Mendelian Randomization (MR) exploits genetic variants as instrumental variables to estimate the causal effect of an "exposure" trait on an "outcome" trait from observational data. However, the validity of such studies is threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to partially mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large genetic databases. Here, we describe a suite of sensitivity analysis tools for MR that enables investigators to properly quantify the robustness of their findings against these (and other) unobserved validity threats. Specifically, we propose the routine reporting of sensitivity statistics that can be used to readily quantify the robustness of a MR result: (i) the partial R2 of the genetic instrument with the exposure and the outcome traits; and, (ii) the robustness value of both genetic associations. These statistics quantify the minimal strength of violations of the MR assumptions that would be necessary to explain away the MR causal effect estimate. We also provide intuitive displays to visualize the sensitivity of the MR estimate to any degree of violation, and formal methods to bound the worst-case bias caused by violations in terms of multiples of the observed strength of principal components, batch effects, as well as putative pleiotropic pathways. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings, by showing that the MR estimate of the causal effect of body mass index (BMI) on diastolic blood pressure is relatively robust, whereas the MR estimate of the causal effect of BMI on Townsend deprivation index is relatively fragile.

中文翻译：

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剩余种群分层，批次效应和水平多效性存在下的鲁棒孟德尔随机化

孟德尔随机化（MR）利用遗传变异作为工具变量，从观测数据中估计“暴露”性状对“结果”性状的因果关系。但是，此类研究的有效性受到人口分层，批次效应和水平多效性的威胁。尽管已提出了各种方法来部分缓解这些问题，但仍可能存在残留偏差，从而在大型遗传数据库中导致具有高度统计意义的假阳性。在这里，我们描述了一套针对MR的敏感性分析工具，使研究人员能够针对这些（和其他）未观察到的有效性威胁正确量化其发现的稳健性。特别，我们提出了敏感性统计数据的常规报告，可用于容易地量化MR结果的鲁棒性：（i）具有暴露和结果特征的遗传仪器的部分R2；（ii）两种遗传关联的稳健性值。这些统计数据量化了违反MR假设的最小强度，这是解释MR因果效应估计所必需的。我们还提供直观的显示，以可视化表示MR估计值对任何违规程度的敏感性，并提供正式方法以观察到的主要成分强度，批次效应以及倍数来约束由违规引起的最坏情况偏差。假定的多效性途径。我们演示了这些工具如何帮助研究人员区分鲁棒性和脆弱性发现，