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F&S science Pub Date : 2025-02-21 DOI:10.1016/j.xfss.2025.02.003

Yanggang Hong

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引用次数: 0

摘要

目的：通过全基因组孟德尔随机化（MR）和共定位分析，确定卵巢相关疾病的关键基因和潜在药物靶点：通过全基因组孟德尔随机化（MR）和共定位分析，确定卵巢相关疾病的关键基因和潜在药物靶点：我们进行了全面的双样本 MR 分析，以估计血液表达定量性状位点（eQTLs）对卵巢相关疾病的因果效应，然后进行共定位分析，以验证表达工具变量（IVs）的稳健性。此外，还进行了全表型关联研究（PheWAS），以评估潜在药物靶点的水平多效性和可能的副作用：公开的全基因组关联研究数据：eQTL数据来自eQTLGen联盟，包括16,987个基因和31,684个顺式-eQTL，这些数据来自欧洲血统的健康个体的血液样本。主要结果指标：主要结果指标是确定与卵巢相关疾病有因果关系的基因，并验证这些基因是潜在的治疗靶点：我们的研究发现，CD163L1、PPP3CA、MTAP、F12、NRM、BANK1、ZNF66、GNA15 和 SLC6A9 等特定基因与卵巢子宫内膜异位症、卵巢囊肿和多囊卵巢综合征有关。通过磁共振和共定位分析，我们确定了潜在的药物靶点，包括 CTNNB1、PTPN7 和 ABCB4，它们与卵巢相关疾病的共定位证据确凿。敏感性分析证实了我们研究结果的稳健性，没有发现横向多效性或异质性的证据：这项研究强调了精准医学方法在确定卵巢相关疾病的遗传因素方面的重要性，并为开发靶向疗法、提高诊断准确性和改善卵巢相关疾病的治疗策略奠定了基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.

Setting: Publicly available genome-wide association study data.

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. eQTL 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 measure: 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 PCOS. 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.

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