Abstract
The aim of the study was to estimate genetic parameters for show-jumping competition performance using random regression model. Show-jumping competition results collected between 1996 and 2009 were analyzed. The database contained 272 951 starts of 8020 horses. Identity number and gender of the horse, rider, competition date, the level of the competition and placing were recorded in the database. Competition levels were categorized into five groups. Weighted – competition level used – square root transformed placing was used to measure performance of horses. The random regression model included fixed effects for gender, year and place of competition, and random effects for rider, animal and permanent environment.
 Later performance of show-jumping horses measured with weighted square root ranks is less influenced by rider and permanent environmental effects than performance at the beginning of a horse’s sporting career. Heritability increased continuously from 6.3 years of age (2296 age in days), values were in the range of 0.07 and 0.37. Higher heritability was found in later ages. Weak genetic and phenotypic correlation was found between the early 4–5–6 years of age and older (7, 8, 8+) age classes. From 8.5 years of age (3132 days old) there were strong genetic and phenotypic correlations between neighboring age groups. For the same age classes moderate and strong genetic and phenotypic correlation was found. Genetic correlation between 13.5 years of age and older horses was very strong.
Highlights
Sporthorse breeder’s interest is focused on changes in performance within individual horses over their career
Weak genetic and phenotypic correlation was found between the early 4–5–6 years of age and older (7, 8, 8+) age classes
The use of random regression model is highly recommended as a very useful tool, because it allows us to estimate the animal's breeding value through all the stages of its trajectory in racing competitions
Summary
Sporthorse breeder’s interest is focused on changes in performance within individual horses over their career. Changes can be observed in means, but there can be heterogeneity in variances and covariances at different ages. The way how to investigate changes in variances and covariances is by defining appropriate covariance functions (Kirkpatrick et al, 1990). Covariance functions are continuous functions that should make possible to estimate covariance within animals between measures at any two age ti and tj, and variances between animals at any age ti. To estimate covariance functions an alternative is using random coefficient regression model. The regression coefficients are assumed to be random variables and there is a covariance structure among regression coefficients. These can be used to estimate covariance functions, i.e. variances and covariances between values for different ages
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