Inclusion of genomic information in estimation of genetic parameters for body weights and visual scores in Nelore cattle

The aim of this study was to evaluate genomic information inclusion in genetic parameter estimation of standardized body weight at birth and at 240, 365, and 450 days of age, and visual scores for body structure, precocity, and body muscularity, measured as yearlings in Nelore cattle. We compared genetic parameters, (co)variance components (estimated from Bayesian inference and Gibbs sampling), breeding value accuracies, genetic trends, and principal component analysis (PCA) obtained through traditional GBLUP and ssGBLUP methods. For all traits analyzed, part of the phenotypic variation was explained by the additive genetic effect, thus indicating the capacity of traits to respond to the selection process. Estimates of genetic correlation, in both methodologies, between body weights and visual scores were, in general, high and positive, showing that the selection for visual scores can lead to heavier animals. Genetic trends showed genetic progress, both when estimated breeding values and genomic estimated breeding values were used. The PCA, genetic trends, and accuracy estimates on breeding values showed that inclusion of single nucleotide polymorphism information contributed towards slightly better estimates of the genetic variability of evaluated traits. Genomic information did not bring greater gains in genetic estimates, due to redundancy of kinship information from the pedigree, which already had complete animal kinship data.