High-dimensional mediation analysis aims to identify mediating pathways and to estimate indirect effects linking an exposure to an outcome. In this paper, we propose a Bayesian framework to address key challenges in these analyses, including high dimensionality, complex dependence among omics mediators, and non-continuous outcomes. Furthermore, commonly used approaches assume independent mediators or ignore correlations in the selection stage, which can reduce power when mediators are highly correlated. Addressing these challenges leads to a non-Gaussian likelihood and specialized selection priors, which in turn require efficient and adaptive posterior computation. Our proposed framework selects active pathways under generalized linear models while accounting for mediator dependence. Specifically, the mediators are modeled using a multivariate distribution, exposure-mediator selection is guided by a Markov random field prior on inclusion indicators, and mediator-outcome activation is restricted to mediators supported in the exposure-mediator model through a sequential subsetting Bernoulli prior. Simulation studies show improved operating characteristics in correlated-mediator settings, with appropriate error control under the global null and stable performance under model misspecification. We illustrate the method using real-world metabolomics data to study metabolites that mediate the association between adherence to the Alternate Mediterranean Diet score and two cardiometabolic outcomes.

翻译：高维中介分析旨在识别连接暴露与结果的中介路径并估计间接效应。本文提出一个贝叶斯框架以应对此类分析中的关键挑战，包括高维性、组学中介变量间的复杂依赖性以及非连续型结果。现有常用方法通常假设中介变量相互独立或在选择阶段忽略相关性，当中介变量高度相关时可能导致检验效能降低。应对这些挑战需要处理非高斯似然函数和特殊的选择先验，进而要求高效且自适应的后验计算。我们提出的框架能够在考虑中介变量依赖性的前提下，在广义线性模型中选择活跃路径。具体而言，我们采用多元分布对中介变量建模，通过包含指标的马尔可夫随机场先验指导暴露-中介变量选择，并借助序列子集伯努利先验将中介-结果激活限制于暴露-中介模型中支持的中介变量。模拟研究表明，在相关中介变量场景下该方法具有更优的操作特性，能在全局零假设下实现适当的误差控制，并在模型误设情况下保持稳定性能。我们通过实际代谢组学数据演示该方法，用于研究介导替代地中海饮食评分依从性与两种心脏代谢结果关联的代谢物。