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Home / Equine Research Bank / Genet Sel Evol / Validation of models for analysis of ranks in horse breeding...
Genetics, selection, evolution : GSE2010; 42(1); 3; doi: 10.1186/1297-9686-42-3

Validation of models for analysis of ranks in horse breeding evaluation.

Abstract: Ranks have been used as phenotypes in the genetic evaluation of horses for a long time through the use of earnings, normal score or raw ranks. A model, ("underlying model" of an unobservable underlying variable responsible for ranks) exists. Recently, a full Bayesian analysis using this model was developed. In addition, in reality, competitions are structured into categories according to the technical level of difficulty linked to the technical ability of horses (horses considered to be the "best" meet their peers). The aim of this article was to validate the underlying model through simulations and to propose a more appropriate model with a mixture distribution of horses in the case of a structured competition. The simulations involved 1000 horses with 10 to 50 performances per horse and 4 to 20 horses per event with unstructured and structured competitions. Results: The underlying model responsible for ranks performed well with unstructured competitions by drawing liabilities in the Gibbs sampler according to the following rule: the liability of each horse must be drawn in the interval formed by the liabilities of horses ranked before and after the particular horse. The estimated repeatability was the simulated one (0.25) and regression between estimated competing ability of horses and true ability was close to 1. Underestimations of repeatability (0.07 to 0.22) were obtained with other traditional criteria (normal score or raw ranks), but in the case of a structured competition, repeatability was underestimated (0.18 to 0.22). Our results show that the effect of an event, or category of event, is irrelevant in such a situation because ranks are independent of such an effect. The proposed mixture model pools horses according to their participation in different categories of competition during the period observed. This last model gave better results (repeatability 0.25), in particular, it provided an improved estimation of average values of competing ability of the horses in the different categories of events. Conclusions: The underlying model was validated. A correct drawing of liabilities for the Gibbs sampler was provided. For a structured competition, the mixture model with a group effect assigned to horses gave the best results.
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Publication Date: 2010-01-28 PubMed ID: 20109204PubMed Central: PMC2832620DOI: 10.1186/1297-9686-42-3Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research paper examines and validates the use of rank-based models for the evaluation of horse breeding, particularly in the context of horse competitions which are categorized by different levels of technical skill. It specifically validates an ‘underlying model’ for unstructured competitions and proposes a more inclusive model for competitions with varying levels of structure.

Research Context and Objective

  • The research focuses primarily on the use of ranks in assessing horse breeding. These ranks have traditionally been derived from various sources such as earnings, normal score, or raw ranks.
  • An existing “underlying model” allows for the use of these ranks for evaluation. This model assumes a hidden variable responsible for determining the ranks.
  • This study aims to validate this underlying model, particularly in the context of unstructured horse competitions.
  • Furthermore, it proposes a new model that can handle structured competitions, where horses are grouped according to their technical abilities.

Methodology

  • The researchers performed simulations involving 1,000 horses, with each horse participating in 10 to 50 individual events. Events had anywhere from 4 to 20 participating horses, and were categorized as either unstructured or structured competitions.
  • The performances of the horses were analyzed based on the existing underlying model, as well as the proposed model for structured competitions.

Results and Conclusions

  • The underlying model performed well in unstructured competitions, providing accurate estimates for repeatability and a strong correlation between estimated competing ability of horses and their true abilities.
  • However, traditional ranking methods resulted in underestimations of repeatability in both unstructured competitions and structured competitions.
  • In structured competitions, the underlying model also underestimated repeatability.
  • Importantly, the study found that the specific category or event has no effect on the ranks, as the ranks are independent of such factors.
  • The proposed model provides more accurate results in structured competitions by separating horses based on their participation in different categories, providing a better estimate of the average competing abilities of horses across different categories.
  • The study concludes by validating the underlying model and highlights dedicated drawing procedures that deliver optimal results in the Gibbs sampler for unstructured competitions. It also indicates that the proposed model works better for structured competitions.

Cite This Article

APA
Ricard A, Legarra A. (2010). Validation of models for analysis of ranks in horse breeding evaluation. Genet Sel Evol, 42(1), 3. https://doi.org/10.1186/1297-9686-42-3

Publication

Genetics, selection, evolution : GSE
ISSN: 1297-9686
NlmUniqueID: 9114088
Country: France
Language: English
Volume: 42
Issue: 1
Pages: 3

Researcher Affiliations

Ricard, Anne
  • INRA, UMR 1313, 78352 Jouy-en-Josas, France. anne.ricard@toulouse.inra.fr
Legarra, Andrés

    MeSH Terms

    • Animals
    • Breeding
    • Computer Simulation
    • Horses / genetics
    • Models, Genetic
    • Models, Statistical
    • Phenotype
    • Physical Conditioning, Animal

    References

    This article includes 22 references
    1. Langlois B, Poirel D, Bresch JL. Estimation de la valeur génétique des chevaux de sport d'après les sommes gagnées dans les compétitions équestres françaises.. Ann Genet Sel Anim 1980;12(1):15-31.
      pmc: PMC2734835pubmed: 22896152doi: 10.1186/1297-9686-12-1-15google scholar: lookup
    2. Tavernier A. Estimation of breeding value of jumping horses from their ranks. Livest Prod Sci 1990;26:277–290.
      doi: 10.1016/0301-6226(90)90065-Egoogle scholar: lookup
    3. Tavernier A. Genetic evaluation of horses based on ranks in competitions. Genet Sel Evol 1991;23:159–173.
      doi: 10.1186/1297-9686-23-2-159google scholar: lookup
    4. Henery RJ. Permutation probabilities as models for horse races. JR Statist Soc 1981;43:86–91.
    5. Jaitner J, Reinhardt F. National genetic evaluation for horses in Germany. Book of abstracts or the 54th annual meeting of the EAAP 31 August-3 September 2003; Roma; p. 402.
    6. Gómez MD, Cervantes I, Bartolomé E, Molina A, Valera M. Genetic evaluation of show jumping performances in young spanish sport horse breed. Book of abstracts or the 57th annual meeting of the EAAP 17-20 September 2006; Antalya; p. 351.
    7. Foran MK, Reilly MP, Kellecher DL, Langan KW, Brophy PO. Genetic evaluation of show jumping horses in Ireland using ranks in competition. Book of abstracts or the 46th annual meeting of the EAAP 4-7 September 1995; Prague; p. 349.
    8. Ruhlmann C, Janssens S, Philipsson J, Thorén-Hellsten E, Crolly H, Quinn K, Manfredi E, Ricard A. Genetic correlations between horse show jumping competition traits in five European countries. Livest Sci 2009;122:234–240.
      doi: 10.1016/j.livsci.2008.09.006google scholar: lookup
    9. Gianola D, Foulley J. Sire evaluation for ordered categorical data with a threshold model.. Genet Sel Evol (1983) 1983;15(2):201-24.
      doi: 10.1186/1297-9686-15-2-201pmc: PMC2714596pubmed: 22879123google scholar: lookup
    10. Gianola D, Simianer H. A Thurstonian model for quantitative genetic analysis of ranks: a Bayesian approach.. Genetics 2006 Nov;174(3):1613-24.
      doi: 10.1534/genetics.106.060673pmc: PMC1667069pubmed: 16980397google scholar: lookup
    11. TM Threshold Model. http://snp.toulouse.inra.fr/~alegarra
    12. David HA. Order statistics. 2. New York: Wiley; 1981; p. 360.
    13. Geweke J. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. Bayesian statistics. Vol. 4. New York: Oxford University press; 1992.
    14. SAS® Institute I. SAS® 9.1.3 Help and documentation. SAS® Institute Inc.; Proc Mixed.
    15. The Numerical Algorithms Group. http://www.nag.co.uk/numeric/FL/FLdocumentation.asp
    16. Luhrs-Behnke H, Rohe R, Kalm E. Genetic analyses of riding test and their connections with traits of stallion performance and breeding mare tests. Zuchtungskunde 2006;78:119–128.
    17. Brockmann A, Bruns E. Estimation of genetic parameters for performance traits of riding horses. Zuchtungskunde 2000;72:4–16.
    18. Aldridge LI, Kelleher DL, Reilly M, Brophy PO. Estimation of the genetic correlation between performances at different levels of show jumping competitions in Ireland. J Anim Breed Genet 2000;117:65–72.
      doi: 10.1046/j.1439-0388.2000.00232.xgoogle scholar: lookup
    19. Janssens S, Buys N, Vandepitte W. Sport status and the genetic evaluation for show jumping in Belgian sport horses. Book of abstracts or the 58th annual meeting of the EAAP 26-29 August 2007; Dublin; p. 151.
    20. Tavernier A. Special problems in genetic evaluation of performance traits in horses. Proceedings of the 5th World Congress on Genetics applied to Livestock Production 7-12 August 1994; Guelph; Vol. 17; pp. 450–457.
    21. Gianola D. Theory and analysis of threshold characters. J Anim Sci 1982;54:1079–1096.
    22. Ricard A, Bruns E, Cunningham EP. Genetics of performance traits. The genetics of the horse Oxon: CABI Publishing; 2000; pp. 411–438.

    Citations

    This article has been cited 6 times.
    1. Chapard L, Van Thillo A, Meyermans R, Gorssen W, Buys N, Janssens S. Adjusted fence height: an improved phenotype for the genetic evaluation of show jumping performance in Warmblood horses. Genet Sel Evol 2023 Feb 23;55(1):12.
      doi: 10.1186/s12711-023-00786-2pubmed: 36823617google 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. 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
    6. 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
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