STATISTICAL GENETICS FRAMEWORKS FOR INTEGRATIVE MULTI-OMICS DATA INTERPRETATION IN DISEASE RESEARCH

Abstract

Background: Integrative multi-omics analysis has emerged as an important strategy to understand complex molecular mechanisms involved in human diseases. Statistical genetics methods enable integration of genomic, transcriptomic, proteomic and epigenomic data to allow disease interpretation and biomarker discovery.

Objective: In this work, we developed a statistical genetics framework for integrative multi-omics data interpretation in disease research and evaluated its performance for disease prediction and molecular pathway identification.

Method: We combined public datasets from TCGA, GEO, UK Biobank and ProteomicsDB using statistical genetics methods like genome-wide association studies (GWAS), eQTL mapping, Bayesian network analysis and machine learning algorithms. Disease-associated biomarkers and molecular interactions were identified through data preprocessing, normalization, feature selection, and pathway enrichment analyses.

Results: Multi-omics integration resulted in significantly improved disease classification accuracy compared to single-omics analysis. XGBoost had the highest predictive accuracy of 95.3 %. PI3K-Akt and MAPK signaling pathways were significantly enriched. Integrative analysis identified numerous differentially expressed genes and disease-associated SNPs.

Conclusion: The proposed statistical genetics framework was successful in improving disease prediction, biomarker discovery and systems-level biological interpretation. Integrative multi-omics approaches may have a significant role in precision medicine and personalized therapeutic approaches.