Bayesian model averaging methods for gene-environment interactions and admixture mapping

Evidence suggests that identifying genetic contributions to the risk of complex diseases requires moving beyond independent tests of association between markers and traits. The purpose of this study is to present two methods within a Bayesian framework to be used in identifying gene-by-environment (GxE) interactions and genomic regions contributing to differential disease risk by ancestry. We first introduce a GxE approach which combines a Bayesian framework with a two-degree-of-freedom (2df) test structure for a simultaneous test of main and interaction effects. Simulations are used to present a comparison study of classical and more complex GxE approaches used currently and demonstrate that our proposed method performs similarly to existing 2df approaches with increased power and robustness in numerous scenarios. A second approach is introduced to perform admixture mapping and map susceptibility loci to complex disease with parental ancestry. Our admixture mapping approach provides a linear regression framework in which we reformulate the often used case-control and case-only statistics as nested regression models which are combined within a Bayesian model selection framework. Simulation is used to demonstrate that this approach is advantagous to using case-control or case-only statistics in increased power and robustness. We conduct two genome-wide interaction studies (GWIS) for childhood asthma using air pollution and ethnicity as environmental factors in a nested case-control sample from the Children?s Health Study (CHS). We conduct an admixture mapping of prostate cancer (PrCa) in African Americans and Latinos from the Multiethnic Cohort as well as multiple sclerosis in Hispanic Whites using our proposed method, as well as case-control and case-only methods.