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孟德尔随机化:一种利用观察性数据评估因果关系的方法。

Mendelian Randomization as an Approach to Assess Causality Using Observational Data.

作者信息

Sekula Peggy, Del Greco M Fabiola, Pattaro Cristian, Köttgen Anna

机构信息

Division of Genetic Epidemiology, Institute for Medical Biometry and Statistics and

Center for Biomedicine, European Academy of Bolzano, Bolzano, Italy.

出版信息

J Am Soc Nephrol. 2016 Nov;27(11):3253-3265. doi: 10.1681/ASN.2016010098. Epub 2016 Aug 2.

Abstract

Mendelian randomization refers to an analytic approach to assess the causality of an observed association between a modifiable exposure or risk factor and a clinically relevant outcome. It presents a valuable tool, especially when randomized controlled trials to examine causality are not feasible and observational studies provide biased associations because of confounding or reverse causality. These issues are addressed by using genetic variants as instrumental variables for the tested exposure: the alleles of this exposure-associated genetic variant are randomly allocated and not subject to reverse causation. This, together with the wide availability of published genetic associations to screen for suitable genetic instrumental variables make Mendelian randomization a time- and cost-efficient approach and contribute to its increasing popularity for assessing and screening for potentially causal associations. An observed association between the genetic instrumental variable and the outcome supports the hypothesis that the exposure in question is causally related to the outcome. This review provides an overview of the Mendelian randomization method, addresses assumptions and implications, and includes illustrative examples. We also discuss special issues in nephrology, such as inverse risk factor associations in advanced disease, and outline opportunities to design Mendelian randomization studies around kidney function and disease.

摘要

孟德尔随机化是一种分析方法,用于评估可改变的暴露因素或风险因素与临床相关结局之间观察到的关联的因果关系。它是一种有价值的工具,特别是在检验因果关系的随机对照试验不可行且观察性研究因混杂因素或反向因果关系而提供有偏差的关联时。通过使用基因变异作为所测试暴露因素的工具变量来解决这些问题:这种与暴露相关的基因变异的等位基因是随机分配的,不受反向因果关系的影响。这一点,再加上已发表的基因关联广泛可用,可用于筛选合适的基因工具变量,使得孟德尔随机化成为一种省时且经济高效的方法,并促使其在评估和筛选潜在因果关联方面越来越受欢迎。基因工具变量与结局之间观察到的关联支持了所讨论的暴露因素与结局存在因果关系的假设。本综述概述了孟德尔随机化方法,阐述了假设和影响,并包含了示例。我们还讨论了肾脏病学中的特殊问题,如晚期疾病中的反向风险因素关联,并概述了围绕肾功能和疾病设计孟德尔随机化研究的机会。

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