Multivariable Mendelian Randomization (MVMR) estimates the direct causal effects of multiple risk factors on an outcome using genetic variants as instruments. The growing availability of summary-level genetic data has created opportunities to apply MVMR in high-dimensional settings with many strongly correlated candidate risk factors. However, existing methods face three major limitations: weak instrument bias, limited interpretability, and the absence of valid post-selection inference. Here we introduce MVMR-PACS, a method that identifies signal-groups -- sets of causal risk factors with high genetic correlation or indistinguishable causal effects -- and estimates the direct effect of each group. MVMR-PACS minimizes a debiased objective function that reduces weak instrument bias while yielding interpretable estimates with theoretical guarantees for variable selection. We adapt a data-thinning strategy to summary-data MVMR to enable valid post-selection inference. In simulations, MVMR-PACS outperforms existing approaches in both estimation accuracy and variable selection. When applied to 27 lipoprotein subfraction traits and coronary artery disease risk, MVMR-PACS identifies biologically meaningful and robust signal-groups with interpretable direct causal effects.

翻译：多变量孟德尔随机化（MVMR）利用遗传变异作为工具变量，估计多个风险因素对结局的直接因果效应。随着汇总水平遗传数据的日益丰富，为在多维且高度相关的候选风险因素场景中应用MVMR提供了机遇。然而，现有方法面临三大局限：弱工具变量偏倚、可解释性有限以及缺乏有效的选择后推断。本文提出MVMR-PACS方法，该方法能够识别信号组——即具有高遗传相关性或因果效应难以区分的因果风险因素集合，并估计每个组的直接效应。MVMR-PACS通过最小化去偏目标函数来降低弱工具变量偏倚，同时获得具有变量选择理论保证的可解释估计。我们将数据稀疏化策略适配至汇总数据MVMR框架，以实现有效的选择后推断。模拟研究表明，MVMR-PACS在估计精度和变量选择方面均优于现有方法。将其应用于27种脂蛋白亚组分性状与冠状动脉疾病风险的分析中，MVMR-PACS识别出具有生物学意义且稳健的信号组，并提供了可解释的直接因果效应估计。