Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes

Molecular quantitative trait loci (xQTLs) are often harnessed to prioritize genes or functional elements under-pinning variant-trait associations identified from genome-wide association studies (GWASs). Here, we intro-duce OPERA, a method that jointly analyzes GWAS and multi-omics xQTL summary statistics to enhance the identification of molecular phenotypes associated with complex traits through shared causal variants. Applying OPERA to summary-level GWAS data for 50 complex traits (n = 20,833-766,345) and xQTL data from seven omics layers (n = 100-31,684) reveals that 50% of the GWAS signals are shared with at least one molecular phenotype. GWAS signals shared with multiple molecular phenotypes, such as those at the MSMB locus for prostate cancer, are particularly informative for understanding the genetic regulatory mech-anisms underlying complex traits. Future studies with more molecular phenotypes, measured considering spatiotemporal effects in larger samples, are required to obtain a more saturated map linking molecular in-termediates to GWAS signals.

Automated summary

This study introduces OPERA, a method that enhances the identification of molecular phenotypes associated with complex traits by analyzing GWAS and multi-omics xQTL summary statistics together. The study finds that 50% of GWAS signals are shared with at least one molecular phenotype, and GWAS signals shared with multiple molecular phenotypes are particularly informative for understanding the genetic regulatory mechanisms of complex traits. Future research with more molecular phenotypes and larger sample sizes is needed to obtain a more saturated map linking molecular intermediates to GWAS signals.