Mendelian randomization is a powerful tool for causal inference in observational studies. The two-sample summary-data design, which estimates genetic associations with exposures and outcomes in separate cohorts, is the most widely used Mendelian randomization approach in large-scale genomic studies. However, this approach relies on a strong assumption of population homogeneity across the two samples. In practice, available samples often differ in ancestry, demographics, socioeconomic factors, covariate adjustment, and measurement protocols. Violations of the homogeneity assumption can bias causal effect estimates and undermine the credibility of Mendelian randomization findings. We introduce a robust, model-free Mendelian randomization framework that directly addresses population heterogeneity in the two-sample summary-data setting. Our method avoids parametric assumptions about population differences and is designed to address real-world challenges, including measurement error, weak instruments, and pleiotropy. We show that the proposed estimator is consistent and asymptotically normal under heterogeneous designs, and may offer efficiency gains over the classic estimator even in homogeneous settings. Through numerical simulations and a real data analysis for estimating the causal effect of body mass index on high-density lipoprotein cholesterol across ancestrally diverse populations, we demonstrate the practical utility, stability, and robustness of our approach.

翻译：孟德尔随机化是观察性研究中因果推断的有力工具。双样本汇总数据设计，即分别在独立队列中估计遗传变异与暴露及结局的关联，是大规模基因组研究中最广泛使用的孟德尔随机化方法。然而，该方法依赖于两个样本群体同质性的强假设。实践中，可获取的样本常在祖先来源、人口学特征、社会经济因素、协变量调整及测量方案等方面存在差异。违反同质性假设会导致因果效应估计偏倚，并削弱孟德尔随机化结论的可信度。我们提出一种稳健的、无模型的孟德尔随机化框架，直接解决双样本汇总数据设定中的群体异质性问题。该方法避免了对群体差异的参数假设，专为应对测量误差、弱工具变量及多效性等现实挑战而设计。研究表明，所提出的估计量在异质性设计下具有一致性及渐近正态性，即使在同质性设定中也可能比经典估计量具有更高的效率。通过数值模拟及一项跨祖先多样人群估算体质指数对高密度脂蛋白胆固醇因果效应的真实数据分析，我们验证了该方法在实际应用中的实用性、稳定性及稳健性。