Wang L N, Zhang Zuofeng
Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
Department of Epidemiology, School of Public Health, University of California, Los Angeles, CA 90095, U.S.
Zhonghua Liu Xing Bing Xue Za Zhi. 2017 Apr 10;38(4):547-552. doi: 10.3760/cma.j.issn.0254-6450.2017.04.027.
Mendelian randomization (MR) approach is based on the Mendelian genetic law, which is called "Parental alleles that randomly assigned to the offspring" . MR refers to the use of genetic variants to develop causal inferences from observational data, if the variant genotype is associated with the phenotype and the variant genotype associated with the risk of disease of interest through the phenotype. Hence, the genotype can be used as Instrumental Variable (IV) to infer the causal relation between the phenotype and the risk of diseases. In recent years, MR approach is widely used in causal inference between the exposure factors and the risks of disease, along with the rapid development of statistical methods, big datasets of GWAS, epigenetics and the various "omics" techniques. This paper provides an overview of the MR strategies and addresses the related assumptions and implications, with reliability and limitations included.
孟德尔随机化(MR)方法基于孟德尔遗传定律,即所谓的“随机分配给后代的亲本等位基因”。MR是指利用基因变异从观察性数据中进行因果推断,前提是变异基因型与表型相关,且变异基因型通过表型与感兴趣疾病的风险相关。因此,基因型可作为工具变量(IV)来推断表型与疾病风险之间的因果关系。近年来,随着统计方法、全基因组关联研究(GWAS)大数据集、表观遗传学和各种“组学”技术的快速发展,MR方法在暴露因素与疾病风险之间的因果推断中得到了广泛应用。本文概述了MR策略,并阐述了相关假设和影响,包括可靠性和局限性。