Empirical Hierarchical Bayes Approach to Gene-Environment Interactions: Development and Application to Genome-Wide Association Studies of Lung Cancer in TRICL
2013 | journal article. A publication with affiliation to the University of Göttingen.
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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
Sohns, M.; Viktorova, E.; Amos, C. I.; Brennan, P. C.; Fehringer, G.; Gaborieau, V. & Han, Y. et al. (2013)
Genetic Epidemiology, 37(6) pp. 551-559. DOI: https://doi.org/10.1002/gepi.21741
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- Authors
- Sohns, Melanie; Viktorova, Elena; Amos, Christopher I.; Brennan, P. C.; Fehringer, Gord; Gaborieau, Valerie; Han, Younghun; Heinrich, Joachim; Chang-Claude, Jenny; Hung, Rayjean J.; Müller-Nurasyid, Martina; Risch, Angela; Lewinger, Juan Pablo; Thomas, Duncan C.; Bickeböller, Heike
- Abstract
- The analysis of gene-environment (G x 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 x 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 x E estimates. We compared EHB-GE with rival methods using simulation. EHB-GE has similar or greater rank power to detect G x 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 x 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.
- Issue Date
- 2013
- Journal
- Genetic Epidemiology
- ISSN
- 0741-0395
- Sponsor
- NIH [TRICL 5U19CA148127-03]; DFG GRK [1034]