Sample size requirements to detect gene‐environment interactions in genome‐wide association studies

Many complex diseases are likely to be a result of the interplay of genes and environmental exposures. The standard analysis in a genome‐wide association study (GWAS) scans for main effects and ignores the potentially useful information in the available exposure data. Two recently proposed methods that exploit environmental exposure information involve a two‐step analysis aimed at prioritizing the large number of SNPs tested to highlight those most likely to be involved in a G × E interaction. For example, Murcray et al. ([2009] Am J Epidemiol 169:219–226) proposed screening on a test that models the G‐E association induced by an interaction in the combined case‐control sample. Alternatively, Kooperberg and LeBlanc ([2008] Genet Epidemiol 32:255–263) suggested screening on genetic marginal effects. In both methods, SNPs that pass the respective screening step at a pre‐specified significance threshold are followed up with a formal test of interaction in the second step. We propose a hybrid method that combines these two screening approaches by allocating a proportion of the overall genome‐wide significance level to each test. We show that the Murcray et al. approach is often the most efficient method, but that the hybrid approach is a powerful and robust method for nearly any underlying model. As an example, for a GWAS of 1 million markers including a single true disease SNP with minor allele frequency of 0.15, and a binary exposure with prevalence 0.3, the Murcray, Kooperberg and hybrid methods are 1.90, 1.27, and 1.87 times as efficient, respectively, as the traditional case‐control analysis to detect an interaction effect size of 2.0. Genet. Epidemiol. 35:201‐210, 2011. © 2011 Wiley‐Liss, Inc.