Multivariable Bidirectional Mendelian Randomization via Bayesian Directed Cyclic Graphical Models with Correlated Errors
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基于相关误差贝叶斯有向循环图模型的多变量双向孟德尔随机化
- 研究问题
- 在包含多个生物变量、反馈循环、未观测混杂与水平多效性的环境中，如何利用遗传工具变量及汇总数据推断因果关系？
- 方法与思路
- 作者建立贝叶斯稀疏条件循环图模型与稀疏误差协方差矩阵，通过两类结构先验联合推断因果连接及潜在混杂结构。
- 材料陈述的贡献
- 摘要称给出存在水平多效性时的充分可识别条件，以模拟及两组基因数据展示关系恢复表现，并用后验概率量化网络模体不确定性。
- 阅读关注点
- 应核对遗传工具变量与水平多效性的建模方式、循环系统的可识别条件，以及结构先验对因果连接和混杂结构推断的影响。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未核实；摘要未展开识别条件，生物学合理性也不能单独证明所推断关系为因果。
原文摘要
Mendelian randomization (MR) is a pivotal tool in genetics, genomics, and epidemiology, leveraging genetic variants as instrumental variables to infer causal relationships between exposures and outcomes. Traditional MR methods, while powerful, often rely on stringent assumptions such as the absence of feedback loops, which are frequently violated in complex biological networks. In addition, many popular MR approaches focus on only two variables (i.e., one exposure and one outcome), whereas our motivating applications of gene regulatory networks have many variables. In this article, we introduce a novel Bayesian framework for multivariable MR that concurrently addresses unmeasured confounding and feedback loops. Central to our approach is a sparse conditional cyclic graphical model with a sparse error variance-covariance matrix. Two structural priors are employed to enable the modeling and inference of causal relationships as well as latent confounding structures. Our method is designed to operate effectively with summary-level data, facilitating its application in contexts where individual-level data are inaccessible, e.g., due to privacy concerns. It can also account for horizontal pleiotropy, under which we establish the sufficient identifiability conditions. Through extensive simulations and applications to the GTEx and OneK1K data, we demonstrate the superior performance of our approach in recovering biologically plausible causal relationships in the presence of possible feedback loops and unmeasured confounding. Using posterior samples, we further quantify uncertainty in inferred network motifs by computing their posterior probabilities. The R package MR.RGM that implements the proposed method is available on CRAN (https://cran.r-project.org/package=MR.RGM).