摘要
孟德尔随机化(Mendelian randomization,MR)研究是一种因果推断方法,利用遗传变异作为工具变量来探索暴露(X)与结局(Y)之间的因果关系。首先,文章介绍了MR方法的核心假设以及如何在生物医学研究中挑选常见的MR设计类型。进而,介绍目前MR研究面对的关键挑战和解决方案,如:如何选择满足核心假设的工具变量(instrumental variable,IV),如何挑选MR中因果估计方法以及如何在生物学上解释MR结果等。此外,文章提出了包括宏基因组数据MR分析、甲基化数据MR分析等未来研究的方向。总之,MR方法作为一种强大的工具,有助于探索因果关系、选择治疗干预靶点,进行长期基于人群的干预研究。尽管MR不能完全替代随机对照试验,但其在生物医学研究和临床实践中具有广泛的应用前景,有助于更深入地理解健康和疾病之间的复杂关系。
Mendelian randomization(MR)is a causal inference method that utilizes genetic variations as instrumental variables to investigate the causal relationships between exposures(various factors)and outcomes(diseases or phenotypes).This article discusses the fundamental assumptions of MR methods in biomedical research,common designs,key challenges,optimizations and the future prospects of MR.The article introduces the core assumptions of MR methods and how to select common MR design types in biomedical research.Furthermore,the article discusses the key challenges currently faced by MR studies and their solutions,such as how to select instrumental variables that meet the core assumptions,how to choose causal estimation methods in MR,and how to interpret MR results biologically.In addition,the article suggests some directions for future research.As a powerful tool,MR contributes to exploring causal relationships,selecting therapeutic intervention targets,and conducting long-term population-based intervention studies.While MR cannot fully replace randomized controlled trials,it has a wide application in biomedical research and clinical practice,helping us to deeply understand the complex relationships between health and disease.
作者
王晶
张国燕
程杉
Wang Jing;Zhang Guoyan;Cheng Shan(Department of Medical Genetics and Developmental Biology,School of Basic Medical Sciences,Capital Medical University,Beijing 100069,China)
出处
《首都医科大学学报》
北大核心
2023年第6期1087-1094,共8页
Journal of Capital Medical University
关键词
孟德尔随机化分析
常见设计
挑战
Mendelian randomization analysis
common designs
challenges