Animal Molecular Breeding 2024, Vol.14, No.3, 239-251 http://animalscipublisher.com/index.php/amb 243 Figure 1 Number of dairy cattle genotypes submitted in the United States by year that first genotype was received (Adopted from Wiggans and Carrillo, 2022) Wiggans and Carrillo (2022) shows the number of genotyped animals included in the US genomic evaluations for dairy cattle. Since the official evaluations began in January 2009, the number of genotyped Holstein and Jersey cattle has significantly increased. The figure indicates a substantial rise in the number of genotyped female cattle since the implementation of genomic evaluations, with a peak observed between 2015 and 2016. This trend highlights the growing importance of genomic selection in the dairy industry. The success of GS in dairy cattle is also evident in the extensive use of genomic data across various breeds, which has improved the prediction of breeding values and allowed for more precise selection strategies (Cole and Silva, 2016). 4.2 Beef cattle GS in beef cattle has shown promising results, although its implementation has been more challenging compared to dairy cattle due to differences in breeding structures. The application of GS in beef cattle primarily focuses on improving traits such as growth rate, carcass quality, and feed efficiency. Studies have demonstrated that selective genotyping, combined with GS, can enhance the prediction accuracy of breeding values even with a limited number of genotyped animals (Esrafili Taze Kand Mohammaddiyeh et al., 2023). Additionally, GS has been used to manage genetic diversity and reduce inbreeding, which are critical for maintaining the long-term viability of beef cattle populations. The integration of GS with traditional breeding programs has resulted in significant genetic gains, although challenges related to cost, genotyping strategies, and the diversity of breeding objectives rem aim (Liang et al., 2020). 4.3Swine The swine industry has rapidly adopted GS, benefiting from the technology's ability to improve traits such as growth rate, feed efficiency, and reproductive performance. GS in swine has been facilitated by the development of high-density SNP panels and advances in genome sequencing. The implementation of GS has led to more accurate breeding value predictions, particularly for traits with low heritability, which are traditionally challenging to improve through conventional selection methods. However, the application of GS in swine faces challenges related to the genetic diversity of crossbred populations and the need for robust reference populations (Samoré and Fontanesi, 2016). Despite these challenges, GS has proven to be a valuable tool in enhancing genetic gain and improving the overall efficiency of swine breeding programs. 4.4 Poultry The poultry industry has leveraged GS to improve a range of economically important traits, including growth rate, egg production, and disease resistance. Poultry breeding programs already benefit from short generation intervals, but GS has further accelerated genetic progress by increasing the accuracy of selection. The unique breeding structures of poultry, such as the use of overlapping generations in broilers and annual generations in layers, have influenced the implementation of GS.
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