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

Autor:	Zhang S; Department of Epidemiology, University of Florida, Gainesville, FL, USA.; Departments of Biostatistics & Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.; Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.; These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou., Shu H; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.; These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou., Zhou J; Arc Institute, Palo Alto, CA, USA.; Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.; These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou., Rubin-Sigler J; Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA., Yang X; Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA., Liu Y; Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA., Cooper-Knock J; Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK., Monte E; Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA., Zhu C; Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA., Tu S; Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA., Li H; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China., Tong M; Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA., Ecker JR; Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.; Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA., Ichida JK; Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA., Shen Y; Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.; Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA., Zeng J; School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China., Tsao PS; VA Palo Alto Healthcare System, Palo Alto, CA, USA.; Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA., Snyder MP; Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 May 14. Date of Electronic Publication: 2024 May 14.
DOI:	10.1101/2024.05.14.594252
Abstrakt:	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 T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14 . We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics. Competing Interests: Competing interests M.P.S. is a co-founder and the scientific advisory board member of Personalis, SensOmics, Qbio, January AI, Fodsel, Filtricine, Protos, RTHM, Iollo, Marble Therapeutics, Crosshair Therapeutics, NextThought and Mirvie. He is a scientific advisor of Jupiter, Neuvivo, Swaza, Mitrix, Yuvan, TranscribeGlass, Applied Cognition. J.K.I. is a co-founder and a scientific advisory board member of AcuraStem, Inc. and Modulo Bio, a scientific advisory board member of Synapticure and Vesalius Therapeutics. J.K.I. is also an employee of BioMarin Pharmaceutical. The remaining authors declare no competing interests.
Databáze:	MEDLINE
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