1. Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models.
- Author
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Chen H, Wang C, Conomos MP, Stilp AM, Li Z, Sofer T, Szpiro AA, Chen W, Brehm JM, Celedón JC, Redline S, Papanicolaou GJ, Thornton TA, Laurie CC, Rice K, and Lin X
- Subjects
- Asthma genetics, Case-Control Studies, Central America, Computer Simulation, Genotyping Techniques, Humans, Logistic Models, Models, Genetic, Phylogeography, Polymorphism, Single Nucleotide, South America, Genetic Association Studies methods, Genetics, Population methods, Linear Models, Phenotype
- Abstract
Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMM's constant-residual variance assumption. To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. We show in simulation studies and real data analysis that GMMAT effectively controls for population structure and relatedness when analyzing binary traits in a wide variety of study designs., (Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)
- Published
- 2016
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