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Tissue-specific enhancer–gene maps from multimodal single-cell data identify causal disease alleles
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Tissue-specific enhancer–gene maps from multimodal single-cell data identify causal disease alleles
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Journal Article
Tissue-specific enhancer–gene maps from multimodal single-cell data identify causal disease alleles
2024
Overview
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.
SCENT is a nonparametric method that models association between chromatin accessibility and gene expression in single-cell multimodal datasets, enabling construction of cell-type-specific enhancer–gene maps to aid mapping of candidate causal variants and genes for common diseases.
Publisher
Nature Publishing Group US,Nature Publishing Group
Subject
/ 38
/ 45/43
/ 45/91
/ Alleles
/ Animal Genetics and Genomics
/ Biomedical and Life Sciences
/ Cells
/ Datasets
/ Disease
/ Genes
/ Genetic Predisposition to Disease - genetics
/ Genome-wide association studies
/ Genome-Wide Association Study - methods
/ Genomes
/ Genomics
/ Humans
/ Nuclei
/ Polymorphism, Single Nucleotide
