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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Genomic prediction of breeding values for carcass traits in Nellore cattle

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Autor(es):
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Fernandes Junior, Gerardo A. [1] ; Rosa, Guilherme J. M. [2] ; Valente, Bruno D. [2] ; Carvalheiro, Roberto [1] ; Baldi, Fernando [1] ; Garcia, Diogo A. [1] ; Gordo, Daniel G. M. [1] ; Espigolan, Rafael [1] ; Takada, Luciana [1] ; Tonussi, Rafael L. [1] ; de Andrade, Willian B. F. [1] ; Magalhaes, Ana F. B. [1] ; Chardulo, Luis A. L. [3] ; Tonhati, Humberto [1] ; de Albuquerque, Lucia G. [1]
Número total de Autores: 15
Afiliação do(s) autor(es):
[1] UNESP, Fac Ciencias Agr & Vet, BR-14884900 Jaboticabal, SP - Brazil
[2] Univ Wisconsin, Dept Anim Sci, Madison, WI 53706 - USA
[3] UNESP, Fac Med Vet & Zootecnia, BR-18618970 Botucatu, SP - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: GENETICS SELECTION EVOLUTION; v. 48, JAN 29 2016.
Citações Web of Science: 10
Resumo

Background: The objective of this study was to evaluate the accuracy of genomic predictions for rib eye area (REA), backfat thickness (BFT), and hot carcass weight (HCW) in Nellore beef cattle from Brazilian commercial herds using different prediction models. Methods: Phenotypic data from 1756 Nellore steers from ten commercial herds in Brazil were used. Animals were offspring of 294 sires and 1546 dams, reared on pasture, feedlot finished, and slaughtered at approximately 2 years of age. All animals were genotyped using a 777k Illumina Bovine HD SNP chip. Accuracy of genomic predictions of breeding values was evaluated by using a 5-fold cross-validation scheme and considering three models: Bayesian ridge regression (BRR), Bayes C (BC) and Bayesian Lasso (BL), and two types of response variables: traditional estimated breeding value (EBV), and phenotype adjusted for fixed effects (Y{*}). Results: The prediction accuracies achieved with the BRR model were equal to 0.25 (BFT), 0.33 (HCW) and 0.36 (REA) when EBV was used as response variable, and 0.21 (BFT), 0.37 (HCW) and 0.46 (REA) when using Y{*}. Results obtained with the BC and BL models were similar. Accuracies increased for traits with a higher heritability, and using Y{*} instead of EBV as response variable resulted in higher accuracy when heritability was higher. Conclusions: Our results indicate that the accuracy of genomic prediction of carcass traits in Nellore cattle is moderate to high. Prediction of genomic breeding values from adjusted phenotypes Y{*} was more accurate than from EBV, especially for highly heritable traits. The three models considered (BRR, BC and BL) led to similar predictive abilities and, thus, either one could be used to implement genomic prediction for carcass traits in Nellore cattle. (AU)

Processo FAPESP: 09/16118-5 - Ferramentas genômicas no melhoramento genético de características de importância econômica direta em bovinos da raça Nelore
Beneficiário:Lucia Galvão de Albuquerque
Linha de fomento: Auxílio à Pesquisa - Temático