Integrative Multiomics Network Analysis of Genetic Risk Factors to Infer Biomarkers and Therapeutic Targets for Rheumatoid Arthritis

Abstract Purpose: Understanding the interplay between multiomics genetic risk factors in rheumatoid arthritis (RA) is key to developing effective treatments. Although network genetic-medicine approaches have identified key regulatory nodes of RA, there has been limited analysis of the molecular interactions between its risk actors. Methods: To identify the significant pathways and predict therapeutic targets of RA, we implemented a workflow to enable the computational multiomics network analysis of its associated risk factors using disease mapping and molecular function similarities. Results: We identified 28 common risk factors and biomarkers between three main biological pathways. Two key proteins are discovered as potential RA drug targets and a causal RA risk factor is associated with Alzheimer’s disease. Conclusion: This analysis suggests that drugs approved for other diseases mapped with RA could be repurposed for other diseases with the same pathways. This study provides insight into disease pathogenesis and enhances drug discovery for RA and other complex multifactorial diseases.