Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases
Sai Mei Zhang, Hantao Shu, Jingtian Zhou, Jasper Rubin-Sigler, Xiaoyu Jenny Yang, Yuxi Liu, Johnathan Cooper‐Knock, Emma Monte et al.. Nature Biotechnology, 2025-07-25
Topic: Brain proteins, microglia and lab models
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.
Key findings
- 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.
Why it matters
The paper introduces scPRS, a graph neural network framework that computes single-cell-resolved polygenic risk scores and outperformed traditional PRS approaches, including for Alzheimer's disease. It is a methodological tool for genetic risk prediction and mechanistic study, not a treatment, cure, or prevention.
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Study details
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Nature Biotechnology) |
| Journal's citation record | 4 / 4 | Nature Biotechnology: journal h-index 631 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 13 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 4.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 7.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 13 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Michael Snyder: h-index 207 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Stanford University: institution h-index 2,684 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 14 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40715455) |
| Free to read | 2 / 2 | Free to read |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41587-025-02725-6
- PubMed: 40715455
- Original source →
- Free full text: Nature Biotechnology →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Zhang, S. M., Shu, H., Zhou, J., Rubin-Sigler, J., Yang, X. J., Liu, Y., et al. (2025). Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases. Nature Biotechnology. https://doi.org/10.1038/s41587-025-02725-6
Vancouver
Zhang SM, Shu H, Zhou J, Rubin-Sigler J, Yang XJ, Liu Y, et al. Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases. Nature Biotechnology. 2025. doi:10.1038/s41587-025-02725-6. PMID: 40715455.
BibTeX
@article{zhang2025-40715455,
author = {Zhang, Sai Mei and Shu, Hantao and Zhou, Jingtian and Rubin-Sigler, Jasper and Yang, Xiaoyu Jenny and Liu, Yuxi and Cooper‐Knock, Johnathan and Monte, Emma and others},
title = {{Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases}},
journal = {Nature Biotechnology},
year = {2025},
doi = {10.1038/s41587-025-02725-6},
pmid = {40715455},
url = {https://doi.org/10.1038/s41587-025-02725-6}
}
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