Efficient generalized least squares method for mixed population and family-based samples in genome-wide association studies

Jia Li, James Yang, Albert M Levin, Courtney G Montgomery, Indrani Datta, Sheri Trudeau, Indra Adrianto, Paul McKeigue, Michael C Iannuzzi, Benjamin A Rybicki

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)430-8
Number of pages9
JournalGenetic Epidemiology
Volume38
Issue number5
DOIs
Publication statusPublished - 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

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