Animal Molecular Breeding 2024, Vol.14, No.5, 307-317 http://animalscipublisher.com/index.php/amb 315 These findings offer transformative potential for the dairy industry. Identifying specific genomic regions and candidate genes associated with milk production traits enables the refinement of genomic selection strategies. Incorporating these genetic markers into breeding programs can substantially improve selection accuracy and accelerate genetic gains. For example, applying weighted single-step GWAS has demonstrated superior genomic prediction accuracy compared to traditional methods. Furthermore, novel genes linked to traits such as heat tolerance, longevity, and fertility present opportunities to breed more resilient and productive dairy cattle. The integration of these genetic insights into breeding programs can facilitate the creation of customized SNP arrays tailored to target specific traits under varying environmental conditions. These advancements in GWAS and their practical applications hold the promise to revolutionize dairy cattle breeding. By enabling higher milk production, better animal health, and increased economic returns, GWAS findings contribute to sustainable and profitable dairy farming practices. Acknowledgements The author is grateful to the reviewers for their in-depth analysis and detailed feedback during the review process. 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 Bakhshalizadeh S., Zerehdaran S., and Javadmanesh A., 2021, Meta-analysis of genome-wide association studies and gene networks analysis for milk production traits in Holstein cows, Livestock Science, 250: 104605. https://doi.org/10.1016/j.livsci.2021.104605 Berg I., Boichard D., and Lund M., 2016, Comparing power and precision of within-breed and multibreed genome-wide association studies of production traits using whole-genome sequence data for 5 French and Danish dairy cattle breeds, Journal of Dairy Science, 99(11): 8932-8945. https://doi.org/10.3168/jds.2016-11073 PMid:27568046 Bindea G., Mlecnik B., Hackl H., Charoentong P., Tosolini M., Kirilovsky A., Fridman W.H., Pagès F., Trajanoski Z., and Galon J., 2009, ClueGO: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks, Bioinformatics, 25(8): 1091-1093. https://doi.org/10.1093/bioinformatics/btp101 PMid:19237447 PMCid:PMC2666812 Bolormaa S., Pryce J., Hayes B., and Goddard M., 2010, Multivariate analysis of a genome-wide association study in dairy cattle, Journal of Dairy Science, 93(8): 3818-3833. https://doi.org/10.3168/jds.2009-2980 PMid:20655452 Buaban S., Lengnudum K., Boonkum W., and Phakdeedindan P., 2021, Genome-wide association study on milk production and somatic cell score for Thai dairy cattle using weighted single-step approach with random regression test-day model, Journal of Dairy Science, 105(1): 468-494. https://doi.org/10.3168/jds.2020-19826 PMid:34756438 Chen Z., Yao Y., Ma P., Wang Q., and Pan Y., 2018, Haplotype-based genome-wide association study identifies loci and candidate genes for milk yield in Holsteins. PLoS ONE, 13(2): e0192695. https://doi.org/10.1371/journal.pone.0192695 PMid:29447209 PMCid:PMC5813974 Dadousis C., Biffani S., Cipolat-Gotet C., Nicolazzi E., Rosa G., Gianola D., Rossoni A., Santus E., Bittante G., and Cecchinato A., 2017a, Genome-wide association study for cheese yield and curd nutrient recovery in dairy cows, Journal of Dairy Science, 100(2): 1259-1271. https://doi.org/10.3168/jds.2016-11586 PMid:27889122 Dadousis C., Pegolo S., Rosa G., Gianola D., Bittante G., and Cecchinato A., 2017b, Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle, Journal of Dairy Science, 100(2): 1223-1231. https://doi.org/10.3168/jds.2016-11587 PMid:27988128 Gebreyesus G., Gebreyesus G., Buitenhuis A., Poulsen N., Visker M., Zhang Q., Valenberg H., Sun D., and Bovenhuis H., 2019, Combining multi-population datasets for joint genome-wide association and meta-analyses: the case of bovine milk fat composition traits, Journal of Dairy Science, 102(12): 11124-11141. https://doi.org/10.3168/jds.2019-16676 PMid:31563305
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