  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.
