An Introduction to Causal Inference Methods with Multi-omics Data

Abstract

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.