Genomic Selection in Animal Breeding: Principles, Applications, and Future Perspectives
Abstract
Genomic selection (GS) has transformed modern animal breeding by enabling the prediction of genetic merit using dense genome-wide molecular markers rather than relying solely on pedigree and phenotypic information. Since its conceptual introduction in 2001, GS has become a cornerstone of genetic improvement programs in livestock species, particularly dairy cattle, and has subsequently expanded to beef cattle, sheep, goats, swine, poultry, and aquaculture. The integration of high-density single nucleotide polymorphism (SNP) genotyping with advanced statistical prediction models has substantially increased the accuracy of breeding value estimation, shortened generation intervals, and accelerated rates of genetic gain. Compared with conventional best linear unbiased prediction (BLUP) and marker-assisted selection (MAS), genomic selection captures the combined effects of thousands of loci distributed throughout the genome, making it highly effective for complex quantitative traits governed by many genes of small effect. Recent advances, including single-step genomic BLUP, Bayesian prediction methods, whole-genome sequence analysis, functional genomics, multi-omics integration, artificial intelligence, and precision livestock farming technologies, have further enhanced the scope and efficiency of genomic prediction. These innovations are facilitating simultaneous improvement in productivity, fertility, feed efficiency, disease resistance, animal welfare, and environmental sustainability. Moreover, genomic information is increasingly being integrated with genome editing technologies such as CRISPR to support precision breeding strategies. This review summarizes the historical evolution, fundamental principles, methodological developments, and practical applications of genomic selection in livestock breeding while highlighting emerging innovations and future research directions that are expected to shape next-generation animal improvement programs.
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Introduction
Genetic improvement has long been one of the primary drivers of increased productivity, efficiency, and profitability in livestock production. Traditionally, breeding programs have relied on phenotypic performance records, pedigree information, and statistical methods such as Best Linear Unbiased Prediction (BLUP) to estimate breeding values. Although these approaches have generated substantial genetic progress over the past several decades, their effectiveness is constrained by long generation intervals, delayed availability of phenotypic records, and reduced prediction accuracy for traits with low heritability or those expressed late in life [1, 2].
The rapid development of molecular genetics and high-throughput genotyping technologies has fundamentally changed animal breeding strategies. The availability of dense genome-wide single nucleotide polymorphism (SNP) markers has enabled direct estimation of genomic relationships among individuals and facilitated prediction of breeding values based on DNA information rather than pedigree alone [1, 3]. This strategy, known as genomic selection (GS), estimates the combined effects of thousands of markers distributed across the entire genome, thereby capturing the genetic architecture underlying complex quantitative traits.
Unlike conventional marker-assisted selection, which focuses on a limited number of significant quantitative trait loci (QTL), genomic selection simultaneously exploits information from all available genome-wide markers regardless of their individual statistical significance [1, 4]. This approach has markedly improved the accuracy of genomic estimated breeding values (GEBVs), particularly for young selection candidates without phenotypic records, enabling earlier and more reliable selection decisions [2, 5].
The widespread adoption of genomic selection in dairy cattle since 2008 has demonstrated remarkable increases in annual genetic gain, reduced breeding costs, and shortened generation intervals. Similar advances are now being realized in beef cattle, sheep, goats, swine, poultry, and aquaculture species through continued improvements in genomic resources, computational methodologies, and sequencing technologies [3, 6–8]. Consequently, genomic selection has become one of the most influential innovations in modern quantitative genetics and animal breeding, laying the foundation for sustainable and precision livestock improvement.
Conclusion
Genomic selection represents one of the most significant innovations in modern animal breeding, fundamentally transforming genetic evaluation through the use of genome-wide molecular information. By combining dense SNP genotyping with advanced statistical prediction models, genomic selection has substantially improved the accuracy of breeding value estimation, accelerated genetic gain, and reduced generation intervals across livestock species. Its successful implementation in dairy cattle has stimulated rapid adoption in beef cattle, sheep, goats, swine, poultry, and aquaculture, where continued improvements in genotyping technologies and computational methodologies are expanding its practical applications.
Recent advances such as single-step GBLUP, Bayesian genomic prediction, whole-genome sequencing, multi-omics integration, artificial intelligence, and precision livestock farming are further enhancing the efficiency and scope of genomic selection. Nevertheless, challenges related to reference population size, computational demands, across-breed prediction, and implementation costs remain important considerations. Continued international collaboration, improved phenotypic recording, and integration of functional genomics with genome editing technologies are expected to further strengthen genomic breeding programs. Consequently, genomic selection will remain a central component of sustainable livestock improvement, supporting increased productivity, improved animal health and welfare, enhanced environmental resilience, and long-term global food security.
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