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

Research output: Contribution to journalArticle

6 Citations (Scopus)

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<0.05) phenotypes associated with HLA SNP variation and show that hypothyroidism is genetically correlated with Type I diabetes (rG=0.31, s.e. 0.12, P=0.003). We also report novel SNP associations for hypothyroidism near HLA-DQA1/HLA-DQB1 at rs6906021 (combined odds ratio (OR)=1.2 (95% confidence interval (CI): 1.1-1.2), P=9.8 × 10-11) and for polymyalgia rheumatica near C6orf10 at rs6910071 (OR=1.5 (95% CI: 1.3-1.6), P=1.3 × 10-10). Phenome-wide application of GLMMs identifies phenotypes with important genetic drivers, and focusing on these phenotypes can identify novel genetic associations.

Original languageEnglish (US)
Article number11433
JournalNature communications
Volume7
DOIs
StatePublished - Apr 25 2016
Externally publishedYes

Fingerprint

phenotype
HLA Antigens
Linear Models
leukocytes
Polymorphism
Single Nucleotide Polymorphism
antigens
Phenotype
Nucleotides
polymorphism
nucleotides
Hypothyroidism
confidence
Medical problems
Odds Ratio
Confidence Intervals
liabilities
Polymyalgia Rheumatica
Exome
intervals

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

Cite this

Mosley, J. D., Witte, J. S., Larkin, E. K., Bastarache, L., Shaffer, C. M., Karnes, J. H., ... Denny, J. C. (2016). Identifying genetically driven clinical phenotypes using linear mixed models. Nature communications, 7, [11433]. https://doi.org/10.1038/ncomms11433

Identifying genetically driven clinical phenotypes using linear mixed models. / Mosley, Jonathan D.; Witte, John S.; Larkin, Emma K.; Bastarache, Lisa; Shaffer, Christian M.; Karnes, Jason H.; Stein, C. Michael; Phillips, Elizabeth; Hebbring, Scott J.; Brilliant, Murray H.; Mayer, John; Ye, Zhan; Roden, Dan M.; Denny, Joshua C.

In: Nature communications, Vol. 7, 11433, 25.04.2016.

Research output: Contribution to journalArticle

Mosley, JD, Witte, JS, Larkin, EK, Bastarache, L, Shaffer, CM, Karnes, JH, Stein, CM, Phillips, E, Hebbring, SJ, Brilliant, MH, Mayer, J, Ye, Z, Roden, DM & Denny, JC 2016, 'Identifying genetically driven clinical phenotypes using linear mixed models', Nature communications, vol. 7, 11433. https://doi.org/10.1038/ncomms11433
Mosley, Jonathan D. ; Witte, John S. ; Larkin, Emma K. ; Bastarache, Lisa ; Shaffer, Christian M. ; Karnes, Jason H. ; Stein, C. Michael ; Phillips, Elizabeth ; Hebbring, Scott J. ; Brilliant, Murray H. ; Mayer, John ; Ye, Zhan ; Roden, Dan M. ; Denny, Joshua C. / Identifying genetically driven clinical phenotypes using linear mixed models. In: Nature communications. 2016 ; Vol. 7.
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