Target Discovery for Drug Development Using Mendelian Randomization.
Target Discovery for Drug Development Using Mendelian Randomization.
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DOI：
10.1007/978-1-0716-2573-6_1
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发表时间：
2022-01-01
期刊：
影响因子：
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通讯作者：
Evans, Daniel S
中科院分区：
文献类型：
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作者：
Evans, Daniel S
通过确定有前景的药物靶标来提高药物开发效率，有助于节省资源。使用人类遗传方法识别有前途的药物靶标可以消除与翻译相关的障碍。此外，遗传信息可用于识别药物靶点与疾病之间的潜在因果关系。孟德尔随机化 (MR) 是一类使用人类遗传学研究数据来识别基因预测特征对之间因果关系的方法。 MR 可用于通过预测疾病结果和药物靶点操作可能导致的不良事件来确定候选药物靶点的优先顺序。回顾了 MR 背后的理论，包括对 MR 假设、不同 MR 分析方法、违反假设的测试以及对某些违反 MR 假设的稳健的 MR 方法的讨论。描述了执行双样本 MR (2SMR) 和总结全基因组关联研究 (GWAS) 结果的协议。提供了 2SMR 检查低密度脂蛋白 (LDL) 与冠状动脉疾病 (CAD) 之间因果关系的示例作为协议说明。
Making drug development more efficient by identifying promising drug targets can contribute to resource savings. Identifying promising drug targets using human genetic approaches can remove barriers related to translation. In addition, genetic information can be used to identify potentially causal relationships between a drug target and disease. Mendelian randomization (MR) is a class of approaches used to identify causal associations between pairs of genetically predicted traits using data from human genetic studies. MR can be used to prioritize candidate drug targets by predicting disease outcomes and adverse events that could result from the manipulation of a drug target. The theory behind MR is reviewed, including a discussion of MR assumptions, different MR analytical methods, tests for violations of assumptions, and MR methods that can be robust to some violations of MR assumptions. A protocol to perform two-sample MR (2SMR) with summary genome-wide association study (GWAS) results is described. An example of 2SMR examining the causal relationship between low-density lipoprotein (LDL) and coronary artery disease (CAD) is provided as an illustration of the protocol.