Gene Environment Interaction And Extension To Empirical Hierarchical Bayes Models In Genome Wide Association Studies
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Gene Environment Interaction And Extension To Empirical Hierarchical Bayes Models In Genome Wide Association Studies
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Author :
language : en
Publisher:
Release Date : 2014
Gene Environment Interaction And Extension To Empirical Hierarchical Bayes Models In Genome Wide Association Studies written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
There are over 100,000 human diseases of which only around 10,000 are known to be monogenic, resulting from modification in a single gene. Many multifactorial diseases, such as cancer and lung cancer in particular, are outcomes of the interplay between genetic and environmental factors. It is well known that smoking is the major environmental risk factor in lung cancer. In recent years, great progress in genotyping technology and cost control has enabled researchers to perform large-scale association studies, involving thousands of individuals genotyped on millions of markers. To date, geno...
Cancer Research
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Author :
language : en
Publisher:
Release Date : 2008-12
Cancer Research written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12 with Cancer categories.
The Empirical Hierarchical Bayes Approach For Pathway Integration And Gene Environment Interactions In Genome Wide Association Studies
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Author : Melanie Sohns
language : en
Publisher:
Release Date : 2012
The Empirical Hierarchical Bayes Approach For Pathway Integration And Gene Environment Interactions In Genome Wide Association Studies written by Melanie Sohns and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Complex diseases such as cancer result from a complicated interplay of multiple genetic and environmental factors. To unveil their genetic component, the simple analysis of single-nucleotide polymorphisms (SNP) as done in genome-wide association studies (GWAS) is not sufficient. Complementary approaches considering the complexity of diseases, such as the incorporation of biological pathway information or detection of gene-environment interaction, are necessary. In this thesis we focus on an empirical hierarchical Bayes model proposed for the integration of external information into genome-w ...
Undergraduate And Graduate Courses And Programs
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Author : Iowa State University
language : en
Publisher:
Release Date : 2009
Undergraduate And Graduate Courses And Programs written by Iowa State University and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Universities and colleges categories.
Flexible Hierarchical Bayesian Modeling Extensions To Improve Whole Genome Prediction And Genome Wide Association Analyses
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Author : Chunyu Chen
language : en
Publisher:
Release Date : 2017
Flexible Hierarchical Bayesian Modeling Extensions To Improve Whole Genome Prediction And Genome Wide Association Analyses written by Chunyu Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Electronic dissertations categories.
Assessing Gene Environment Interactions In Genome Wide Association Studies Statistical Approaches
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Author : Philip C. Cooley
language : en
Publisher: RTI Press
Release Date : 2014-05-14
Assessing Gene Environment Interactions In Genome Wide Association Studies Statistical Approaches written by Philip C. Cooley and has been published by RTI Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-14 with Science categories.
In this report, we address a scenario that uses synthetic genotype case-control data that is influenced by environmental factors in a genome-wide association study (GWAS) context. The precise way the environmental influence contributes to a given phenotype is typically unknown. Therefore, our study evaluates how to approach a GWAS that may have an environmental component. Specifically, we assess different statistical models in the context of a GWAS to make association predictions when the form of the environmental influence is questionable. We used a simulation approach to generate synthetic data corresponding to a variety of possible environmental-genetic models, including a “main effects only” model as well as a “main effects with interactions” model. Our method takes into account the strength of the association between phenotype and both genotype and environmental factors, but we focus on low-risk genetic and environmental risks that necessitate using large sample sizes (N = 10,000 and 200,000) to predict associations with high levels of confidence. We also simulated different Mendelian gene models, and we analyzed how the collection of factors influences statistical power in the context of a GWAS. Using simulated data provides a “truth set” of known outcomes such that the association-affecting factors can be unambiguously determined. We also test different statistical methods to determine their performance properties. Our results suggest that the chances of predicting an association in a GWAS is reduced if an environmental effect is present and the statistical model does not adjust for that effect. This is especially true if the environmental effect and genetic marker do not have an interaction effect. The functional form of the statistical model also matters. The more accurately the form of the environmental influence is portrayed by the statistical model, the more accurate the prediction will be. Finally, even with very large samples sizes, association predictions involving recessive markers with low risk can be poor
Assessing Gene Environment Interactions In Genome Wide Association Studies
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Author : Philip Chester Cooley
language : en
Publisher:
Release Date : 2014
Assessing Gene Environment Interactions In Genome Wide Association Studies written by Philip Chester Cooley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
In this report, we address a scenario that uses synthetic genotype case-control data that is influenced by environmental factors in a genome-wide association study (GWAS) context. The precise way the environmental influence contributes to a given phenotype is typically unknown. Therefore, our study evaluates how to approach a GWAS that may have an environmental component. Specifically, we assess different statistical models in the context of a GWAS to make association predictions when the form of the environmental influence is questionable. We used a simulation approach to generate synthetic data corresponding to a variety of possible environmental-genetic models, including a "main effects only" model as well as a "main effects with interactions" model. Our method takes into account the strength of the association between phenotype and both genotype and environmental factors, but we focus on low-risk genetic and environmental risks that necessitate using large sample sizes (N = 10,000 and 200,000) to predict associations with high levels of confidence. We also simulated different Mendelian gene models, and we analyzed how the collection of factors influences statistical power in the context of a GWAS. Using simulated data provides a "truth set" of known outcomes such that the association-affecting factors can be unambiguously determined. We also test different statistical methods to determine their performance properties. Our results suggest that the chances of predicting an association in a GWAS is reduced if an environmental effect is present and the statistical model does not adjust for that effect. This is especially true if the environmental effect and genetic marker do not have an interaction effect. The functional form of the statistical model also matters. The more accurately the form of the environmental influence is portrayed by the statistical model, the more accurate the prediction will be. Finally, even with very large samples sizes, association predictions involving recessive markers with low risk can be poor.
Between The Lines Of Genetic Code
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Author : Bo Ding
language : en
Publisher: Elsevier Inc. Chapters
Release Date : 2013-09-28
Between The Lines Of Genetic Code written by Bo Ding and has been published by Elsevier Inc. Chapters this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-28 with Science categories.
Genome-wide interaction studies is an extremely challenging problem in statistics, in which conventional methods are often inadequate in terms of both power and computational efficiency. An exhaustive search for genome-wide gene–gene interactions becomes feasible with modern cluster computing run on graphics processing units. However, the large number of tests accompanying the search raises a serious multiple testing problem. A way to overcome these limits is to apply a filtering step prior to the combinatorial method and to analyze only interesting single nucleotide polymorphisms selected based on a priori (defined by statistical evidence, genetic impact, or biological plausibility). The advantage of the filter approach is speed, and the disadvantage is that attributes with poor quality scores are disregarded. Genome-wide gene–environment interaction is less problematic computational demand compared with pairwise genome-wide gene–gene interaction. Accounting for gene–gene and gene–environment interactions is important for future strategies of diagnosis, prognosis, and management of human diseases and will bring new data regarding pathogenetic mechanisms for human complex diseases.
Sparse Model Building From Genome Wide Variation With Graphical Models
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Author : Benjamin Alexander Logsdon
language : en
Publisher:
Release Date : 2011
Sparse Model Building From Genome Wide Variation With Graphical Models written by Benjamin Alexander Logsdon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
High throughput sequencing and expression characterization have lead to an explosion of phenotypic and genotypic molecular data underlying both experimental studies and outbred populations. We develop a novel class of algorithms to reconstruct sparse models among these molecular phenotypes (e.g. expression products) and genotypes (e.g. single nucleotide polymorphisms), via both a Bayesian hierarchical model, when the sample size is much smaller than the model dimension (i.e. p n) and the well characterized adaptive lasso algo- rithm. Specifically, we propose novel approaches to the problems of increasing power to detect additional loci in genome-wide association studies using our variational algorithm, efficiently learning directed cyclic graphs from expression and genotype data using the adaptive lasso, and constructing genomewide undirected graphs among genotype, expression and downstream phenotype data using an extension of the variational feature selection algorithm. The Bayesian hierarchical model is derived for a parametric multiple regression model with a mixture prior of a point mass and normal distribution for each regression coefficient, and appropriate priors for the set of hyperparameters. When combined with a probabilistic consistency bound on the model dimension, this approach leads to very sparse solutions without the need for cross validation. We use a variational Bayes approximate inference approach in our algorithm, where we impose a complete factorization across all parameters for the approximate posterior distribution, and then minimize the KullbackLeibler divergence between the approximate and true posterior distributions. Since the prior distribution is non-convex, we restart the algorithm many times to find multiple posterior modes, and combine information across all discovered modes in an approximate Bayesian model averaging framework, to reduce the variance of the posterior probability estimates. We perform analysis of three major publicly available data-sets: the HapMap 2 genotype and expression data collected on immortalized lymphoblastoid cell lines, the genome-wide gene expression and genetic marker data collected for a yeast intercross, and genomewide gene expression, genetic marker, and downstream phenotypes related to weight in a mouse F2 intercross. Based on both simulations and data analysis we show that our algorithms can outperform other state of the art model selection procedures when including thousands to hundreds of thousands of genotypes and expression traits, in terms of aggressively controlling false discovery rate, and generating rich simultaneous statistical models.
Statistical Approaches To Gene X Environment Interactions For Complex Phenotypes
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Author : Michael Windle
language : en
Publisher: MIT Press
Release Date : 2016-07-08
Statistical Approaches To Gene X Environment Interactions For Complex Phenotypes written by Michael Windle and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-08 with Science categories.
Diverse methodological and statistical approaches for investigating the role of gene-environment interactions in a range of complex diseases and traits. Findings from the Human Genome Project and from Genome-Wide Association (GWA) studies indicate that many diseases and traits manifest a more complex genomic pattern than previously assumed. These findings, and advances in high-throughput sequencing, suggest that there are many sources of influence—genetic, epigenetic, and environmental. This volume investigates the role of the interactions of genes and environment (G × E) in diseases and traits (referred to by the contributors as complex phenotypes) including depression, diabetes, obesity, and substance use. The contributors first present different statistical approaches or strategies to address G × E and G × G interactions with high-throughput sequenced data, including two-stage procedures to identify G × E and G × G interactions, marker-set approaches to assessing interactions at the gene level, and the use of a partial-least square (PLS) approach. The contributors then turn to specific complex phenotypes, research designs, or combined methods that may advance the study of G × E interactions, considering such topics as randomized clinical trials in obesity research, longitudinal research designs and statistical models, and the development of polygenic scores to investigate G × E interactions. Contributors Fatima Umber Ahmed, Yin-Hsiu Chen, James Y. Dai, Caroline Y. Doyle, Zihuai He, Li Hsu, Shuo Jiao, Erin Loraine Kinnally, Yi-An Ko, Charles Kooperberg, Seunggeun Lee, Arnab Maity, Jeanne M. McCaffery, Bhramar Mukherjee, Sung Kyun Park, Duncan C. Thomas, Alexandre Todorov, Jung-Ying Tzeng, Tao Wang, Michael Windle, Min Zhang