Prioritization of potential drug targets in ovarian-related diseases: Mendelian randomization and colocalization analyses

OBJECTIVE: To identify key genes and potential drug targets for ovarian-related diseases through genome-wide Mendelian randomization (MR) and colocalization analyses. DESIGN: We conducted a comprehensive two-sample MR analysis to estimate the causal effects of blood expression quantitative trait loci (eQTLs) on ovarian-related diseases, followed by colocalization analyses to verify the robustness of the expression instrumental variables (IVs). Phenome-wide association studies (PheWAS) were also performed to evaluate the horizontal pleiotropy of potential drug targets and possible side effects. SUBJECTS: Large cohorts of European ancestry. EXPOSURE: The exposure in this study was the genetic variants (eQTLs) associated with gene expression levels, considered a form of lifelong exposure. Expression quantitative trait loci data were obtained from the eQTLGen Consortium, encompassing 16,987 genes and 31,684 cis-eQTLs derived from blood samples of healthy individuals of European ancestry. MAIN OUTCOME MEASURES: The primary outcome measures were the identification of genes causally associated with ovarian-related diseases and the validation of these genes as potential therapeutic targets. RESULTS: Our study revealed that specific genes such as CD163L1, PPP3CA, MTAP, F12, NRM, BANK1, ZNF66, GNA15, and SLC6A9 were associated with ovarian endometriosis, ovarian cysts, and polycystic ovarian syndrome. Through MR and colocalization analyses, we identified potential drug targets, including CTNNB1, PTPN7, and ABCB4, with strong evidence of colocalization with ovarian-related diseases. Sensitivity analyses confirmed the robustness of our findings, showing no evidence of horizontal pleiotropy or heterogeneity. CONCLUSION: This research highlights the significance of precision medicine approaches in identifying genetic factors underlying ovarian-related diseases and provides a foundation for developing targeted therapies, enhancing diagnostic accuracy, and improving treatment strategies for ovarian-related diseases.