The number of Mendelian randomization (MR) analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. Since it is unlikely that all genetic variants will be valid instrumental variables, several robust methods have been proposed. We compare nine robust methods for MR based on summary data that can be implemented using standard statistical software. Methods were compared in three ways: by reviewing their theoretical properties, in an extensive simulation study, and in an empirical example. In the simulation study, the best method, judged by mean squared error was the contamination mixture method. This method had well-controlled Type 1 error rates with up to 50% invalid instruments across a range of scenarios. Other methods performed well according to different metrics. Outlier-robust methods had the narrowest confidence intervals in the empirical example. With isolated exceptions, all methods performed badly when over 50% of the variants were invalid instruments. Our recommendation for investigators is to perform a variety of robust methods that operate in different ways and rely on different assumptions for valid inferences to assess the reliability of MR analyses.

中文翻译：

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使用汇总数据比较稳健的孟德尔随机化方法。

包括大量遗传变异的孟德尔随机化 (MR) 分析的数量正在迅速增加。这是由于全基因组关联研究的激增以及对因果效应获得更精确估计的愿望。由于不可能所有遗传变异都是有效的工具变量，因此提出了几种稳健的方法。我们基于可使用标准统计软件实施的汇总数据比较了九种稳健的 MR 方法。通过三种方式对方法进行了比较：回顾其理论特性、广泛的模拟研究和实证示例。在模拟研究中，根据均方误差判断，最佳方法是污染物混合法。该方法可以很好地控制 1 类错误率，在一系列场景中无效仪器高达 50%。根据不同的指标，其他方法表现良好。在经验示例中，异常值稳健方法的置信区间最窄。除了个别例外，当超过 50% 的变体是无效仪器时，所有方法都表现不佳。我们对研究人员的建议是采用各种稳健的方法，这些方法以不同的方式运行，并依赖不同的假设进行有效的推论，以评估 MR 分析的可靠性。