Animal Molecular Breeding 2024, Vol.14, No.1, 95-105 http://animalscipublisher.com/index.php/amb 104 In conclusion, the future of livestock breeding is intricately linked to the development and implementation of precision breeding technologies and sustainable breeding strategies. These advancements will enable breeders to meet the growing demand for animal products while addressing the critical need for environmental stewardship. 6 Concluding Remarks The advent of genomic prediction (GP) technology has revolutionized the field of animal breeding, particularly in the selection for complex traits in livestock. The importance of this technology cannot be overstated, as it has enabled breeders to make more accurate and efficient selection decisions, which are crucial for the improvement of traits that are economically significant in livestock production. Genomic prediction leverages dense single nucleotide polymorphism (SNP) genotypes to estimate breeding values, taking into account the genetic architecture of traits, which often involves a large number of loci with small effects. The impact of genomic prediction on breeding strategies is evident in various livestock species, including dairy and beef cattle, pigs, and poultry. It has been shown to contribute significantly to increased accuracy in selection decisions, particularly in dairy cattle, where the practical application of GP has been clearly illustrated. Moreover, the integration of genomic data has been successfully applied to local breeds, demonstrating its utility even in populations with smaller sizes. However, the necessity for continued research and development in this field remains paramount. The genetic architecture of complex traits is intricate, and the identification of causal polymorphisms continues to be a challenge. The use of whole-genome sequence data is anticipated to improve the accuracy of genomic selection, but it also presents new challenges, such as managing inbreeding and ensuring the sustainability of genetic diversity. Furthermore, the design of breeding programs and the optimization of training population structures are critical factors that influence the accuracy of genomic predictors. The development of new and improved genomic prediction algorithms, including non-linear approaches like artificial neural networks and gradient tree boosting, is an ongoing area of research. Benchmarking these algorithms to identify the most effective ones for specific traits and species is essential for the advancement of genomic prediction. Additionally, the potential of genomic selection to facilitate the introgression of beneficial alleles from exotic germplasm into elite breeding populations is an exciting prospect that warrants further investigation. In conclusion, genomic prediction technology has already made a substantial impact on livestock breeding strategies for complex traits. To fully realize its potential and to continue enhancing breeding programs, it is crucial to invest in research that addresses the challenges associated with the genetic complexity of these traits, the development of advanced prediction models, and the integration of comprehensive genomic information. References Boison S., Utsunomiya A., Santos D., Neves H., Carvalheiro R., Mészáros G., Utsunomiya Y., Carmo A., Verneque R., Machado M., Panetto J., Garcia J., Sölkner J., and Silva M., 2017, Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle, Journal of dairy science, 100(7): 5479-5490. https://doi.org/10.3168/jds.2016-11811 Bolormaa S., Pryce J., Kemper K., Savin K., Hayes B., Barendse W., Zhang Y., Reich C., Mason B., Bunch R., Harrison B., Reverter A., Herd R., Tier B., Graser H., and Goddard M., 2013, Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle.. Journal of animal science, 91(7): 3088-3104. https://doi.org/10.2527/jas.2012-5827 Campos G., Hickey J., Pong-Wong R., Daetwyler H., and Calus M., 2013, Whole-genome regression and prediction methods applied to plant and animal breeding, Genetics, 193: 327-345. https://doi.org/10.1534/genetics.112.143313 Christensen O., and Lund M., 2010, Genomic prediction when some animals are not genotyped. Genetics, Selection, Evolution: GSE, 42: 2. https://doi.org/10.1186/1297-9686-42-2 Ding X., Zhang Z., Zhang Z., Li X., Wang S., Wu X., Sun D., Yu Y., Liu J., Wang Y., Zhang Y., Zhang S., and Zhang Q., 2013, Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows, Journal of Dairy Science, 96(8): 5315-5323. https://doi.org/10.3168/jds.2012-6194
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