Causal Inference Methods to Integrate Omics and Complex Traits.

Area of Science:

• Biotechnology
• Genetics
• Epidemiology

Background:

• Advances in omics technologies generate large-scale datasets linking molecular phenotypes to diseases.
• Genome-wide association studies (GWAS) identify genetic markers associated with diseases, but causality remains unclear.
• Omics-disease associations may represent consequences rather than causes of disease.

Purpose of the Study:

• To review the application of Mendelian randomization in omics research.
• To highlight the potential of Mendelian randomization in establishing causal relationships between molecular traits and diseases.
• To discuss current challenges and future directions in this field.

Main Methods:

• Utilizing genetic variants as instrumental variables.
• Integrating genetic association data from omics studies and disease studies.
• Applying Mendelian randomization frameworks to infer causality.

Main Results:

• Mendelian randomization offers a robust method to distinguish causal molecular drivers from mere correlates of disease.
• This approach enhances the biological interpretability of omics findings.
• Identified causal variants can pinpoint specific molecular pathways involved in disease development.

Conclusions:

• Mendelian randomization is a powerful tool for causal inference in omics research.
• Addressing challenges in data integration and methodology will further advance the field.
• This framework holds significant promise for understanding disease etiology and biomarker discovery.