Towards more plausible point-identifying assumptions in two-sample Mendelian randomization
两样本孟德尔随机化中更具可信度的点识别假设AI 解读 ↓
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两样本孟德尔随机化中更具可信度的点识别假设
- 研究问题
- 两样本孟德尔随机化依赖的汇总层面同质性，能否允许个体因果效应异质，并仍使常用方法识别平均因果效应？
- 方法与思路
- 作者研究各人群层内加性线性效应，允许层间异质，并要求效应异质性与遗传变异对暴露的关联异质性不相关。
- 材料陈述的贡献
- 摘要称上述条件蕴含汇总层面同质性，使典型两样本方法可解释为平均因果效应估计，放宽此前的恒定效应要求。
- 阅读关注点
- 应核对不相关条件的精确定义、平均效应对应的人群，以及无效工具变量和两样本人群差异如何影响结论。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；较弱同质性条件仍需成立，摘要未说明其实证可检验程度。
原文摘要
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.