The analysis of gene-environment (GxE) interactions remains one of the greatest challenges in the post-genome-wide-association-studies (GWAS) 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 fails. I present a novel empirical hierarchical Bayes approach to GxE interaction (EHB-GE), which benefits from greater power while accounting for G-E dependence. Building on Lewinger et al.â€™s ( Genet Epidemiol 31:871-882) hierarchical Bayes prioritization approach, the method utilizes posterior G-E association estimates in controls based on G-E information across the genome to adjust for it in resulting test statistics. These posteriori estimates are subtracted from the corresponding G-E association coefficients within cases. Compared with existing methods, EHB-GE reaches higher rank power to detect markers with GxE interaction effects, accounting for G-E association in the population. An exception is given by no or only a few weak G-E associations, then Murcray et al.â€™s method ( Am J Epidemiol 169:219-226) identifies markers with low GxE interaction effects better. We applied EHB-GE and competing methods to four lung cancer case-control GWAS from the TRICL/ILCCO consortium with smoking as environmental factor. Genes identified by the EHB-GE approach are reasonable candidates, suggesting usefulness of the method. In addition we combined the search for GxE interactions with the investigation of pathways in TRICL/ILCCO.