scholarly journals ESTIMATION OF GENETIC PARAMETERS FOR MILK YIELD IN THE FIRST THREE LACTATIONS OF HOLSTEIN COWS USING RANDOM REGRESSION MODEL

2007 ◽  
Vol 44 (1) ◽  
pp. 49-62
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Genetic Parameters ◽  
Random Regression ◽  
Holstein Cows ◽  
Random Regression Model ◽  
Estimation Of Genetic Parameters

scholarly journals Estimation of genetic parameters for test-day milk yield in Holstein cows using a random regression model

2005 ◽  
Vol 28 (1) ◽  
pp. 75-83 ◽  
Author(s):  
Jaime Araujo Cobuci ◽  
Ricardo Frederico Euclydes ◽  
Paulo Sávio Lopes ◽  
Claudio Napolis Costa ◽  
Robledo de Almeida Torres ◽  
...  
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Genetic Parameters ◽  
Random Regression ◽  
Holstein Cows ◽  
Random Regression Model ◽  
Estimation Of Genetic Parameters

Importance of genotype by environment interaction on genetic analysis of milk yield in Iranian Holstein cows using a random regression model

2019 ◽  
Vol 59 (8) ◽  
pp. 1438
Author(s):  
Y. Fazel ◽  
A. Esmailizadeh ◽  
M. Momen ◽  
M. Asadi Fozi
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Milk Yield ◽  
Genetic Parameter ◽  
Genotype By Environment Interaction ◽  
Random Regression ◽  
Holstein Cows ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Random Regression Model

Changes in the relative performance of genotypes (sires) across different environments, which are referred to as genotype–environment interactions, play an important role in dairy production systems, especially in countries that rely on imported genetic material. Importance of genotype by environment interaction on genetic analysis of milk yield was investigated in Holstein cows by using random regression model. In total, 68945 milk test-day records of first, second and third lactations of 8515 animals that originated from 100 sires and 7743 dams in 34 herds, collected by the Iranian animal breeding centre during 2007–2009, were used. The different sires were considered as different genotypes, while factors such as herd size, herd milk average (HMA), herd protein average and herd fat average were used as criteria to define the different environments. The inclusion of the environmental descriptor improved not only the log-likelihood of the model, but also the Bayesian information criterion. The results showed that defining the environment on the basis of HMA affected genetic parameter estimations more than did the other environmental descriptors. The heritability of milk yield during lactating days reduced when sire × HMA was fitted to the model as an additional random effect, while the genetic and phenotypic correlations between lactating months increased. Therefore, ignoring this interaction term can lead to the biased genetic-parameter estimates, reduced selection accuracy and, thus, different ranking of the bulls in different environments.

scholarly journals Estimation of genetic parameters for test-day milk yield in Girolando cows using a random regression model

2021 ◽  
Vol 73 (1) ◽  
pp. 18-24
Author(s):  
E.P.B. Santos ◽  
G.L. Feltes ◽  
R. Negri ◽  
J.A. Cobuci ◽  
M.V.G.B. Silva
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Legendre Polynomials ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Random Regression ◽  
Third Order ◽  
Good Tool ◽  
Components Of Variance ◽  
Random Regression Model

ABSTRACT The objective of this study was to estimate the components of variance and genetic parameters of test-day milk yield in first lactation Girolando cows, using a random regression model. A total of 126,892 test-day milk yield (TDMY) records of 15,351 first-parity Holstein, Gyr, and Girolando breed cows were used, obtained from the Associação Brasileira dos Criadores de Girolando. To estimate the components of (co) variance, the additive genetic functions and permanent environmental covariance were estimated by random regression in three functions: Wilmink, Legendre Polynomials (third order) and Linear spline Polynomials (three knots). The Legendre polynomial function showed better fit quality. The genetic and permanent environment variances for TDMY ranged from 2.67 to 5.14 and from 9.31 to 12.04, respectively. Heritability estimates gradually increased from the beginning (0.13) to mid-lactation (0.19). The genetic correlations between the days of the control ranged from 0.37 to 1.00. The correlations of permanent environment followed the same trend as genetic correlations. The use of Legendre polynomials via random regression model can be considered as a good tool for estimating genetic parameters for test-day milk yield records.

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