Mixed model with correction for case-control ascertainment increases association power

Hayeck, Tristan J., Zaitlen, Noah A., Loh, Po-Ru, Vilhjalmsson, Bjarni, Pollack, Samuela, Gusev, Alexander, Yang, Jian, Chen, Guo-Bo, Goddard, Michael E., Visscher, Peter M., Patterson, Nick and Price, Alkes L. (2015) Mixed model with correction for case-control ascertainment increases association power. American Journal of Human Genetics, 96 5: 720-730. doi:10.1016/j.ajhg.2015.03.004

Author Hayeck, Tristan J.
Zaitlen, Noah A.
Loh, Po-Ru
Vilhjalmsson, Bjarni
Pollack, Samuela
Gusev, Alexander
Yang, Jian
Chen, Guo-Bo
Goddard, Michael E.
Visscher, Peter M.
Patterson, Nick
Price, Alkes L.
Title Mixed model with correction for case-control ascertainment increases association power
Journal name American Journal of Human Genetics   Check publisher's open access policy
ISSN 1537-6605
Publication date 2015-05-07
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.ajhg.2015.03.004
Open Access Status
Volume 96
Issue 5
Start page 720
End page 730
Total pages 11
Place of publication Cambridge, MA, United States
Publisher Cell Press
Collection year 2016
Language eng
Formatted abstract
We introduce a liability-threshold mixed linear model (LTMLM) association statistic for case-control studies and show that it has a well-controlled false-positive rate and more power than existing mixed-model methods for diseases with low prevalence. Existing mixed-model methods suffer a loss in power under case-control ascertainment, but no solution has been proposed. Here, we solve this problem by using a χ2 score statistic computed from posterior mean liabilities (PMLs) under the liability-threshold model. Each individual's PML is conditional not only on that individual's case-control status but also on every individual's case-control status and the genetic relationship matrix (GRM) obtained from the data. The PMLs are estimated with a multivariate Gibbs sampler; the liability-scale phenotypic covariance matrix is based on the GRM, and a heritability parameter is estimated via Haseman-Elston regression on case-control phenotypes and then transformed to the liability scale. In simulations of unrelated individuals, the LTMLM statistic was correctly calibrated and achieved higher power than existing mixed-model methods for diseases with low prevalence, and the magnitude of the improvement depended on sample size and severity of case-control ascertainment. In a Wellcome Trust Case Control Consortium 2 multiple sclerosis dataset with >10,000 samples, LTMLM was correctly calibrated and attained a 4.3% improvement (p = 0.005) in χ2 statistics over existing mixed-model methods at 75 known associated SNPs, consistent with simulations. Larger increases in power are expected at larger sample sizes. In conclusion, case-control studies of diseases with low prevalence can achieve power higher than that in existing mixed-model methods.
Keyword Liability-threshold model
Case control studies
Mixed model
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2016 Collection
UQ Diamantina Institute Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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