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Animals : an open access journal from MDPI2020; 10(6); 1001; doi: 10.3390/ani10061001

GIBBSTHUR: Software for Estimating Variance Components and Predicting Breeding Values for Ranking Traits Based on a Thurstonian Model.

Abstract: (1) Background: Ranking traits are used commonly for breeding purposes in several equine populations; however, implementation is complex, because the position of a horse in a competition event is discontinuous and is influenced by the performance of its competitors. One approach to overcoming these limitations is to assume an underlying Gaussian liability that represents a horse's performance and dictates the observed classification in a competition event. That approach can be implemented using Montecarlo Markov Chain (McMC) techniques with a procedure known as the Thurstonian model. (2) Methods: We have developed software (GIBBSTHUR) that analyses ranking traits along with other continuous or threshold traits. The software implements a Gibbs Sampler scheme with a data-augmentation step for the liability of the ranking traits and provides estimates of the variance and covariance components and predictions of the breeding values and the average performance of the competitors in competition events. (3) Results: The results of a simple example are presented, in which it is shown that the procedure can recover the simulated variance and covariance components. In addition, the correlation between the simulated and predicted breeding values and between the estimates of the event effects and the average additive genetic effect of the competitors demonstrates the ability of the software to produce useful predictions for breeding purposes. (4) Conclusions: the GIBBSTHUR software provides a useful tool for the breeding evaluation of ranking traits in horses and is freely available in a public repository (https://github.com/lvaronaunizar/Gibbsthur).
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Publication Date: 2020-06-08 PubMed ID: 32521773PubMed Central: PMC7341208DOI: 10.3390/ani10061001Google 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 article is about a software called GIBBSTHUR that has been designed for predicting breeding values and estimating variance components based on the Thurstonian model. It is specifically used for analysing ranking traits in horse breeding setups.

Background of the Study

  • The study provides a solution for issues related to the ranking of traits utilised for breeding horses. These issues include the discontinuous and competing influenced positions of horses in competition events.
  • The study proposes the use of a Gaussian liability, representing the horse’s performance responsible for dictating its observed classification in a competition event.
  • This concept is realised through the application Montecarlo Markov Chain (McMC) techniques and a procedure known as the Thurstonian model.

Methods Used

  • Authors developed and presented GIBBSTHUR, a software tool which is capable of analysing ranking traits and other continuous or threshold traits.
  • The software carries out its functions using a Gibbs Sampler and a data-augmentation step for the liability of the ranking traits.
  • It is designed to provide estimates of the variance and covariance components. It also predicts the breeding values and the average performance of the competitors found in the competition events.

Results of the Study

  • The results are showcased through an example where the software successfully recovers the simulated variance and covariance components.
  • The software showed a correlation between the simulated and predicted breeding values, as well as between the estimates of the event effects and the average additive genetic effect of the competitors.
  • This demonstrates the software’s prediction functionality useful for breeding purposes.

Conclusions Derived

  • The GIBBSTHUR software is found to be practical for the breeding evaluation of ranking traits in horses.
  • The software is publicly available for free, positioned in the public repository (https://github.com/lvaronaunizar/Gibbsthur).

Cite This Article

APA
Varona L, Legarra A. (2020). GIBBSTHUR: Software for Estimating Variance Components and Predicting Breeding Values for Ranking Traits Based on a Thurstonian Model. Animals (Basel), 10(6), 1001. https://doi.org/10.3390/ani10061001

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 10
Issue: 6
PII: 1001

Researcher Affiliations

Varona, Luis
  • Departamento de Anatomía, Embriología y Genética Animal, Instituto Agroalimentario de Aragón (IA2), Universidad de Zaragoza, 50018 Zaragoza, Spain.
Legarra, Andrés
  • Génétique Physiologie et Systèmes d'Elevage (GenPhySE), Institut National de la Recherche Agronomique de Toulouse, 31326 Castanet-Tolosan, France.

Conflict of Interest Statement

The authors declare no conflict of interest.

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Citations

This article has been cited 3 times.
  1. 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
  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
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