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.
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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
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