Evidence map›Paper›PMID 38168419›Full record

ArticlebioRxiv : the preprint server for biology2024

Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation.

Arushi Varshney, Nandini Manickam, Peter Orchard, Adelaide Tovar, Christa Ventresca, Zhenhao Zhang, Fan Feng, Joseph Mears, Michael R Erdos, Narisu Narisu and 26 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 10 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

36 authors at 9 institutions in 3 countries.

Arushi VarshneyDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0001-9177-9707
Nandini ManickamDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Peter OrchardDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Adelaide TovarDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Christa VentrescaDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Zhenhao ZhangDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Fan FengDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Joseph MearsDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Michael R ErdosCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Narisu NarisuCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Kirsten NishinoDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Vivek RaiDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0002-2058-7238
Heather M StringhamDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Anne U JacksonDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Tricia TamsenBiomedical Research Core Facilities Advanced Genomics Core, University of Michigan, Ann Arbor, MI, USA.
Chao GaoDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Mao YangDepartment of Internal Medicine, Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, USA.
Olivia I KouesBiomedical Research Core Facilities Advanced Genomics Core, University of Michigan, Ann Arbor, MI, USA.
Joshua D WelchDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0002-5869-2391
Charles F BurantDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
L Keoki WilliamsDepartment of Internal Medicine, Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, USA.
Chris JenkinsonSouth Texas Diabetes and Obesity Research Institute, School of Medicine, University of Texas, Rio Grande Valley, TX, USA.
Ralph A DeFronzoDepartment of Medicine/Diabetes Division, University of Texas Health, San Antonio, TX, USA.
Luke NortonDepartment of Medicine/Diabetes Division, University of Texas Health, San Antonio, TX, USA.ORCID 0000-0002-0231-5722
Jouko SaramiesSavitaipale Health Center, South Karelia Central Hospital, Lappeenranta, Finland.
Timo A LakkaInstitute of Biomedicine, University of Eastern Finland, Kuopio, Finland.
Markku LaaksoInstitute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.
Jaakko TuomilehtoDept. of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.
Karen L MohlkeDept. of Genetics, University of North Carolina, Chapel Hill, NC, USA.
Jacob O KitzmanDept. of Human Genetics, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0002-6145-882X
Heikki A KoistinenDept. of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.
Jie LiuDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Michael BoehnkeDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Francis S CollinsCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Laura J ScottDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Stephen C J ParkerDept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
University of Michigan · USNational Institutes of Health · USHenry Ford Hospital · USThe University of Texas Health Science Center at San Antonio · USUniversity of Eastern Finland · FIUniversity of Helsinki · FISouth Karelia Central Hospital · FIThe University of Texas Rio Grande Valley · USUniversity of North Carolina at Chapel Hill · US

Funding

PREDOCTORAL TRAINING IN GENETICST32GM007544 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MORAN, JOHN V. · 1985 to 2022
$11.3M
Targeted Genetic Analysis of T2D and Quantitative TraitsR01DK072193 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN L. MOHLKE · 2005 to 2026
$11.3M
Bridging the gap between type 2 diabetes GWAS and therapeutic targetsUM1DK126185 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI CLAUSSNITZER, MELINA C, GLOYN, ANNA LOUISE · 2020 to 2024
$9.5M
Structure, Composition, & Histology Core - Core BP30AR069620 · NIAMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KARL J JEPSEN · 2016 to 2026
$8.4M
Identifying Genes for Type 2 Diabetes:FUSIONR01DK062370 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L, SCOTT, LAURA J. · 2003 to 2025
$6.7M
Context-specific and combinatorial genetic regulatory grammars in diabetesR01DK117960 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Stephen CJ Parker · 2018 to 2026
$2.8M
NIAMS NIH HHS P30 AR069620NIDDK NIH HHS R01 DK062370NIDDK NIH HHS R01 DK072193NIDDK NIH HHS R01 DK117960NIDDK NIH HHS UM1 DK126185NIGMS NIH HHS T32 GM007544
6 · The paper itself

Abstract

Skeletal muscle, the largest human organ by weight, is relevant in several polygenic metabolic traits and diseases including type 2 diabetes (T2D). Identifying genetic mechanisms underlying these traits requires pinpointing cell types, regulatory elements, target genes, and causal variants. Here, we use 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 nearly half a million nuclei. We identify 13 cell types and integrate genetic variation to discover >7,000 expression quantitative trait loci (eQTL) and >100,000 chromatin accessibility QTLs (caQTL) across cell types. Learning patterns of e/caQTL sharing across cell types increased precision of effect estimates. We identify high-resolution cell-states and context-specific e/caQTL with significant genotype by context 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-e/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. Finally, our GWAS-caQTL colocalization data reveal distinct cell-specific regulatory paradigms. Our 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.

Identifiers

PMID38168419
PMCPMC10760134
OpenAlexW4389817837

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.