Integrating Multi-Omics and Phenotypic Datasets for Genomic Breeding Value Prediction

Abstract

Genomic Breeding Value (GBV) prediction plays a crucial role in modern livestock breeding by enabling early identification of genetically superior animals. Traditional genomic selection approaches primarily rely on single-omics data, particularly single nucleotide polymorphisms (SNPs), which limits their ability to capture the biological complexity underlying economically important traits. This paper proposes an integrated framework that combines multi-omics data, including genomics, transcriptomics, epigenomics, metabolomics, microbiomics, along with phenotypic information to enhance GBV prediction. The proposed approach provides a comprehensive representation of genotype–phenotype relationships, improves predictive accuracy, and increases robustness. By integrating multiple biological layers, the framework supports biologically informed, data-driven breeding decisions and contributes to sustainable precision livestock breeding.