Empirical Hierarchical Bayes Approach to Gene‐Environment Interactions: Development and Application to Genome‐Wide Association Studies of Lung Cancer in TRICL

The analysis of gene‐environment (G × E) interactions remains one of the greatest challenges in the postgenome‐wide association studies (GWASs) era. Recent methods constitute a compromise between the robust but underpowered case‐control and powerful case‐only methods. Inferences of the latter are biased when the assumption of gene‐environment (G‐E) independence in controls fails. We propose a novel empirical hierarchical Bayes approach to G × E interaction (EHB‐GE), which benefits from greater rank power while accounting for population‐based G‐E correlation. Building on Lewinger et al.'s ([2007] Genet Epidemiol 31:871–882) hierarchical Bayes prioritization approach, the method first obtains posterior G‐E correlation estimates in controls for each marker, borrowing strength from G‐E information across the genome. These posterior estimates are then subtracted from the corresponding case‐only G × E estimates. We compared EHB‐GE with rival methods using simulation. EHB‐GE has similar or greater rank power to detect G × E interactions in the presence of large numbers of G‐E correlations with weak to strong effects or only a low number of such correlations with large effect. When there are no or only a few weak G‐E correlations, Murcray et al.'s method ([2009] Am J Epidemiol 169:219–226) identifies markers with low G × E interaction effects better. We applied EHB‐GE and competing methods to four lung cancer case‐control GWAS from the Interdisciplinary Research in Cancer of the Lung/International Lung Cancer Consortium with smoking as environmental factor. A number of genes worth investigating were identified by the EHB‐GE approach.