01

Motivation: For many complex traits, including height, the majority of variants identified by genome-wide association studies (GWAS) have small effects, leaving a significant proportion of the heritable variation unexplained. Although many penalized multiple regression methodologies have been proposed to increase the power to detect associations for complex genetic architectures, they generally lack mechanisms for false-positive control and diagnostics for model over-fitting. Our methodology is the first penalized multiple regression approach that explicitly controls Type I error rates and provide model over-fitting diagnostics through a novel normally distributed statistic defined for every marker within the GWAS, based on results from a variational Bayes spike regression algorithm. Results: We compare the performance of our method to the lasso and single marker analysis on simulated data and demonstrate that our approach has superior performance in terms of power and Type I error control. In addition, using the Women's Health Initiative (WHI) SNP Health Association Resource (SHARe) GWAS of African-Americans, we show that our method has power to detect additional novel associations with body height. These findings replicate by reaching a stringent cutoff of marginal association in a larger cohort. Availability: An R-package, including an implementation of our variational Bayes spike regression (vBsr) algorithm, is available at http://kooperberg.fhcrc.org/soft.html. Contact: blogsdon@fhcrc.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22563072

Genetic models of absence epilepsy: New concepts and insights

NARCIS (Netherlands)

Luijtelaar, E.L.J.M. van; Stein, J.

2017-01-01

The discovery, development and use of genetic rodent models of absence epilepsy have led to a new theory about the origin of absence seizures, which has gained impact within the international epilepsy community. A focal zone has been identified in the perioral region of the somatosensory cortex in

Revised models and genetic parameter estimates for production and ...

African Journals Online (AJOL)

Genetic parameters for production and reproduction traits in the Elsenburg Dormer sheep stud were estimated using records of 11743 lambs born between 1943 and 2002. An animal model with direct and maternal additive, maternal permanent and temporary environmental effects was fitted for traits considered traits of the ...

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

Science.gov (United States)

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

Genetics in endocrinology: genetic variation in deiodinases: a systematic review of potential clinical effects in humans

NARCIS (Netherlands)

Verloop, H.; Dekkers, O.M.; Peeters, R.P.; Schoones, J.W.; Smit, J.W.

2014-01-01

Iodothyronine deiodinases represent a family of selenoproteins involved in peripheral and local homeostasis of thyroid hormone action. Deiodinases are expressed in multiple organs and thyroid hormone affects numerous biological systems, thus genetic variation in deiodinases may affect multiple

Multiple Time Series Ising Model for Financial Market Simulations

International Nuclear Information System (INIS)

Takaishi, Tetsuya

2015-01-01

In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated

Tailoring weld geometry during keyhole mode laser welding using a genetic algorithm and a heat transfer model

International Nuclear Information System (INIS)

Rai, R; DebRoy, T

2006-01-01

Tailoring of weld attributes based on scientific principles remains an important goal in welding research. The current generation of unidirectional laser keyhole models cannot determine sets of welding variables that can lead to a particular weld attribute such as specific weld geometry. Here we show how a computational heat transfer model of keyhole mode laser welding can be restructured for systematic tailoring of weld attributes based on scientific principles. Furthermore, the model presented here can calculate multiple sets of laser welding variables, i.e. laser power, welding speed and beam defocus, with each set leading to the same weld pool geometry. Many sets of welding variables were obtained via a global search using a real number-based genetic algorithm, which was combined with a numerical heat transfer model of keyhole laser welding. The reliability of the numerical heat transfer calculations was significantly improved by optimizing values of the uncertain input parameters from a limited volume of experimental data. The computational procedure was applied to the keyhole mode laser welding of the 5182 Al-Mg alloy to calculate various sets of welding variables to achieve a specified weld geometry. The calculated welding parameter sets showed wide variations of the values of welding parameters, but each set resulted in a similar fusion zone geometry. The effectiveness of the computational procedure was examined by comparing the computed weld geometry for each set of welding parameters with the corresponding experimental geometry. The results provide hope that systematic tailoring of weld attributes via multiple pathways, each representing alternative welding parameter sets, is attainable based on scientific principles

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier.

Science.gov (United States)

Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J; Kwapinski, Witold

2015-03-01

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well. Copyright © 2014 Elsevier Ltd. All rights reserved.

Nonhomologous recombination between defective poliovirus and coxsackievirus genomes suggests a new model of genetic plasticity for picornaviruses.

Science.gov (United States)

Holmblat, Barbara; Jégouic, Sophie; Muslin, Claire; Blondel, Bruno; Joffret, Marie-Line; Delpeyroux, Francis

2014-08-05

Most of the circulating vaccine-derived polioviruses (cVDPVs) implicated in poliomyelitis outbreaks in Madagascar have been shown to be recombinants between the type 2 poliovirus (PV) strain of the oral polio vaccine (Sabin 2) and another species C human enterovirus (HEV-C), such as type 17 coxsackie A virus (CA17) in particular. We studied intertypic genetic exchanges between PV and non-PV HEV-C by developing a recombination model, making it possible to rescue defective type 2 PV RNA genomes with a short deletion at the 3' end by the cotransfection of cells with defective or infectious CA17 RNAs. We isolated over 200 different PV/CA17 recombinants, using murine cells expressing the human PV receptor (PVR) and selecting viruses with PV capsids. We found some homologous (H) recombinants and, mostly, nonhomologous (NH) recombinants presenting duplications of parental sequences preferentially located in the regions encoding proteins 2A, 2B, and 3A. Short duplications appeared to be stable, whereas longer duplications were excised during passaging in cultured cells or after multiplication in PVR-transgenic mice, generating H recombinants with diverse sites of recombination. This suggests that NH recombination events may be a transient, intermediate step in the generation and selection of the fittest H recombinants. In addition to the classical copy-choice mechanism of recombination thought to generate mostly H recombinants, there may also be a modular mechanism of recombination, involving NH recombinant precursors, shaping the genomes of recombinant enteroviruses and other picornaviruses. Importance: The multiplication of circulating vaccine-derived polioviruses (cVDPVs) in poorly immunized human populations can render these viruses pathogenic, causing poliomyelitis outbreaks. Most cVDPVs are intertypic recombinants between a poliovirus (PV) strain and another human enterovirus, such as type 17 coxsackie A viruses (CA17). For further studies of the genetic exchanges

Invasion genetics of a freshwater mussel (Dreissena rostriformis bugensis) in eastern Europe: high gene flow and multiple introductions.

Science.gov (United States)

Therriault, T W; Orlova, M I; Docker, M F; Macisaac, H J; Heath, D D

2005-07-01

In recent years, the quagga mussel, Dreissena rostriformis bugensis, native to the Dnieper and Bug Limans of the northern Black Sea, has been dispersed by human activities across the basin, throughout much of the Volga River system, and to the Laurentian Great Lakes. We used six published microsatellite markers to survey populations throughout its native and introduced range to identify relationships among potential source populations and introduced ones. Mussels from 12 sites in Eurasia, including the central Caspian Sea and one in North America (Lake Erie), were sampled. Field surveys in the Volga River basin suggested that the species first colonized the middle reach of the river near Kubyshev Reservoir, and thereafter spread both upstream and downstream. Evidence of considerable gene flow among populations was observed and genetic diversity was consistent with a larger, metapopulation that has not experienced bottlenecks or founder effects. We propose that high gene flow, possibly due to multiple invasions, has facilitated establishment of quagga mussel populations in the Volga River system. The Caspian Sea population (D. rostriformis rostriformis (=distincta)) was genetically more distinct than other populations, a finding that may be related to habitat differences. The geographical pattern of genetic divergence is not characteristic of isolation-by-distance but, rather, of long-distance dispersal, most likely mediated by commercial ships' ballast water transfer.

Genetic algorithms and Monte Carlo simulation for optimal plant design

International Nuclear Information System (INIS)

Cantoni, M.; Marseguerra, M.; Zio, E.

2000-01-01

We present an approach to the optimal plant design (choice of system layout and components) under conflicting safety and economic constraints, based upon the coupling of a Monte Carlo evaluation of plant operation with a Genetic Algorithms-maximization procedure. The Monte Carlo simulation model provides a flexible tool, which enables one to describe relevant aspects of plant design and operation, such as standby modes and deteriorating repairs, not easily captured by analytical models. The effects of deteriorating repairs are described by means of a modified Brown-Proschan model of imperfect repair which accounts for the possibility of an increased proneness to failure of a component after a repair. The transitions of a component from standby to active, and vice versa, are simulated using a multiplicative correlation model. The genetic algorithms procedure is demanded to optimize a profit function which accounts for the plant safety and economic performance and which is evaluated, for each possible design, by the above Monte Carlo simulation. In order to avoid an overwhelming use of computer time, for each potential solution proposed by the genetic algorithm, we perform only few hundreds Monte Carlo histories and, then, exploit the fact that during the genetic algorithm population evolution, the fit chromosomes appear repeatedly many times, so that the results for the solutions of interest (i.e. the best ones) attain statistical significance

Modelling the genetic risk in age-related macular degeneration.

Directory of Open Access Journals (Sweden)

Felix Grassmann

Full Text Available Late-stage age-related macular degeneration (AMD is a common sight-threatening disease of the central retina affecting approximately 1 in 30 Caucasians. Besides age and smoking, genetic variants from several gene loci have reproducibly been associated with this condition and likely explain a large proportion of disease. Here, we developed a genetic risk score (GRS for AMD based on 13 risk variants from eight gene loci. The model exhibited good discriminative accuracy, area-under-curve (AUC of the receiver-operating characteristic of 0.820, which was confirmed in a cross-validation approach. Noteworthy, younger AMD patients aged below 75 had a significantly higher mean GRS (1.87, 95% CI: 1.69-2.05 than patients aged 75 and above (1.45, 95% CI: 1.36-1.54. Based on five equally sized GRS intervals, we present a risk classification with a relative AMD risk of 64.0 (95% CI: 14.11-1131.96 for individuals in the highest category (GRS 3.44-5.18, 0.5% of the general population compared to subjects with the most common genetic background (GRS -0.05-1.70, 40.2% of general population. The highest GRS category identifies AMD patients with a sensitivity of 7.9% and a specificity of 99.9% when compared to the four lower categories. Modeling a general population around 85 years of age, 87.4% of individuals in the highest GRS category would be expected to develop AMD by that age. In contrast, only 2.2% of individuals in the two lowest GRS categories which represent almost 50% of the general population are expected to manifest AMD. Our findings underscore the large proportion of AMD cases explained by genetics particularly for younger AMD patients. The five-category risk classification could be useful for therapeutic stratification or for diagnostic testing purposes once preventive treatment is available.

Multiple models for Rosaceae genomics.

Science.gov (United States)

Shulaev, Vladimir; Korban, Schuyler S; Sosinski, Bryon; Abbott, Albert G; Aldwinckle, Herb S; Folta, Kevin M; Iezzoni, Amy; Main, Dorrie; Arús, Pere; Dandekar, Abhaya M; Lewers, Kim; Brown, Susan K; Davis, Thomas M; Gardiner, Susan E; Potter, Daniel; Veilleux, Richard E

2008-07-01

The plant family Rosaceae consists of over 100 genera and 3,000 species that include many important fruit, nut, ornamental, and wood crops. Members of this family provide high-value nutritional foods and contribute desirable aesthetic and industrial products. Most rosaceous crops have been enhanced by human intervention through sexual hybridization, asexual propagation, and genetic improvement since ancient times, 4,000 to 5,000 B.C. Modern breeding programs have contributed to the selection and release of numerous cultivars having significant economic impact on the U.S. and world markets. In recent years, the Rosaceae community, both in the United States and internationally, has benefited from newfound organization and collaboration that have hastened progress in developing genetic and genomic resources for representative crops such as apple (Malus spp.), peach (Prunus spp.), and strawberry (Fragaria spp.). These resources, including expressed sequence tags, bacterial artificial chromosome libraries, physical and genetic maps, and molecular markers, combined with genetic transformation protocols and bioinformatics tools, have rendered various rosaceous crops highly amenable to comparative and functional genomics studies. This report serves as a synopsis of the resources and initiatives of the Rosaceae community, recent developments in Rosaceae genomics, and plans to apply newly accumulated knowledge and resources toward breeding and crop improvement.

Alternative approaches to reliability modeling of a multiple engineered barrier system

International Nuclear Information System (INIS)

Ananda, M.M.A.; Singh, A.K.

1994-01-01

The lifetime of the engineered barrier system used for containment of high-level radioactive waste will significantly impact the total performance of a geological repository facility. Currently two types of designs are under consideration for an engineered barrier system, single engineered barrier system and multiple engineered barrier system. Multiple engineered barrier system consists of several metal barriers and the waste form (cladding). Some recent work show that a significant improvement of performance can be achieved by utilizing multiple engineered barrier systems. Considering sequential failures for each barrier, we model the reliability of the multiple engineered barrier system. Weibull and exponential lifetime distributions are used through out the analysis. Furthermore, the number of failed engineered barrier systems in a repository at a given time is modeled using a poisson approximation

Race, Ethnicity and Ancestry in Unrelated Transplant Matching for the National Marrow Donor Program: A Comparison of Multiple Forms of Self-Identification with Genetics.

Directory of Open Access Journals (Sweden)

Jill A Hollenbach

Full Text Available We conducted a nationwide study comparing self-identification to genetic ancestry classifications in a large cohort (n = 1752 from the National Marrow Donor Program. We sought to determine how various measures of self-identification intersect with genetic ancestry, with the aim of improving matching algorithms for unrelated bone marrow transplant. Multiple dimensions of self-identification, including race/ethnicity and geographic ancestry were compared to classifications based on ancestry informative markers (AIMs, and the human leukocyte antigen (HLA genes, which are required for transplant matching. Nearly 20% of responses were inconsistent between reporting race/ethnicity versus geographic ancestry. Despite strong concordance between AIMs and HLA, no measure of self-identification shows complete correspondence with genetic ancestry. In certain cases geographic ancestry reporting matches genetic ancestry not reflected in race/ethnicity identification, but in other cases geographic ancestries show little correspondence to genetic measures, with important differences by gender. However, when respondents assign ancestry to grandparents, we observe sub-groups of individuals with well- defined genetic ancestries, including important differences in HLA frequencies, with implications for transplant matching. While we advocate for tailored questioning to improve accuracy of ancestry ascertainment, collection of donor grandparents' information will improve the chances of finding matches for many patients, particularly for mixed-ancestry individuals.

Race, Ethnicity and Ancestry in Unrelated Transplant Matching for the National Marrow Donor Program: A Comparison of Multiple Forms of Self-Identification with Genetics

Science.gov (United States)

Hollenbach, Jill A.; Saperstein, Aliya; Albrecht, Mark; Vierra-Green, Cynthia; Parham, Peter; Norman, Paul J.; Maiers, Martin

2015-01-01

We conducted a nationwide study comparing self-identification to genetic ancestry classifications in a large cohort (n = 1752) from the National Marrow Donor Program. We sought to determine how various measures of self-identification intersect with genetic ancestry, with the aim of improving matching algorithms for unrelated bone marrow transplant. Multiple dimensions of self-identification, including race/ethnicity and geographic ancestry were compared to classifications based on ancestry informative markers (AIMs), and the human leukocyte antigen (HLA) genes, which are required for transplant matching. Nearly 20% of responses were inconsistent between reporting race/ethnicity versus geographic ancestry. Despite strong concordance between AIMs and HLA, no measure of self-identification shows complete correspondence with genetic ancestry. In certain cases geographic ancestry reporting matches genetic ancestry not reflected in race/ethnicity identification, but in other cases geographic ancestries show little correspondence to genetic measures, with important differences by gender. However, when respondents assign ancestry to grandparents, we observe sub-groups of individuals with well- defined genetic ancestries, including important differences in HLA frequencies, with implications for transplant matching. While we advocate for tailored questioning to improve accuracy of ancestry ascertainment, collection of donor grandparents’ information will improve the chances of finding matches for many patients, particularly for mixed-ancestry individuals. PMID:26287376

Multiple surveys employing a new sample-processing protocol reveal the genetic diversity of placozoans in Japan.

Science.gov (United States)

Miyazawa, Hideyuki; Nakano, Hiroaki

2018-03-01

Placozoans, flat free-living marine invertebrates, possess an extremely simple bauplan lacking neurons and muscle cells and represent one of the earliest-branching metazoan phyla. They are widely distributed from temperate to tropical oceans. Based on mitochondrial 16S rRNA sequences, 19 haplotypes forming seven distinct clades have been reported in placozoans to date. In Japan, placozoans have been found at nine locations, but 16S genotyping has been performed at only two of these locations. Here, we propose a new processing protocol, "ethanol-treated substrate sampling," for collecting placozoans from natural environments. We also report the collection of placozoans from three new locations, the islands of Shikine-jima, Chichi-jima, and Haha-jima, and we present the distribution of the 16S haplotypes of placozoans in Japan. Multiple surveys conducted at multiple locations yielded five haplotypes that were not reported previously, revealing high genetic diversity in Japan, especially at Shimoda and Shikine-jima Island. The observed geographic distribution patterns were different among haplotypes; some were widely distributed, while others were sampled only from a single location. However, samplings conducted on different dates at the same sites yielded different haplotypes, suggesting that placozoans of a given haplotype do not inhabit the same site constantly throughout the year. Continued sampling efforts conducted during all seasons at multiple locations worldwide and the development of molecular markers within the haplotypes are needed to reveal the geographic distribution pattern and dispersal history of placozoans in greater detail.

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

Science.gov (United States)

Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

2015-05-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.