Winner’s Curse Free Robust Mendelian Randomization with Summary Data
提出一种统一框架，同时解决汇总数据孟德尔随机化中的赢家诅咒和多效性问题，无需假设多效性分布或完美工具变量筛选，通过模拟和案例验证了有效性。
In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental variables, are known for their robustness against reverse causation bias and unmeasured confounders. Nevertheless, classical MR analyses using summary data may still produce biased causal effect estimates due to the winner's curse and pleiotropy issues. To address these two issues and establish valid causal conclusions, we propose a unified robust Mendelian Randomization framework with summary data, which systematically removes the winner's curse and screens out invalid genetic instruments with pleiotropic effects. Unlike existing robust MR literature, our framework delivers valid statistical inference on the causal effect without requiring the genetic pleiotropy effects to follow any parametric distribution or relying on perfect instrument screening property. Under appropriate conditions, we demonstrate that our proposed estimator converges to a normal distribution, and its variance can be well estimated. We demonstrate the performance of our proposed estimator through Monte Carlo simulations and two case studies. The corresponding R package MRcare is available at https://chongwulab.github.io/MRcare/. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.