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Estimating Genetic Parameters using a Random Regression Model
53-55Views:135One 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. -
Estimation of Inheritabilities from Holstein-Friesian Test Day Data in Hungary
23-25Views:176Recently, test day models (TDM) began to be increasingly used for the genetic evaluation of dairy cattle. The main advantage of the TDM compared with the 305 days lactation yield models is that more effects can be used in the evaluation. Therefore, the TDM is more accurate than the lactation models. The main disadvantage is the increased computational requirement, but this can be offset by improvements in computer capabilities.
The topic of this paper is the use of a fix regression test day model to estimate the inheritabilities of test day data from Hungarian Holstein-Friesian dairy cattle. The inheritability was 0.26 for milk production, 0.2 for fat production, 0.24 for protein production and 0.06 for the somatic cell count.