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孟德尔随机化方法在观察性研究因果推断中的应用

[Application of mendelian randomization methods in causal inference of observational study].

作者信息

Lin L J, Wei Y Y, Zhang R Y, Chen F

机构信息

Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.

出版信息

Zhonghua Yu Fang Yi Xue Za Zhi. 2019 Jun 6;53(6):619-624. doi: 10.3760/cma.j.issn.0253-9624.2019.06.015.

Abstract

Mendelian randomization (MR) approach follows the Mendel's law of inheritance, which is called "Parental alleles randomly assigned to the offspring", and refers to use genetic variants as an instrumental variable to develop causal inference between the exposure factor and the outcome from observational study. In recent years, with the rapid development of genome-wide association study (GWAS) and various omics data,the disclosure of a large number of aggregated data provides an opportunity for the wide application of MR approach in causal inference. We introduce three methods widely used in MR and then apply them to explore causal relationship between blood metabolites and depressive. The advantages and disadvantages of three methods in causal inference are compared in order to provide reference for the application of MR in observational studies.

摘要

孟德尔随机化(MR)方法遵循孟德尔遗传定律,即“亲代等位基因随机分配给后代”,是指利用基因变异作为工具变量,从观察性研究中推断暴露因素与结局之间的因果关系。近年来,随着全基因组关联研究(GWAS)和各种组学数据的快速发展,大量汇总数据的公开为MR方法在因果推断中的广泛应用提供了契机。我们介绍了三种在MR中广泛使用的方法,然后将它们应用于探究血液代谢物与抑郁症之间的因果关系。比较了三种方法在因果推断中的优缺点,以便为MR在观察性研究中的应用提供参考。

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