A key challenge for genetic analysis today is to account for the bulk of the phenotypic variance in complex traits that is attributable to genetic factors, but remains unexplained after multiple well-powered GWAS (Goldstein 2009;Hirschhorn 2009;Kraft and Hunter 2009). Many believe that systematically testing datasets for joint gene-environment effects (GxE) and gene-gene (GxG) effects will be essential in order to understand the genetics of complex phenotypes. However, the new era of high throughput, high-density genomic data (genotypes and sequencing) has given rise to serious computational and statistical challenges for the analysis of joint effects. The goal of this project is to develop effective strategies for the statistical analysis of joint-gene environment effects.
The specific aims are to (1) identify strengths and weaknesses of multiple analytic approaches to joint GxE effects, and (2) develop new methods for coordinated meta-analysis. To accomplish Aim 1, we will systematically examine and compare traditional (e.g. regression based) and modern (e.g. partitioning, machine learning, information theoretic) analysis methods for case-control and quantitative phenotypes. The primary product will be specific guidance as to the conditions under which each method performs well. To accomplish Aim 2, we will extend recent joint-effects methods to a meta-analytic framework and develop improvements to currently used methods. For both aims, we will also analyze real data related to smoking, thereby improving our understanding of genetic and environmental contributors to smoking and addiction risk. These analyses of real data will be guided by bioinformatics-based variant prioritization. This study therefore will provide both guidance and tools needed to move the field of joint effects analysis forward. As a result, it will ultimately have a significant impact on our ability to account for the currently unexplained genetic contribution to phenotypic variance for complex traits.
Many diseases that greatly impact public health (such as heart disease, diabetes, and nicotine addiction) are the result of a complex interplay between genes and environmental factors. The purpose of this grant is to improve data analysis strategies (applicable to a wide variety of diseases) for the detection of joint effects of genes and environment. We will apply these methods to analyze existing data on smokers to improve our understanding of gene-environment effects on nicotine addiction.
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