Statistical Approaches To Gene X Environment Interactions For Complex Phenotypes
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Statistical Approaches To Gene Environment Interactions For Complex Phenotypes
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Author : Michael T. Windle
language : en
Publisher:
Release Date : 2016
Statistical Approaches To Gene Environment Interactions For Complex Phenotypes written by Michael T. Windle and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with SCIENCE categories.
Diverse methodological and statistical approaches for investigating the role of gene-environment interactions in a range of complex diseases and traits.
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.
Gene Environment Interaction Analysis
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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 book is the first to deal with the theme of GE interaction analysis. It compiles and details cutting-edge research in bioinformatics
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
Modelling Of Genotype By Environment Interaction And Prediction Of Complex Traits Across Multiple Environments As A Synthesis Of Crop Growth Modelling Genetics And Statistics
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Author :
language : en
Publisher:
Release Date : 2017
Modelling Of Genotype By Environment Interaction And Prediction Of Complex Traits Across Multiple Environments As A Synthesis Of Crop Growth Modelling Genetics And Statistics written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
High Dimensional Statistical Methods For Gene Environment Interactions
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Author : Cen Wu
language : en
Publisher:
Release Date : 2013
High Dimensional Statistical Methods For Gene Environment Interactions written by Cen Wu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Electronic dissertations categories.
Statistical Methods To Understand The Genetic Architecture Of Complex Traits
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Author : Farhad Hormozdiari
language : en
Publisher:
Release Date : 2016
Statistical Methods To Understand The Genetic Architecture Of Complex Traits written by Farhad Hormozdiari and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Genome-wide association studies (GWAS) have successfully identified thousands of risk loci for complex traits. Identifying these variants requires annotating all possible variations between any two individuals, followed by detecting the variants that affect the disease status or traits. High-throughput sequencing (HTS) advancements have made it possible to sequence cohort of individuals in an efficient manner both in term of cost and time. However, HTS technologies have raised many computational challenges. I first propose an efficient method to recover dense genotype data by leveraging low sequencing and imputation techniques. Then, I introduce a novel statistical method (CNVeM) to identify Copy-number variations (CNVs) loci using HTS data. CNVeM was the first method that incorporates multi-mapped reads, which are discarded by all existing methods. Unfortunately, among all GWAS variants only a handful of them have been successfully validated to be biologically causal variants. Identifying causal variants can aid us to understand the biological mechanism of traits or diseases. However, detecting the causal variants is challenging due to linkage disequilibrium (LD) and the fact that some loci contain more than one causal variant. In my thesis, I will introduce CAVIAR (CAusal Variants Identification in Associated Regions) that is a new statistical method for fine mapping. The main advantage of CAVIAR is that we predict a set of variants for each locus that will contain all of the true causal variants with a high confidence level (e.g. 95%) even when the locus contains multiple causal variants. Next, I aim to understand the underlying mechanism of GWAS risk loci. A standard approach to uncover the mechanism of GWAS risk loci is to integrate results of GWAS and expression quantitative trait loci (eQTL) studies; we attempt to identify whether or not a significant GWAS variant also influences expression at a nearby gene in a specific tissue. However, detecting the same variant being causal in both GWAS and eQTL is challenging due to complex LD structure. I will introduce eCAVIAR (eQTL and GWAS CAusal Variants Identification in Associated Regions), a statistical method to compute the probability that the same variant is responsible for both the GWAS and eQTL signal, while accounting for complex LD structure. We integrate Glucose and Insulin-related traits meta-analysis with GTEx to detect the target genes and the most relevant tissues. Interestingly, we observe that most loci do not colocalize between GWAS and eQTL. Lastly, I propose an approach called phenotype imputation that allows one to perform GWAS on a phenotype that is difficult to collect. In our approach, we leverage the correlation structure between multiple phenotypes to impute the uncollected phenotype. I demonstrate that we can analytically calculate the statistical power of association test using imputed phenotype, which can be helpful for study design purposes
Biosocial Surveys
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Author : National Research Council
language : en
Publisher: National Academies Press
Release Date : 2008-01-06
Biosocial Surveys written by National Research Council and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-06 with Social Science categories.
Biosocial Surveys analyzes the latest research on the increasing number of multipurpose household surveys that collect biological data along with the more familiar interviewerâ€"respondent information. This book serves as a follow-up to the 2003 volume, Cells and Surveys: Should Biological Measures Be Included in Social Science Research? and asks these questions: What have the social sciences, especially demography, learned from those efforts and the greater interdisciplinary communication that has resulted from them? Which biological or genetic information has proven most useful to researchers? How can better models be developed to help integrate biological and social science information in ways that can broaden scientific understanding? This volume contains a collection of 17 papers by distinguished experts in demography, biology, economics, epidemiology, and survey methodology. It is an invaluable sourcebook for social and behavioral science researchers who are working with biosocial data.
Epigenomics In Health And Disease
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Author : Mario Fraga
language : en
Publisher: Academic Press
Release Date : 2015-10-12
Epigenomics In Health And Disease written by Mario Fraga and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-12 with Science categories.
Epigenomics in Health and Disease discusses the next generation sequencing technologies shaping our current knowledge with regards to the role of epigenetics in normal development, aging, and disease. It includes the consequences for diagnostics, prognostics, and disease-based therapies made possible by the study of the complete set of epigenetic modifications to the genetic material of human cells. With coverage pertinent to both basic biology and translational research, the book will be of particular interest for medical and bioscience researchers and students seeking current translational knowledge in epigenesis and epigenomics. Coverage includes the latest findings on epigenome-wide research in disease-based profiling, epidemiological implications, epigenome-wide epigenetic studies, the cancer epigenome, and other pervasive disease categories. Presents critical reviews that provide the means for reviewing and analyzing the epigenome as a whole, also discussing its translational potential Combines basic epigenomic knowledge with methodological and biostatistical topics related to technology and data analysis Includes coverage of relatively new topics, including DNA methylation dynamics during development and differentiation, genome-wide histone post-translational modifications during development and differentiation, and genome-wide DNA methylation changes during aging