Comparison of models to estimate genetic parameters for scores of competitive sport horse events in the Czech Republic

https://doi.org/10.17221/8453-CJASCitation:Novotná A., Svitáková A., Schmidová J. (2015): Comparison of models to estimate genetic parameters for scores of competitive sport horse events in the Czech Republic. Czech J. Anim. Sci., 60: 383-390.
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The objective of the present study was to estimate genetic parameters and predict breeding values of sport horses in the Czech Republic using animal model variations. The data set for the evaluation was composed of edited records of show jumping competitions in the Czech Republic in years 1991–2013. Input data were not normally distributed; hence Blom transformation was used for the variable filtration. The Gibbs sampling algorithm was used for the genetic parameters estimation. Two models were examined. The first was a random regression model including the effect of a horse’s experience in competition (expressed as the length of the horse’s sporting career in days), fixed effects of sex, age, and event, and random effects of rider, permanent environment, and animal. The second model was a multi-trait model with fixed effects for sex, age, and event and random effects for rider, permanent environment, and animal. In this latter case, horse performance was classified as three traits. The first trait was jumping results from obstacle heights of 90–110 cm, the second of 120–135 cm, and the third of 140–155 cm. In the random regression model, heritability estimates ranged from 0.01 to 0.11; whereas in the multi-trait model, heritabilities were 0.07, 0.11, and 0.14 for the first, second, and third trait, respectively. Results indicate that both models could be used to predict breeding values of sport horses in the Czech Republic. The multi-trait model revealed that heritability estimates increased with the increasing height of obstacle. In the random regression model, breeding values differed according to a horse’s experience in competition, allowing adjustment of the breeding value for the environmental effect of a past experience.
References:
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Bugislaus A.-E., Roehe R., Willms F., Kalm E. (2006): The use of a random regression model to account for change in racing speed of German trotters with increasing age. Journal of Animal Breeding and Genetics, 123, 239-246 https://doi.org/10.1111/j.1439-0388.2006.00596.x
 
Buxadera Alberto Menéndez, da Mota Marcilio Dias Silveira (2008): Variance component estimations for race performance of thoroughbred horses in Brazil by random regression model. Livestock Science, 117, 298-307 https://doi.org/10.1016/j.livsci.2007.12.027
 
Ducro B. (2011): Relevance of test information in horse breeding. PhD Diss. Wageningen, the Netherlands: Wageningen Univ. 170 p.
 
Janssens S., Geysen D., Vandepitte W. (1997): Genetic parameters for show jumping in Belgian sporthorses. In: Proc. 48th Annu. Mtg., European Association for Animal Production (EAAP), Vienna, Austria.
 
Luehrs-Behnke H., Roehe R., Kalm E. (2002): Genetic associations among traits of the new integrated breeding evaluation method used for selection of German warmblood horses. Veterinarija ir Zootechnika, 18, 90–93.
 
Meyer Karin (2005): Random regression analyses using B-splines to model growth of Australian Angus cattle. Genetics Selection Evolution, 37, 473- https://doi.org/10.1186/1297-9686-37-6-473
 
Misztal I., Wiggans G.R. (1988): Approximation of Prediction Error Variance in Large-Scale Animal Models. Journal of Dairy Science, 71, 27-32 https://doi.org/10.1016/S0022-0302(88)79976-2
 
Posta J., Komlosi I., Mihok S. (2009a): Breeding value estimation in the Hungarian Sport Horse population. Veterinary Journal, 181, 19–23.
 
Posta J., Mihok S., Markus S., Komlosi I. (2009b): Analysis of Hungarian sport horse show jumping results using different transformations and models. Archiv fur Tierzucht, 52, 451–458.
 
Posta J., Malovhr S., Mihók S., Komlósi I. (2010): Random regression model estimation of genetic parameters for show-jumping results of Hungarian Sporthorses. Journal of Animal Breeding and Genetics, 127, 280-288 https://doi.org/10.1111/j.1439-0388.2009.00848.x
 
Pribyl J., Krejcova H., Pribylova J., Misztal I., Bohmanova J., Stipkova M. (2007): Trajectory of body weight of performance tested dual-purpose bulls. Czech Journal of Animal Science, 52, 315–324.
 
Reilly M., Foran M. K., Kelleher D. L., Flanagan M. J., Brophy P. O. (1998): Estimation of genetic value of showjumping horses from the ranking of all performances in all competitions. Journal of Animal Breeding and Genetics, 115, 17-25 https://doi.org/10.1111/j.1439-0388.1998.tb00324.x
 
Ricard Anne, Legarra Andrés (2010): Validation of models for analysis of ranks in horse breeding evaluation. Genetics Selection Evolution, 42, 3- https://doi.org/10.1186/1297-9686-42-3
 
Viklund Å., Näsholm A., Strandberg E., Philipsson J. (2011): Genetic trends for performance of Swedish Warmblood horses. Livestock Science, 141, 113-122 https://doi.org/10.1016/j.livsci.2011.05.006
 
Aldridge By L. I., Kelleher D. L., Reilly M., Brophy P. O. (2000): Estimation of the genetic correlation between performances at different levels of show jumping competitions in Ireland. Journal of Animal Breeding and Genetics, 117, 65-72 https://doi.org/10.1046/j.1439-0388.2000.00232.x
 
Bugislaus A.-E., Roehe R., Willms F., Kalm E. (2006): The use of a random regression model to account for change in racing speed of German trotters with increasing age. Journal of Animal Breeding and Genetics, 123, 239-246 https://doi.org/10.1111/j.1439-0388.2006.00596.x
 
Buxadera Alberto Menéndez, da Mota Marcilio Dias Silveira (2008): Variance component estimations for race performance of thoroughbred horses in Brazil by random regression model. Livestock Science, 117, 298-307 https://doi.org/10.1016/j.livsci.2007.12.027
 
Ducro B. (2011): Relevance of test information in horse breeding. PhD Diss. Wageningen, the Netherlands: Wageningen Univ. 170 p.
 
Janssens S., Geysen D., Vandepitte W. (1997): Genetic parameters for show jumping in Belgian sporthorses. In: Proc. 48th Annu. Mtg., European Association for Animal Production (EAAP), Vienna, Austria.
 
Luehrs-Behnke H., Roehe R., Kalm E. (2002): Genetic associations among traits of the new integrated breeding evaluation method used for selection of German warmblood horses. Veterinarija ir Zootechnika, 18, 90–93.
 
Meyer Karin (2005): Random regression analyses using B-splines to model growth of Australian Angus cattle. Genetics Selection Evolution, 37, 473- https://doi.org/10.1186/1297-9686-37-6-473
 
Misztal I., Wiggans G.R. (1988): Approximation of Prediction Error Variance in Large-Scale Animal Models. Journal of Dairy Science, 71, 27-32 https://doi.org/10.1016/S0022-0302(88)79976-2
 
Posta J., Komlosi I., Mihok S. (2009a): Breeding value estimation in the Hungarian Sport Horse population. Veterinary Journal, 181, 19–23.
 
Posta J., Mihok S., Markus S., Komlosi I. (2009b): Analysis of Hungarian sport horse show jumping results using different transformations and models. Archiv fur Tierzucht, 52, 451–458.
 
Posta J., Malovhr S., Mihók S., Komlósi I. (2010): Random regression model estimation of genetic parameters for show-jumping results of Hungarian Sporthorses. Journal of Animal Breeding and Genetics, 127, 280-288 https://doi.org/10.1111/j.1439-0388.2009.00848.x
 
Pribyl J., Krejcova H., Pribylova J., Misztal I., Bohmanova J., Stipkova M. (2007): Trajectory of body weight of performance tested dual-purpose bulls. Czech Journal of Animal Science, 52, 315–324.
 
Reilly M., Foran M. K., Kelleher D. L., Flanagan M. J., Brophy P. O. (1998): Estimation of genetic value of showjumping horses from the ranking of all performances in all competitions. Journal of Animal Breeding and Genetics, 115, 17-25 https://doi.org/10.1111/j.1439-0388.1998.tb00324.x
 
Ricard Anne, Legarra Andrés (2010): Validation of models for analysis of ranks in horse breeding evaluation. Genetics Selection Evolution, 42, 3- https://doi.org/10.1186/1297-9686-42-3
 
Viklund Å., Näsholm A., Strandberg E., Philipsson J. (2011): Genetic trends for performance of Swedish Warmblood horses. Livestock Science, 141, 113-122 https://doi.org/10.1016/j.livsci.2011.05.006
 
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