Evidence map›Paper›PMID 40992379›Full record

SynthesisAmerican journal of human genetics2025

Skeletal muscle eQTL meta-analysis implicates genes in the genetic architecture of muscular and cardiometabolic traits.

Emma P Wilson, K Alaine Broadaway, Victoria A Parsons, Swarooparani Vadlamudi, Narisu Narisu, Sarah M Brotman, Kevin W Currin, Heather M Stringham, Michael R Erdos, Ryan Welch and 10 more

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in American journal of human genetics, 2025. 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.

  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

20 authors.

Emma P WilsonDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
K Alaine BroadawayDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Victoria A ParsonsDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Swarooparani VadlamudiDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Narisu NarisuCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Sarah M BrotmanDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Kevin W CurrinDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Heather M StringhamDepartment of Biostatistics, Center for Statistical Genetics, 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.
Ryan WelchDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Jeffrey K HoltzmanDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Timo A LakkaInstitute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.
Markku LaaksoInstitute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.
Jaakko TuomilehtoDepartment of Public Health, University of Helsinki, Helsinki, Finland.
Michael BoehnkeDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Heikki A KoistinenDepartment of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland; Research Programs Unit, Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland; Department of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Minerva Foundation Institute for Medical Research, Helsinki, Finland.
Francis S CollinsCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Stephen C J ParkerDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
Laura J ScottDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Karen L MohlkeDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: mohlke@med.unc.edu.

Funding

Genetic analysis of type II diabetes in Finnish populationZIAHG000024 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI ERDOS, MICHAEL · 2009 to 2025
$38.2M
Regional Pilot And Feasibility Study Grants ProgramP30DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Mehboob A Hussain · 2013 to 2026
$24.3M
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
Genetic epidemiology of rare and regulatory variants for metabolic traitsR01DK093757 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN L. MOHLKE · 2011 to 2026
$8.2M
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
The Genetic Epidemiology of Heart, Lung, and Blood TraitsTraining Grant (GenHLB)T32HL129982 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Christy Leigh Avery, KAREN L. MOHLKE · 2016 to 2026
$3.8M
Predoctoral Training Program in Bioinformatics and Computational BiologyT32GM135123 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Michael Isaiah Love, William Valdar · 2021 to 2026
$1.7M
Analyzing gene expression in adipose tissue to identify candidate genes at cardiometabolic trait GWAS lociF31HL154730 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ZWEIFEL, SARAH · 2021 to 2023
$86k
Intramural NIH HHS ZIA HG000024NHLBI NIH HHS F31 HL154730NHLBI NIH HHS T32 HL129982NIDDK NIH HHS P30 DK020572NIDDK NIH HHS R01 DK062370NIDDK NIH HHS R01 DK072193NIDDK NIH HHS R01 DK093757NIDDK NIH HHS UM1 DK126185NIGMS NIH HHS T32 GM135123
6 · The paper itself

Abstract

Identifying genetic variants that regulate gene expression can help uncover mechanisms underlying complex traits. We performed a meta-analysis of skeletal muscle expression quantitative trait locus (eQTL) using data from 1,002 individuals from two studies. A stepwise analysis identified 18,818 conditionally distinct signals for 12,283 genes, and 35% of these genes contained two or more signals. Colocalization of these eQTL signals with 26 muscular and cardiometabolic trait genome-wide association studies (GWASs) identified 2,252 GWAS-eQTL colocalizations that nominated 1,342 candidate genes. Notably, 22% of the GWAS-eQTL colocalizations involved non-primary eQTL signals. Additionally, 37% of the colocalized GWAS-eQTL signals corresponded to the closest protein-coding gene, while 44% were located >50 kb from the transcription start site of the nominated gene. To assess tissue specificity for a heterogeneous trait, we compared colocalizations with type 2 diabetes (T2D) signals across muscle, adipose, liver, and islet eQTLs; we identified 551 candidate genes for 309 T2D signals representing 36% of T2D signals tested and over 100 more than were detected with any one tissue alone. We then functionally validated the allelic regulatory effect of an eQTL variant for INHBB linked to T2D in both muscle and adipose tissue. Together, these results further demonstrate the value of skeletal muscle eQTLs in elucidating mechanisms underlying complex traits.

Indexed as

Diabetes Mellitus, Type 2Muscle, SkeletalQuantitative Trait LociGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansPolymorphism, Single Nucleotideallelic heterogeneitycolocalizationcomplex traiteQTLGWASINHBBsignal identificationskeletal muscletranscriptomicstype 2 diabetes

Identifiers

PMID40992379
PMCPMC12614742

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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.