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Comparative Study
. 2013 Sep;37(6):551-559.
doi: 10.1002/gepi.21741. Epub 2013 Jul 26.

Empirical hierarchical bayes approach to gene-environment interactions: development and application to genome-wide association studies of lung cancer in TRICL

Melanie Sohns #  1 , Elena Viktorova #  1 , Christopher I Amos  2 , Paul Brennan  3 , Gord Fehringer  4 , Valerie Gaborieau  3 , Younghun Han  2 , Joachim Heinrich  5 , Jenny Chang-Claude  6 , Rayjean J Hung  4 , Martina Müller-Nurasyid  7   8 , Angela Risch  9 , Duncan Thomas  10 , Heike Bickeböller  1
Affiliations
Comparative Study

Empirical hierarchical bayes approach to gene-environment interactions: development and application to genome-wide association studies of lung cancer in TRICL

Melanie Sohns et al. 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 ([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.

Keywords: GEWIS; GWAS; lung cancer; population G-E correlation; rank power.

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Figures

Figure 1
Figure 1
Rank power to detect a GxE interaction in the top 25 ranking SNPs by EHB-GE with various ratios of cases and controls (ccr = 1:1, 1:2 and 2:1 => 1,500:1,500, 1,000:2,000 and 2,000:1,000 cases and controls) and varying number of G-E associations NG-E with different effect sizes ORG-E(low), ORG-E(medium) and ORG-E(high)., ORGxE = 2, pg = 30%, pe = 10%, pd = 5%.
Figure 2
Figure 2
Rank power comparison to detect a GxE interaction in the top 25 ranking SNPs between EHB-GE and competing method. On the x-axis EHB-GE rank power, on the y-axis difference between the rank power of EHB-GE and competing method: a) Case-Control (CC), b) Case-Only (CO), c) Intuitive two-step approach (TWO), d) Mukherjee's empirical Bayes-type shrinkage estimator (MUK-EB), e) Murcray's two step approach (MUR)
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