ArticleNature communications2016
Identifying genetically driven clinical phenotypes using linear mixed models.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- A genome-wide Association study of the Count of Codeine prescriptions.Scientific reports · 2024Article
- Clinical associations with a polygenic predisposition to benign lower white blood cell counts.Nature communications · 2024Article
- A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.Radiology · 2019Article
- Probing the Virtual Proteome to Identify Novel Disease Biomarkers.Circulation · 2018Article
- Defining Phenotypes from Clinical Data to Drive Genomic Research.Annual review of biomedical data science · 2018Article
- Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank.Nature genetics · 2017Article
- Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes.Circulation. Cardiovascular genetics · 2017Article
- Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data.Circulation. Cardiovascular genetics · 2016Observational
Corrections and comments
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Authors and funding
14 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.