Chaddock, N.J.M., Crossfield, S.S.R., Pujades-Rodriguez, M. et al. (2 more authors) (2025) Genetic proxies for clinical traits are associated with increased risk of severe COVID-19. Scientific Reports, 15. 2083. ISSN 2045-2322
Abstract
Routine use of genetic data in healthcare is much-discussed, yet little is known about its performance in epidemiological models including traditional risk factors. Using severe COVID-19 as an exemplar, we explore the integration of polygenic risk scores (PRS) into disease models alongside sociodemographic and clinical variables. PRS were optimized for 23 clinical variables and related traits previously-associated with severe COVID-19 in up to 450,449 UK Biobank participants, and tested in 9,560 individuals diagnosed in the pre-vaccination era. Associations were further adjusted for (i) sociodemographic and (ii) clinical variables. Pathway analyses of PRS were performed to improve biological understanding of disease. In univariate analyses, 17 PRS were associated with increased risk of severe COVID-19 and, of these, four remained associated with COVID-19 outcomes following adjustment for sociodemographic/clinical variables: hypertension PRS (OR = 1.1, 95%CI 1.03–1.18), atrial fibrillation PRS (OR = 1.12, 95%CI 1.03–1.22), peripheral vascular disease PRS (OR = 0.9, 95%CI 0.82–0.99), and Alzheimer’s disease PRS (OR = 1.14, 95%CI 1.05–1.25). Pathway analyses revealed enrichment of genetic variants in pathways for cardiac muscle contraction (genes N = 5; beta[SE] = 3.48[0.60]; adjusted-P = 1.86 × 10⁻⁵). These findings underscore the potential for integrating genetic data into epidemiological models and highlight the advantages of utilizing multiple trait PRS rather than a single PRS for a specific outcome of interest.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Discovery & Translational Science Dept (Leeds) |
Funding Information: | Funder Grant number MRC (Medical Research Council) Exception P.O. 4050781788 NIHR National Inst Health Research Not Known NIHR National Inst Health Research Not Known NIHR National Inst Health Research NIHR202395 |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Jan 2025 12:00 |
Last Modified: | 30 Jan 2025 15:38 |
Status: | Published |
Publisher: | Nature |
Identification Number: | 10.1038/s41598-025-86260-z |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:221603 |