Animal Molecular Breeding 2024, Vol.14, No.2, 141-153 http://animalscipublisher.com/index.php/amb 150 In practice, the adoption of precision breeding techniques that utilize multi-omics information should be encouraged. This approach can lead to more accurate and personalized breeding strategies, ultimately improving production efficiency and sustainability. Additionally, integrating omics data with traditional breeding methods can help fine-tune nutritional management and other husbandry practices to optimize animal health and productivity. Finally, continued support for initiatives like FAANG and the development of open-access databases will be crucial for advancing livestock breeding research and translating scientific discoveries into practical applications. Acknowledgements Author would like to express our gratitude to the two anonymous peer reviewers for their critical assessment and constructive suggestions on our manuscript. Conflict of Interest Disclosure Author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Arishima T., Wakaguri H., Nakashima R., Sakakihara S., Kawashima K., Sugimoto Y., Suzuki Y., and Sasaki S., 2022, Comprehensive analysis of 124 transcriptomes from 31 tissues in developing, juvenile, and adult Japanese Black cattle, DNA Research, 29(5): dsac032. https://doi.org/10.1093/dnares/dsac032 PMID: 36047829 PMCID: PMC9555877 Baes C.F., Rochus C.M., Houlahan K., Jr G.A., van Staaveren N., and Miglior F., 2022, 22 sustainable livestock breeding: challenges and opportunities, Journal of Animal Science, 100(Suppl 3): 13. https://doi.org/10.1093/jas/skac247.023 Banerjee P., Cesar A.S.M., and De Lima A.O., 2022, Editorial: gene regulation explored by systems biology in livestock science, Frontiers in Genetics, 13: 859061. https://doi.org/10.3389/fgene.2022.859061 PMID: 35464851 PMCID: PMC9027333 Benedetto A., Pezzolato M., Biasibetti E., and Bozzetta E., 2021, Omics applications in the fight against abuse of anabolic substances in cattle: challenges, perspectives and opportunities, Current Opinion in Food Science, 40: 112-120. https://doi.org/10.1016/J.COFS.2021.03.001 Berry D.P., Meade K.G., Mullen M.P., Butler S., Diskin M.G., Morris D., and Creevey C.J., 2011, The integration of 'omic' disciplines and systems biology in cattle breeding, Animal, 5(4): 493-505. https://doi.org/10.1017/S1751731110002120 Bhattacharya A., Li Y., and Love M.I., 2020, MOSTWAS: multi-omic strategies for transcriptome-wide association studies, PLoS Genetics, 17(3): e1009398. https://doi.org/10.1101/2020.04.17.047225 Blum B.C., Mousavi F., and Emili A., 2018, Single-platform 'multi-omic' profiling: unified mass spectrometry and computational workflows for integrative proteomics-metabolomics analysis, Molecular Omics, 14(5): 307-319. https://doi.org/10.1039/c8mo00136g Camara Y., Moula N., Sow F., Sissokho M.M., and Antoine-Moussiaux N., 2019, Analysing innovations among cattle smallholders to evaluate the adequacy of breeding programs, Animal, 13(2): 417-426. https://doi.org/10.1017/S1751731118001544 Chakraborty D., Sharma N., Kour S., Sodhi S.S., Gupta M.K., Lee S.J., and Son Y.O., 2022, Applications of omics technology for livestock selection and improvement, Frontiers in Genetics, 13: 774113. https://doi.org/10.3389/fgene.2022.774113 PMID: 35719396 PMCID: PMC9204716 D’Alessandro A., and Zolla L., 2013, Meat science: from proteomics to integrated omics towards system biology, Journal of Proteomics, 78: 558-577. https://doi.org/10.1016/j.jprot.2012.10.023 PMID: 23137709 Dihazi H., Asif A.R., Beissbarth T., Bohrer R., Feussner K., Feussner I., Jahn O., Lenz C., Majcherczyk A., Schmidt B., Schmitt K., Urlaub H., and Valerius O., 2018, Integrative omics - from data to biology, Expert Review of Proteomics, 15: 463-466. https://doi.org/10.1080/14789450.2018.1476143 PMID: 29757692 Diniz W., and Ward A., 2021, 282 Multi-omics approaches to improve animal production, Journal of Animal Science, 99: 20-21. https://doi.org/10.1093/JAS/SKAB054.036 Duruflé H., Selmani M., Ranocha P., Jamet E., Dunand C., and Déjean S., 2020, A powerful framework for an integrative study with heterogeneous omics data: from univariate statistics to multi-block analysis, Briefings in Bioinformatics, 22(3): bbaa166.
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