ArticleNature genetics2020
Identifying cross-disease components of genetic risk across hospital data in the UK Biobank.
Article in Nature genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-wide meta-analysis of muscle weakness identifies 15 susceptibility loci in older men and women.Nature communications · 2021Pooled it
- Context-specific genetic effects inform endotypes and treatment in asthma.The Journal of allergy and clinical immunology · 2026Article
- The Genetic Background of Ankylosing Spondylitis Reveals a Distinct Overlap with Autoimmune Diseases: A Systematic Review.Journal of clinical medicine · 2025Review
- Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research.Molecular psychiatry · 2025Review
- Article
- An observational and genetic investigation into the association between psoriasis and risk of malignancy.Nature communications · 2024Observational
- Identifying genetic subtypes of disease from hospital diagnosis records.Nature genetics · 2023Article
- Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk.Nature genetics · 2023Article
- Inflammatory human leucocyte antigen genotypes are not a risk factor in chronic subdural hematoma development.Acta neurochirurgica · 2023Article
- A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank.Communications biology · 2022Article
- Elucidating mechanisms of genetic cross-disease associations at the PROCR vascular disease locus.Nature communications · 2022Article
- Identification of putative genetic variants in major depressive disorder patients in Pakistan.Molecular biology reports · 2022Article
- From biobank and data silos into a data commons: convergence to support translational medicine.Journal of translational medicine · 2021Article
- SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits.Genome biology · 2021Article
- Predictions, Pivots, and a Pandemic: a Review of 2020's Top Translational Bioinformatics Publications.Yearbook of medical informatics · 2021Review
- The impact of age on genetic risk for common diseases.PLoS genetics · 2021Article
- Mendelian randomization and pleiotropy analysis.Quantitative biology (Beijing, China) · 2021Article
- An iterative approach to detect pleiotropy and perform Mendelian Randomization analysis using GWAS summary statistics.Bioinformatics (Oxford, England) · 2021Article
- Common genetic associations between age-related diseases.Nature aging · 2021Article
- Characterisation, identification, clustering, and classification of disease.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Genetic risk factors frequently affect multiple common human diseases, providing insight into shared pathophysiological pathways and opportunities for therapeutic development. However, systematic identification of genetic profiles of disease risk is limited by the availability of both comprehensive clinical data on population-scale cohorts and the lack of suitable statistical methodology that can handle the scale of and differential power inherent in multi-phenotype data. Here, we develop a disease-agnostic approach to cluster the genetic risk profiles for 3,025 genome-wide independent loci across 19,155 disease classification codes from 320,644 participants in the UK Biobank, representing a large and heterogeneous population. We identify 339 distinct disease association profiles and use multiple approaches to link clusters to the underlying biological pathways. We show how clusters can decompose the variance and covariance in risk for disease, thereby identifying underlying biological processes and their impact. We demonstrate the use of clusters in defining disease relationships and their potential in informing therapeutic strategies.
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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.