Genetic parameters for traditional and novel ultrasound carcass traits in Nellore cattle

Marestone, Bruna Silva; Torres, Roberto Augusto de Almeida; Silva, L. O. C.; Menezes, G.R.O.; Muniz, Carolina Amália Souza Dantas; Ribeiro, E. L. A.

doi:10.1007/s11250-021-03028-z

Cited by 3 publications

(1 citation statement)

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“…Several efforts have been made to improve the beef quality‐related traits in beef cattle breeding programs, that is, ultrasound imaging has been widely used to measure carcass traits such as rib‐eye area, fat thickness at the rump and over the 12th‐13th ribs, and marbling (Caetano et al, 2013; Marestone et al, 2022; Yokoo et al, 2010). However, meat tenderness has not been used as a selection criterion, due to the difficulty in obtaining phenotypic information and the polygenic nature of the trait (Pacheco et al, 2020; Tizioto et al, 2013; Wheeler et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

Lopes

Baldi

Brunes

et al. 2022

J Animal Breeding Genetics

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This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low‐density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross‐validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low‐density and customized arrays using Markov blanket might be cost‐effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.

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