Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis

TLDR

Many disease‑associated variants identified by GWAS have unclear downstream molecular consequences. The study aims to identify the downstream effects of these variants. The authors conducted cis‑ and trans‑eQTL analyses on blood samples from 31,684 individuals using the eQTLGen Consortium. Cis‑eQTLs were detected for 88 % of genes but differ genetically from disease variants, limiting causal inference, whereas trans‑eQTLs—identified for 37 % of trait‑associated variants—more effectively link multiple variants to key disease genes, a pattern also seen with polygenic scores where 13 % of genes’ expression correlated with scores and drive traits.

Abstract

Summary While many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear. To identify these effects, we performed cis- and trans-expression quantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium. We observed that cis -eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to use cis -eQTLs to pinpoint causal genes within susceptibility loci. In contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology. We observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.