ArticleNature biotechnology2026
Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases.
Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Polygenic risk scores in human genetics for study design discovery and translation.Human genetics · 2026Review
- Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation.Bioinformatics (Oxford, England) · 2026Article
- Cell-type-specific polygenic risk scores reveal adipocyte-related interactions with lipids in coronary artery disease.medRxiv : the preprint server for health sciences · 2026Article
- When network biology meets human genetics.Cell genomics · 2026Article
- A dish-to-biobank framework links β-cell nutrient-stress programs to genetic and dietary risk for Type 2 Diabetes.bioRxiv : the preprint server for biology · 2026Article
- Navigating the Genetic Risk of Chemotherapy-Induced Hearing Loss in the Stria Vascularis.Clinical pharmacology and therapeutics · 2026Review
- Medea: An omics AI agent for therapeutic discovery.bioRxiv : the preprint server for biology · 2026Article
- Genetic burden across core genes of the PI3K-AKT-mTOR pathway is associated with susceptibility to microscopic polyangiitis: a Chinese cohort study.Frontiers in immunology · 2026Article
- The First Advent of the Whole Genome Sequencing Cohort in Japan.JMA journal · 2025Article
- Modeling gene interactions in polygenic prediction via geometric deep learning.Genome research · 2025Article
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18 authors.
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.