Integrating Multi-Omics Summary Data Identifies Candidate Molecular Mechanisms for Major Depression.

BackgroundMajor depression (MD) is the most common psychiatric disorder. However, despite having a significant genetic component, the underlying biological mechanisms remain poorly understood. Our analyses leveraged molecular quantitative trait loci (xQTL) data to identify molecular biomarkers for MD.MethodsWe used OPERA (Omics Pleiotropic Association) software to identify molecular phenotypes associated with MD through shared causal variants, using genome-wide association study (GWAS) summary statistics and xQTL data for 5 phenotypes in blood and brain tissues. The xQTL phenotypes were gene expression, DNA methylation, splicing variation, chromatin accessibility, and protein abundance.ResultsWe identified 939 genes in blood tissues and 607 genes in brain tissues associated with MD via at least 1 molecular phenotype. Drug targets were enriched in our significant genes in both tissues. A total of 23 genes showed associations via 3 or more molecular phenotypes, providing robust evidence for their causal role in MD and offering insights into their biomolecular mechanisms. These high-priority associations included genes that have been previously identified by GWASs of MD such as CDH13 and RAB27B as well as novel associations such as H6PD.ConclusionsOur results highlight promising new targets for biomarker and drug target identification and successfully expand on GWAS findings to identify novel associations with MD. However, our study took a broad approach using bulk brain and blood tissues. Future research should expand these analyses into cell- and region-specific contexts.