Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models

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 gener...

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Published inAmerican journal of human genetics Vol. 98; no. 4; pp. 653 - 666
Main Authors Chen, Han, Wang, Chaolong, Conomos, Matthew P., Stilp, Adrienne M., Li, Zilin, Sofer, Tamar, Szpiro, Adam A., Chen, Wei, Brehm, John M., Celedón, Juan C., Redline, Susan, Papanicolaou, George J., Thornton, Timothy A., Laurie, Cathy C., Rice, Kenneth, Lin, Xihong
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 07.04.2016
Cell Press
Elsevier
Subjects
Asthma
Asthma - genetics
Case-Control Studies
Central America
Computer Simulation
Data analysis
Genetic Association Studies - methods
Genetics
Genetics, Population - methods
Genomes
Genotyping Techniques
Humans
Linear Models
Logistic Models
Models, Genetic
Phenotype
Phylogeography
Polymorphism, Single Nucleotide
Simulation
South America
South America
Central America
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Summary: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.
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These authors contributed equally to this work
ISSN:0002-9297
1537-6605
DOI:10.1016/j.ajhg.2016.02.012