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Empirical hierarchical Bayes approach to gene-environment interactions: Development and application to genome-wide association studies of lung cancer in TRICL.
Genet. Epidemiol. 37, 551-559 (2013)
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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
GEWIS; GWAS; lung cancer; population G-E correlation; rank power; Susceptibility Locus ; Independence ; Inference ; Variants ; Designs ; Models ; Scan
ISSN (print) / ISBN
0741-0395
e-ISSN
1098-2272
Journal
Genetic Epidemiology
Quellenangaben
Volume: 37,
Issue: 6,
Pages: 551-559
Publisher
Wiley
Non-patent literature
Publications
Reviewing status
Peer reviewed