title: Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association Study.
abstract: 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 the interplay of genes and environmental factors. These SNPs may have a weak marginal effect and thus unlikely to be detected from a scan of marginal effects, but may be detectable in a gene-environment (G × E) interaction analysis. However, a genome-wide interaction scan (GWIS) using a standard test of G × E interaction is known to have low power, particularly when one corrects for testing multiple SNPs. Two 2-step methods for GWIS have been previously proposed, aimed at improving efficiency by prioritizing SNPs most likely to be involved in a G × E interaction using a screening step. For a quantitative trait, these include a method that screens on marginal effects [Kooperberg and Leblanc, 2008] and a method that screens on variance heterogeneity by genotype [Paré et al., 2010] In this paper, we show that the Paré et al. approach has an inflated false-positive rate in the presence of an environmental marginal effect, and we propose an alternative that remains valid. We also propose a novel 2-step approach that combines the two screening approaches, and provide simulations demonstrating that the new method can outperform other GWIS approaches. Application of this method to a G × Hispanic-ethnicity scan for childhood lung function reveals a SNP near the MARCO locus that was not identified by previous marginal-effect scans.
authors.affiliation: Department of Preventive Medicine, University of Southern California, Los Angeles, United States of America.
authors.forename: Pingye
authors.initials: P
authors.lastname: Zhang
authors.affiliation: Department of Preventive Medicine, University of Southern California, Los Angeles, United States of America.
authors.forename: Juan Pablo
authors.initials: JP
authors.lastname: Lewinger
authors.affiliation: Department of Preventive Medicine, University of Southern California, Los Angeles, United States of America.
authors.forename: David
authors.initials: D
authors.lastname: Conti
authors.affiliation: Department of Preventive Medicine, University of Southern California, Los Angeles, United States of America.
authors.forename: John L
authors.initials: JL
authors.lastname: Morrison
authors.affiliation: Department of Preventive Medicine, University of Southern California, Los Angeles, United States of America.
authors.forename: W James
authors.initials: WJ
authors.lastname: Gauderman
mesh_terms: D003198:Computer Simulation; D059647:Gene-Environment Interaction; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D008168:Lung / Q000503:physiopathology; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D019655:Quantitative Trait, Heritable
keywords: Linear regression; Two-step methods; Variance heterogeneity