标题： 基于上下文的孟德尔随机化：利用区域暴露变异来探索因果效应异质性和非线性 显示英文标题

标题： Context-stratified Mendelian randomization: exploiting regional exposure variation to explore causal effect heterogeneity and non-linearity

摘要： 孟德尔随机化（MR）使用遗传变异作为工具变量来进行因果推断。 标准的MR方法通常只报告一个总体平均估计值，限制了它们探索效应异质性或非线性剂量反应关系的能力。 现有的分层方法，如基于残差和双重排序分层的MR，试图克服这一问题，但依赖于强且不可验证的假设。 我们提出了一种替代方法，即上下文分层的孟德尔随机化，该方法利用暴露在子群体中的外生变化——如招募中心、地理区域或时间段——来研究效应异质性和非线性。 在每个上下文中分别进行MR分析，并使用Cochran的Q统计量和元回归来评估结果估计值的异质性。 通过模拟，我们证明了当存在异质性时，该方法能够检测到异质性，并且在适当的方法下，同质性情况下保持名义上的假阳性率。 在一个使用英国生物库数据的应用示例中，我们评估了维生素D水平对冠状动脉疾病风险的影响，跨越20个招募中心。 尽管维生素D分布在某些地区存在差异，但没有证据表明存在因果效应或估计值的异质性。 与需要模型假设的分层方法相比，上下文分层方法实现起来简单，并且在上下文变量是外生的情况下对碰撞偏差具有鲁棒性。 然而，该方法的效力和可解释性关键取决于不同上下文之间暴露分布的有意义的外生变化。 在维生素D的例子中，其他分层方法探索的子群体涵盖了更广泛的暴露分布范围。 显示英文摘要

摘要： Mendelian randomization (MR) uses genetic variants as instrumental variables to make causal claims. Standard MR approaches typically report a single population-averaged estimate, limiting their ability to explore effect heterogeneity or non-linear dose-response relationships. Existing stratification methods, such as residual-based and doubly-ranked stratified MR, attempt to overcome this but rely on strong and unverifiable assumptions. We propose an alternative, context-stratified Mendelian randomization, which exploits exogenous variation in the exposure across subgroups -- such as recruitment centres, geographic regions, or time periods -- to investigate effect heterogeneity and non-linearity. Separate MR analyses are performed within each context, and heterogeneity in the resulting estimates is assessed using Cochran's Q statistic and meta-regression. We demonstrate through simulations that the approach detects heterogeneity when present while maintaining nominal false positive rates under homogeneity when appropriate methods are used. In an applied example using UK Biobank data, we assess the effect of vitamin D levels on coronary artery disease risk across 20 recruitment centres. Despite some regional variation in vitamin D distributions, there is no evidence for a causal effect or heterogeneity in estimates. Compared to stratification methods requiring model-based assumptions, the context-stratified approach is simple to implement and robust to collider bias, provided the context variable is exogenous. However, the method's power and interpretability depend critically on meaningful exogenous variation in exposure distributions between contexts. In the example of vitamin D, subgroups from other stratification methods explored a much wider range of the exposure distribution.