Identifying genetically driven clinical phenotypes using linear mixed models

Jonathan D. Mosley, John S. Witte, Emma K. Larkin, Lisa Bastarache, Christian M. Shaffer, Jason H. Karnes, C. Michael Stein, Elizabeth Phillips, Scott J. Hebbring, Murray H. Brilliant, John Mayer, Zhan Ye, Dan M. Roden and Joshua C. Denny ()
Additional contact information
Jonathan D. Mosley: Vanderbilt University
John S. Witte: University of California
Emma K. Larkin: Vanderbilt University
Lisa Bastarache: Biomedical Informatics, Vanderbilt University
Christian M. Shaffer: Vanderbilt University
Jason H. Karnes: Vanderbilt University
C. Michael Stein: Vanderbilt University
Elizabeth Phillips: Vanderbilt University
Scott J. Hebbring: Center for Human Genetics, Marshfield Clinic Research Foundation
Murray H. Brilliant: Center for Human Genetics, Marshfield Clinic Research Foundation
John Mayer: Biomedical Informatics Research Center, Marshfield Clinic Research Foundation
Zhan Ye: Biomedical Informatics Research Center, Marshfield Clinic Research Foundation
Dan M. Roden: Vanderbilt University
Joshua C. Denny: Vanderbilt University

Nature Communications, 2016, vol. 7, issue 1, 1-8

Abstract: Abstract We hypothesized that generalized linear mixed models (GLMMs), which estimate the additive genetic variance underlying phenotype variability, would facilitate rapid characterization of clinical phenotypes from an electronic health record. We evaluated 1,288 phenotypes in 29,349 subjects of European ancestry with single-nucleotide polymorphism (SNP) genotyping on the Illumina Exome Beadchip. We show that genetic liability estimates are primarily driven by SNPs identified by prior genome-wide association studies and SNPs within the human leukocyte antigen (HLA) region. We identify 44 (false discovery rate q

Date: 2016
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DOI: 10.1038/ncomms11433

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