Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK.
Res Synth Methods. 2019 Dec;10(4):486-496. doi: 10.1002/jrsm.1346. Epub 2019 Apr 23.
Mendelian randomization (MR) uses genetic variants as instrumental variables to infer whether a risk factor causally affects a health outcome. Meta-analysis has been used historically in MR to combine results from separate epidemiological studies, with each study using a small but select group of genetic variants. In recent years, it has been used to combine genome-wide association study (GWAS) summary data for large numbers of genetic variants. Heterogeneity among the causal estimates obtained from multiple genetic variants points to a possible violation of the necessary instrumental variable assumptions. In this article, we provide a basic introduction to MR and the instrumental variable theory that it relies upon. We then describe how random effects models, meta-regression, and robust regression are being used to test and adjust for heterogeneity in order to improve the rigor of the MR approach.
孟德尔随机化（MR）使用遗传变异作为工具变量来推断风险因素是否会对健康结果产生因果影响。元分析历史上一直用于 MR，以合并来自单独的流行病学研究的结果，每个研究使用一小部分但经过选择的遗传变异。近年来，它已被用于合并大量遗传变异的全基因组关联研究（GWAS）汇总数据。从多个遗传变异中获得的因果估计值之间的异质性表明，可能违反了必要的工具变量假设。在本文中，我们提供了 MR 及其所依赖的工具变量理论的基本介绍。然后，我们描述了如何使用随机效应模型、元回归和稳健回归来测试和调整异质性，以提高 MR 方法的严谨性。
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