Journal
GENETIC EPIDEMIOLOGY
Volume 40, Issue 5, Pages 394-403Publisher
WILEY-BLACKWELL
DOI: 10.1002/gepi.21977
Keywords
Linear regression; Two-step methods; Variance heterogeneity
Funding
- NCI NIH HHS [P01 CA196569] Funding Source: Medline
- NHLBI NIH HHS [R21 HL115606, R01 HL118455, R01 HL087680] Funding Source: Medline
- NICHD NIH HHS [R01 HD061968, U01 HD061968] Funding Source: Medline
- NIEHS NIH HHS [P30 ES007048, R03 ES022719, R21 ES024844, R01 ES021801] Funding Source: Medline
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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 (GxE) interaction analysis. However, a genome-wide interaction scan (GWIS) using a standard test of GxE 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 GxE 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 [Pare etal., 2010] In this paper, we show that the Pare etal. 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 x Hispanic-ethnicity scan for childhood lung function reveals a SNP near the MARCO locus that was not identified by previous marginal-effect scans.
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