Objective. Determine the genetic and phenotypic parameters for milk yield, fat percentage, protein percentage and somatic cell score. Materials and methods. 18134 lactation records were used to Holstein and 1377 lactations for Jersey in different herds. The (co) variance components and genetic parameters were estimated using the software Multiple Trait Derivative-Free Restricted Maximum Likelihood MTDFREML. Results. The Holstein and Jersey heritability’s (and standard error) for milk yield were: 0.16 (0.082) and 0.15 (0.306), 0.30 (0.079) and 0.37 (0.319) for protein percentage, 0.32 (0.076) and 0.46 (0.313) for fat percentage and for somatic cell score were: 0.01 (0.054) and 0.01 (0.233), respectively. The largest genetic correlations were found between the percentage of fat and percentage of protein, with values of 0.82 (0.126) and 0.98 (0.852) for Holstein and Jersey respectively. The lowest correlations were between fat percentage and somatic cell score with -0.01 (1.147) and -0.01 (1. 734). Phenotypic correlations were generally found low and repeatability showed a significant effect of permanent environment on milk production per lactation. Conclusions. It is important to emphasize the development of research to help guide breeding programs in the tropics, using selection indices of multi-traits.

Objetive. To estimate and compare breeding values (EBV) using the conventional method (BLUP) and genomic breeding values (MEBV and GEBV) estimated through bayes C method for milk yield and milk quality traits in dairy cattle in Antioquia, Colombia. Materials and methods. Two methods were used to estimate breeding values: BLUP to estimate conventional breeding value (EBV) and bayes C to estimate genomic values (MEBV and GEBV). The traits evaluated were: milk yield (PL), protein percentage (PPRO), fat percentage (PGRA) and score somatic cell (SCS). The methods (BLUP and bayes C) were compared using Person correlation (r p ), Spearman rank correlation (r s ) and linear regression coefficient (b). Results. The Pearson and Spearman correlations among EBVs and genomic values (MEBV and GEBV) (r pMEBV;EBV and r sGEBV;EBV ) were greater than 0.93 and the linear regression coefficients of EBVs on genomic values (MEBV and GEBV) (b MEBV;EBV , and b GEBV;EBV ) ranged between 0.954 and 1.051 in all traits evaluated. Conclusions. The predictions of genomic values (MEBV and GEBV), using bayes C method were consistent with the predictions of the EBVs estimate through the conventional method (BLUP) in conditions of high Colombian tropic, allowing to obtain high associations between the breeding values.