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

Am J Hum Genet. 2016 Apr 7;98(4):653-66. doi: 10.1016/j.ajhg.2016.02.012. Epub 2016 Mar 24.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Asthma / genetics
  • Case-Control Studies
  • Central America
  • Computer Simulation
  • Genetic Association Studies / methods*
  • Genetics, Population / methods*
  • Genotyping Techniques
  • Humans
  • Linear Models*
  • Logistic Models
  • Models, Genetic
  • Phenotype*
  • Phylogeography
  • Polymorphism, Single Nucleotide
  • South America