MRGN：面向基因组三元组、可处理高维混杂的因果网络推断方法
Inferring causal networks for genomic trios with high-dimensional confounding variables
作者提出 MRGN（Mendelian randomization for genomic networks），一种基于回归、在孟德尔随机化原则下从个体水平数据推断基因组三元组因果模型并同时校正大量混杂变量的方法。MRGN 超出经典中介模型的范围，可区分多种因果关系；它以给定遗传变异和混杂变量后的条件独立性检验为优先，因此即使两个表型的边际关联较弱也能检出因果关系。大量模拟显示，MRGN 在推断准确性、稳健性和计算效率上优于现有方法。
来源：Briefings in Bioinformatics · doi.org
Causal structure inference under the principle of Mendelian randomization (PMR) enables the study of causal relationships between two molecular phenotypes (e.g. gene expression) by leveraging genetic variants as instrumental variables to impose constraints on edge direction. In practice, inference is often affected by numerous confounding variables, such as other genes, other processes and demographic variables, making it essential to robustly account for confounders. However, existing methods have limited ability to accommodate high-dimensional confounding. Here, we introduce Mendelian randomization for genomic networks (MRGN), a regression-based method under the PMR for inferring causal models in genomic trios from individual-level data while accounting for many confounders. MRGN extends beyond the canonical mediation model to distinguish among multiple causal relationships. By prioritizing conditional independence tests given the genetic variant and confounders, MRGN can detect causal relationships between phenotypes even when their marginal association is weak. Through extensive simulations, we demonstrate improved inference accuracy, robustness, and computational efficiency over existing approaches. We further apply MRGN to data from the Genotype-Tissue Expression Consortium to investigate how genetic variants may regulate gene expression in cis and trans.