Random regression models were used to estimate genetic parameters for test-day milk yield (PLDC) of Alpine dairy goats, implemented by Bayesian methods with Gibbs Sampling. The estimates were compared with those obtained by random regression analysis, using REML. Heritability estimates obtained by Bayesian analysis ranged from 0.18 to 0.37, while those obtained by REML ranged from 0.09 to 0.32. Genetic correlations between yields of close test days approached the unit, but decreased gradually as the interval between test days increased. Results indicated that random regression models are appropriate to model the covariance structure of PLDC and to predict genetic gains and select animals along the lactation trajectory of dairy goats. Results obtained by Bayesian and REML approaches were similar, although genetic variance and heritability estimates were slightly higher with Bayesian methods.
Bayesian methods; dairy goats; genetic correlation; variance components
