Using Bayes model averaging to leverage both gene main effects and G  ×  E interactions to identify genomic regions in genome‐wide association studies

We present a framework that (a) balances the robustness of a CC approach with the power of the case ‐only (CO) approach; (b) incorporates main SNP effects; (c) allows for incorporation of prior information; and (d) allows the data to determine the most appropriate model. Our framework is based on Bayes model averaging, which provides a principled statistical method for incorporating model uncert ainty. We average over inclusion of parameters corresponding to the main andG × E interaction effects and theG –E association in controls. The resulting method exploits the joint evidence for main and interaction effects while gaining power from a CO equivalent analysis. Through simulations, we demonstrate that our approach detects SNPs within a wide range of scenarios with increased power over current methods. We illustrate the approach on a gene ‐environment scan in the USC Children's Health Study.
Source: Genetic Epidemiology - Category: Epidemiology Authors: Tags: RESEARCH ARTICLE Source Type: research