Brian Chi-Fung Wong1, Carlos Kwan-Long Chau1, Fu-Kiu Ao1, Cheuk-Hei Mo2, Sze-Yung Wong2, Yui-Hang Wong1, Hon-Cheong So1,3. 1. School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong. 2. Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong. 3. KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Shatin, Hong Kong.
Abstract
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.
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.
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