Estimation of genetic variation for macro- and micro-environmental sensitivities of milk yield and composition in Holstein cows using double hierarchical generalized linear models

Abstract

AbstractThe aim of this study was to estimate genetic parameters for environmental sensitivities in milk yield and composition of Iranian Holstein cows using the double hierarchical generalized linear model (DHGLM) method. Data set included test-day productive records of cows which were provided by the Animal Breeding Center and Promotion of Animal Products of Iran during 1983 to 2014. In the DHGLM method, a random regression model was fitted which included two parts of mean and residual variance. A random regression model (mean model) and a residual variance model were used to study the genetic variation of micro-environmental sensitivities. In order to consider macro-environmental sensitivities, DHGLM was extended using a reaction norm model, and a sire model was applied. Based on the mean model, additive genetic variances for the mean were 38.25 for milk yield, 0.23 for fat yield and 0.03 for protein yield in the first lactation, respectively. Based on the residual variance model, additive genetic variances for residual variance were 0.039 for milk yield, 0.030 for fat yield and 0.020 for protein yield in the first lactation, respectively. Estimates of genetic correlation between milk yield and macro- and micro-environmental sensitivities were 0.660 and 0.597 in the first lactation, respectively. The results of this study indicated that macro- and micro-environmental sensitivities were present for milk production traits of Iranian Holsteins. High genetic coefficient of variation for micro-environmental sensitivities indicated the possibility of reducing environmental variation and increase in uniformity via selection.