Title: Empirical hierarchical bayes approach to gene-environment interactions: development and application to genome-wide association studies of lung cancer in TRICL.
Authors: Sohns, Melanie; Viktorova, Elena; Amos, Christopher I; Brennan, Paul; Fehringer, Gord; Gaborieau, Valerie; Han, Younghun; Heinrich, Joachim; Chang-Claude, Jenny; Hung, Rayjean J; Müller-Nurasyid, Martina; Risch, Angela; Thomas, Duncan; Bickeböller, Heike
Published In Genet Epidemiol, (2013 Sep)
Abstract: 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 ( 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 ( 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.
PubMed ID: 23893921
MeSH Terms: Bayes Theorem*; Bias; Case-Control Studies; Computer Simulation; Gene-Environment Interaction*; Genome, Human; Genome-Wide Association Study; Humans; Lung Neoplasms/genetics*; Models, Genetic*; Smoking