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

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.

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Journal
The American Journal of Human Genetics
Published
2025-09-24
DOI
https://doi.org/10.1016/j.ajhg.2025.09.003
Primary Topic
Adipose Tissue and Metabolism
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article
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article

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

Michael Boehnke, Laura J. Scott, Narisu Narisu, Heather M. Stringham et al.
The American Journal of Human Genetics
Adipose Tissue and Metabolism
article

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

Michael Boehnke, Laura J. Scott, Narisu Narisu, Heather M. Stringham, Michael R. Erdos, Kevin W. Currin, Ryan Welch, Timo A. Lakka, Karen L. Mohlke, Stephen C.J. Parker, K. Alaine Broadaway, Swarooparani Vadlamudi, Francis S. Collins, Sarah M. Brotman, Victoria A. Parsons, Jaakko Tuomilehto, Markku Laakso, Emma P Wilson, Heikki A. Koistinen, Jeffrey K Holtzman
article en

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.

The American Journal of Human GeneticsVol. 112(11)
University of North Carolina at Chapel Hill (US), National Institutes of Health (US), University of North Carolina Health Care (US), University of Helsinki (FI), University of Eastern Finland (FI), University of Michigan (US), Finnish Institute for Health and Welfare (FI), National Human Genome Research Institute (US), Institute for Molecular Medicine Finland (FI)
Openalex Percentile: Top 67%
Adipose Tissue and Metabolism
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