An Introduction to Causal Inference Methods with Multi-omics Data.

Area of Science:

• Genetics and Bioinformatics
• Personalized Medicine
• Causal Inference

Background:

• Omics biomarkers are crucial for personalized medicine, offering molecular insights into disease etiology, diagnostics, and therapies.
• Advancements in omics technologies generate vast multimodal data, enabling novel biomarker discovery for human diseases.
• Mendelian randomization (MR) is a causal inference method using genetic variants as instrumental variables to address confounding bias.

Purpose of the Study:

• To address the challenges in performing MR analysis with omics data.
• To present and describe four MR methods for analyzing multi-omics data.
• To provide R-executable methods for epigenomics, transcriptomics, proteomics, and metabolomics data analysis.

Main Methods:

• Review of current challenges in applying MR to omics data.
• Description of four distinct MR methodologies tailored for multi-omics datasets.
• Implementation guidance for these methods within the R statistical software environment.

Main Results:

• Identification of key challenges in omics data-driven MR.
• Detailed explanation of four MR methods applicable to diverse omics data types.
• Demonstration of R-based execution for practical application.

Conclusions:

• The presented MR methods offer a robust framework for causal inference with multi-omics data.
• These R-executable tools facilitate the identification of omics biomarkers for disease etiology and targeted therapies.
• This work advances the application of causal inference in personalized medicine using integrated omics approaches.