Animal Molecular Breeding 2024, Vol.14, No.3, 239-251 http://animalscipublisher.com/index.php/amb 242 These models are capable of handling large, noisy datasets and capturing non-linear relationships between markers and traits, which are often missed by traditional statistical methods. The use of ensemble learning techniques, such as Adaboost, has further improved the robustness and stability of genomic predictions. Additionally, machine learning models have shown promise in integrating multi-omics data, including transcriptomics, proteomics, and metabolomics, to refine predictions and identify novel genetic markers associated with complex traits. The continued development of these models is expected to further optimize GS and lead to more precise and targeted breeding strategies (Liang et al., 2020; Chafai et al., 2023). 3.3 Advances in Genomic Prediction Accuracy Improvements in genomic prediction accuracy are central to the success of GS. The accuracy of genomic predictions depends on several factors, including the density and quality of SNP data, the size of the reference population, and the statistical models used. Recent advances have focused on optimizing these factors to enhance prediction accuracy. High-density SNP arrays and whole-genome sequencing have increased the resolution of genetic data, allowing for the capture of more genetic variation and improving the accuracy of estimated breeding values (EBVs). Additionally, the development of single-step genomic best linear unbiased prediction (ssGBLUP) models has allowed for the simultaneous use of pedigree, phenotypic, and genomic data, resulting in more accurate predictions. The incorporation of non-additive genetic effects, such as dominance and epistasis, into prediction models has also contributed to improved accuracy. As the field continues to evolve, the use of more sophisticated models that account for genotype-by-environment interactions and other complex genetic architectures is expected to further enhance the precision of GS (Wang et al., 2022; Passamonti et al., 2021). 3.4 Improvements in breeding program efficiency The adoption of GS has led to significant improvements in the efficiency of breeding programs. By enabling the early selection of genetically superior animals, GS has reduced the generation interval and accelerated the rate of genetic gain. The ability to predict breeding values with high accuracy at a young age has also reduced the need for extensive phenotyping, lowering the overall cost and time required for breeding programs. Additionally, the integration of GS with other breeding tools, such as marker-assisted selection and genome editing, has created more efficient and targeted breeding strategies. The use of digital tools and automation in the selection process has further streamlined breeding programs, allowing for the rapid deployment of new genetic lines. As the technology continues to advance, the efficiency gains achieved through GS are expected to play a crucial role in meeting the growing global demand for animal products while ensuring the sustainability of livestock production systems (Xu et al., 2019; Rosa et al., 2023). 4 Applications of Genomic Selection in Livestock Breeding Genomic selection (GS) has been a transformative tool in livestock breeding, offering precise and efficient selection methods across various species. Its application spans several key livestock categories, including dairy cattle, beef cattle, swine, poultry, small ruminants, and aquaculture species. This section explores the specific applications of GS in these livestock categories, highlighting the advancements, challenges, and successes in each area. 4.1 Dairy cattle The dairy cattle industry has been at the forefront of implementing GS, with the technology significantly enhancing genetic gain and breeding efficiency. GS has been widely adopted in dairy cattle due to the economic importance of traits like milk yield, fertility, and longevity. The introduction of GS has reduced generation intervals and increased the accuracy of selecting young animals, leading to accelerated genetic improvement. In the United States, for example, the adoption of GS has doubled the rate of genetic gain since its implementation, with traits such as feed efficiency and health traits benefiting from the enhanced accuracy provided by genomic evaluations (Wiggans et al., 2017; Wiggans and Carrillo, 2022) (Figure 1).
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