Animal Molecular Breeding 2024, Vol.14, No.1, 95-105 http://animalscipublisher.com/index.php/amb 95 Research Report Open Access Genomic Prediction: Enhancing Breeding Strategies for Complex Traits in Livestock Xiao Zhu, Siping Zhang Tropical Animal Medicine Center, Hainan Institute of Tropical Agricultural Resources, Sanya, 572024, Hainan, China Corresponding author: 2495757304@qq.com Animal Molecular Breeding, 2024, Vol.14, No.1 doi: 10.5376/amb.2024.14.0012 Received: 06 Jan., 2024 Accepted: 16 Feb., 2024 Published: 26 Feb., 2024 Copyright © 2024 Zhu and Zhang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhu X., and Zhang S.P., 2024, Genomic prediction: enhancing breeding strategies for complex traits in livestock, Animal Molecular Breeding, 14(1): 95-105 (doi: 10.5376/amb.2024.14.0012) Abstract Genomic prediction has become a cornerstone in livestock breeding programs, aiming to enhance the selection process for complex traits. This approach leverages dense single nucleotide polymorphism (SNP) genotypes to estimate breeding values, which are pivotal for making informed selection decisions. The accuracy of genomic predictions is influenced by the genetic architecture of the trait, including the number and effect distribution of loci involved. Studies have shown that traits with a mix of large and small effect loci, such as coat color and milk-fat percentage in Holstein cattle, tend to yield higher prediction accuracies than traits governed solely by small effect loci. Incorporating biological priors, such as gene ontology terms, into prediction models can further refine these estimates, particularly when considering traits with immunological relevance like mastitis. The effectiveness of genomic selection is also dependent on the statistical models employed, with whole-genome regression methods demonstrating significant promise in both plant and animal breeding. Moreover, the integration of genome-wide association study (GWAS) results into prediction models has been proposed to enhance the accuracy of whole genome predictions, especially for traits with lower heritability. The application of genomic selection is not without challenges, including the management of inbreeding and the need for large reference populations to achieve accurate predictions. Nonetheless, the paradigm shift towards genomic selection in animal breeding is anticipated to continue evolving, with the potential inclusion of whole-genome sequence data to capture all genetic variance. Keywords Genomic prediction; Complex traits; Livestock breeding; Genetic architecture; SNP genotypes The advent of genomic selection (GS) has marked a transformative period in the field of animal breeding, particularly in the context of complex traits in livestock. Traditional breeding methods, which have been the cornerstone of genetic improvement for centuries, rely on phenotypic selection and pedigree information to estimate breeding values. However, these methods have limitations, particularly when it comes to the accuracy and efficiency of selection for complex traits, which are typically influenced by many genes with small effects (Meuwissen et al., 2016). The development of genomic selection has been driven by significant technological advancements, including the discovery of a vast number of genetic markers known as single nucleotide polymorphisms (SNPs) and the ability to genotype animals at a high throughput and reduced cost (Meuwissen et al., 2016). Genomic selection assumes that all markers are linked to genes affecting the trait and focuses on estimating their effects rather than testing for significance, a paradigm shift from traditional marker-assisted selection (MAS) (Meuwissen et al., 2016). Genomic prediction technologies leverage dense SNP genotypes to estimate breeding values, with the accuracy of these predictions depending on the genetic architecture of the trait in question (Hayes et al., 2010). For instance, traits like coat color in Holstein cattle and fat concentration in milk have been shown to have a mix of large-effect loci and many loci with small effects, while other traits like overall type are affected only by loci with small effects (Hayes et al., 2010). The application of whole-genome regression (WGR) models allows for the concurrent regression of phenotypes on thousands of markers, enhancing the prediction of complex traits (Campos et al., 2013).
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