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

Abstract

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.