发表机构
Federal University of Pelotas; University of Bristol; University College London; Norwegian University of Science and Technology（佩洛塔斯联邦大学; 布里斯托大学; 伦敦大学学院; 挪威科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文证明两样本孟德尔随机化的点识别假设比先前认为的更弱，即汇总水平同质性可由加性线性因果效应及异质性不相关性隐含，从而支持将方法解释为估计平均因果效应。
AI 中文摘要
两样本孟德尔随机化（MR）是流行病学中广泛应用的方法。在两样本MR中，汇总数据（通常是回归系数和标准误）用于量化多个遗传变异与暴露及结局之间的关联，并在工具变量框架下用于估计暴露对结局的因果效应。大多数两样本MR方法是在数据生成模型下开发的，其中每个候选遗传工具与暴露的关联以及暴露对结局的因果效应在加性尺度上是恒定的。这些假设之所以有用，是因为它们意味着，如果所有遗传变异都是有效的工具变量，它们都将估计相同的因果参数——即恒定的因果效应。我们将此条件称为汇总水平同质性。然而，这些是相当强的同质性条件，可能引发对这些方法在实践中合理性的担忧。在本文中，我们表明汇总水平同质性由以下条件隐含：因果效应是加性线性的，但在所有人口阶层中不一定是恒定的；以及因果效应中的异质性与每个遗传变异和暴露之间关联中的异质性之间不相关。在这些条件下，典型的两样本MR方法可以被解释为平均因果效应的估计量。这些结果阐明，两样本MR方法所需的点识别假设比先前预期的要弱，这有助于提高其合理性，并在至少一些实际应用中有助于其解释。
英文摘要
Two-sample Mendelian randomization (MR) is a widely applied methodology in epidemiology. In two-sample MR, summary data (typically, regression coefficients and standard errors) quantifying the association between multiple genetic variants and the exposure and the outcome are used in an instrumental variable framework aimed at estimating the causal effect of the exposure on the outcome. Most two-sample MR methods were developed under data-generating models where the association of for each candidate genetic instrument with the exposure, as well as the causal effect of the exposure on the outcome, are constant in the additive scale. These assumptions are useful because they imply that, had all genetic variants been valid IVs, they would all estimate the same causal parameter - namely, the constant causal effect. We refer to this condition as summary-level homogeneity. However, these are rather strong homogeneity conditions which may raise concerns about the plausibility of these methods in practice. In this paper, we show that summary-level homogeneity is implied by the following conditions: the causal effect is additive linear, but not necessarily constant across, all strata of the population; and uncorrelatedness between heterogeneity in the causal effect and in the association between each genetic variant and the exposure. Under these conditions, typical two-sample MR methods can be interpreted as estimators of the average causal effect. These results clarify that point-identifying assumptions required for two-sample MR methods are weaker than previously anticipated, which contributes to their plausibility and interpretation in at least some practical applications.
