Nature Communications (Apr 2016)

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,
  • Joshua C. Denny

DOI
https://doi.org/10.1038/ncomms11433
Journal volume & issue
Vol. 7, no. 1
pp. 1 – 8

Abstract

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Use of general linear mixed models (GLMMs) in genetic variance analysis can quantify the relative contribution of additive effects from genetic variation on a given trait. Here, Jonathan Mosley and colleagues apply GLMM in a phenome-wide analysis and show that genetic variations in the HLA region are associated with 44 phenotypes, 5 phenotypes which were not previously reported in GWASes.