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Estimating variance components and predicting breeding values for eventing disciplines and grades in sport horses

Published online by Cambridge University Press:  21 March 2012

I. D. Stewart ,
I. M. S. White ,
A. R. Gilmour ,
R. Thompson ,
J. A. Woolliams  and
S. Brotherstone
I. D. Stewart*
Affiliation:
Institute of Evolutionary Biology, University of Edinburgh, Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK
I. M. S. White
Affiliation:
Institute of Evolutionary Biology, University of Edinburgh, Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK
A. R. Gilmour
Affiliation:
School of Mathematics and Applied Statistics, Faculty of Informatics, University of Wollongong, Wollongong, NSW 2522, Australia
R. Thompson
Affiliation:
Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK
J. A. Woolliams
Affiliation:
The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, Scotland, UK
S. Brotherstone
Affiliation:
Institute of Evolutionary Biology, University of Edinburgh, Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK
*
†E-mail: [email protected]
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Abstract

Eventing competitions in Great Britain (GB) comprise three disciplines, each split into four grades, yielding 12 discipline-grade traits. As there is a demand for tools to estimate (co)variance matrices with a large number of traits, the aim of this work was to investigate different methods to produce large (co)variance matrices using GB eventing data. Data from 1999 to 2008 were used and penalty points were converted to normal scores. A sire model was utilised to estimate fixed effects of gender, age and class, and random effects of sire, horse and rider. Three methods were used to estimate (co)variance matrices. Method 1 used a method based on Gibbs sampling and data augmentation and imputation. Methods 2a and 2b combined sub-matrices from bivariate analyses; one took samples from a multivariate Normal distribution defined by the covariance matrix from each bivariate analysis, then analysed these data in a 12-trait multivariate analysis; the other replaced negative eigenvalues in the matrix with positive values to obtain a positive definite (co)variance matrix. A formal comparison of models could not be conducted; however, estimates from all methods, particularly Methods 2a/2b, were in reasonable agreement. The computational requirements of Method 1 were much less compared with Methods 2a or 2b. Method 2a heritability estimates were as follows: for dressage 7.2% to 9.0%, for show jumping 8.9% to 16.2% and for cross-country 1.3% to 1.4%. Method 1 heritability estimates were higher for the advanced grades, particularly for dressage (17.1%) and show jumping (22.6%). Irrespective of the model, genetic correlations between grades, for dressage and show jumping, were positive, high and significant, ranging from 0.59 to 0.99 for Method 2a and 0.78 to 0.95 for Method 1. For cross-country, using Method 2a, genetic correlations were only significant between novice and pre-novice (0.75); however, using Method 1 estimates were all significant and low to moderate (0.36 to 0.70). Between-discipline correlations were all low and of mixed sign. All methods produced positive definite 12 × 12 (co)variance matrices, suitable for the prediction of breeding values. Method 1 benefits from much reduced computational requirements, and by performing a true multivariate analysis.

Keywords

genetic evaluationsport horseeventing(co)variance matrices
Type
Full Paper
Information
animal , Volume 6 , Issue 9 , September 2012 , pp. 1377 - 1388
Copyright
Copyright © The Animal Consortium 2012

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References

Clayton, D, Rasbash, J 1999. Estimation in large crossed random-effect models by data augmentation. Journal of the Royal Statistical Society, Series A 162, 425–436.CrossRefGoogle Scholar
Ducro, BJ, Koenen, EPC, van Tartwijk, JMFM, van Arendonk, JAM 2007. Genetic relations of first stallion inspection traits with dressage and show-jumping performance in competition of Dutch warmblood horses. Livestock Science 107, 81–85.CrossRefGoogle Scholar
Entin, P 2008. Do racehorses and Greyhound dogs exhibit a gender difference in running speed? Equine and Comparative Exercise Physiology 4, 135–140.CrossRefGoogle Scholar
Gilmour, AR, Gogel, BJ, Cullis, BR, Thompson, R 2009. ASReml user guide release 3.0. VSN International Ltd, Hemel Hempstead, UK. www.vsni.co.uk.Google Scholar
Higham, NJ 2002. Computing the nearest correlation matrix – a problem from finance. IMA Journal of Numerical Analysis 22, 329–343.CrossRefGoogle Scholar
Hill, WG, Thompson, R 1978. Probabilities of non-positive definite between-group or genetic covariance matrices. Biometrics 34, 429–439.CrossRefGoogle Scholar
Huizinga, HA, Van Der Meij, GJW 1989. Estimated parameters of performance in jumping and dressage competition of the Dutch Warmblood horse. Livestock Production Science 21, 333–345.CrossRefGoogle Scholar
Janssens, S 2008. Breeding programs and estimated breeding values. Retrieved May 12, 2011, from http://www.biw.kuleuven.be/genlog/livgen/chgs_is_Breedprog.html Google Scholar
Kearsley, CGS, Woolliams, JA, Coffey, MP, Brotherstone, S 2008. Use of competition data for genetic evaluations of eventing horses in Britain: analysis of the dressage, showjumping and cross country phases of eventing competition. Livestock Science 118, 72–81.CrossRefGoogle Scholar
Knol, DL, Ten Berge, JMF 1989. Least-squares approximation of an improper correlation matrix by a proper one. Psychometrika 54, 53–61.CrossRefGoogle Scholar
Langlois, B 1980. Estimation de la valeur génétique des chevaux de sport d'après les sommes gagnées dans les compétitions équestres françaises. Annales de Génétique et de Sélection Animale 12, 15–31.CrossRefGoogle Scholar
Mantysaari, EA 2004. Multiple-trait across-country evaluations using singular (co)variance matrix and random regression model. Proceedings of the 2004 Interbull Meeting Sousse, Tunisia, 4pp.Google Scholar
Misztal, I 2008. Reliable computing in estimation of variance components. Journal of Animal Breeding and Genetics 125, 363–370.CrossRefGoogle ScholarPubMed
Olsson, E, Näsholm, A, Strandberg, E, Philipsson, J 2008. Use of field records and competition results in genetic evaluation of station performance tested Swedish warmblood stallions. Livestock Science 117, 287–297.CrossRefGoogle Scholar
Ricard, A, Chanu, I 2001. Genetic parameters of eventing horse competition in France. Genetics Selection Evolution 33, 175–190.CrossRefGoogle ScholarPubMed
Ricard, A, Bruns, E, Cunningham, EP 2000. Genetics of performance traits. In The Genetics of the Horse (ed. AT Bowling and A Ruvinsky), pp. 411–438. CABI Publishing, Wallingford, UK.CrossRefGoogle Scholar
Royston, JP 1982. Expected normal-order statistics (exact and approximate). Journal of the Royal Statistical Society. Series C 31, 161–165.Google Scholar
Sorensen, AC, Pong-Wong, R, Windig, JJ, Woolliams, JAW 2002. Precision of methods for calculating identity-by-descent matrices using multiple markers. Genetics Selection Evolution 34, 557–579.CrossRefGoogle ScholarPubMed
Stewart, ID, Woolliams, JA, Brotherstone, S 2010. Genetic evaluation of horses for performance in dressage competitions in Great Britain. Livestock Science 128, 36–45.CrossRefGoogle Scholar
Stock, KF, Hoeschele, I, Distl, O 2007. Estimation of genetic parameters and prediction of breeding values for multivariate threshold and continuous data in a simulated horse population using Gibbs sampling and residual maximum likelihood. Journal of Animal Breeding and Genetics 124, 308–319.CrossRefGoogle Scholar
Thompson, R 1994. Integrating best linear unbiased prediction and maximum likelihood estimation. Fifth World Congress on Genetics Applied to Livestock Production 18, 337–340.Google Scholar
Thorén Hellsten, E, Viklund, Å, Koenen, EPC, Ricard, A, Bruns, E, Philipsson, J 2006. Review of genetic parameters estimated at stallion and young horse performance tests and their correlations with later results in dressage and show-jumping competition. Livestock Science 103, 1–12.CrossRefGoogle Scholar
Wall, E, White, IMS, Coffey, MP, Brotherstone, S 2005. The relationship between fertility, rump angle, and selected type information in Holstein–Friesian cows. Journal of Dairy Science 88, 1521–1528.CrossRefGoogle ScholarPubMed