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.

翻译：孟德尔随机化（MR）利用遗传变异作为工具变量进行因果推断。标准MR方法通常报告单一的人群平均估计值，限制了其探究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MR）试图解决此问题，但依赖于强且不可验证的假设。我们提出一种替代方案——基于情境分层的孟德尔随机化，该方法利用暴露在亚组（如招募中心、地理区域或时间段）间的外生变异来研究效应异质性和非线性。在每个情境内部独立进行MR分析，并使用Cochran's Q统计量和元回归评估所得估计值的异质性。通过模拟实验，我们证明该方法能在存在异质性时有效检测，并在使用适当方法时，于同质性条件下维持名义假阳性率。在英国生物银行数据的应用实例中，我们评估了维生素D水平对冠状动脉疾病风险在20个招募中心的影响。尽管维生素D分布存在区域差异，但未发现因果效应或估计值异质性的证据。与需要基于模型假设的分层方法相比，基于情境分层的方法实施简单，且在情境变量具有外生性时对碰撞偏倚具有稳健性。然而，该方法的效能与可解释性高度依赖于不同情境间暴露分布存在有意义的外生变异。在维生素D的案例中，其他分层方法所探索的亚组覆盖了更广泛的暴露分布范围。