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Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation.
Varshney, Arushi; Manickam, Nandini; Orchard, Peter; Tovar, Adelaide; Zhang, Zhenhao; Feng, Fan; Erdos, Michael R; Narisu, Narisu; Ventresca, Christa; Nishino, Kirsten; Rai, Vivek; Stringham, Heather M; Jackson, Anne U; Tamsen, Tricia; Gao, Chao; Yang, Mao; Koues, Olivia I; Welch, Joshua D; Burant, Charles F; Williams, L Keoki; Jenkinson, Chris; DeFronzo, Ralph A; Norton, Luke; Saramies, Jouko; Lakka, Timo A; Laakso, Markku; Tuomilehto, Jaakko; Mohlke, Karen L; Kitzman, Jacob O; Koistinen, Heikki A; Liu, Jie; Boehnke, Michael; Collins, Francis S; Scott, Laura J; Parker, Stephen C J.
Affiliation
  • Varshney A; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Manickam N; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Orchard P; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Tovar A; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Zhang Z; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Feng F; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Erdos MR; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
  • Narisu N; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
  • Ventresca C; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Nishino K; Dept. of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
  • Rai V; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Stringham HM; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Jackson AU; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
  • Tamsen T; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
  • Gao C; Biomedical Research Core Facilities Advanced Genomics Core, University of Michigan, Ann Arbor, MI, USA.
  • Yang M; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Koues OI; Department of Internal Medicine, Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, USA.
  • Welch JD; Biomedical Research Core Facilities Advanced Genomics Core, University of Michigan, Ann Arbor, MI, USA.
  • Burant CF; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Williams LK; Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
  • Jenkinson C; Department of Internal Medicine, Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, USA.
  • DeFronzo RA; South Texas Diabetes and Obesity Research Institute, School of Medicine, University of Texas, Rio Grande Valley, TX, USA.
  • Norton L; Department of Medicine/Diabetes Division, University of Texas Health, San Antonio, TX, USA.
  • Saramies J; Department of Medicine/Diabetes Division, University of Texas Health, San Antonio, TX, USA.
  • Lakka TA; Savitaipale Health Center, South Karelia Central Hospital, Lappeenranta, Finland.
  • Laakso M; Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland.
  • Tuomilehto J; Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.
  • Mohlke KL; Dept. of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.
  • Kitzman JO; Dept. of Public Health, University of Helsinki, Helsinki, Finland.
  • Koistinen HA; Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia.
  • Liu J; Dept. of Genetics, University of North Carolina, Chapel Hill, NC, USA.
  • Boehnke M; Dept. of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
  • Collins FS; Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Scott LJ; Dept. of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.
  • Parker SCJ; Department of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
bioRxiv ; 2023 Dec 15.
Article in En | MEDLINE | ID: mdl-38168419
ABSTRACT
Skeletal muscle, the largest human organ by weight, is relevant to several polygenic metabolic traits and diseases including type 2 diabetes (T2D). Identifying genetic mechanisms underlying these traits requires pinpointing the relevant cell types, regulatory elements, target genes, and causal variants. Here, we used genetic multiplexing to generate population-scale single nucleus (sn) chromatin accessibility (snATAC-seq) and transcriptome (snRNA-seq) maps across 287 frozen human skeletal muscle biopsies representing 456,880 nuclei. We identified 13 cell types that collectively represented 983,155 ATAC summits. We integrated genetic variation to discover 6,866 expression quantitative trait loci (eQTL) and 100,928 chromatin accessibility QTL (caQTL) (5% FDR) across the five most abundant cell types, cataloging caQTL peaks that atlas-level snATAC maps often miss. We identified 1,973 eGenes colocalized with caQTL and used mediation analyses to construct causal directional maps for chromatin accessibility and gene expression. 3,378 genome-wide association study (GWAS) signals across 43 relevant traits colocalized with sn-e/caQTL, 52% in a cell-specific manner. 77% of GWAS signals colocalized with caQTL and not eQTL, highlighting the critical importance of population-scale chromatin profiling for GWAS functional studies. GWAS-caQTL colocalization showed distinct cell-specific regulatory paradigms. For example, a C2CD4A/B T2D GWAS signal colocalized with caQTL in muscle fibers and multiple chromatin loop models nominated VPS13C, a glucose uptake gene. Sequence of the caQTL peak overlapping caSNP rs7163757 showed allelic regulatory activity differences in a human myocyte cell line massively parallel reporter assay. These results illuminate the genetic regulatory architecture of human skeletal muscle at high-resolution epigenomic, transcriptomic, and cell state scales and serve as a template for population-scale multi-omic mapping in complex tissues and traits.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: BioRxiv Year: 2023 Type: Article Affiliation country: United States
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: BioRxiv Year: 2023 Type: Article Affiliation country: United States