️ Episode 153: Skeletal muscle eQTL meta-analysis implicates genes in the genetic architecture of muscular and cardiometabolic traits

In this episode of PaperCast Base by Base, we explore a large skeletal muscle eQTL meta-analysis that integrates GTEx and FUSION data to pinpoint regulatory variants and genes underlying muscular and cardiometabolic traits.

Study Highlights:
Combining RNA-seq and whole-genome data from 1,002 individuals across two cohorts, the authors identified 18,818 conditionally distinct eQTL signals affecting 12,283 genes, with 35% of genes harboring multiple signals. Colocalization with 26 GWAS datasets yielded 2,252 signal pairs and nominated 1,342 candidate genes, and strikingly 22% of the colocalizations involved non‑primary eQTL signals while many mapped far from the nearest transcription start site. A focused multi‑tissue analysis for type 2 diabetes linked 309 of 862 tested signals to 551 genes across skeletal muscle, adipose, liver, and islet, representing 36% of T2D signals and exceeding the yield of any single tissue. The study also functionally validated a T2D‑linked variant at the INHBB locus, where the risk allele increased enhancer activity and aligned with higher gene expression in both muscle and adipose models.

Conclusion:
This work delivers a well‑powered skeletal muscle eQTL resource and shows how multi‑signal, multi‑tissue integration clarifies the molecular mechanisms and candidate targets underlying cardiometabolic disease.

Reference:
Wilson EP, Broadaway KA, Parsons VA, Vadlamudi S, Narisu N, Brotman SM, Currin KW, Stringham HM, Erdos MR, Welch R, Holtzman JK, Lakka TA, Laakso M, Tuomilehto J, Boehnke M, Koistinen HA, Collins FS, Parker SCJ, Scott LJ, Mohlke KL. Skeletal muscle eQTL meta-analysis implicates genes in the genetic architecture of muscular and cardiometabolic traits. The American Journal of Human Genetics. 2025;112:1–15. https://doi.org/10.1016/j.ajhg.2025.09.003

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

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