Mixed model with correction for case-control ascertainment increases association power.
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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 χ(2) 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 χ(2) 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. |
Year of Publication | 2015
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Journal | Am J Hum Genet
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Volume | 96
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Issue | 5
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Pages | 720-30
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Date Published | 2015 May 07
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ISSN | 1537-6605
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DOI | 10.1016/j.ajhg.2015.03.004
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PubMed ID | 25892111
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PubMed Central ID | PMC4570278
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Grant list | R01 HG006399 / HG / NHGRI NIH HHS / United States
T32 GM074897 / GM / NIGMS NIH HHS / United States
F32 HG007805 / HG / NHGRI NIH HHS / United States
K25 HL121295 / HL / NHLBI NIH HHS / United States
Wellcome Trust / United Kingdom
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