title: Sample size requirements to detect gene-environment interactions in genome-wide association studies.
abstract: 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 GE 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 genomewide 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.
authors.affiliation: Department of Preventive Medicine, University of Southern California, Los Angeles, California 90089-9010, USA. Murcray@usc.edu
authors.forename: Cassandra E
authors.initials: CE
authors.lastname: Murcray
authors.forename: Juan Pablo
authors.initials: JP
authors.lastname: Lewinger
authors.forename: David V
authors.initials: DV
authors.lastname: Conti
authors.forename: Duncan C
authors.initials: DC
authors.lastname: Thomas
authors.forename: W James
authors.initials: WJ
authors.lastname: Gauderman
mesh_terms: D016022:Case-Control Studies; D004194:Disease / Q000235:genetics; D004777:Environment; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D017720:Molecular Epidemiology / Q000706:statistics & numerical data; D020641:Polymorphism, Single Nucleotide; D018401:Sample Size; D012984:Software