增强子
生物
全基因组关联研究
基因
计算生物学
遗传学
染色质
电池类型
基因表达
细胞
单核苷酸多态性
基因型
作者
Saori Sakaue,Kathryn Weinand,Shakson Isaac,Kushal K. Dey,Karthik A. Jagadeesh,Masahiro Kanai,Gerald F. Watts,Zhu Zhu,Michael B. Brenner,Andrew McDavid,Laura T. Donlin,Kevin Wei,Alkes L. Price,Soumya Raychaudhuri
出处
期刊:Cold Spring Harbor Laboratory - medRxiv
日期:2022-11-01
被引量:9
标识
DOI:10.1101/2022.10.27.22281574
摘要
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. We developed a new non-parametric statistical method, SCENT (Single-Cell ENhancer Target gene mapping) which models association between enhancer chromatin accessibility and gene expression in single-cell multimodal RNA-seq and ATAC-seq data. We applied SCENT to 9 multimodal datasets including > 120,000 single cells and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in eQTLs and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases. In addition, we were able to link 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 non-coding variant function.
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