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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月25日

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  • Sai Zhang,
  • Hantao Shu,
  • Jingtian Zhou,
  • Jasper Rubin-Sigler,
  • Xiaoyu Yang,
  • Yuxi Liu,
  • Johnathan Cooper-Knock,
  • Emma Monte,
  • Chenchen Zhu,
  • Sharon Tu,
  • Han Li,
  • Mingming Tong,
  • Joseph R. Ecker,
  • Justin K. Ichida,
  • Yin Shen,
  • Jianyang Zeng,
  • Philip S. Tsao,
  • Michael P. Snyder

摘要

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.