Journal
AMERICAN JOURNAL OF HUMAN GENETICS
Volume 98, Issue 4, Pages 653-666Publisher
CELL PRESS
DOI: 10.1016/j.ajhg.2016.02.012
Keywords
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Categories
Funding
- China Scholarship Council
- National Heart, Lung, and Blood Institute (NHLBI) [N01-HC65233]
- University of Miami [N01-HC65234]
- Albert Einstein College of Medicine [N01-HC65235]
- Northwestern University [N01-HC65236]
- San Diego State University [N01-HC65237]
- NHLBI: National Institute on Minority Health and Health Disparities
- National Institute on Deafness and Other Communication Disorders
- National Institute of Dental and Craniofacial Research (NIDCR)
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Institute of Neurological Disorders and Stroke
- NIH Office of Dietary Supplements
- NIDCR [HHSN268201300005C AM03, MOD03]
- NHLBI [HSN26220/20054C]
- National Center for Advancing Translational Science Clinical Translational Science Institute [UL1TR000124]
- NIDDK Diabetes Research Center [DK063491]
- [P01 CA134294]
- [R35 CA197449]
- [R01 HL113338]
- [K99 HL130593]
- [R37 CA076404]
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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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