Revolutionizing Livestock Breeding: Multi-omics applications and Sustainable Agriculture

The field of livestock genomics has experienced remarkable progress over the past two decades, ushering in a new era of data-driven breeding practices. Traditional breeding approaches, which relied heavily on pedigree-based selection, have evolved significantly with the introduction of genomic selection methods. These advances have revolutionized the ability to estimate breeding values more accurately and efficiently across diverse livestock species. Despite such progress, several challenges remain regarding the optimization of prediction accuracy, computational efficiency, and integration of advanced genomic data into selection pipelines. Modern breeding programs are now confronted with the task of balancing productivity gains with the maintenance of genetic diversity and sustainability. Recent studies highlight how innovative computational techniques—such as Bayesian inference, machine learning, and cross-validation frameworks—have enhanced genomic predictions. However, there is still a gap in understanding how these tools can be systematically harmonized to address scaling issues, the handling of non-genotyped individuals, and long-term sustainability objectives in agricultural systems.This Research Topic aims to deepen scientific understanding and methodological innovation in genomic predictions applied to livestock breeding. It seeks to elucidate how emerging computational models and genomic data integration can foster sustainable genetic improvement across species. Specific goals include advancing algorithms that improve prediction accuracy, developing frameworks for effectively incorporating non-genotyped data, and investigating the impact of genomic selection on genetic diversity. By synthesizing multi-disciplinary insights from genomics, bioinformatics, and quantitative genetics, this Research Topic aims to set new standards for integrating advanced genomic tools into practical breeding programs aligned with sustainable agriculture objectives.To gather further insights into genomic prediction methodologies and their applications in livestock breeding, we welcome articles addressing, but not limited to, the following themes:o Development and optimization of Bayesian and machine learning methods for genomic predictiono Computational strategies for managing and analyzing large-scale genomic datasetso Approaches to incorporate non-genotyped individuals in predictive modelso Assessment of genetic diversity and sustainability outcomes under genomic selectiono Integration of genomic predictions into national and global breeding programso Cross-species applicability and translational models in livestock genomicso Future directions for sustainable genomic selection aligned with global food security goals Topic editors: Themistoklis Giannoulis, Beata Horecka Source: https://www.frontiersin.org/research-topics/82548/revolutionizing-livestock-breeding-multi-omics-applications-and-sustainable-agriculture