title: Bayesian inference in genetic parameter estimation of visual scores in Nellore beef-cattle.
abstract: The aim of this study was to estimate the components of variance and genetic parameters for the visual scores which constitute the Morphological Evaluation System (MES), such as body structure (S), precocity (P) and musculature (M) in Nellore beef-cattle at the weaning and yearling stages, by using threshold Bayesian models. The information used for this was gleaned from visual scores of 5,407 animals evaluated at the weaning and 2,649 at the yearling stages. The genetic parameters for visual score traits were estimated through two-trait analysis, using the threshold animal model, with Bayesian statistics methodology and MTGSAM (Multiple Trait Gibbs Sampler for Animal Models) threshold software. Heritability estimates for S, P and M were 0.68, 0.65 and 0.62 (at weaning) and 0.44, 0.38 and 0.32 (at the yearling stage), respectively. Heritability estimates for S, P and M were found to be high, and so it is expected that these traits should respond favorably to direct selection. The visual scores evaluated at the weaning and yearling stages might be used in the composition of new selection indexes, as they presented sufficient genetic variability to promote genetic progress in such morphological traits.
authors.affiliation: Universidade Federal de Goiás, Campus de Jataí, Jataí, GO Brazil.
authors.forename: Carina Ubirajara
authors.initials: CU
authors.lastname: de Faria
authors.forename: William
authors.initials: W
authors.lastname: Koury
authors.forename: Cláudio Ulhôa
authors.initials: CU
authors.lastname: Magnabosco
authors.forename: Lucia Galvão
authors.initials: LG
authors.lastname: de Albuquerque
authors.forename: Luiz Antônio Framartino
authors.initials: LA
authors.lastname: Bezerra
authors.forename: Raysildo Barbosa
authors.initials: RB
authors.lastname: Lôbo