Animal Molecular Breeding 2024, Vol.14, No.3, 239-251 http://animalscipublisher.com/index.php/amb 244 Studies have shown that GS can improve selection efficiency in poultry by integrating genomic data into breeding programs, leading to faster and more targeted genetic improvement (Wolc et al., 2016). However, the success of GS in poultry also depends on the availability of large, well-characterized reference populations, which are essential for accurate genomic predictions. 4.5 Small ruminants (sheep and goats) The application of GS in small ruminants, such as sheep and goats, has been more recent but is showing significant potential. GS has primarily been applied to enhance production traits, such as growth, milk yield, and wool quality, as well as functional traits like disease resistance and reproductive performance. The smaller reference populations and the use of multi-breed datasets pose challenges, but they have also driven innovations in genomic prediction models. For example, the integration of molecular data has improved the accuracy of breeding value predictions and provided valuable insights into parentage verification and QTL identification (Zhao and Zhang, 2019; Mrode et al., 2018). In developing countries, GS offers opportunities to enhance breeding programs for small ruminants, particularly in regions where traditional genetic improvement strategies are limited. 4.6 Aquaculture species GS has been increasingly applied in aquaculture species, including fish and shellfish, to improve traits such as growth rate, disease resistance, and feed efficiency. The success of GS in aquaculture is attributed to its ability to predict breeding values at an early age, thereby accelerating genetic gain. The use of high-density SNP arrays and advanced genomic tools has facilitated the application of GS in species like salmon, tilapia, and shrimp. However, challenges related to the diversity of breeding objectives, environmental interactions, and the cost of genotyping remain. Despite these challenges, GS has proven to be a powerful tool in enhancing the efficiency of breeding programs in aquaculture, leading to significant improvements in production traits and overall industry sustainability (Jonas and de Koning, 2015). 5 Case Study: Genomic Selection in Dairy Cattle Breeding 5.1 Background of the case study Genomic selection (GS) has revolutionized dairy cattle breeding, allowing for significant improvements in genetic gain and breeding program efficiency. This case study examines the implementation of GS in dairy cattle, focusing on the background, outcomes, challenges, and lessons learned from its adoption. The dairy cattle industry has long sought to improve genetic gain through selective breeding. Traditionally, this was accomplished using progeny testing, a time-consuming and expensive process that involved evaluating the offspring of breeding animals for desirable traits. The introduction of GS in the late 2000s marked a turning point, enabling the use of genomic data to predict the breeding value of animals at a much earlier stage, thus reducing the generation interval and accelerating genetic progress. By 2009, the USDA began releasing official genomic evaluations for Holsteins and Jerseys, marking the widespread adoption of GS in the dairy industry (Wiggans et al., 2017). This case study focuses on the implementation of GS in the U.S. dairy industry, with particular attention to the experiences of Holstein and Jersey breeders. 5.2 Implementation of genomic selection in dairy cattle The implementation of GS in dairy cattle began with the development and use of high-density SNP chips, which allowed for the genotyping of thousands of genetic markers across the genome. These markers were then used to predict the breeding values of young animals, thereby enabling more accurate and timely selection decisions. The USDA's genomic evaluation program for dairy cattle utilized a combination of pedigree, phenotypic, and genomic data to estimate the genomic estimated breeding values (GEBVs) of animals. The program significantly reduced the reliance on traditional progeny testing, as young bulls could now be selected based on their genomic profiles, even before they had produced offspring (Wiggans et al., 2017). Additionally, the integration of genomic data into breeding programs has facilitated the identification of deleterious alleles and has improved the management of genetic diversity within the population (Mäntysaari et al., 2020).
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