Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

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

Nature biotechnology - 1 May 2026

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

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cardiomyopathy, Hypertrophic
Share to Chromatin

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to COVID-19

Share to Genetic Risk Score

Share to Multifactorial Inheritance

Share to Neural Networks, Computer

Share to SARS-CoV-2

Share to Single-Cell Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 40715455
DOI: 10.1038/s41587-025-02725-6
PMCID: PMC13180658

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases.PMID 40715455Read ↗

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseasesSemantic proximity Read ↗

DPCGS: a computational framework for linking GWAS to single-cell transcriptomics in complex traits and diseasesSemantic proximity Read ↗

Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibilitySemantic proximity Read ↗

Modeling gene interactions in polygenic prediction via geometric deep learning.Semantic proximity Read ↗

Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility.Semantic proximity Read ↗

Conditional polygenic enrichment distinguishes causal from tagging disease-critical cell populations in single-cell RNA-seqSemantic proximity Read ↗

A probabilistic framework to dissect functional cell-type-specific regulatory elements and risk loci underlying the genetics of complex traitsSemantic proximity Read ↗

PRS-Net: Interpretable polygenic risk scores via geometric learningSemantic proximity Read ↗

scEPS integrates genetic and single-cell disease atlas data to provide granular mechanistic insights into complex human diseasesSemantic proximity Read ↗

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq dataSemantic proximity Read ↗

scRiskDB: a single-cell epigenomic resource linking complex traits to regulatory mechanisms across human tissues.Semantic proximity Read ↗

Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human geneticsSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.