Performance of deep-learning-based approaches to improve polygenic scores

Martin Kelemen (), Yu Xu, Tao Jiang, Jing Hua Zhao, Carl A. Anderson, Chris Wallace, Adam Butterworth and Michael Inouye
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Martin Kelemen: University of Cambridge
Yu Xu: University of Cambridge
Tao Jiang: University of Cambridge
Jing Hua Zhao: University of Cambridge
Carl A. Anderson: Wellcome Sanger Institute
Chris Wallace: University of Cambridge
Adam Butterworth: University of Cambridge
Michael Inouye: University of Cambridge

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-60056-1

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