Animal Molecular Breeding 2024, Vol.14, No.1, 95-105 http://animalscipublisher.com/index.php/amb 99 In summary, the theoretical foundations of genomic selection are built upon the intricate relationship between genetic markers and phenotypes, the implementation of genome-wide selection models, and the utilization of advanced statistical and machine learning algorithms. These foundations are crucial for enhancing breeding strategies and improving the selection of livestock with desirable complex traits. 2 Practical Applications of Genomic Prediction Technologies 2.1 Cattle breeding Genomic prediction technologies have significantly advanced cattle breeding by enabling more precise selection for desirable traits. One of the key applications is in increasing milk production and improving milk quality. By using genomic selection, breeders can identify and select for alleles that are associated with higher milk yield and better milk composition. This is achieved through methods such as whole-genome regression (WGR) models, which allow for the prediction of complex traits by regressing phenotypes on thousands of markers concurrently (Campos et al., 2013). Additionally, genomic information has been utilized to optimize genetic resistance to diseases in cattle. This not only improves the health and welfare of the animals but also reduces economic losses due to illness. The integration of genomic, pedigree, and phenotype information into a one-step procedure for genomic prediction has been shown to result in more accurate estimated breeding values (EBVs), which is particularly beneficial for disease resistance traits (Christensen and Lund, 2010). 2.2 Swine and poultry breeding In swine and poultry breeding, improvements in growth rate and meat quality are paramount. Genomic selection has been applied to enhance these traits by predicting genomic estimated breeding values (GEBVs) for economically important traits such as carcass weight, live weight, and meat quality (Liang et al., 2020). The use of ensemble learning algorithms, which combine multiple machine learning methods, has shown to outperform traditional genomic best linear unbiased prediction (GBLUP) methods, leading to higher prediction accuracy (Liang et al., 2020). Furthermore, genomic selection aids in identifying genotypes that are better suited to cope with environmental stresses. This is crucial for maintaining productivity and animal welfare in varying climatic conditions (Meuwissen et al., 2016). 2.3 Rare and economically valuable animals For rare and economically valuable animals like alpacas and water buffalos, genomic prediction offers a way to achieve genetic improvement while preserving genetic diversity. The application of genomic selection in these species can help identify individuals with superior traits for breeding programs, ensuring the sustainability and profitability of these niche markets. Although genomic selection is more established in common livestock species, its principles are equally applicable to rare breeds, and its implementation can be tailored to the specific needs and challenges associated with these animals (Georges, 2001). In conclusion, genomic prediction technologies have provided breeders with powerful tools to enhance breeding strategies for complex traits in livestock. These advancements have led to significant improvements in cattle, swine, and poultry breeding, and they hold promise for the genetic improvement of rare and economically valuable animals. 3 Case Studies in Genomic Prediction 3.1 Enhancing dairy cattle productivity through genomic selection Genomic selection has revolutionized dairy cattle breeding programs by enabling the accurate selection of superior candidates for productivity traits. The use of genomic estimated breeding values (GEBVs) based on intensive progeny testing for production and reproductive traits has been crucial in increasing dairy cattle productivity. Key genes and haplotypes have been identified as markers for productivity traits, improving the strategies for dairy cattle selection. Genome-wide association studies (GWAS), quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), and single-step genomic best linear unbiased prediction (ssGBLUP) methods have been incorporated into global dairy programs to estimate marker-assisted selection effects. Despite the progress, inbreeding depression remains a challenge due to closed bloodlines in several milk breeds. Genomic
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