Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association Study

摘要: A genome-wide association study (GWAS) typically is focused on detecting marginal genetic effects. However, many complex traits are likely to be the result of interplay genes and environmental factors. These SNPs may have a weak effect thus unlikely detected from scan effects, but detectable in gene-environment (G × E) interaction analysis. (GWIS) using standard test G × E known low power, particularly when one corrects for testing multiple SNPs. Two 2-step methods GWIS been previously proposed, aimed at improving efficiency by prioritizing most involved screening step. For quantitative trait, these include method that screens effects [Kooperberg Leblanc, 2008] variance heterogeneity genotype [Pare et al., 2010] In this paper, we show Pare et al. approach has an inflated false-positive rate presence effect, propose alternative remains valid. We also novel combines two approaches, provide simulations demonstrating new can outperform other approaches. Application G × Hispanic-ethnicity childhood lung function reveals SNP near MARCO locus was not identified previous marginal-effect scans.