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. 2016 Apr 25:7:11433.
doi: 10.1038/ncomms11433.

Identifying genetically driven clinical phenotypes using linear mixed models

Jonathan D Mosley  1 , John S Witte  2 , Emma K Larkin  1 , Lisa Bastarache  3 , Christian M Shaffer  1 , Jason H Karnes  1 , C Michael Stein  1 , Elizabeth Phillips  1 , Scott J Hebbring  4 , Murray H Brilliant  4 , John Mayer  5 , Zhan Ye  5 , Dan M Roden  1 , Joshua C Denny  1   3
Affiliations

Identifying genetically driven clinical phenotypes using linear mixed models

Jonathan D Mosley et al. Nat Commun. .

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.

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Figures

Figure 1
Figure 1. Genetic liability estimates and P-values for PheWAS phenotypes.
Each point represents the results from a mixed-models analysis using all SNPs with MAF>0.01 on the exome chip, adjusted for age, sex and 20 principal components. PheWAS phenotypes with P-values<10−16 (3 phenotypes) were set to 10−16 for display purposes. Phenotypes with a genetic liability estimate<0 are not shown.
Figure 2
Figure 2. SNP association analysis for hypothyroidism and polymyalgia rheumatica.
All analyses used an additive genetic model adjusted for three principal components, age and sex. (a) Manhattan plot for the hypothyroidism phenotype (3,242 cases, 6,484 controls) and (b) LocusZoom plot highlighting SNP rs6906021. (c) Manhattan plot for polymyalgia rheumatica (413 cases, 5,782 controls) and (d) LocusZoom plot highlighting SNP rs6910071.
See this image and copyright information in PMC

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