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Animals : an open access journal from MDPI2020; 10(6); 1075; doi: 10.3390/ani10061075

Combining Threshold, Thurstonian and Classical Linear Models in Horse Genetic Evaluations for Endurance Competitions.

Abstract: The racing time and rank at finish traits are commonly used for endurance horse breeding programs as a measure of their performance. Even so, given the nature of endurance competitions, many horses do not finish the race. However, the exclusion of non placed horses from the dataset could have an influence on the prediction of individual breeding values. The objective of the present paper was to develop a multitrait model including race time (T), rank (R) and placing (P), with different methodologies, to improve the genetic evaluation in endurance competitions in Spain. The database contained 6135 records from 1419 horses, with 35% of the records not placed. Horse pedigree included 10868 animals, with 52% Arab Horses. All models included gender, age and race effect as systematic effects and combined different random effects beside the animal and residual effects: rider, permanent environmental effect, and interaction horse-rider. The kilometers per race was included as a covariate for T. Heritabilities were estimated as moderately low, ranging from 0.06 to 0.14 for T, 0.09 to 0.15 for P, and 0.07 to 0.17 for R, depending on the model. T and R appeared mostly as inverse measures of the same trait due to their high genetic correlation, suggesting that T can be ignored in future genetic evaluations. P was the most independent trait from the genetic correlations. The possibility of simultaneously processing the threshold, Thurstonian and continuous traits has opened new opportunities for genetic evaluation in horse populations, and much more practical genetic evaluations can be done to help a proper genetic selection.
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Publication Date: 2020-06-22 PubMed ID: 32580415PubMed Central: PMC7341300DOI: 10.3390/ani10061075Google Scholar: Lookup
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Summary

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The research article focuses on combining various models in genetic evaluations of endurance horses in Spain. The aim is to get a more comprehensive understanding of their performance, including race duration, position, and placing in races, taking into account the substantial percentage of horses that do not finish competitions.

Research Scope and Methodology

  • The research took into account 6135 records of 1419 horses, about 35% of which did not finish the races they participated in.
  • Arab Horses, known for their endurance, made up 52% of the total horse pedigree that included 10868 animals.
  • The researchers employed different methodologies including the Threshold model, Thurstonian model, and Classical Linear model to evaluate the characteristics for each horse.
  • They also considered additional variables such as gender, age, race effect as systematic effects, and combined diverse random effects like the rider effect, environmental effects, and the interaction between the horse and rider.
  • The distance covered per race (kilometers per race) was used as a covariate for the race time (T).

Findings

  • Estimates of heritability (a measure of how much of the traits are genetically inherited) were moderately low, ranging from 0.06 to 0.14 for race time (T), 0.09 to 0.15 for placing (P), and 0.07 to 0.17 for rank (R) in the race.
  • Race time and rank were found to have a high genetic correlation, indicating that they essentially measured the same trait inversely. Because of this, the research suggests that race time could be excluded in future genetic evaluations.
  • Placing (whether or not a horse finished the race) was found to be the most independent trait, with low correlation to other traits.

Implications

  • The study demonstrates that combining different traits and models can yield a comprehensive and robust genetic evaluation in endurance horses.
  • The findings provide insights for future research and practical genetic evaluations, by highlighting that race placing is an independent trait and indicating the possibility to ignore race time because of its correlation with rank.
  • This research thus opens new possibilities for genetic evaluations in horse populations, contributing to more efficient and effective genetic selection in the future.

Cite This Article

APA
Cervantes I, Gutiérrez JP, García-Ballesteros S, Varona L. (2020). Combining Threshold, Thurstonian and Classical Linear Models in Horse Genetic Evaluations for Endurance Competitions. Animals (Basel), 10(6), 1075. https://doi.org/10.3390/ani10061075

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 10
Issue: 6
PII: 1075

Researcher Affiliations

Cervantes, Isabel
  • Departamento de Producción Animal, Universidad Complutense de Madrid, Avda. Puerta de Hierro s/n, E-28040 Madrid, Spain.
Gutiérrez, Juan Pablo
  • Departamento de Producción Animal, Universidad Complutense de Madrid, Avda. Puerta de Hierro s/n, E-28040 Madrid, Spain.
García-Ballesteros, Silvia
  • Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, E-28040 Madrid, Spain.
Varona, Luis
  • Departamento de Anatomía, Embriología y Genética Animal, Instituto Agroalimentario de Aragón (IA2), Universidad de Zaragoza, E-50013 Zaragoza, Spain.

Conflict of Interest Statement

The authors declare no conflict of interest.

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Citations

This article has been cited 4 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. Bussiman F, Richter J, Hidalgo J, Silva FFE, Ventura RV, Carvalho RSB, Mattos EC, Ferraz JBS, Eler JP, de Carvalho Balieiro JC. Bayesian Recursive and Structural Equation Models to Infer Causal Links Among Gait Visual Scores on Campolina Horses. J Anim Breed Genet 2025 Sep;142(5):463-477.
    doi: 10.1111/jbg.12919pubmed: 39698947google scholar: lookup
  3. Bussiman F, Alves AAC, Richter J, Hidalgo J, Veroneze R, Oliveira T. Supervised Machine Learning Techniques for Breeding Value Prediction in Horses: An Example Using Gait Visual Scores. Animals (Basel) 2024 Sep 20;14(18).
    doi: 10.3390/ani14182723pubmed: 39335312google scholar: lookup
  4. 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).
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