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Animal : an international journal of animal bioscience2017; 12(1); 20-27; doi: 10.1017/S1751731117001331

Cross-validation analysis for genetic evaluation models for ranking in endurance horses.

Abstract: Ranking trait was used as a selection criterion for competition horses to estimate racing performance. In the literature the most common approaches to estimate breeding values are the linear or threshold statistical models. However, recent studies have shown that a Thurstonian approach was able to fix the race effect (competitive level of the horses that participate in the same race), thus suggesting a better prediction accuracy of breeding values for ranking trait. The aim of this study was to compare the predictability of linear, threshold and Thurstonian approaches for genetic evaluation of ranking in endurance horses. For this purpose, eight genetic models were used for each approach with different combinations of random effects: rider, rider-horse interaction and environmental permanent effect. All genetic models included gender, age and race as systematic effects. The database that was used contained 4065 ranking records from 966 horses and that for the pedigree contained 8733 animals (47% Arabian horses), with an estimated heritability around 0.10 for the ranking trait. The prediction ability of the models for racing performance was evaluated using a cross-validation approach. The average correlation between real and predicted performances across genetic models was around 0.25 for threshold, 0.58 for linear and 0.60 for Thurstonian approaches. Although no significant differences were found between models within approaches, the best genetic model included: the rider and rider-horse random effects for threshold, only rider and environmental permanent effects for linear approach and all random effects for Thurstonian approach. The absolute correlations of predicted breeding values among models were higher between threshold and Thurstonian: 0.90, 0.91 and 0.88 for all animals, top 20% and top 5% best animals. For rank correlations these figures were 0.85, 0.84 and 0.86. The lower values were those between linear and threshold approaches (0.65, 0.62 and 0.51). In conclusion, the Thurstonian approach is recommended for the routine genetic evaluations for ranking in endurance horses.
Publication Date: 2017-06-21 PubMed ID: 28633685DOI: 10.1017/S1751731117001331Google Scholar: Lookup
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  • Comparative Study
  • Journal Article

Summary

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The research investigates the best model for estimating breeding values for competitive horses based on their racing performance. The Thurstonian approach was found to yield best prediction accuracy compared to linear and threshold models.

Research Aim and Methodology

  • The study aimed at comparing the predictability of linear, threshold and Thurstonian approaches for the genetic evaluation of racing performance in endurance horses.
  • Eight genetic models were used for each method, combining different random effects such as the rider, the rider-horse interaction and the environmental permanent effect. All models included the systematic impact of gender, age, and competition.
  • The research was conducted on a dataset with 4065 ranking records from 966 horses, 47% of which were Arabian. The pedigree dataset contained details for 8733 animals. The estimated heritability for the ranking trait was around 0.10.
  • The prediction ability of each model was evaluated using a cross-validation approach.

Findings

  • The average correlation between actual and predicted performances was around 0.25 for the threshold approach, 0.58 for the linear approach, and 0.60 for the Thurstonian approach.
  • While there were no significant differences found among models within the same approach, the best performing models for each approach included various combinations of random effects.
  • The correlations of predicted breeding values were highest between the threshold and Thurstonian approaches for all animals, and especially the top-performing 20% and 5% of animals.
  • The lowest correlation values were found between the linear and threshold approaches.

Conclusion

  • Based on the research findings, the Thurstonian approach to predicting the breeding values for ranking in endurance horses is recommended for routine genetic evaluations.

Cite This Article

APA
García-Ballesteros S, Varona L, Valera M, Gutiérrez JP, Cervantes I. (2017). Cross-validation analysis for genetic evaluation models for ranking in endurance horses. Animal, 12(1), 20-27. https://doi.org/10.1017/S1751731117001331

Publication

ISSN: 1751-732X
NlmUniqueID: 101303270
Country: England
Language: English
Volume: 12
Issue: 1
Pages: 20-27

Researcher Affiliations

García-Ballesteros, S
  • 1Departamento de Producción Animal,Universidad Complutense de Madrid,Avda. Puerta de Hierro s/n,E-28040 Madrid,Spain.
Varona, L
  • 2Unidad de Genética Cuantitativa y Mejora Animal,Universidad de Zaragoza,E-50013 Zaragoza,Spain.
Valera, M
  • 3Departamento de Ciencias Agro-Forestales,Universidad de Sevilla,Ctra. Utrera km 1,41013 Sevilla,Spain.
Gutiérrez, J P
  • 1Departamento de Producción Animal,Universidad Complutense de Madrid,Avda. Puerta de Hierro s/n,E-28040 Madrid,Spain.
Cervantes, I
  • 1Departamento de Producción Animal,Universidad Complutense de Madrid,Avda. Puerta de Hierro s/n,E-28040 Madrid,Spain.

MeSH Terms

  • Animals
  • Breeding
  • Female
  • Horses / genetics
  • Horses / physiology
  • Male
  • Models, Genetic
  • Models, Statistical
  • Pedigree
  • Phenotype
  • Physical Endurance

Citations

This article has been cited 7 times.
  1. Giontella A, Sarti FM, Biggio GP, Giovannini S, Cherchi R, Silvestrelli M, Pieramati C. Elo Method and Race Traits: A New Integrated System for Sport Horse Genetic Evaluation. Animals (Basel) 2020 Jul 6;10(7).
    doi: 10.3390/ani10071145pubmed: 32640698google scholar: lookup
  2. Cervantes I, Gutiérrez JP, García-Ballesteros S, Varona L. Combining Threshold, Thurstonian and Classical Linear Models in Horse Genetic Evaluations for Endurance Competitions. Animals (Basel) 2020 Jun 22;10(6).
    doi: 10.3390/ani10061075pubmed: 32580415google scholar: lookup
  3. Varona L, Legarra A. GIBBSTHUR: Software for Estimating Variance Components and Predicting Breeding Values for Ranking Traits Based on a Thurstonian Model. Animals (Basel) 2020 Jun 8;10(6).
    doi: 10.3390/ani10061001pubmed: 32521773google scholar: lookup
  4. Cervantes I, Bodin L, Valera M, Molina A, Gutiérrez JP. Challenging the selection for consistency in the rank of endurance competitions. Genet Sel Evol 2020 Apr 10;52(1):20.
    doi: 10.1186/s12711-020-00539-5pubmed: 32276582google scholar: lookup
  5. Chute M, Aujla P, Jana S, Kassiri Z. The Non-Fibrillar Side of Fibrosis: Contribution of the Basement Membrane, Proteoglycans, and Glycoproteins to Myocardial Fibrosis. J Cardiovasc Dev Dis 2019 Sep 23;6(4).
    doi: 10.3390/jcdd6040035pubmed: 31547598google scholar: lookup
  6. Hegedűs B, Galoro Leite N, Bolhuis JE, Bijma P. Genetic parameters and potential of reducing tail and ear damage in pigs through breeding. Genet Sel Evol 2025 Jul 14;57(1):39.
    doi: 10.1186/s12711-025-00976-0pubmed: 40660110google scholar: lookup
  7. Sánchez-Guerrero MJ, Ripollés-Lobo M, Bartolomé E, Perdomo-González DI, Valera M. The Relevance of the Expected Value of the Proportion of Arabian Genes in Genetic Evaluations for Eventing Competitions. Animals (Basel) 2023 Jun 13;13(12).
    doi: 10.3390/ani13121973pubmed: 37370483google scholar: lookup