Winner's Curse Free Robust Mendelian Randomization with Summary Data

TL;DR

This paper introduces a new robust Mendelian Randomization framework that effectively addresses winner's curse and pleiotropy issues in summary data, enabling more accurate causal inference in genetic studies.

Contribution

It proposes a unified method that removes winner's curse and screens invalid instruments without assuming specific pleiotropy distributions, improving causal effect estimation.

Findings

Estimator converges to a normal distribution under certain conditions.

Method performs well in simulations and case studies.

Variance of estimator can be accurately estimated.

Abstract

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 utilizing summary data may still produce biased causal effect estimates due to the winner's curse and pleiotropic 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. Different from existing robust MR literature, our framework delivers valid statistical inference on the causal effect neither requiring the genetic pleiotropy effects to follow any parametric distribution nor relying on perfect instrument screening property. Under appropriate conditions, we show 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 codes implementing the procedures are available at https://github.com/ChongWuLab/CARE/.