AMB_2025v15n2

Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 57 Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Bertolini F., Servin B., Talenti A., Rochat E., Kim E., Oget C., Palhière I., Crisà A., Catillo G., Steri R., Amills M., Colli L., Marras G., Milanesi M., Nicolazzi E., Rosen B., Van Tassell C., Guldbrandtsen B., Sonstegard T., Tosser-Klopp G., Stella A., Rothschild M., Joost S., and Crepaldi P., 2018, Signatures of selection and environmental adaptation across the goat genome post-domestication, Genetics, Selection, Evolution, 50: 57. https://doi.org/10.1186/s12711-018-0421-y Bhat J., Feng X., Mir Z., Raina A., and Siddique K., 2023, Recent advances in artificial intelligence, mechanistic models and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding, Physiologia Plantarum, 175(4): e13969. https://doi.org/10.1111/ppl.13969 Bhat S., Malik A., Ahmad S., Shah R., Ganai N., Shafi S., and Shabir N., 2017, Advances in genome editing for improved animal breeding: a review, Veterinary World, 10(11): 1361-1366. https://doi.org/10.14202/vetworld.2017.1361-1366 Bishop T., and Van Eenennaam A., 2020, Genome editing approaches to augment livestock breeding programs, Journal of Experimental Biology, 223: jeb207159. https://doi.org/10.1242/jeb.207159 Brito L., Kijas J., Ventura R., Sargolzaei M., Porto-Neto L., Cánovas Á., Feng Z., Jafarikia M., and Schenkel F., 2017, Genetic diversity and signatures of selection in various goat breeds revealed by genome-wide SNP markers, BMC Genomics, 18: 229. https://doi.org/10.1186/s12864-017-3610-0 Chafai N., Hayah I., Houaga I., and Badaoui B., 2023, A review of machine learning models applied to genomic prediction in animal breeding, Frontiers in Genetics, 14: 1150596. https://doi.org/10.3389/fgene.2023.1150596 Dhakate P., Sehgal D., Vaishnavi S., Chandra A., Singh A., Raina S., and Rajpal V., 2022, Comprehending the evolution of gene editing platforms for crop trait improvement, Frontiers in Genetics, 13: 876987. https://doi.org/10.3389/fgene.2022.876987 Dórea J., and Menezes G., 2024, 462 artificial intelligence and machine learning to improve livestock farming, Journal of Animal Science, 102: 296. https://doi.org/10.1093/jas/skae234.338 Farooq M., Gao S., Hassan M., Huang Z., Rasheed A., Hearne S., Prasanna B., Li X., and Li H., 2024, Artificial intelligence in plant breeding, Trends in Genetics, 40(10): 891-908. https://doi.org/10.1016/j.tig.2024.07.001 Feng R., Zhao J., Zhang Q., Zhu Z., Zhang J., Liu C., Zheng X., Wang F., Su J., Ma X., Mi X., Guo L., Yan X., Liu Y., Li H., Chen X., Deng Y., Wang G., Zhang Y., Liu X., and Liu J., 2024, Generation of anti-mastitis gene-edited dairy goats with enhancing lysozyme expression by inflammatory regulatory sequence using ISDra2-TnpB system, Advanced Science, 11(38): 2404408. https://doi.org/10.1002/advs.202404408 Ferreira R., Balaguer M., Bresolin T., Chandra R., Rosa G., White H., and Dórea J., 2024, Multi-modal machine learning for the early detection of metabolic disorder in dairy cows using a cloud computing framework, Comput. Electron. Agric., 227: 109563. https://doi.org/10.1016/j.compag.2024.109563 Ghanatsaman Z., Mehrgardi A., Nanaei A., and Esmailizadeh A., 2023, Comparative genomic analysis uncovers candidate genes related with milk production and adaptive traits in goat breeds, Scientific Reports, 13: 8722. https://doi.org/10.1038/s41598-023-35973-0 Gore D., Okeno T., Muasya T., and Mburu J., 2021, Improved response to selection in dairy goat breeding programme through reproductive technology and genomic selection in the tropics, Small Ruminant Research, 200: 106397. https://doi.org/10.1016/j.smallrumres.2021.106397 Guo J., Tao H., Li P., Li L., Zhong T., Wang L., Ma J., Chen X., Song T., and Zhang H., 2018, Whole-genome sequencing reveals selection signatures associated with important traits in six goat breeds, Scientific Reports, 8: 10405. https://doi.org/10.1038/s41598-018-28719-w Hay E., 2024, Machine learning for the genomic prediction of growth traits in a composite beef cattle population, Animals, 14(20): 3014. https://doi.org/10.3390/ani14203014 Jones H., and Wilson P., 2022, Progress and opportunities through use of genomics in animal production, Trends in Genetics, 38(12): 1228-1252. https://doi.org/10.1016/j.tig.2022.06.014 Lei J., Xu Z.W., Shao X.W., Jiang H., and Zhang Y.M., 2024, Integrating GWAS and genomic selection to enhance soybean breeding, Legume Genomics and Genetics, 15(6): 270-279. https://doi.org/10.5376/lgg.2024.15.0026

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