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Genomic selection in pig breeding: comparative analysis of machine learning algorithms.

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  • Additional Information
    • Source:
      Publisher: BioMed Central Country of Publication: France NLM ID: 9114088 Publication Model: Electronic Cited Medium: Internet ISSN: 1297-9686 (Electronic) Linking ISSN: 0999193X NLM ISO Abbreviation: Genet Sel Evol Subsets: MEDLINE
    • Publication Information:
      Publication: London : BioMed Central
      Original Publication: Paris : Elsevier, c1989-
    • Subject Terms:
      Machine Learning* ; Breeding*/methods ; Genomics*/methods ; Selection, Genetic*; Animals ; Swine/genetics ; Phenotype ; Algorithms ; Polymorphism, Single Nucleotide ; Models, Genetic
    • Abstract:
      Competing Interests: Declarations. Ethics approval and consent to participate: All the data we used were collected through the Internet, which did not harm the pigs. They complied with the regulations of the school and college, passed the review of the ethics committee, and met the requirements of humanitarianism. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests.
      Background: The effectiveness of genomic prediction (GP) significantly influences breeding progress, and employing SNP markers to predict phenotypic values is a pivotal aspect of pig breeding. Machine learning (ML) methods are usually used to predict phenotypic values since their advantages in processing high dimensional data. While, the existing researches have not indicated which ML methods are suitable for most pig genomic prediction. Therefore, it is necessary to select appropriate methods from a large number of ML methods as long as genomic prediction is performed. This paper compared the performance of popular ML methods in predicting pig phenotypes and then found out suitable methods for most traits.
      Results: In this paper, five commonly used datasets from other literatures were utilized to compare the performance of different ML methods. The experimental results demonstrate that Stacking performs best on the PIC dataset where the trait information is hidden, and the performs of kernel ridge regression with rbf kernel (KRR-rbf) closely follows. Support vector regression (SVR) performs best in predicting reproductive traits, followed by genomic best linear unbiased prediction (GBLUP). GBLUP achieves the best performance on growth traits, with SVR as the second best.
      Conclusions: GBLUP achieves good performance for GP problems. Similarly, the Stacking, SVR, and KRR-RBF methods also achieve high prediction accuracy. Moreover, LR statistical analysis shows that Stacking, SVR and KRR are stable. When applying ML methods for phenotypic values prediction in pigs, we recommend these three approaches.
      (© 2025. The Author(s).)
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    • Grant Information:
      2024YFF1000100 National Key R&D Programs of China; 31972563 National Natural Science Foundations of China
    • Publication Date:
      Date Created: 20250311 Date Completed: 20250311 Latest Revision: 20250512
    • Publication Date:
      20250513
    • Accession Number:
      PMC11892316
    • Accession Number:
      10.1186/s12711-025-00957-3
    • Accession Number:
      40065232