Background

Mendelian randomization (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilizing genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome.

Methods and results

We use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single-sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK Biobank to estimate the effect of education and cognitive ability on body mass index.

Conclusion

MVMR analysis consistently estimates the direct causal effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual- or summary-level data.

中文翻译：

---

单样本和两样本汇总数据设置中多变量孟德尔随机化的检查

背景

孟德尔随机化 (MR) 是流行病学中的一个强大工具，可通过利用作为暴露的工具变量 (IV) 的遗传变异来估计暴露对存在未观察到的混杂因素的结果的因果影响。这已扩展到多变量 MR (MVMR)，以估计两种或多种暴露对结果的影响。

方法和结果

我们使用模拟和理论来阐明在一系列潜在场景下 MVMR 分析中估计效应的解释，其中二次暴露以不同方式充当混杂因素、中介因素、多效性途径和碰撞者。然后，我们描述如何在单样本设置中评估 MVMR 分析的工具强度和有效性，并开发测试以在流行的两样本汇总数据设置中评估这些假设。我们使用英国生物银行的数据来说明我们的方法，以估计教育和认知能力对体重指数的影响。

结论

MVMR 分析一致地估计感兴趣的一个或多个暴露的直接因果效应，并提供了一个强大的工具，用于通过个人或摘要级别的数据确定各种场景中的因果效应。