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

## TL;DR

This study uses genetic data from muscle tissue to identify genes linked to muscular and cardiometabolic traits, revealing new insights into their genetic mechanisms.

## Contribution

The study provides a large-scale eQTL meta-analysis in skeletal muscle, identifying novel gene-trait associations and validating regulatory effects in multiple tissues.

## Key findings

- 18,818 distinct eQTL signals were identified for 12,283 genes in skeletal muscle.
- 2,252 GWAS-eQTL colocalizations were found, implicating 1,342 candidate genes in muscular and cardiometabolic traits.
- 551 candidate genes were linked to type 2 diabetes signals across multiple tissues, exceeding single-tissue analyses.

## 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