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Baldi F.,Sao Paulo State University | de Albuquerque L.G.,Sao Paulo State University | de Albuquerque L.G.,Brazilian National Council for Scientific and Technological Development | de Albuquerque L.G.,Instituto Nacional Of Ciencia E Tecnologia Ciencia Animal Inct Ca | And 5 more authors.
Livestock Science | Year: 2012

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. © 2011 Elsevier B.V.

Espigolan R.,Sao Paulo State University | Baldi F.,Sao Paulo State University | Baldi F.,University of Sao Paulo | Boligon A.A.,Sao Paulo State University | And 17 more authors.
BMC Genomics | Year: 2013

Background: Knowledge of the linkage disequilibrium (LD) between markers is important to establish the number of markers necessary for association studies and genomic selection. The objective of this study was to evaluate the extent of LD in Nellore cattle using a high density SNP panel and 795 genotyped steers.Results: After data editing, 446,986 SNPs were used for the estimation of LD, comprising 2508.4 Mb of the genome. The mean distance between adjacent markers was 4.90 ± 2.89 kb. The minor allele frequency (MAF) was less than 0.20 in a considerable proportion of SNPs. The overall mean LD between marker pairs measured by r2 and |D'| was 0.17 and 0.52, respectively. The LD (r2) decreased with increasing physical distance between markers from 0.34 (1 kb) to 0.11 (100 kb). In contrast to this clear decrease of LD measured by r2, the changes in |D'| indicated a less pronounced decline of LD. Chromosomes BTA1, BTA27, BTA28 and BTA29 showed lower levels of LD at any distance between markers. Except for these four chromosomes, the level of LD (r2) was higher than 0.20 for markers separated by less than 20 kb. At distances < 3 kb, the level of LD was higher than 0.30. The LD (r2) between markers was higher when the MAF threshold was high (0.15), especially when the distance between markers was short.Conclusions: The level of LD estimated for markers separated by less than 30 kb indicates that the High Density Bovine SNP BeadChip will likely be a suitable tool for prediction of genomic breeding values in Nellore cattle. © 2013 Espigolan et al.; licensee BioMed Central Ltd.

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