biostudies-literatureSakaue SU.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)Doris Duke Charitable FoundationNIAID NIH HHSRheumatology Research FoundationU.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin DiseasesNHGRI NIH HHSUehara Memorial FoundationU.S. Department of Health & Human Services | NIH | National Human Genome Research InstituteNIAMS NIH HHS615-626https://www.ebi.ac.uk/biostudies/studies/S-EPMC11456345biostudies-literatureUnknown56(4)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 disNature geneticsTissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles.PMC11456345K08 AR077037T32AR007530R01 AR063759R56 HG013083UC2 AR081023U01 HG012009T32 AR007530R00 HG012203UH2 AR067677U01HG012009R01AR063759UC2AR081023K08AR077037T32 HG002295P01 AI148102Zhang FMandelin AMRobinson WHLiao KPRaychaudhuri SDunn PGravallese EMIshigaki KBykerk VPGurajala SRangel-Moreno JAccelerating Medicines Partnership® RA/SLE Program and NetworkMeednu NScheel-Toellner DMaybury MBoyle DLFirestein GSGutierrez-Arcelus MHughes LBRaza KDiCarlo EMoreland LWReshef YJames JANathan ARivellese FPrice ALSmith MHSahbudin ILi YNerviani AMantel IFiler ABrenner MBRumker LKanai MUtz PJMichael Holers VGeraldino-Pardilla LOrange DEKorsunsky IIsaac SZhu ZBanda NIvashkiv LBBarnas JLWeinand KWatts GFMGuthridge JMLi ZJKeras GAlbrecht JLederer JARitchlin CWeisenfeld DDey KKDonlin LTGoodman SMHorowitz DApruzzese WChicoine AWei KBathon JMGregersen PKJagadeesh KCarr HLSeifert JASakaue SDeane KDSlowikowski KCordle AForbess LLakhanpal ARao DAAnolik JHBoyce BFPerlman HMillard NCurtis MXiao QMears JBen-Artzi ABridges SLCampbell DMcDavid ATabechian DPitzalis CCeponis AJonsson AHKang JBWeisman MHfalseTissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles.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 dis2024-01-01T00:00:00Z2024 Apr2026-06-02T22:16:21.976Z2026-04-21T03:14:29.723ZS-EPMC114563453859430510.1038/s41588-024-01682-1