Omics biomarkers play a pivotal role in personalized medicine by providing molecular-level insights into the etiology of diseases, guiding precise diagnostics, and facilitating targeted therapeutic interventions. Recent advancements in omics technologies have resulted in an increasing abundance of multimodal omics data, providing unprecedented opportunities for identifying novel omics biomarkers for human diseases. Mendelian randomization (MR) is a practically useful causal inference method that uses genetic variants as instrumental variables (IVs) to infer causal relationships between omics biomarkers and complex traits/diseases by removing hidden confounding bias. In this article, we first present current challenges in performing MR analysis with omics data, and then describe four MR methods for analyzing multi-omics data including epigenomics, transcriptomics, proteomics, and metabolomics data, all executable within the R software environment.

翻译：组学生物标志物通过提供疾病病因的分子层面见解、指导精准诊断并促进靶向治疗干预，在个体化医疗中发挥着关键作用。近年来组学技术的进步使得多模态组学数据日益丰富，为识别人类疾病的新型组学生物标志物带来了前所未有的机遇。孟德尔随机化（MR）是一种实用的因果推断方法，它利用遗传变异作为工具变量（IVs），通过消除隐蔽混杂偏倚来推断组学生物标志物与复杂特征/疾病之间的因果关系。本文首先阐述了当前利用组学数据进行MR分析面临的挑战，随后介绍了四种适用于分析多组学数据（包括表观基因组学、转录组学、蛋白质组学和代谢组学数据）的MR方法，所有方法均可在R软件环境中执行。