Here is an open access link to the manuscript! rdcu.be/d5ov4

Thanks for the advice!

Further eQTL sample size increases would enable detection of more signals and more colocalizations with GWAS. 8/8

We detected examples of eQTL genes with multiple signals that colocalized with multiple GWAS signals for the same trait, increasing our confidence in the colocalization. Many of these signals showed a gene-based dosage effect on the GWAS trait. 7/8

The proportion of GWAS signals with a colocalized eQTL signal was 34% on average but increased for GWAS traits that were the most directly related to adipose tissue such as fat depot measurements. 6/8

Colocalization of eQTL with conditionally distinct GWAS signals for 28 cardiometabolic traits identified 3,605 eQTL signals for 1,861 genes. Inclusion of non-primary eQTL signals increased detection of colocalized GWAS-eQTL signals by 46%. 5/8

Highly expressed, more heritable genes that were more tolerant to loss-of-function variants had more detected eQTL signals. 4/8

Primary eQTL signals, detected first, had distinct characteristics from subsequently detected eQTL signals. Primary adipose eQTL signals had larger effect sizes, higher minor allele frequency, and were more enriched in promoters than subsequent signals. 3/8

Current eQTL datasets are not well-powered to detect the full genetic regulation of gene expression. We increased our ability to detect conditionally distinct eQTL signals in adipose tissue through meta-analysis of 2,344 samples using APEX. 50% of eQTL genes had at least 2 signals. 2/8