%0 Journal Article
%T Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models
%A Adam£¿A. Szpiro
%A Adrienne£¿M. Stilp
%A Cathy£¿C. Laurie
%A Chaolong Wang
%A George£¿J. Papanicolaou
%A Han Chen
%A John£¿M. Brehm
%A Juan£¿C. Celed¨®n
%A Kenneth Rice
%A Matthew£¿P. Conomos
%A Susan Redline
%A Tamar Sofer
%A Timothy£¿A. Thornton
%A Wei Chen
%A Zilin Li
%J Archive of "American Journal of Human Genetics".
%D 2016
%R 10.1016/j.ajhg.2016.02.012
%X 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
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833218/