To maximize the statistical power to detect eQTLs and eQTSs, we performed a large-scale meta-analysis in up to 31,684 blood samples from 37 eQTLGen Consortium cohorts. This allowed us to identify cis-eQTLs for 16,987 genes, trans-eQTLs for 6,298 genes and eQTS effects for 2,568 genes (false discovery rate (FDR) <0.05, determined by permutations; Methods, Figure 1). We replicated these eQTLs across gene expression platforms, in other tissues and in single cell (sc)RNA-seq data. While the overall concordance was good, formal replication remained limited, possibly due to the effects of genetics on blood cell composition, the limited sample size of the available replication datasets and the cell-type-specific nature of distal effects. To demonstrate the utility of our resource, we combined the associations with additional data layers to gain biological insights into the mechanisms of blood eQTLs and complex traits.
