Análise genética de escores de avaliação visual de bovinos com modelos bayesianos de limiar e linear

Abstract

Resumo -O objetivo deste trabalho foi comparar as estimativas de parâmetros genéticos obtidas em análises bayesianas uni-característica e bi-característica, em modelo animal linear e de limiar, considerando-se as características categóricas morfológicas de bovinos da raça Nelore. Os dados de musculosidade, estrutura física e conformação foram obtidos entre 2000 e 2005, em 3.864 animais de 13 fazendas participantes do Programa Nelore Brasil. Foram realizadas análises bayesianas uni e bi-características, em modelos de limiar e linear. De modo geral, os modelos de limiar e linear foram eficientes na estimação dos parâmetros genéticos para escores visuais em análises bayesianas uni-características. Nas análises bi-características, observou-se que: com utilização de dados contínuos e categóricos, o modelo de limiar proporcionou estimativas de correlação genética de maior magnitude do que aquelas do modelo linear; e com o uso de dados categóricos, as estimativas de herdabilidade foram semelhantes. A vantagem do modelo linear foi o menor tempo gasto no processamento das análises. Na avaliação genética de animais para escores visuais, o uso do modelo de limiar ou linear não influenciou a classificação dos animais, quanto aos valores genéticos preditos, o que indica que ambos os modelos podem ser utilizados em programas de melhoramento genético. Termos para indexação: amostragem de Gibbs, características morfológicas, gado de corte, valores genéticos. Genetic analysis for visual scores of bovines with the linear and threshold bayesian modelsAbstract -The objective of this work was to compare the estimates of genetic parameters obtained in singletrait and two-trait bayesian analyses, under linear and threshold animal models, considering categorical morphological traits of bovines of the Nelore breed. Data of musculature, physical structure and conformation were obtained between years 2000 and 2005, from 3,864 bovines of the Nelore breed from 13 participant farms of the Nelore Brazil Program. Single-trait and two-trait bayesian analyses were performed under linear and threshold animal models. In general, the linear and threshold models were efficient in estimating genetic parameters for visual scores under single-trait bayesian analyses. In the two-trait analyses, it was observed that: using continuous and categorical data, the threshold model provided greater estimates of genetic correlation than those of the linear model; with categorical data, the heritability estimates were similar. One major advantage of the linear models was its smaller requirements in the analyses processing time. In the genetic evaluation of animals for visual scores, the use of the linear or threshold model did not influence the classification of the animals, based on their predicted breeding values, which suggests that both models can be used in genetic improvement programs.

Index proposal for Nelore bulls classification using traits groups of weight, carcass and reproductive

“…The body shape scores are used as a selection criterion to infer the quality of the carcass (SHIOTSUKI et al, 2009) and, in this way, can meet market requirements (FARIA et al, 2009). …”

Preweaning Calf Survival of a Nellore Beef Cattle Population

“…(Gianola & Foulley, 1983) Bayesian inference with Gibbs sampling approach has been used to estimate genetic parameters for categorical data on sire and maternal grandfather-sire models. Some difficulties can be verified in obtaining Gibbs chain convergence in animal models because the algorithm used is characterized as an iterative process (Faria et al, 2008). Nevertheless, Bayesian inference is recommended to obtain genetic correlations between categorical (survival and visual scores) and continuous traits (weights) through multi-trait analysis (Everling et al, 2014).…”

Categorical Visual Score Traits of a Nellore Beef Cattle Population

“…Some difficulties can be verified in obtaining Gibbs chain convergence in animal models because the algorithm used is characterized as an iterative process (Faria et al, 2008). Nevertheless, Bayesian inference is recommended to obtain genetic correlations between categorical (survival and visual scores) and continuous traits (weights) through multi-trait analysis (Everling et al, 2014).…”

“…The establishment of an analysis model that correctly describes the data is an important factor in obtaining the genetic parameters (Faria et al, 2008). The methodologies vary according to the number of evaluated traits, the nature of the phenotypic data distribution, the genetic structure adopted and the estimation method of the variance components.…”