The Empirical Hierarchical Bayes Approach For Pathway Integration And Gene Environment Interactions In Genome Wide Association Studies
DOWNLOAD
Download The Empirical Hierarchical Bayes Approach For Pathway Integration And Gene Environment Interactions In Genome Wide Association Studies PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get The Empirical Hierarchical Bayes Approach For Pathway Integration And Gene Environment Interactions In Genome Wide Association Studies book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
The Empirical Hierarchical Bayes Approach For Pathway Integration And Gene Environment Interactions In Genome Wide Association Studies
DOWNLOAD
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 ...
Cancer Research
DOWNLOAD
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.
Phenotypes And Endophenotypes
DOWNLOAD
Author :
language : en
Publisher:
Release Date : 2009
Phenotypes And Endophenotypes written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Nicotine categories.
Gene Environment Interaction And Extension To Empirical Hierarchical Bayes Models In Genome Wide Association Studies
DOWNLOAD
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...
Assessing Gene Environment Interactions In Genome Wide Association Studies Statistical Approaches
DOWNLOAD
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
A Bayesian Hierarchical Framework For Pathway Analysis In Genome Wide Association Studies
DOWNLOAD
Author : Lei Zhang
language : en
Publisher:
Release Date : 2018
A Bayesian Hierarchical Framework For Pathway Analysis In Genome Wide Association Studies written by Lei Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Bayesian statistical decision theory categories.
The genome-wide association studies (GWAS) aim to identify genetic variants, typically single nucleotide polymorphisms (SNPs), associated with a disease/trait. A commonly used analytic strategy in GWAS is to test for association with one single SNP at a time. However, such a strategy lacks power to detect associations that are caused by joint effects of multiple variants, each with a modest effect of its own. Pathway analysis jointly tests the combined effects of all SNPs in all genes belonging to a molecular pathway. This analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway. Moreover, due to biological functionality of pathways, a significant result lends itself more easily to interpretation. In this dissertation, we develop a Bayesian hierarchical model that fully models the natural three-level hierarchy inherent in pathway structure, namely SNP—gene—pathway, unlike most other methods that use ad hoc ways of combining such information. We model the effects at each level conditional on the effects of the levels preceding them within the generalized linear model framework. This joint modeling allows detection of not only the associated pathways but also testing for association with genes and SNPs within significant pathways and significant genes in a hierarchical manner, which can be useful for follow-up studies. To deal with the high dimensionality of such a unified model, we regularize the regression coefficients through an appropriate choice of priors. We fit the model using a combination of Iteratively Weighted Least Squares and Expectation-Maximization algorithms to estimate the posterior modes and their standard errors. The inference is carried out in a hierarchical manner from pathways to genes to SNPs. Hierarchical false discovery rate (FDR) is used for multiplicity adjustment of the entire inference procedure. We also explore the utility of effective number of parameters proposed in the Bayesian literature in our context of multiplicity adjustment using the hierarchical FDR. To study the proposed approach, we conduct simulations with samples generated under realistic linkage disequilibrium patterns obtained from the HapMap project. We find that our method has higher power than some standard approaches in several settings for identifying pathways that have multiple modest-sized variants. Moreover, it can also pinpoint associated genes once a pathway is implicated, a feature unavailable in other methods. We also find that the use of the effective number of parameters can boost the power to detect associated genes and helps in distinguishing them from the null genes. We apply the proposed method to two GWAS datasets on breast and renal cancer.
Assessing Gene Environment Interactions In Genome Wide Association Studies
DOWNLOAD
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.
Statistical Approaches To Gene X Environment Interactions For Complex Phenotypes
DOWNLOAD
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
Gene Environment Interaction Analysis
DOWNLOAD
Author : Sumiko Anno
language : en
Publisher: CRC Press
Release Date : 2016-03-30
Gene Environment Interaction Analysis written by Sumiko Anno and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-30 with Mathematics categories.
Gene-environment (GE) interaction analysis is a statistical method for clarifying GE interactions applicable to a phenotype or a disease that is the result of interactions between genes and the environment. This is the first book dealing with the theme of gene-environment (G ) interaction analysis. The book compiles and details cutting-edge research in bioinformatics and computational biology. Edited by Sumiko Anno.
Between The Lines Of Genetic Code
DOWNLOAD
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.