Mediation analysis is a powerful tool for studying causal pathways between exposure, mediator, and outcome variables of interest. While classical mediation analysis using observational data often requires strong and sometimes unrealistic assumptions, such as unconfoundedness, Mendelian Randomization (MR) avoids unmeasured confounding bias by employing genetic variations as instrumental variables. We develop a novel MR framework for mediation analysis with genome-wide associate study (GWAS) summary data, and provide solid statistical guarantees. Our framework employs carefully crafted estimating equations, allowing for different sets of genetic variations to instrument the exposure and the mediator, to efficiently integrate information stored in three independent GWAS. As part of this endeavor, we demonstrate that in mediation analysis, the challenge raised by instrument selection goes beyond the well-known winner's curse issue, and therefore, addressing it requires special treatment. We then develop bias correction techniques to address the instrument selection issue and commonly encountered measurement error bias issue. Collectively, through our theoretical investigations, we show that our framework provides valid statistical inference for both direct and mediation effects with enhanced statistical efficiency compared to existing methods. We further illustrate the finite-sample performance of our approach through simulation experiments and a case study.

翻译：中介效应分析是研究暴露变量、中介变量与结局变量之间因果路径的有力工具。虽然基于观测数据的经典中介分析通常需要强且有时不切实际的假设（如无混杂性），但孟德尔随机化（Mendelian Randomization, MR）通过采用遗传变异作为工具变量，避免了未测量混杂偏倚。我们针对全基因组关联研究（GWAS）汇总数据开发了一种新颖的MR中介分析框架，并提供了坚实的统计保证。该框架采用精心设计的估计方程，允许使用不同组的遗传变异分别工具化暴露变量与中介变量，从而高效整合存储于三个独立GWAS中的信息。在此过程中，我们证明中介分析中工具选择带来的挑战远超广为人知的优胜者诅咒问题，因此需要特殊处理。随后我们开发了偏倚校正技术，以解决工具选择问题及常见的测量误差偏倚问题。通过理论研究表明，相较于现有方法，我们的框架能够为直接效应和中介效应提供有效的统计推断，并具有更高的统计效率。我们进一步通过模拟实验和案例研究展示了方法的有限样本性能。