Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases

Abstract

Polygenic risk scores (PRSs) predict an individual’s genetic risk for complex diseases, yet their utility in elucidating disease biology remains limited. We introduce scPRS, a graph neural network-based framework that computes single-cell-resolved PRSs by integrating reference single-cell chromatin accessibility profiles. scPRS outperforms traditional PRS approaches in genetic risk prediction, as demonstrated across multiple diseases including type 2 diabetes, hypertrophic cardiomyopathy, Alzheimer disease and severe COVID-19. Beyond risk prediction, scPRS prioritizes disease-critical cells and, when combined with a layered multiomic analysis, links risk variants to gene regulation in a cell-type-specific manner. Applied to these diseases, scPRS fine-maps causal cell types and cell-type-specific variants and genes, demonstrating its ability to bridge genetic risk with cell-specific biology. scPRS provides a unified framework for genetic risk prediction and mechanistic dissection of complex diseases, laying a methodological foundation for single-cell genetics.

Metadata

Item Type:	Article
Authors/Creators:	Zhang, S. https://orcid.org/0000-0001-5996-6086 Shu, H. https://orcid.org/0000-0002-7306-5644 Zhou, J. Rubin-Sigler, J. Yang, X. Liu, Y. Cooper-Knock, J. https://orcid.org/0000-0002-0873-8689 Monte, E. Zhu, C. Tu, S. Li, H. https://orcid.org/0000-0002-7380-6174 Tong, M. https://orcid.org/0000-0001-8422-5108 Ecker, J.R. https://orcid.org/0000-0001-5799-5895 Ichida, J.K. Shen, Y. https://orcid.org/0000-0001-9901-5613 Zeng, J. https://orcid.org/0000-0003-0950-7716 Tsao, P.S. https://orcid.org/0000-0001-7274-9318 Snyder, M.P. https://orcid.org/0000-0003-0784-7987
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/.
Keywords:	Diseases; Gene regulation; Genetic association study; Genomics; Machine learning
Dates:	Submitted: 15 May 2024 Accepted: 28 May 2025 Published (online): 25 July 2025 Published: 25 July 2025
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health
Funding Information:	Funder Grant number TARGET ALS BB-2022-C1-L1 PACKARD CENTER 1417355 MOTOR NEURONE DISEASE ASSOCIATION Cooper-Knock/Apr23/894-791
Depositing User:	Symplectic Sheffield
Date Deposited:	06 Aug 2025 13:48
Last Modified:	06 Aug 2025 13:48
Status:	Published online
Publisher:	Springer Science and Business Media LLC
Refereed:	Yes
Identification Number:	10.1038/s41587-025-02725-6
Related URLs:	Author
Sustainable Development Goals:
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:230148

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Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases

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