Many common complex traits are believed to be a result of the combined effect of genes, environmental factors and their interactions. Understanding the relationship between genetic polymorphisms and environmental exposures can help to identify high risk subgroups in the population and provide a better understanding of pathway mechanisms for complex diseases. However, most investigators conducting a search for new genes in a genome-wide association study (GWAS) do not consider gene- environment (GxE) interactions. This is in part due to a current lack of efficient statistical methods and software designed to detect interactions in high-volume genetic data. In this project, we will develop new and powerful methods for the detection of genes involved in a GxE interaction, including efficient screening techniques and Bayesian methods. These methods will be applicable to both disease (e.g. cancer, asthma) and quantitative (e.g. cholesterol, lung function) outcomes, with forms applicable to case- control and case-parent trio designs. We will also develop approaches for the detection GxE interactions using GWA data from a consortium setting. In both the single-study and consortium settings, we will establish that these methods provide improved power for finding interactions relative to standard tests, while also controlling the false positive rate. We will also develop new user-friendly software for the analysis of GxE interactions in a GWAS, and will freely distribute these programs via our website.
Gene-environment (GxE) interactions are important contributors to the etiology of complex traits such as cancer, heart disease, and asthma. We will develop new statistical methods and freely-distributed software for efficiently detecting GxE interactions in a genome-wide association (GWA) study, a consortium of GWA studies, and in post-GWAS investigations of a candidate gene or region.
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