Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies.
Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies.
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DOI：
10.3945/ajcn.115.118216
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发表时间：
2016-04
期刊：
影响因子：
--
通讯作者：
Davey Smith G
中科院分区：
文献类型：
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作者：
Haycock PC;Burgess S;Wade KH;Bowden J;Relton C;Davey Smith G
在观察流行病学中，孟德尔随机化（MR）是一种越来越重要的评价因果关系的工具。该技术利用了基因型通常不容易受到反向因果偏差和混淆的影响的原理，反映了它们的固定性质和孟德尔的第一和第二遗传定律。然而，这种方法受到重要的限制和假设的影响，如果不加以解决或由于糟糕的研究设计而复杂化，可能导致错误的结论。然而，双样本方法（在单独的样本中测量暴露和结果）的出现以及来自全基因组关联研究和种群生物库的大型联盟开放获取数据的日益增加意味着该方法可能成为证据合成和因果推理研究中的常规做法。在本文中，我们概述了MR研究的设计、分析和解释，特别强调了假设和局限性。我们还考虑了不同的分析策略来加强因果推理。虽然不可能用任何单一方法证明因果关系，但MR是一种具有高度成本效益的战略，可为疾病预防确定干预目标的优先次序，并加强公共卫生政策的证据基础。
Mendelian randomization (MR) is an increasingly important tool for appraising causality in observational epidemiology. The technique exploits the principle that genotypes are not generally susceptible to reverse causation bias and confounding, reflecting their fixed nature and Mendel’s first and second laws of inheritance. The approach is, however, subject to important limitations and assumptions that, if unaddressed or compounded by poor study design, can lead to erroneous conclusions. Nevertheless, the advent of 2-sample approaches (in which exposure and outcome are measured in separate samples) and the increasing availability of open-access data from large consortia of genome-wide association studies and population biobanks mean that the approach is likely to become routine practice in evidence synthesis and causal inference research. In this article we provide an overview of the design, analysis, and interpretation of MR studies, with a special emphasis on assumptions and limitations. We also consider different analytic strategies for strengthening causal inference. Although impossible to prove causality with any single approach, MR is a highly cost-effective strategy for prioritizing intervention targets for disease prevention and for strengthening the evidence base for public health policy.