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Home / Equine Research Bank / PLoS One / Genetic consistency between gait analysis by accelerometry a...
PloS one2020; 15(12); e0244064; doi: 10.1371/journal.pone.0244064

Genetic consistency between gait analysis by accelerometry and evaluation scores at breeding shows for the selection of jumping competition horses.

Abstract: The aim was to assess the efficiency of gaits characteristics in improving jumping performance of sport horses and confront accelerometers and judge scores for this purpose. A sample of 1,477 young jumping horses were measured using accelerometers for walk, trot, and canter. Of these, 702 were genotyped with 541,175 SNPs after quality control. Dataset of 26,914 horses scored by judges in breeding shows for gaits and dataset of 142,682 horses that performed in jumping competitions were used. Analysis of accelerometric data defined three principal components from 64% to 89% of variability explained for each gait. Animal mixed models were used to estimate genetic parameters with the inclusion to up 308,105 ancestors for the relationship matrix. Fixed effects for the accelerometric variables included velocity, gender, age, and event. A GWAS was performed on residuals with the fixed effect of each SNP. The GWAS did not reveal other QTLs for gait traits than the one related to the height at withers. The accelerometric principal components were highly heritable for the one linked to stride frequency and dorsoventral displacement at trot (0.53) and canter (0.41) and moderately for the one linked to longitudinal activities (0.33 for trot, 0.19 for canter). Low heritabilities were found for the walk traits. The genetic correlations of the accelerometric principal components with the jumping competition were essentially nil, except for a negative correlation with longitudinal activity at canter (-0.19). The genetic correlation between the judges' scores and the jumping competition reached 0.45 for canter (0.31 for trot and 0.17 for walk). But these correlations turned negative when the scores were corrected for the known parental breeding value for competition at the time of the judging. In conclusion, gait traits were not helpful to select for jumping performances. Different gaits may be suitable for a good jumping horse.
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Publication Date: 2020-12-16 PubMed ID: 33326505PubMed Central: PMC7743953DOI: 10.1371/journal.pone.0244064Google Scholar: Lookup
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  • Evaluation Study
  • Journal Article
  • Research Support
  • Non-U.S. Gov't

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.

This research aimed to find out if the characteristics of a horse’s gait, which can be measured through accelerometers and evaluated by judges during breeding shows, can help in improving its performance in jumping competitions.

Methodology

  • A total of 1,477 young jumping horses had their walking, trotting, and canting evaluated using accelerometers and scored by judges during breeding shows.
  • From this sample, 702 horses were genotyped with over 540,000 SNPs, or genetic markers, following quality control checks.
  • A dataset of more than 26,000 horses were evaluated for their gaits during breeding shows and another dataset of over 142,000 horses were assessed based on their performance in jumping competitions.
  • 64% to 89% of variability for each gait was explained by analyzing three principal components derived from the accelerometer data.
  • Fixed effects for the accelerometric variables taken into consideration during the analysis included velocity, gender, age, and event.
  • The genetic relationship between these variables was determined using animal mixed models that incorporated up to over 308,000 ancestors.

Results

  • The study did not find any other Quantitative Trait Loci (QTLs), or regions of DNA that correlate with the trait being studied, for gait traits aside from the one related to the height at withers, the highest point on a horse’s back.
  • The factors related to the frequency and height of strides during trotting and canting had high heritability, implying a major genetic contribution to these traits.
  • The traits related to forward or backward movement during trotting and cantering had moderate heritability.
  • Low heritability was found for traits related to walking.
  • There was virtually no genetic correlation between the accelerometer-measured traits and jumping competition performance, apart from a negative correlation with forward or backward movement during cantering.

Conclusion

  • The research concluded that gait traits did not significantly impact jumping performance, indicating that different types of gaits may be suitable for a good jumping horse.
  • The study also revealed a notable discrepancy between genetic correlation and judges’ scores. While genetic correlation turned negative when scores were adjusted for known parental breeding value at judging time, the unadjusted scores had a positive correlation with jumping performance. This suggests that judging scores may not be a reliable indicator of genetic potential for jumping performance.

Cite This Article

APA
Ricard A, Dumont Saint Priest B, Chassier M, Sabbagh M, Danvy S. (2020). Genetic consistency between gait analysis by accelerometry and evaluation scores at breeding shows for the selection of jumping competition horses. PLoS One, 15(12), e0244064. https://doi.org/10.1371/journal.pone.0244064

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 15
Issue: 12
Pages: e0244064

Researcher Affiliations

Ricard, Anne
  • Génétique Animale et Biologie Intégrative, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France.
  • Institut Français du Cheval et de l'Equitation, Pôle Développement, Innovation et Recherche, Exmes, France.
Dumont Saint Priest, Bernard
  • Institut Français du Cheval et de l'Equitation, Pôle Développement, Innovation et Recherche, Exmes, France.
Chassier, Marjorie
  • Génétique Animale et Biologie Intégrative, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France.
Sabbagh, Margot
  • Institut Français du Cheval et de l'Equitation, Pôle Développement, Innovation et Recherche, Exmes, France.
Danvy, Sophie
  • Institut Français du Cheval et de l'Equitation, Pôle Développement, Innovation et Recherche, Exmes, France.

MeSH Terms

  • Accelerometry
  • Animals
  • Female
  • Gait / physiology
  • Gait Analysis
  • Horses / physiology
  • Physical Conditioning, Animal

Conflict of Interest Statement

The authors have declared that no competing interests exist.

References

This article includes 39 references
  1. Koenen EPC, Aldridge LI, Philipsson J. An overview of breeding objectives for warmblood sport horses. Livestock Production Science 2004;88(1–2):77–84.
    doi: 10.1016/j.livprodsci.2003.10.011google scholar: lookup
  2. Ablondi M, Eriksson S, Tetu S, Sabbioni A, Viklund A, Mikko S. Genomic Divergence in Swedish Warmblood Horses Selected for Equestrian Disciplines. Genes 2019;10(12).
    doi: 10.3390/genes10120976pmc: PMC6947233pubmed: 31783652google scholar: lookup
  3. Rovere G, Madsen P, Norberg E, van Arendonk JAM, Ducro BJ. Effect of specialization on genetic parameters of studbook-entry inspection in Dutch Warmblood horses. Journal of Animal Breeding and Genetics 2015;132(6):441–8.
    doi: 10.1111/jbg.12166pubmed: 26012787google scholar: lookup
  4. Welker V, Stock KF, Schopke K, Swalve HH. Genetic parameters of new comprehensive performance traits for dressage and show jumping competitions performance of German riding horses. Livestock Science 2018;212:93–8.
    doi: 10.1016/j.livsci.2018.04.002google scholar: lookup
  5. Rovere G, Ducro BJ, van Arendonk JAM, Norberg E, Madsen P. Genetic correlations between dressage, show jumping and studbook-entry inspection traits in a process of specialization in Dutch Warmblood horses. Journal of Animal Breeding and Genetics 2017;134(2):162–71.
    doi: 10.1111/jbg.12241pubmed: 27678258google scholar: lookup
  6. Duensing J, Stock KF, Krieter J. Implementation and Prospects of Linear Profiling in the Warmblood Horse. Journal of Equine Veterinary Science 2014;34(3):360–8.
    doi: 10.1016/j.jevs.2013.09.002google scholar: lookup
  7. Viklund Å, Eriksson S. Genetic analyses of linear profiling data on 3-year-old Swedish Warmblood horses. Journal of Animal Breeding and Genetics 2018;135(1):62–72.
    doi: 10.1111/jbg.12311pubmed: 29345075google scholar: lookup
  8. Rustin M, Janssens S, Buys N, Gengler N. Multi-trait animal model estimation of genetic parameters for linear type and gait traits in the Belgian warmblood horse. Journal of Animal Breeding and Genetics 2009;126(5):378–86.
    doi: 10.1111/j.1439-0388.2008.00798.xpubmed: 19765164google scholar: lookup
  9. Novotna A, Svitakova A, Vesela Z, Vostry L. Estimation of genetic parameters for linear type traits in the population of sport horses in the Czech Republic. Livestock Science 2017;202:1–6.
    doi: 10.1016/j.livsci.2017.05.012google scholar: lookup
  10. Viklund A, Nasholm A, Strandberg E, Philipsson J. Effects of long-time series of data on genetic evaluations for performance of Swedish Warmblood riding horses. Animal 2010;4(11):1823–31.
    doi: 10.1017/S1751731110001175pubmed: 22445143google scholar: lookup
  11. Ducro BJ, Koenen EPC, van Tartwijk J, Bovenhuis H. Genetic relations of movement and free-jumping traits with dressage and show-jumping performance in competition of Dutch Warmblood horses. Livestock Science 2007;107(2–3):227–34.
    doi: 10.1016/j.livsci.2006.09.018google scholar: lookup
  12. Schopke K, Wensch-Dorendorf M, Swalve HH. Genetic evaluations of the German Sport Horse: Population structure and use of data from foal and mare inspections and performance tests of mares. Archiv Fur Tierzucht-Archives of Animal Breeding 2013;56:658–74.
    doi: 10.7482/0003-9438-56-066google scholar: lookup
  13. Egan S, Brama P, McGrath D. Research trends in equine movement analysis, future opportunities and potential barriers in the digital age: A scoping review from 1978 to 2018. Equine Veterinary Journal 2019;51(6):813–24.
    doi: 10.1111/evj.13076pubmed: 30659639google scholar: lookup
  14. Barrey E, Hermelin M, Vaudelin JL, Poirel D, Valette JP. Utilization of an accelometric device in equine gait analysis. Equine Veterinary Journal 1994;17:7–12.
  15. Barrey E, Auvinet B, Couroucé A. Gait evaluation of race trotters using an accelerometric device. Equine Veterinary Journal 1995;18(suppl):156–60.
  16. Leleu C, Bariller F, Cotrel C, Barrey E. Reproducibility of a locomotor test for trotter horses. Veterinary Journal 2004;168(2):160–6.
    doi: 10.1016/S1090-0233(03)00109-6pubmed: 15301764google scholar: lookup
  17. Meyer K. WOMBAT—A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University Science B 2007;8(11):815–21.
    doi: 10.1631/jzus.2007.B0815pmc: PMC2064953pubmed: 17973343google scholar: lookup
  18. Lê S, Josse J, Husson F. FactoMineR: An R Package for Multivariate Analysis. 2008;25(1):18 Epub 2008-03-18.
    doi: 10.18637/jss.v025.i01google scholar: lookup
  19. Aulchenko YS, Ripke S, Isaacs A, Van Duijn CM. GenABEL: an R library for genome-wide association analysis. Bioinformatics 2007;23(10):1294–6.
    doi: 10.1093/bioinformatics/btm108pubmed: 17384015google scholar: lookup
  20. Aulchenko YS, de Koning D-J, Haley C. Genomewide Rapid Association Using Mixed Model and Regression: A Fast and Simple Method For Genomewide Pedigree-Based Quantitative Trait Loci Association Analysis. Genetics 2007;177(1):577–85.
    doi: 10.1534/genetics.107.075614pmc: PMC2013682pubmed: 17660554google scholar: lookup
  21. Teyssedre S, Elsen JM, Ricard A. Statistical distributions of test statistics used for quantitative trait association mapping in structured populations. Genetics Selection Evolution 2012;44.
    doi: 10.1186/1297-9686-44-32pmc: PMC3817592pubmed: 23146127google scholar: lookup
  22. Gianola D, Sorensen D. Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics 2004;167(3):1407–24.
    doi: 10.1534/genetics.103.025734pmc: PMC1470962pubmed: 15280252google scholar: lookup
  23. Rosa GJM, Valente BD, de los Campos G, Wu XL, Gianola D, Silva MA. Inferring causal phenotype networks using structural equation models. Genetics Selection Evolution 2011;43.
    doi: 10.1186/1297-9686-43-6pmc: PMC3056759pubmed: 21310061google scholar: lookup
  24. Signer-Hasler H, Flury C, Haase B, Burger D, Simianer H, Leeb T. A Genome-Wide Association Study Reveals Loci Influencing Height and Other Conformation Traits in Horses. Plos One 2012;7(5) e37282.
    doi: 10.1371/journal.pone.0037282pmc: PMC3353922pubmed: 22615965google scholar: lookup
  25. Frischknecht M, Signer-Hasler H, Leeb T, Rieder S, Neuditschko M. Genome-wide association studies based on sequence-derived genotypes reveal new QTL associated with conformation and performance traits in the Franches-Montagnes horse breed. Animal Genetics 2016;47(2):227–9.
    doi: 10.1111/age.12406pubmed: 26767322google scholar: lookup
  26. Makvandi-Nejad S, Hoffman GE, Allen JJ, Chu E, Gu E, Chandler AM. Four Loci Explain 83% of Size Variation in the Horse. Plos One 2012;7(7) e39929.
    doi: 10.1371/journal.pone.0039929pmc: PMC3394777pubmed: 22808074google scholar: lookup
  27. Barrey E. Methods, applications and limitations of gait analysis in horses. Veterinary Journal 1999;157:07–22.
    doi: 10.1053/tvjl.1998.0297pubmed: 10030124google scholar: lookup
  28. Pfau T, Witte TH, Wilson AM. Centre of mass movement and mechanical energy fluctuation during gallop locomotion in the Thoroughbred racehorse. Journal of Experimental Biology 2006;209(19):3742–57.
    doi: 10.1242/jeb.02439pubmed: 16985191google scholar: lookup
  29. Robilliard JJ, Pfau T, Wilson AM. Gait characterisation and classification in horses. Journal of Experimental Biology 2007;210(2):187–97.
    doi: 10.1242/jeb.02611pubmed: 17210956google scholar: lookup
  30. Witte TH, Hirst CV, Wilson AM. Effect of speed on stride parameters in racehorses at gallop in field conditions. Journal of Experimental Biology 2006;209(21):4389–97.
    doi: 10.1242/jeb.02518pubmed: 17050854google scholar: lookup
  31. Andersson LS, Larhammar M, Memic F, Wootz H, Schwochow D, Rubin CJ. Mutations in DMRT3 affect locomotion in horses and spinal circuit function in mice. Nature 2012;488(7413):642–6.
    doi: 10.1038/nature11399pmc: PMC3523687pubmed: 22932389google scholar: lookup
  32. Promerova M, Andersson LS, Juras R, Penedo MCT, Reissmann M, Tozaki T. Worldwide frequency distribution of the ‘Gait keeper’ mutation in the DMRT3 gene. Animal Genetics 2014;45(2):274–82.
    doi: 10.1111/age.12120pubmed: 24444049google scholar: lookup
  33. Becker AC, Stock KF, Distl O. Genetic analyses of new movement traits using detailed evaluations of warmblood foals and mares. Journal of Animal Breeding and Genetics 2012;129(5):390–401.
    doi: 10.1111/j.1439-0388.2011.00980.xpubmed: 22963360google scholar: lookup
  34. Molina A, Valera M, Galisteo AM, Vivo J, Gomez MD, Rodero A. Genetic parameters of biokinematic variables at walk in the Spanish Purebred (Andalusian) horse using experimental treadmill records. Livestock Science 2008;116(1–3):137–45.
    doi: 10.1016/j.livsci.2007.09.021google scholar: lookup
  35. Sole M, Santos R, Molina A, Galisteo A, Valera M. Genetic analysis of kinematic traits at the trot in Lusitano horse subpopulations with different types of training. Animal 2014;8(2):192–9.
    doi: 10.1017/S1751731113002036pubmed: 24230460google scholar: lookup
  36. Valera M, Galisteo AM, Molina A, Miro F, Gomez MD, Cano MR. Genetic parameters of biokinematic variables of the trot in Spanish Purebred horses under experimental treadmill conditions. Veterinary Journal 2008;178(2):219–26.
    doi: 10.1016/j.tvjl.2007.07.031pubmed: 17897847google scholar: lookup
  37. Barrey E. Biomechanics of locomotion in the athletic horse In: Kenneth W. Hinchcliff AJK, Geor Raymond J editor. In Equine Sports Medicine & Surgery: Basic and clinical sciences of the equine athlete. Edingburgh-Toronto: Saunders Elsevier; 2014. p. 189–211.
  38. Wallin L, Strandberg E, Philipsson J. Genetic correlations between field test results of Swedish Warmblood Riding Horses as 4-year-olds and lifetime performance results in dressage and show jumping. Livestock Production Science 2003;82(1):61–71.
    doi: 10.1016/s0301-6226(02)00307-xgoogle scholar: lookup
  39. Viklund A, Braam A, Nasholm A, Strandberg E, Philipsson J. Genetic variation in competition traits at different ages and time periods and correlations with traits at field tests of 4-year-old Swedish Warmblood horses. Animal 2010;4(5):682–91.
    doi: 10.1017/S1751731110000017pubmed: 22444120google scholar: lookup

Citations

This article has been cited 5 times.
  1. Nazari-Ghadikolaei A, Fikse WF, Viklund ÅG, Mikko S, Eriksson S. Single-Step Genome-Wide Association Study of Factors for Evaluated and Linearly Scored Traits in Swedish Warmblood Horses. J Anim Breed Genet 2025 Sep;142(5):499-512.
    doi: 10.1111/jbg.12923pubmed: 39754479google scholar: lookup
  2. Ricard A, Crevier-Denoix N, Pourcelot P, Crichan H, Sabbagh M, Dumont-Saint-Priest B, Danvy S. Genetic analysis of geometric morphometric 3D visuals of French jumping horses. Genet Sel Evol 2023 Sep 18;55(1):63.
    doi: 10.1186/s12711-023-00837-8pubmed: 37723416google scholar: lookup
  3. 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
  4. Legarra A, Christensen OF. Genomic evaluation methods to include intermediate correlated features such as high-throughput or omics phenotypes. JDS Commun 2023 Jan;4(1):55-60.
    doi: 10.3168/jdsc.2022-0276pubmed: 36713125google scholar: lookup
  5. Dugué M, Dumont Saint Priest B, Crichan H, Danvy S, Ricard A. Genomic Correlations Between the Gaits of Young Horses Measured by Accelerometry and Functional Longevity in Jumping Competition. Front Genet 2021;12:619947.
    doi: 10.3389/fgene.2021.619947pubmed: 33584826google scholar: lookup
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