ArticleNature genetics2024
Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles.
Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Article
- Mapping enhancer-gene regulatory interactions from single-cell data.Nature genetics · 2026Article
- Accurate, sensitive, and efficient chromatin accessibility quantification at target loci using UNIChro-seq.Nature communications · 2026Article
- From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.Biomolecules · 2026Review
- 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
- Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.Bioinformatics (Oxford, England) · 2026Article
- Teaching an old dog new cells.Nature methods · 2026Article
- Integrated mapping of human meniscus and cartilage eQTLs reveals shared and distinct osteoarthritis genetic drivers.medRxiv : the preprint server for health sciences · 2026Article
- Single-nucleus multiomic profiling of the aging mouse substantia nigra reveals conserved gene alterations linked to Parkinson's disease.Genome research · 2026Article
- Boolean logic links chromatin accessibility states to gene expression variability across cell types.Nucleic acids research · 2026Article
- Article
- Multi-Tissue Genetic Regulation of RNA Editing in Pigs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review
- CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data.Bioinformatics (Oxford, England) · 2026Article
- Distinguishing causal from tagging enhancers using single-cell multiome data.medRxiv : the preprint server for health sciences · 2026Article
- Donor-matched iPSC model reveals context-dependent T2D genetic signals in fibro-adipogenic progenitors.bioRxiv : the preprint server for biology · 2026Article
- Integrating polygenic signals and single-cell multiomics identifies cell-type-specific regulomes critical for immune- and aging-related diseases.Nature aging · 2026Article
- Multi-omics Data Integration.Advances in experimental medicine and biology · 2026Review
- ThebioRxiv : the preprint server for biology · 2025Article
- Quantifying the impact of genetic mutations on enhancer dynamics.bioRxiv : the preprint server for biology · 2025Article
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15 authors.
Funding
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
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