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.

将全基因组关联研究 (GWAS) 位点转化为因果变异和基因需要来自疾病相关组织的准确的细胞类型特异性增强子基因图谱。构建增强子基因图谱是必要的，但对于目前在原代人体组织中的实验方法来说具有挑战性。在这里，我们开发了一种非参数统计方法 SCENT（单细胞增强子靶基因作图），该方法模拟单细胞或细胞核多模式 RNA 测序和 ATAC 测序数据中增强子染色质可及性与基因表达之间的关联。我们将 SCENT 应用于 9 个多模式数据集，包括超过 120,000 个单细胞或细胞核，并创建了 23 个细胞类型特异性增强子基因图谱。这些图谱高度富集了 1,143 种疾病和性状的表达数量位点和 GWAS 的因果变异。我们确定了常见和罕见疾病的可能致病基因，并将体细胞突变热点与目标基因联系起来。我们证明，将 SCENT 应用到疾病相关人体组织的多模态数据中，能够可扩展地构建准确的细胞类型特异性增强子基因图谱，这对于定义非编码变异功能至关重要。