Abstract
Genome-wide association studies (GWAS) that draw samples from multiple studies with a mixture of relationship structures are becoming more common. Analytical methods exist for using mixed-sample data, but few methods have been proposed for the analysis of genotype-by-environment (G×E) interactions. Using GWAS data from a study of sarcoidosis susceptibility genes in related and unrelated African Americans, we explored the current analytic options for genotype association testing in studies using both unrelated and family-based designs. We propose a novel method-generalized least squares (GLX)-to estimate both SNP and G×E interaction effects for categorical environmental covariates and compared this method to generalized estimating equations (GEE), logistic regression, the Cochran-Armitage trend test, and the WQLS and MQLS methods. We used simulation to demonstrate that the GLX method reduces type I error under a variety of pedigree structures. We also demonstrate its superior power to detect SNP effects while offering computational advantages and comparable power to detect G×E interactions versus GEE. Using this method, we found two novel SNPs that demonstrate a significant genome-wide interaction with insecticide exposure-rs10499003 and rs7745248, located in the intronic and 3' UTR regions of the FUT9 gene on chromosome 6q16.1.
Original language | English |
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Pages (from-to) | 430-8 |
Number of pages | 9 |
Journal | Genetic Epidemiology |
Volume | 38 |
Issue number | 5 |
DOIs | |
Publication status | Published - 20 May 2014 |
Keywords
- African Americans
- Environment
- Family
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Genotype
- Humans
- Least-Squares Analysis
- Logistic Models
- Models, Genetic
- Pedigree
- Polymorphism, Single Nucleotide
- Sarcoidosis