Research Report

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, China
Author    Correspondence author
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
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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)


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.

Genomic prediction; Complex traits; Livestock breeding; Genetic architecture; SNP genotypes
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