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:10.1038/ncomms11433

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 journal › Article › peer-review

10 Scopus citations

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 language	English (US)
Article number	11433
Journal	Nature communications
Volume	7
DOIs	https://doi.org/10.1038/ncomms11433
State	Published - Apr 25 2016
Externally published	Yes

ASJC Scopus subject areas

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

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@article{ebc0042e545a4c19a788dda629e481d2,

title = "Identifying genetically driven clinical phenotypes using linear mixed models",

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.",

author = "Mosley, {Jonathan D.} and Witte, {John S.} and Larkin, {Emma K.} and Lisa Bastarache and Shaffer, {Christian M.} and Karnes, {Jason H.} and Stein, {C. Michael} and Elizabeth Phillips and Hebbring, {Scott J.} and Brilliant, {Murray H.} and John Mayer and Zhan Ye and Roden, {Dan M.} and Denny, {Joshua C.}",

note = "Funding Information: This work was supported by a career development award from the Vanderbilt Faculty Research Scholars Fund, American Heart Association (15MCPRP25620006), National Institutes of Health (PGRN U01HG04603, R01LM010685, 1K22LM011938, 1R01GM114128 (Marshfield), and NCATS UL1TR000427 (Marshfield)). BioVU is supported by institutional funding and by the Vanderbilt CTSA grant UL1TR000445 from NCATS/National Institutes of Health. Genome-wide genotyping was funded by National Institutes of Health (NIGMS/OD (RC2GM092618) and NHGRI/NIGMS (U01HG004603)).",

year = "2016",

month = apr,

day = "25",

doi = "10.1038/ncomms11433",

language = "English (US)",

volume = "7",

journal = "Nature communications",

issn = "2041-1723",

publisher = "Nature Research",

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TY - JOUR

T1 - Identifying genetically driven clinical phenotypes using linear mixed models

AU - Mosley, Jonathan D.

AU - Witte, John S.

AU - Larkin, Emma K.

AU - Bastarache, Lisa

AU - Shaffer, Christian M.

AU - Karnes, Jason H.

AU - Stein, C. Michael

AU - Phillips, Elizabeth

AU - Hebbring, Scott J.

AU - Brilliant, Murray H.

AU - Mayer, John

AU - Ye, Zhan

AU - Roden, Dan M.

AU - Denny, Joshua C.

N1 - Funding Information: This work was supported by a career development award from the Vanderbilt Faculty Research Scholars Fund, American Heart Association (15MCPRP25620006), National Institutes of Health (PGRN U01HG04603, R01LM010685, 1K22LM011938, 1R01GM114128 (Marshfield), and NCATS UL1TR000427 (Marshfield)). BioVU is supported by institutional funding and by the Vanderbilt CTSA grant UL1TR000445 from NCATS/National Institutes of Health. Genome-wide genotyping was funded by National Institutes of Health (NIGMS/OD (RC2GM092618) and NHGRI/NIGMS (U01HG004603)).

PY - 2016/4/25

Y1 - 2016/4/25

N2 - 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.

AB - 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.

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U2 - 10.1038/ncomms11433

DO - 10.1038/ncomms11433

M3 - Article

C2 - 27109359

AN - SCOPUS:84964587187

SN - 2041-1723

VL - 7

JO - Nature communications

JF - Nature communications

M1 - 11433

ER -

Identifying genetically driven clinical phenotypes using linear mixed models

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