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Chau, C. K.-L.

Publications and source records attributed to Chau, C. K.-L..

2 recordsLinked to original sources

Uncovering bi-directional causal relationships between plasma proteins and psychiatric disorders: A proteome-wide study leveraging GWAS summary data

Psychiatric disorders represent a major public health burden yet their etiologies remain poorly understood, and treatment advances are limited. In addition, there are no reliable biomarkers for diagnosis or progress monitoring. Here we performed a proteome-wide causal association study covering 3522 plasma proteins and 24 psychiatric traits or disorders, based on large-scale GWAS data and the principle of Mendelian randomization (MR). We have conducted ~95,000 MR analyses in total; to our knowledge, this is the most comprehensive study on the causal relationship between plasma proteins and psychiatric traits. The analysis was bi-directional: we studied how proteins may affect psychiatric disorder risks, but also looked into how psychiatric traits/disorders may be causal risk factors for changes in protein levels. We also performed a variety of additional analysis to prioritize protein-disease associations, including HEIDI test for distinguishing functional association from linkage, analysis restricted to cis- acting variants and replications in independent datasets from the UK Biobank. Based on the MR results, we constructed directed networks linking proteins, drugs and different psychiatric traits, hence shedding light on their complex relationships and drug repositioning opportunities. Interestingly, many top proteins were related to inflammation or immune functioning. The full results were also made available online in searchable databases. In conclusion, identifying proteins causal to disease development have important implications on drug discovery or repurposing. Findings from this study may also guide the development of blood-based biomarkers for the prediction or diagnosis of psychiatric disorders, as well as assessment of disease progression or recovery.

genomics↗

Genetic Overlap Of Depression With Cardiometabolic Diseases And Implications For Drug Repurposing For Comorbidities

Numerous studies have suggested associations between depression and cardiometabolic abnormalities or diseases, such as coronary artery disease and type 2 diabetes. However, little is known about the mechanism underlying this comorbidity, and whether the relationship differs by depression subtypes. Using the polygenic risk score (PRS) approach and linkage disequilibrium (LD) score regression, we investigated the genetic overlap of various depression-related phenotypes with a comprehensive panel of 20 cardiometabolic 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 also identified the shared genetic variants and inferred enriched pathways. In addition, we looked for drugs over-represented among the top shared genes, with an aim to finding repositioning opportunities for comorbidities.\n\nWe found significant polygenic sharing between MDD, DS and neuroticism with various cardiometabolic traits. In general, positive polygenic associations with CV risks were observed for most depression phenotypes except MDD-CONVERGE. Counterintuitively, PRS representing severe melancholic depression was associated with reduced CV risks. Enrichment analyses of shared SNPs revealed many interesting pathways, such as those related to inflammation, that underlie the comorbidity of depressive and cardiometabolic traits. Using a gene-set analysis approach, we also revealed a number of repositioning candidates, some of which were supported by prior studies, such as bupropion and glutathione. Our study highlights shared genetic bases of depression with cardiometabolic traits, and suggests the associations vary by depression subtypes. To our knowledge, this is the also first study to make use of human genomic data to guide drug discovery or repositioning for comorbid disorders.

genomics↗