Animal Molecular Breeding 2024, Vol.14, No.3, 239-251 http://animalscipublisher.com/index.php/amb 247 7.1 Emerging technologies (e.g., CRISPR, AI) Emerging technologies such as CRISPR and AI are poised to play a transformative role in the future of GS. CRISPR, a powerful tool for genome editing, offers unprecedented precision in modifying specific genes, enabling the enhancement of desirable traits or the elimination of detrimental ones in livestock. CRISPR-based techniques, such as RNA-guided DNA integration, allow for more precise and predictable genetic modifications, making it possible to rapidly improve complex traits that are otherwise difficult to enhance through traditional breeding methods (Park et al., 2021; Cheng et al., 2022). AI, on the other hand, is being increasingly used to analyze large genomic datasets, identify patterns, and predict breeding outcomes with greater accuracy. AI algorithms, including machine learning models, can integrate genomic, phenotypic, and environmental data to optimize selection decisions, ultimately leading to more efficient and sustainable breeding programs (Manghwar et al., 2020). The combination of CRISPR and AI holds the potential to accelerate the pace of genetic improvement in livestock, making it possible to achieve genetic gains that were previously unimaginable. 7.2 Expanding genomic selection to new species While GS has been widely adopted in cattle, swine, and poultry, its application in other species, such as aquaculture and small ruminants, is still in its early stages. The expansion of GS to these new species presents both opportunities and challenges. In aquaculture, for instance, GS could significantly improve traits such as growth rate, disease resistance, and feed efficiency, but the development of reliable reference populations and the adaptation of existing models to the specific genetic architectures of these species remain challenging (Figure 3) (Elmore et al., 2023). Similarly, in species like sheep and goats, GS offers the potential to enhance productivity and health traits, yet the limited availability of genomic resources and the high costs associated with genotyping pose significant barriers (Bhat et al., 2016). As genomic technologies become more affordable and accessible, the expansion of GS to a broader range of species will likely drive significant improvements in global livestock production. 7.3 Enhancing genomic data integration The integration of diverse genomic data types is critical for enhancing the accuracy and effectiveness of GS. Advances in next-generation sequencing (NGS) and other high-throughput genomic technologies have generated vast amounts of data that can be leveraged to improve the prediction of breeding values. However, integrating these data with traditional phenotypic and pedigree information remains a challenge. Emerging approaches, such as multi-omics data integration and the use of AI for data analysis, offer promising solutions. By combining genomic, transcriptomic, proteomic, and metabolomic data, researchers can gain a more comprehensive understanding of the genetic basis of complex traits, leading to more accurate predictions and better-informed selection decisions (Nidhi et al., 2021; Arribas et al., 2021). Additionally, the development of new bioinformatics tools and databases that facilitate the seamless integration of these diverse data types will be essential for maximizing the potential of GS. 7.4 Potential for global collaboration in livestock breeding Global collaboration in livestock breeding is becoming increasingly important as the challenges facing the industry grow more complex. The need for sustainable practices, the impacts of climate change, and the demands of a growing global population all underscore the importance of international cooperation in advancing GS. Collaborative initiatives that share genomic resources, reference populations, and breeding strategies across borders can help to accelerate the development and dissemination of GS technologies. Moreover, global efforts to establish standardized protocols and data-sharing frameworks will be crucial for ensuring that the benefits of GS are realized worldwide (Policante and Borg, 2023). Such collaboration will not only enhance the effectiveness of breeding programs but also contribute to the equitable distribution of genetic gains, particularly in developing regions where access to genomic technologies has been limited.
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