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• Random regression models for genetic evaluation of performance of the Hungarian show-jumping horse population

  87-91

  Anita Rudiné Mezei

  János Posta

  Sándor Mihók

  Views:

  148

  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.

• Estimating Genetic Parameters using a Random Regression Model

  53-55

  Szilárd Márkus

  Eva Němcová

  István Fazekas

  István Komlósi

  Views:

  135

  One of the most important part of the genetic evaluation using a random regression model is the estimation of variance components. This is the topic of many papers because the large computational costs. We can use restricted maximum likelihood (REML), Gibbs sampling and ℜ method for the estimation of genetic parameters. The variance components are necessary to calculate the heritabilities and repeatabilities.
  The aim of our paper is to estimate the variance components using a random regression repeatability model from test day data set of Hungarian Holstein-Friesian dairy cows and to analyse the change of additive genetic and permanent environmental variance, heritability and repeatability over lactation.