Translating GWAS findings into therapies for depression and anxiety disorders: gene-set analyses reveal enrichment of psychiatric drug classes and implications for drug repositioning.

BACKGROUND: Depression and anxiety disorders (AD) are the first and sixth leading causes of disability worldwide. Despite their high prevalence and significant disability resulted, there are limited advances in new drug development. Recently, genome-wide association studies (GWAS) have greatly advanced our understanding of the genetic basis underlying psychiatric disorders. METHODS: Here we employed gene-set analyses of GWAS summary statistics for drug repositioning. We explored five related GWAS datasets, including two on major depressive disorder (MDD2018 and MDD-CONVERGE, with the latter focusing on severe melancholic depression), one on AD, and two on depressive symptoms and neuroticism in the population. We extracted gene-sets associated with each drug from DSigDB and examined their association with each GWAS phenotype. We also performed repositioning analyses on meta-analyzed GWAS data, integrating evidence from all related phenotypes. RESULTS: Importantly, we showed that the repositioning hits are generally enriched for known psychiatric medications or those considered in clinical trials. Enrichment was seen for antidepressants and anxiolytics but also for antipsychotics. We also revealed new candidates or drug classes for repositioning, some of which were supported by experimental or clinical studies. For example, the top repositioning hit using meta-analyzed p values was fendiline, which was shown to produce antidepressant-like effects in mouse models by inhibition of acid sphingomyelinase. CONCLUSION: Taken together, our findings suggest that human genomic data such as GWAS are useful in guiding drug discoveries for depression and AD.

Differential associations of depression-related phenotypes with cardiometabolic risks: Polygenic analyses and exploring shared genetic variants and pathways.

BACKGROUND: Numerous studies have suggested associations between depression and cardiometabolic (CM) diseases. However, little is known about the mechanism underlying this comorbidity, and whether the relationship differs by depression subtypes. METHODS: Using polygenic risk scores (PRS) and linkage disequilibrium (LD) score regression, we investigated the genetic overlap of various depression-related phenotypes with a comprehensive panel of 20 CM traits. GWAS results for major depressive disorder (MDD) were taken from the PGC and CONVERGE studies, with the latter focusing on severe melancholic depression. GWAS results on general depressive symptoms (DS) and neuroticism were also included. We identified the shared genetic variants and inferred enriched pathways. We also looked for drugs over-represented among the top-shared genes, with an aim to finding repositioning opportunities for comorbidities. RESULTS: We found significant genetic overlap between MDD, DS, and neuroticism with cardiometabolic traits. In general, positive polygenic associations with CM abnormalities were observed except for MDD-CONVERGE. Counterintuitively, PRS representing severe melancholic depression was associated with reduced CM risks. Enrichment analyses of shared SNPs revealed many interesting pathways such as those related to inflammation that underlie the comorbidity of depressive and CM traits. Using a gene-set analysis approach, we also revealed several repositioning candidates with literature support (e.g., bupropion). CONCLUSIONS: Our study highlights shared genetic bases of depression with CM traits, and suggests the associations vary by depression subtypes, which may have implications in targeted prevention of cardiovascular events for patients. Identification of shared genetic factors may also guide drug discovery for the comorbidities.