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PQG Working Group
February 14 @ 1:00 pm - 2:00 pm
A scalable approach to use single-cell multimodal data to fine-map disease causal variants and links them to target genes
After 20 years of genome-wide association study (GWAS), rarely have we identified causal variants or genes. Translating 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, which outperformed previous bulk-tissue based enhancer map (e.g., EpiMap) and single-cell based method (e.g., Signac). We identified novel 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 variant function.