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.

中文翻译：

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荟萃分析和孟德尔随机化：综述。


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因果影响健康结果。荟萃分析历来被用于 MR 中，将单独的流行病学研究的结果结合起来，每项研究都使用一小部分但经过选择的遗传变异。近年来，它已被用于结合大量遗传变异的全基因组关联研究（GWAS）汇总数据。从多个遗传变异获得的因果估计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，我们对 MR 及其所依赖的工具变量理论进行了基本介绍。然后，我们描述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。