AbstractSkeletal muscle tissue underlies several polygenic metabolic traits and diseases. Identifying their genetic mechanisms requires pinpointing cell types, regulatory elements, target genes and causal variants. Here we use genetic multiplexing to generate population-scale single-nucleus chromatin accessibility (single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC–seq)) and transcriptome (single-nucleus RNA sequencing (snRNA-seq)) maps across 287 frozen human skeletal muscle biopsies. We identify 13 cell types and integrate genetic variation to discover >7,000 expression quantitative trait loci (eQTLs) and >100,000 chromatin accessibility QTLs (caQTLs) across cell types, including context-specific eQTL/caQTL with significant G × C interaction. We identify nearly 2,000 eGenes colocalized with caQTL and construct causal directional maps for chromatin accessibility and gene expression. Almost 3,500 genome-wide association study (GWAS) signals across 38 relevant traits colocalize with sn-eQTL/caQTL, most in a cell-specific manner. These signals typically colocalize with caQTL and not eQTL, highlighting the importance of population-scale chromatin profiling for GWAS functional studies. The results of this study illuminate the genetic regulatory architecture of human skeletal muscle.This is a preview of subscription content, access via your institutionAccess options Access through your institutionAccess Nature and 54 other Nature Portfolio journalsGet Nature+, our best-value online-access subscription34.99 € / 30 days cancel any timeLearn moreSubscribe to this journalReceive 12 print issues and online access269.00 € per year only 22.42 € per issueLearn moreBuy this articlePurchase on SpringerLinkInstant access to the full article PDF.39,95 €Prices may be subject to local taxes which are calculated during checkoutFig. 1: snRNA and snATAC–seq data generation and integration identify 13 high-quality cell-type clusters.Fig. 2: Thousands of eQTLs/caQTLs identified in clusters.Fig. 3: Learning patterns of eQTL/caQTL signal sharing across clusters informs effect estimates.Fig. 4: Identifying context-specific eQTL/caQTL.Fig. 5: eQTL/caQTL fine-mapping, colocalization and causal inference inform regulatory grammar in clusters.Fig. 6: Enrichment of GWAS traits in cluster snATAC peaks.Fig. 7: Integrating eQTL/caQTL signals with GWAS informs disease/trait-relevant regulatory mechanisms.Data availabilityThe raw data are available from the dbGaP repository with other FUSION molecular data phs001048.v3.p1 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001048.v3.p1). MPRA data are available at GEO accession GSE247455 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE247455). Processed datasets are submitted to Zenodo (https://zenodo.org/records/19670721)107. Large processed datasets such as ATAC fragment files, full cis-eQTL/caQTL scans and data objects are available on the Common Metabolic Diseases Genomic Atlas portal (https://cmdga.org/search/?searchTerm=stephen-parker%3AVarshney2024). A browser session with snATAC–seq profiles in each cluster is available at http://genome.ucsc.edu/s/arushiv/f3_atac_hg38.Code availabilityThe code used to run analyses in this work is available on GitHub (https://github.com/ParkerLab/sn_muscle_2023) and a freeze zip on Zenodo (https://zenodo.org/records/19670721)107. The analysis code with an assigned DOI is archived on Zenodo107.ReferencesFrontera, W. R. & Ochala, J. Skeletal muscle: a brief review of structure and function. Calcif. 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Population-scale single-nucleus multi-omic profiling of skeletal muscle reveals extensive context-specific genetic regulation [data set]. Zenodo https://doi.org/10.5281/zenodo.19670721 (2026).Download referencesAcknowledgementsThe authors thank S. Graham, C. Willer, S. Hanks (University of Michigan), M. den Hoed (Uppsala University) and Parker lab members for their help and feedback. The authors thank the dbGaP team, A. Sturcke, N. Sharopova and R. Lapoint, and the Common Metabolic Diseases Genomic Atlas team, Y. Sun and K. Gaulton, for helping organize our datasets for sharing.FundingThis work was supported by the Foundation for the National Institutes of Health and the Accelerating Medicines Partnership Type 2 Diabetes initiative, and by National Institutes of Health (grants R01DK072193 to K.L.M., R01DK062370 to L.J.S. and M.B., P30AR069620 and R01DK117960 to S.C.J.P., UM1DK126185 to K.L.M. and S.C.J.P. and R01DK128247 and R01DK129676 to L.N.).Author informationAuthors and AffiliationsGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USAArushi Varshney, Nandini Manickam, Peter Orchard, Adelaide Tovar, Christa Ventresca, Zhenhao Zhang, Fan Feng, Joseph Mears, Kirsten Nishino, Vivek Rai, Chao Gao, Joshua D. Welch, Jacob O. Kitzman, Jie Liu & Stephen C. J. ParkerDepartment of Human Genetics, University of Michigan, Ann Arbor, MI, USAChrista Ventresca, Jacob O. Kitzman & Stephen C. J. ParkerCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USAMichael R. Erdos, Narisu Narisu & Francis S. CollinsDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USAHeather M. Stringham, Anne U. Jackson, Michael Boehnke, Laura J. Scott & Stephen C. J. ParkerBiomedical Research Core Facilities Advanced Genomics Core, University of Michigan, Ann Arbor, MI, USATricia Tamsen & Olivia I. KouesCenter for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health, Detroit, MI, USAMao Yang & L. Keoki WilliamsDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI, USACharles F. BurantSouth Texas Diabetes and Obesity Research Institute, School of Medicine, University of Texas, Rio Grande Valley, TX, USAChris JenkinsonDepartment of Medicine/Diabetes, UT Health San Antonio, San Antonio, TX, USARalph A. DeFronzo & Luke NortonDepartment of Cellular and Integrative Physiology, UT Health San Antonio, San Antonio, TX, USALuke NortonDepartment of Cell Systems and Anatomy, UT Health San Antonio, San Antonio, TX, USALuke NortonWellbeing Services County of South Karelia, South Karelia, FinlandJouko SaramiesInstitute of Biomedicine, University of Eastern Finland, Kuopio, FinlandTimo A. LakkaDepartment of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, FinlandTimo A. LakkaKuopio Research Institute of Exercise Medicine, Kuopio, FinlandTimo A. LakkaInstitute of Clinical Medicine, University of Eastern Finland, Kuopio, FinlandMarkku LaaksoDepartment of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, FinlandJaakko Tuomilehto & Heikki A. KoistinenDepartment of Public Health, University of Helsinki, Helsinki, FinlandJaakko TuomilehtoDiabetes Research Group, King Abdulaziz University, Jeddah, Saudi ArabiaJaakko TuomilehtoDepartment of Genetics, University of North Carolina, Chapel Hill, NC, USAKaren L. MohlkeDepartment of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, FinlandHeikki A. KoistinenUniversity of Helsinki, Faculty of Medicine, Research Programs Unit, Clinical and Molecular Metabolism (CAMM), Helsinki, FinlandHeikki A. KoistinenMinerva Foundation Institute for Medical Research, Helsinki, FinlandHeikki A. KoistinenAuthorsArushi VarshneyView author publicationsSearch author on:PubMed Google ScholarNandini ManickamView author publicationsSearch author on:PubMed Google ScholarPeter OrchardView author publicationsSearch author on:PubMed Google ScholarAdelaide TovarView author publicationsSearch author on:PubMed Google ScholarChrista VentrescaView author publicationsSearch author on:PubMed Google ScholarZhenhao ZhangView author publicationsSearch author on:PubMed Google ScholarFan FengView author publicationsSearch author on:PubMed Google ScholarJoseph MearsView author publicationsSearch author on:PubMed Google ScholarMichael R. ErdosView author publicationsSearch author on:PubMed Google ScholarNarisu NarisuView author publicationsSearch author on:PubMed Google ScholarKirsten NishinoView author publicationsSearch author on:PubMed Google ScholarVivek RaiView author publicationsSearch author on:PubMed Google ScholarHeather M. 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LakkaView author publicationsSearch author on:PubMed Google ScholarMarkku LaaksoView author publicationsSearch author on:PubMed Google ScholarJaakko TuomilehtoView author publicationsSearch author on:PubMed Google ScholarKaren L. MohlkeView author publicationsSearch author on:PubMed Google ScholarJacob O. KitzmanView author publicationsSearch author on:PubMed Google ScholarHeikki A. KoistinenView author publicationsSearch author on:PubMed Google ScholarJie LiuView author publicationsSearch author on:PubMed Google ScholarMichael BoehnkeView author publicationsSearch author on:PubMed Google ScholarFrancis S. CollinsView author publicationsSearch author on:PubMed Google ScholarLaura J. ScottView author publicationsSearch author on:PubMed Google ScholarStephen C. J. ParkerView author publicationsSearch author on:PubMed Google ScholarContributionsA.V. performed batch design, data processing and computational analyses, interpreted and visualized the results, prepared figures and wrote the paper. N.M. processed samples, isolated nuclei and performed the snATAC–seq and snRNA-seq experiments. P.O., Z.Z., F.F. and J.M. performed computational analyses and generated figures. A.T. and K.N. performed MPRA experiments. A.T. analyzed the data. V.R. contributed to visualization. M.R.E. processed FUSION biopsy samples. H.M.S. and A.U.J. organized sample information and metadata. N.N. processed FUSION genotyping data. C.V. performed experiments. T.T. and O.I.K. performed sequencing. C.G. and J.D.W. assisted in joint clustering. M.Y., L.K.W., C.J., R.A.D., L.N., J.S., T.A.L., M.L., J.T. and H.A.K. contributed biopsy samples. J.D.W., C.F.B., K.L.M., J.O.K., J.L., M.B., F.S.C., L.J.S. and S.C.J.P. provided supervision and mentoring. L.J.S. and S.C.J.P. designed the study and obtained funding. S.C.J.P. organized the project and supervised all aspects of data generation, analyses and interpretation, and edited the paper.Corresponding authorCorrespondence to Stephen C. J. Parker.Ethics declarationsCompeting interestsS.C.J.P. has a research grant from Pfizer. The other authors declare no competing interests.Peer reviewPeer review informationNature Genetics thanks Clint Miller and other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Study design and quality control.a, Batch design showing cohort representation across batches. Other key variables blocked on during batch design include: b, Age. c, BMI. d, Sex. e, OGTT status for individuals with samples across batches. IFG: impaired fasting glucose; IGT: impaired glucose tolerance; NGT: normal glucose tolerance; T2D: type 2 diabetic. f, snRNA-seq barcode rank plot showing number of UMIs across droplets. g, snRNA UMIs versus mitochondrial read fraction across batches (columns). h, snATAC–seq barcode rank plot showing high-quality autosomal alignments (HQAA) across droplet. i, snATAC HQAA versus mitochondrial read fraction across batches. j, snATAC HQAA versus TSS enrichment across batches. Panels g, i and j show nuclei from one 10x channel per batch.Extended Data Fig. 2 Joint clustering of the snRNA-seq and snATAC–seq modalities identified 13 cell-type clusters.a, UMAP plots by batch and modality. Columns show 10 FUSION batches and one multiome sample, which was processed in a separate batch. b, UMAP plots showing the Louvain clusters and snRNA-seq expression of known marker genes. c, UMAP plots and percentage abundance plots by modality, colored by annotated clusters. d, Concordance between cluster annotations for the RNA and ATAC modalities of the multiome nuclei. Plotted are the fraction of nuclei in the RNA cluster that are assigned each annotation in the ATAC cluster.Extended Data Fig. 3 Identifying snATAC–seq peak features in clusters.a–c, Example genomic locations where consensus summit(s) (red) were identified among nearby summit calls (black) (top track), along with the respective cluster ATAC–seq signal tracks. d, Aggregated ATAC–seq signal across all broad peaks in a cluster while centering on the leftmost summit in the peak. e, Heatmap showing peak summits identified as cluster-specific. f, Motif enrichment in cluster-specific peak summits calculated using a logistic regression approach. ‘*’ indicates significant enrichment (5% FDR).Extended Data Fig. 4 Identifying eQTL in clusters.a, PC scan to maximize eQTL discovery. b, Identifying testable genes with minimum n counts across at least 10 samples that maximize eQTL discovery. Number of testable genes and the number of eGenes (FDR 0.5) at the default p12 of 5 × 10−6 were considered. e,f, Heatmaps showing the coloc PPH4 for BMI (e) and fasting insulin (f) GWAS loci that colocalize with e/caQTL across clusters. g, caQTL-GWAS colocalization comparison between DIAMANTE (European) and FinnGen (Finnish) T2D GWAS datasets. The same genomic region was considered for colocalization with the two datasets.Extended Data Fig. 10 Integrating e/caQTL signals with GWAS inform disease/trait relevant regulatory mechanisms.a, GLI2 genomic locus where a T2D GWAS signal is colocalized with an endothelial caQTL. snATAC profiles in five clusters by caSNP rs11688682 genotype in its ±1 kb neighborhood, followed by aggregate profiles in 13 clusters. b, LocusZoom plots for the GLI2 GWAS signal (top) followed by the caQTL signal in the five clusters. The peak was not testable for caQTL in the type 1, 2a, 2x and FAP clusters due to low counts. c, EPCOT-imputed micro-C chromatin contacts using endothelial ATAC data at 1 kb resolution at the 500 kb neighborhood centered at rs11688682. d, Difference in the predicted normalized chromatin contacts using endothelial snATAC–seq from samples with the high (GG) and low (CC) accessibility genotype rs11688682. Interactions with rs11688682 highlighted in black are shown as a signal track below. e, Endothelial chromatin co-accessibility scores between the GLI2 caPeak and TSS peaks of neighboring genes, classified by the caSNP genotype. Distance between the peaks is noted in parentheses. f, C2CD4A/B genomic locus, followed by snATAC–seq profiles by caSNP rs7163757 genotypes in clusters, followed by aggregate snATAC profiles. g, NMJ chromatin co-accessibility scores between the caPeak and TSS-peaks classified by caSNP genotype. Distance between the peaks is noted in parentheses.Supplementary informationSupplementary Information (download PDF )Supplementary Figs. 1–4, Supplementary Note, Supplementary Methods and Supplementary Protocols.Reporting Summary (download PDF )Peer Review File (download PDF )Supplementary Tables (download XLSX )Supplementary Table 1: Marker genes known from literature used to annotate clusters. Supplementary Table 2: n pass-QC nuclei per sample-cluster. Supplementary Table 3: snRNA GO term enrichment. Supplementary Table 4: eQTL/caQTL scan information. Supplementary Table 5: List of GWAS traits. Supplementary Table 6: List of annotations included in the LDSC baseline model. Supplementary Table 7: GWAS enrichment in cluster peaks. Supplementary Table 8: GWAS enrichment in QTL peaks. Supplementary Table 9: T2D GWAS–caQTL coloc. Supplementary Table 10: T2D GWAS–eQTL coloc. Supplementary Table 11: MPRA results. Supplementary Table 12: MPRA primer sequences.Rights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this article