Detecting Gene–Environment Interactions for a Quantitative Trait in a Genome‐Wide Association Study
Year: 2016
Type: article
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.
Source: Genetic Epidemiology
DOI: 10.1002/gepi.21977
Institution: University of Southern California
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: G1113415418, G1210143792, G1448761013, Air pollution effects on asthma and lung function in Hispanic children, FLEXIBLE MULTI-LEVEL MODELS FOR LONGITUDINAL ANALYSIS OF CHILDHOOD OBESITY, G2080154552, A Genome-wide Association Study of Childhood Respiratory Outcomes, G2844970199, Integrative Genomics of Acute Asthma Control, G4177996928, G5050719901, Gene-Environment Analyses of Early Life Exposures and Asthma in Ethnically Diverse Children, Detecting GxE Interactions in Genome-wide Association Studies, FLEXIBLE MULTI-LEVEL MODELS FOR LONGITUDINAL ANALYSIS OF CHILDHOOD OBESITY, INTEGRATIVE GENETIC APPROACHES TO GENE-AIR POLLUTION INTERACTIONS IN ASTHMA, G8732294816, G8734275420, Environmental Exposures, Host Factors and Human Disease, Statistical Methods for Integrative Genomics in Cancer
OpenAlex ID: W2418811587