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

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

Author information

1
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
2
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore.
3
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
4
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Mathematics, Tsinghua University, Beijing 100084, P. R. China.
5
Division of Pediatric Pulmonary Medicine, Allergy and Immunology, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA 15224, USA.
6
Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA.
7
Prevention and Population Sciences Program, Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892, USA.
8
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA. Electronic address: xlin@hsph.harvard.edu.

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.

PMID:
27018471
PMCID:
PMC4833218
DOI:
10.1016/j.ajhg.2016.02.012
[Indexed for MEDLINE]
Free PMC Article

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