Performance of deep-learning-based approaches to improve polygenic scores
Polygenic scores, which estimate an individual's genetic propensity for a disease or trait, have the potential to become part of genomic healthcare. Neural-network based deep-learning has emerged as a method of intense interest to model complex, nonlinear phenomena, which may be adapted to exploit gene-gene and gene-environment interactions to potentially improve polygenic scores. We fit neural-network models to both simulated and 28 real traits in the UK Biobank. To infer the amount of nonlinearity present in a phenotype, we also present a framework using neural-networks, which controls for the potential confounding effect of linkage disequilibrium. Although we found evidence for small amounts of nonlinear effects, neural-network models were outperformed by linear regression models for both genetic-only and genetic+environmental input scenarios. In this work, we find that the usefulness of neural-networks for generating polygenic scores may currently be limited and confounded by joint tagging effects due to linkage disequilibrium.
Authors
- Chris Wallace (ORCID: https://orcid.org/0000-0001-9755-1703)
- Carl A. Anderson (ORCID: https://orcid.org/0000-0003-1719-7009)
- Michael Inouye (ORCID: https://orcid.org/0000-0001-9413-6520)
- Yu Xu (ORCID: https://orcid.org/0000-0002-7304-5045)
- Martin Kelemen (ORCID: https://orcid.org/0000-0002-4050-1817)
- Adam S. Butterworth (ORCID: https://orcid.org/0000-0002-6915-9015)
- Jing Hua Zhao (ORCID: https://orcid.org/0000-0002-1463-5870)
- Tao Jiang (ORCID: https://orcid.org/0009-0007-6524-5382)
Institutions
- Baker Heart and Diabetes Institute (AU)
- University of Cambridge (GB)
- Wellcome Sanger Institute (GB)
- British Heart Foundation (GB)
- National Institute for Health and Care Research (GB)
- Lung Institute (US)
- MRC Biostatistics Unit (GB)
- Health Data Research UK (GB)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1038/s41467-025-60056-1
- Citations
- 11
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 19.99
Funders
- Wellcome Trust
- Scottish Government
- National Institute for Health and Care Research
- British Heart Foundation
- Department of Health and Social Care
- Public Health Agency
- Chief Scientist Office, Scottish Government Health and Social Care Directorate
- Medical Research Council
- Engineering and Physical Sciences Research Council
- Economic and Social Research Council
- Health and Social Care Research and Development Division
- NIHR Cambridge Biomedical Research Centre