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

Abstract

Test-day milk yield records of 11,023 first-parity Holstein cows were used to estimate genetic parameters for milk yield during different lactation periods. (Co)variance components were estimated using two random regression models, RRM1 and RRM2, and the restricted maximum likelihood method, compared by the likelihood ratio test. Additive genetic variances determined by RRM1 and additive genetic and permanent environmental variances estimated by RRM2 were described, using the Wilmink function. Residual variance was constant throughout lactation for the two models. The heritability estimates obtained by RRM1 (0.34 to 0.56) were higher than those obtained by RRM2 (0.15 to 0.31). Due to the high heritability estimates for milk yield throughout lactation and the negative genetic correlation between test-day yields during different lactation periods, the RRM1 model did not fit the data. Overall, genetic correlations between individual test days tended to decrease at the extremes of the lactation trajectory, showing values close to unity for adjacent test days. The inclusion of random regression coefficients to describe permanent environmental effects led to a more precise estimation of genetic and non-genetic effects that influence milk yield.

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

“…B-spline and Wilmink showed similar estimates. Several studies in Holstein cattle using RRM reported similar trend for TD heritability estimates as obtained in this study, Olori et al (1999) and Kettunen et al (2000), and in Brazil by Cobuci et al (2005), Araújo et al (2006), Melo et al (2007) and Dorneles et al (2009).…”

“…The highest permanent environmental variances (not shown) estimated with the RRM were observed at the beginning and at the end of lactation. The same trend for Holstein cattle has been reported by Melo et al (2003), Cobuci et al (2005) and Costa et al (2008) and indicates that environmental factors exerted a marked influence during these periods.…”

“…These negative genetic correlations can be attributed to the difficulty of modeling initial test-days milk yields of lactation, a phase during which the cow suffers from post-calving stress and also from an energy deficit. Negative genetic correlations estimated by RRM using different functions have also been reported for Holstein cattle by , Olori et al (1999), Brotherstone et al (2000) and Kettunen et al (2000), and in Brazil by Cobuci et al (2005), Costa et al (2008) and Bignardi et al (2009a,b). The surfaces of the permanent environmental correlations differed between the four models ( Figure 5).…”

Estimação de parâmetros genéticos para produção de leite de vacas da raça Holandesa via regressão aleatória

“…Esse aumento nas estimativas de herdabilidade está parcialmente associado à redução da variância de ambiente permanente que promoveu redução na variância total e, conseqüente, aumento da contribuição proporcional da variância genética aditiva. Essa tendência crescente assemelha-se às relatadas por Melo (2003) e Cobuci et al (2005), para a raça Holandesa, usando a função de Wilmink para modelar as variâncias genéticas e de ambiente permanente.…”

Aplicação de modelos de regressão aleatória utilizando diferentes estruturas de dados

“…Da mesma maneira, como o arquivo CT possuía menor número de informações, a superfície apresentou oscilações mais visíveis que para as demais estruturas. De maneira geral, as correlações fenotípicas foram maiores entre os controles adjacentes e apresentaram menores magnitudes que as correlações genéticas, sendo as magnitudes, próximas às encontradas por COBUCI et al (2005), FERREIRA et al (2003 e MELO et al (2005). As correlações de ambiente permanente para os diferentes arquivos de dados analisados apresentaram superfícies e tendências muito próximas, mantendo o mesmo comportamento das correlações anteriores.…”