Genetic parameter estimates for live weight and daily live weight gain obtained for Nellore bulls in a test station using different models

Abstract

The objective of this study was to estimate (co)variance components and genetic parameters for live weight (LW) and daily live weight gain (LWG) of Nellore bulls in a test station using multi-trait and random regression models. In addition, breeding values for these traits were predicted by multi-trait and random regression analyses, and the rank of animals based on breeding values was compared with the current selection criterion of the test station (own performance). A total of 4758 Nellore bulls tested in a central station of the Beef Cattle Research Center (CPPC) between 1978 and 2007, including 2211 bulls from the CPPC herd and 2547 from commercial herds, were used. During the test, four LWs were recorded at intervals of 56days (LW1d, LW56d, LW112d and LW168d). LWG was calculated as the difference between two consecutive weights for three periods: 1 to 55 (LWG1), 56 to 111 (LWG2), and 112 to 168 (LWG3) days on test. For LW and LWG, the multi-trait model included the fixed effects of contemporary group (year-month of birth), dam age class, and animal age at recording as covariate. For random regression analysis, direct additive genetic and animal permanent environmental effects were modeled using linear, quadratic and cubic polynomial functions. Residual variances for LW and LWG were modeled using a step function with 1 or 3 classes, respectively. Contemporary group (year-month of birth and month of recording) and dam age class were included as fixed effects. The (co)variance components were estimated by the Restricted Maximum Likelihood method using the WOMBAT software. According to model comparison criterion, the model including cubic and quadratic Legendre polynomials to fit genetic and animal permanent environmental effects, respectively, was the most appropriate to describe the covariance structure of LW. For LWG, the BIC value indicated that the model including quadratic and linear Legendre polynomials was the most appropriate to fit genetic and animal permanent environmental effects, respectively. The variance component and genetic parameter estimates for LW and LWG obtained by random regression and multi-trait analyses were similar. Random regression on Legendre polynomials of days on test was more appropriate than multi-trait models to describe the genetic variation of growth traits in station-tested Nellore bulls. Selection based on breeding values for LWG during the test would result in the selection of bulls different from those chosen if final weight is applied as a selection criterion.