孟德尔随机化
孟德尔遗传
混淆
生物
人口
药物开发
计算生物学
药品
遗传学
基因
遗传变异
医学
药理学
基因型
环境卫生
病理
作者
Dipender Gill,Marios K. Georgakis,Venexia Walker,Amand F. Schmidt,Apostolos Gkatzionis,Daniel F. Freitag,Chris Finan,Aroon D. Hingorani,Joanna M. M. Howson,Stephen Burgess,Daniel I. Swerdlow,George Davey Smith,Michael V Holmes,Martin Dichgans,Robert A. Scott,Jie Zheng,Bruce M. Psaty,Neil M Davies
标识
DOI:10.12688/wellcomeopenres.16544.2
摘要
Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.
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