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 overrepresented 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. K E Y W O R D Sbiological markers, cardiovascular/cardiac/heart disease, depression, epidemiology, genetics 330

Recent studies have suggested an important role of de novo mutations (DNMs) in neuropsychiatric disorders. As DNMs are not subject to elimination due to evolutionary pressure, they are likely to have greater disruptions on biological functions. While a number of sequencing studies have been performed on neuropsychiatric disorders, the implications of DNMs for drug discovery remain to be explored.In this study, we employed a gene-set analysis approach to address this issue. Four neuropsychiatric disorders were studied, including schizophrenia (SCZ), autistic spectrum disorders (ASD), intellectual disability (ID) and epilepsy. We first identified gene-sets associated with different drugs, and analyzed whether the gene-set pertaining to each drug overlaps with DNMs more than expected by chance. We also assessed which medication classes are enriched among the prioritized drugs. We discovered that neuropsychiatric drug classes were indeed significantly enriched for DNMs of all four disorders; in particular, antipsychotics and antiepileptics were the most strongly enriched drug classes for SCZ and epilepsy respectively. Interestingly, we revealed enrichment of several unexpected drug classes, such as lipid-lowering agents for SCZ and anti-neoplastic agents. By inspecting individual hits, we also uncovered other interesting drug candidates or mechanisms (e.g. histone deacetylase inhibition and retinoid signaling) that might warrant further investigations.Taken together, this study provided evidence for the usefulness of DNMs in guiding drug discovery or repositioning.

Depression and cardiometabolic diseases, such as coronary artery disease (CAD) and type 2 diabetes (DM), are commonly considered as risk factors to each other. However, little is known about the mechanism underlying the relationship between depression and cardiometabolic traits. Using a polygenic risk score approach, we investigated the genetic overlap of major depressive disorder (MDD) with various cardiometabolic traits based on summary statistics from large-scale meta-analyses of genome-wide association studies (GWAS). GWAS results for MDD were taken from MDD-CONVERGE which represents a relatively homogenous sample of severe depression. We also identified shared genetic variants and inferred the enriched pathways. In addition, we looked for drugs over-represented among the top shared genes, with an aim to finding repositioning opportunities for both kinds of disorders.We found significant polygenic sharing between MDD and cardiometabolic traits, including positive associations with CAD, fat percentage, LDL, triglyceride, body mass index (BMI), waist-hip ratio (WHR) and WHR adjusted for BMI, and an inverse association with HDL. We also observed a modest association of MDD with DM but no significant associations with other glycemic traits or leptin. Some of the shared pathways include lipoprotein metabolism, neurotrophin and oxytocin pathways. Using a gene-set analysis approach, we revealed drugs that may be repositioned for both types of disorders, many of which are supported by previous studies, such as statins, bupropion, verapamil and s-adenosylmethionine. Our study highlights shared genetic bases of MDD with cardiometabolic traits, and implicates the potential of repurposing drugs for comorbidities based on overlapping genetic factors.

Recent studies have suggested an important role of de novo mutations (DNMs) in neuropsychiatric disorders. As DNMs are not subject to elimination due to evolutionary pressure, they are likely to have greater disruptions on biological functions. While a number of sequencing studies have been performed on neuropsychiatric disorders, the implications of DNMs for drug discovery remain to be explored.In this study, we employed a gene-set analysis approach to address this issue. Four neuropsychiatric disorders were studied, including schizophrenia (SCZ), autistic spectrum disorders (ASD), intellectual disability (ID) and epilepsy. We first identified gene-sets associated with different drugs, and analyzed whether the gene-set pertaining to each drug overlaps with DNMs more than expected by chance. We also assessed which medication classes are enriched among the prioritized drugs. We discovered that neuropsychiatric drug classes were indeed significantly enriched for DNMs of all four disorders; in particular, antipsychotics and antiepileptics were the most strongly enriched drug classes for SCZ and epilepsy respectively. Interestingly, we revealed enrichment of several unexpected drug classes, such as lipid-lowering agents for SCZ and anti-neoplastic agents. By inspecting individual hits, we also uncovered other interesting drug candidates or mechanisms (e.g. histone deacetylase inhibition and retinoid signaling) that might warrant further investigations.Taken together, this study provided evidence for the usefulness of DNMs in guiding drug discovery or repositioning.