A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care data
Background: Multimorbidity, the presence of two or more conditions in one person, is common but studies are often limited to observational data and single datasets. We address this gap by integrating large-scale primary-care and genetic data from multiple studies to interrogate multimorbidity patterns and producing digital resources to support future research.
Methods: We defined chronic, common, and heritable conditions in individuals aged ≥65 years, using two large primary-care databases [CPRD (UK) N = 2,425,014 and SIDIAP (Spain) N = 1,053,640], and estimated heritability using the same definitions in UK Biobank (N = 451,197). We used logistic regression to estimate the co-occurrence of pairs of conditions in the primary care data. Linkage disequilibrium score regression was used to estimate genetic similarity between pairs of conditions. Meta-analyses were conducted across databases, and up to three sources of genetic data, for each pair of conditions. We classified pairs of conditions as across or within-domain based on the international classification of disease.
Findings: We identified 72 chronic conditions, with 43.6% of 2546 pairs showing higher co-occurrence than chance in primary care and evidence of shared genetics. Many across-domain pairs exhibited substantial shared genetics (e.g., iron deficiency anaemia and peripheral arterial disease: genetic correlation Rg = 0.45 [95% Confidence Intervals 0.27:0.64]). 33 pairs displayed negative genetic correlations, such as skin cancer and rheumatoid arthritis (Rg = −0.14 [−0.21:−0.06]), due to potential adverse drug effects. Discordance between genetic and primary care data was also observed, e.g., abdominal aortic aneurysm and bladder cancer co-occurred in primary care but were not genetically correlated (Odds-Ratio = 2.23 [2.09:2.37], Rg = 0.04 [−0.20:0.28]) and schizophrenia and fibromyalgia were less likely to co-occur together in primary care but were positively genetically correlated (OR = 0.84 [0.75:0.94], Rg = 0.20 [0.11:0.29]).
Interpretation: Most pairs of chronic conditions show evidence of shared genetics, and co-occurrence in primary care, suggesting shared mechanisms. The identified patterns of shared genetics, negative correlations and discordance between genetic and observational data provide a foundation for future multimorbidity research.
Funding
Genetic Evaluation of Multimorbidity towards INdividualisation of Interventions (GEMINI)
UK Research and Innovation
Find out more...History
Author affiliation
College of Life Sciences Population Health SciencesVersion
- VoR (Version of Record)
Published in
eBioMedicineVolume
113Pagination
105584 - 105584Publisher
Elsevier BVissn
2352-3964eissn
2352-3964Acceptance date
2025-01-20Copyright date
2025Available date
2025-03-07Publisher DOI
Spatial coverage
NetherlandsLanguage
enPublisher version
Deposited by
Professor Frank DudbridgeDeposit date
2025-02-28Data Access Statement
We cannot make individual-level data available. Researchers can apply to UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/), CPRD (https://www.cprd.com/research-applications), and SIDIAP (https://www.sidiap.org/index.php/en/solicituds-en). We have made our diagnostic code lists, code and results available on our GitHub (https://github.com/GEMINI-multimorbidity/) site and Shiny website (https://gemini-multimorbidity.shinyapps.io/atlas/). GWAS summary statistics will be available following acceptance at the GWAS Catalog (https://www.ebi.ac.uk/gwas/home).Rights Retention Statement
- Yes