Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases.

ABSTRACT: Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2

SUBMITTER: Zhang S 

PROVIDER: S-EPMC11118500 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature