Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases
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, M Snyder
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.