Genomic prediction of breeding values for carcass traits in Nellore cattleReportar como inadecuado




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Abstract : 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.MethodsPhenotypic 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*.ResultsThe 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.ConclusionsOur 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.





Autor: Gerardo A. Fernandes Júnior Guilherme J. M. Rosa Bruno D. Valente Roberto Carvalheiro Fernando Baldi Diogo A. Garcia Daniel G. M

Fuente: https://hal.archives-ouvertes.fr/



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