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Publication Detail

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

Authors: 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

Published In Am J Hum Genet, (2015 May 07)

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.

PubMed ID: 25892111 Exiting the NIEHS site

MeSH Terms: Case-Control Studies; Chromosome Mapping; Computer Simulation; Genetic Association Studies*; Humans; Models, Genetic*; Models, Theoretical*; Multiple Sclerosis/genetics; Multiple Sclerosis/pathology; Phenotype; Polymorphism, Single Nucleotide; Sample Size

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