FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Genet Sel Evol / Challenging the selection for consistency in the rank of end...
Genetics, selection, evolution : GSE2020; 52(1); 20; doi: 10.1186/s12711-020-00539-5

Challenging the selection for consistency in the rank of endurance competitions.

Abstract: Control of the environmental variability by genetic selection offers possibilities for new selection objectives for productive traits. This methodology aims at reducing heterogeneity in productive traits and has been applied to several traits and species for which animal homogeneity is profitable. In horse breeding programmes, rank in competitions is a common selection objective but has been challenging to model. In this study, the parameters of environmental variability for the rank of a horse were computed to analyse the capability of a horse to maintain the best ranking across competitions that consist of long-distance races in which the adapted physical condition of the horse is essential. The genetic component of the environmental variance for the rank in endurance competitions was evaluated, which resulted in proposing a new transformation of horse scores in competitions. Results: Homogeneous and heterogeneous variance models were compared by assaying three random effects that affect both the rank and its variability, using endurance ride data consisting of 2863 records. The pedigree relationship matrix contained 5931 animals. The rank trait was transformed into a normalized variable to prevent false estimates of the genetic correlation by inappropriate artificial skewness. The models included the number of participants in the race, sex, and age as systematic effects. The rider, the rider-horse interaction, or an environmental permanent effect were tested as random effects, in addition to additive genetic and residual effects. The models were analysed using the GSEVM program. Estimates of heritability for rank ranged from 0.12 to 0.15. The heterogeneous variance model that fitted the rider was assessed as the best model based on the deviance information criterion. Estimates of genetic variance for rank variability ranged from 0.12 to 0.13. The genetic correlation between the rank and its environmental variability was low and did not differ from 0. Conclusions: These results offer an opportunity to select animals for canalization by reducing the variability of race results and achieving the best positions, which could be a new selection objective by weighting estimated breeding values for rank and its variability in a selection index.
Get Full Text
Publication Date: 2020-04-10 PubMed ID: 32276582PubMed Central: PMC7149905DOI: 10.1186/s12711-020-00539-5Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

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 explores how the genetic selection of horses impacts their performance consistency in long-distance endurance competitions. It presents a new methodology that could be used in horse breeding programs to reduce variability and improve rankings.

Methodology

  • The researchers set out to compute the environmental variability parameters for the ranking of a horse in long-distance competitions.
  • The focus was on analyzing the capability of a horse to maintain top rankings across different competitions; an attribute which strongly hinges on the physical condition of the horse.
  • A significant part of the study was dedicated to evaluating the genetic component of the environmental variance of horses concerning their ranking in endurance competitions.
  • This investigation, in turn, birthed a new method of transforming horse scores in competitions.

Data Analysis

  • The study analysed endurance ride data from 2863 records, lodged in a pedigree relationship matrix containing 5931 animals.
  • The research team transformed the rank trait into a normalized variable to prevent inaccurate estimates of the genetic correlation due to inappropriate artificial skewness.
  • The systematic effects considered in the models include the number of participants in the race, as well as the sex and age of the horses.
  • On the other hand, the random effects tested comprise the rider, the rider-horse interaction, and an environmental permanent effect. The additive genetic and residual effects were also examined.
  • All the models were analyzed using the GSEVM program.

Results

  • The heritability estimates for rank ranged from 0.12 to 0.15.
  • The heterogeneous variance model that fitted the rider was adjudged the best model, following assessment using the deviance information criterion.
  • Estimates of genetic variance for rank variability fluctuated between 0.12 and 0.13.
  • The research found a low genetic correlation between the rank and its environmental variability, which did not differ significantly from 0.

Conclusions

  • This study offers a fresh perspective on how to select animals for canalization. The aim is to reduce the variability of race results and achieve higher positions consistently.
  • The results of this research could herald a new selection objective, which entails weighting estimated breeding values for rank and its variability in a selection index.

Cite This Article

APA
Cervantes I, Bodin L, Valera M, Molina A, Gutiérrez JP. (2020). Challenging the selection for consistency in the rank of endurance competitions. Genet Sel Evol, 52(1), 20. https://doi.org/10.1186/s12711-020-00539-5

Publication

Genetics, selection, evolution : GSE
ISSN: 1297-9686
NlmUniqueID: 9114088
Country: France
Language: English
Volume: 52
Issue: 1
Pages: 20

Researcher Affiliations

Cervantes, Isabel
  • Departamento de Producción Animal, Universidad Complutense de Madrid, Avda. Puerta del Hierro s/n, 28040, Madrid, Spain. icervantes@vet.ucm.es.
Bodin, Loys
  • GenPhySE, INRAE, 31320, Castanet-Tolosan, France.
Valera, Mercedes
  • Departamento de Ciencias Agro-Forestales, Universidad de Sevilla, Ctra. Utrera km 1, 41013, Seville, Spain.
Molina, Antonio
  • Departamento de Genética, Universidad de Córdoba, Campus de Rabanales, 14071, Córdoba, Spain.
Gutiérrez, Juan Pablo
  • Departamento de Producción Animal, Universidad Complutense de Madrid, Avda. Puerta del Hierro s/n, 28040, Madrid, Spain.

MeSH Terms

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

Conflict of Interest Statement

The authors declare that they have no competing interests.

References

This article includes 31 references
  1. 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
  2. Ricard A, Chanu I. Genetic parameters of eventing horse competition in France.. Genet Sel Evol 2001;33:175–190.
    doi: 10.1186/1297-9686-33-2-175pmc: PMC2705390pubmed: 11333833google scholar: lookup
  3. Snell EJ. A scaling procedure for ordered categorical data.. Biometrics 1964;20:592–607.
    doi: 10.2307/2528498google scholar: lookup
  4. Gianola D. Theory and analysis of threshold characters.. J Anim Sci 1982;54:1079–1096.
    doi: 10.2527/jas1982.5451079xgoogle scholar: lookup
  5. Ruhlmann C, Janssens S, Philipsson J, Thorén-Hellsten E, Crolly H, Quinn K. 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
  6. Ricard A, Legarra A. Validation of models for analysis of ranks in horse breeding evaluation.. Genet Sel Evol 2010;42:3.
    doi: 10.1186/1297-9686-42-3pmc: PMC2832620pubmed: 20109204google scholar: lookup
  7. Gómez MD, Varona L, Molina A, Valera M. Genetic evaluation of racing performance in trotter horses by competitive models.. Livest Sci 2011;140:155–160.
    doi: 10.1016/j.livsci.2011.03.024google scholar: lookup
  8. García-Ballesteros S, Varona L, Valera M, Gutiérrez JP, Cervantes I. Cross-validation analysis for genetic evaluation models for ranking in endurance horses.. Animal 2018;12:20–27.
    doi: 10.1017/S1751731117001331pubmed: 28633685google scholar: lookup
  9. Mulder HA, Bijma P, Hill WG. Prediction of breeding values and selection responses with genetic heterogeneity of environmental variance.. Genetics 2007;175:1895–1910.
    doi: 10.1534/genetics.106.063743pmc: PMC1855112pubmed: 17277375google scholar: lookup
  10. Gutiérrez JP, Nieto B, Piqueras P, Ibáñez N, Salgado C. Genetic parameters for canalisation analysis of litter size and litter weight traits at birth in mice.. Genet Sel Evol 2006;38:445–462.
    doi: 10.1186/1297-9686-38-5-445pmc: PMC2689256pubmed: 16954039google scholar: lookup
  11. Formoso-Rafferty N, Cervantes I, Ibáñez-Escriche N, Gutiérrez JP. Genetic control of the environmental variance for birth weight in seven generations of a divergent selection experiment in mice.. J Anim Breed Genet 2016;133:227–237.
    doi: 10.1111/jbg.12174pubmed: 26150168google scholar: lookup
  12. Formoso-Rafferty N, Cervantes I, Ibáñez-Escriche N, Gutiérrez JP. Correlated genetic trends for production and welfare traits in a mouse population divergently selected for birth weight environmental variability.. Animal 2016;10:1770–1777.
    doi: 10.1017/S1751731116000860pubmed: 27170448google scholar: lookup
  13. Garreau H, SanCristobal M, Hurtaud J, Bodin L, Ros M, Robert-Granie C. Can we select on within litter homogeneity for rabbit birth weight? A divergent selection experiment.. In: Proceedings of the 8th world rabbit congress: 7–10 September 2004; Puebla; 2004.
  14. Garreau H, Bolet G, Larzul C, Robert-Granie C, Saleil G, SanCristobal M. Results of four generations of a canalising selection for rabbit birth weight.. Livest Sci 2008;119:55–62.
    doi: 10.1016/j.livsci.2008.02.009google scholar: lookup
  15. Bodin L, Garcia M, Bolet G, Garreau H. Results of canalizing selection for rabbit birth weight.. In: Proceeding of the 9th world congress genetic applied livestock production: 1–6 August 2010; Liepzig; 2010.
  16. Argente MJ, Calle EW, García ML, Blasco A. Correlated response in litter size components in rabbits selected for litter size variability.. J Anim Breed Genet 2017;134:505–511.
    doi: 10.1111/jbg.12283pubmed: 28695620google scholar: lookup
  17. Larzul C, Gondret F, Combes S, de Rochambeau H. Divergent selection on 63-day body weight in the rabbit: response on growth, carcass and muscle traits.. Genet Sel Evol 2005;37:105–122.
    doi: 10.1186/1297-9686-37-1-105pmc: PMC2697246pubmed: 15588570google scholar: lookup
  18. Ibáñez-Escriche N, Varona L, Sorensen D, Noguera JL. A study of heterogeneity of environmental variance for slaughter weight in pig.. Animal 2008;2:19–26.
    doi: 10.1017/S1751731107001000pubmed: 22444959google scholar: lookup
  19. Hill WG, Mulder HA. Genetic analysis of environmental variation.. Genet Res 2010;92:381–395.
    doi: 10.1017/S0016672310000546pubmed: 21429270google scholar: lookup
  20. Yang Y, Christensen O, Sorensen D. Analysis of a genetically structured variance heterogeneity model using the box-cox transformation.. Genet Res (Camb) 2011;93:33–46.
    doi: 10.1017/S0016672310000418pubmed: 21349235google scholar: lookup
  21. Hausberger M, Roche H, Henry S, Visser EK. A review of the human–horse relationship.. Appl Anim Behav Sci 2008;109:1–24.
    doi: 10.1016/j.applanim.2007.04.015google scholar: lookup
  22. Visser EK, Van Reenen CG, Blokhuis MZ, Morgan EKM, Hassmén P, Rundgren TMM. Does horse temperament influence horse-rider cooperation?. J Appl Anim Welf Sci 2008;11:267–284.
    doi: 10.1080/10888700802101254pubmed: 18569223google scholar: lookup
  23. McLean AN, McGreevy PD. Ethical equitation: capping the price horses pay for human glory.. J Vet Behav 2010;5:203–209.
    doi: 10.1016/j.jveb.2010.04.003google scholar: lookup
  24. Bartolomé E, Menéndez-Buxadera A, Molina A, Valera M. Plasticity effect of rider–horse interaction on genetic evaluations for Show Jumping discipline in sport horses.. J Anim Breed Genet 2018;135:138–148.
    doi: 10.1111/jbg.12315pubmed: 29363192google scholar: lookup
  25. Sánchez-Guerrero MJ, Cervantes I, Valera M, Gutiérrez JP. Modelling genetic evaluation for dressage in Pura Raza Español horses with focus on the rider effect.. J Anim Breed Genet 2014;131:395–402.
    doi: 10.1111/jbg.12088pubmed: 24673743google scholar: lookup
  26. Gutiérrez JP, Goyache F. A note on ENDOG: a computer program for analysing pedigree information.. J Anim Breed Genet 2005;122:172–176.
    doi: 10.1111/j.1439-0388.2005.00512.xpubmed: 16130468google scholar: lookup
  27. Ibáñez-Escriche N, Moreno A, Nieto B, Piqueras P, Salgado C, Gutiérrez JP. Genetic parameters related to environmental variability of weight traits in a selection experiment for weight gain in mice; signs of correlated canalised response.. Genet Sel Evol 2008;40:279–293.
    pmc: PMC2674902pubmed: 18400150
  28. Ibáñez-Escriche N, García M, Sorensen D. GSEVMv.2: MCMC software to analyze genetically structured environmental variance models.. J Anim Breed Genet 2010;127:249–251.
    doi: 10.1111/j.1439-0388.2009.00846.xpubmed: 20536643google scholar: lookup
  29. Formoso-Rafferty N, Cervantes I, Ibáñez-Escriche N, Gutiérrez JP. Modulating birth weight heritability in mice.. J Anim Sci 2017;95:531–537.
    pubmed: 28380591
  30. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit.. J R Statist Soc B 2002;64:583–639.
    doi: 10.1111/1467-9868.00353google scholar: lookup
  31. Bartolomé E, Menéndez-Buxadera A, Valera M, Cervantes I, Molina A. Genetic (co)variance components across age for Show Jumping performance as an estimation of phenotypic plasticity ability in Spanish horses.. J Anim Breed Genet 2013;130:190–198.
    doi: 10.1111/jbg.12001pubmed: 23679944google scholar: lookup

Citations

This article has been cited 2 times.
  1. 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
  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
Subscribe to our newsletter
Join 99,344 horse owners like you who receive our email updates on horse health and nutrition.