Identification of quantitative trait loci affecting economic traits based on divergently selected genomic regions between beef and dairy cattle

Yamaji, Keita; Hosokawa, Daisuke; Ishii, Atsushi; Oyama, Kenji; Mannen, Hideyuki; Sasazaki, Shinji

doi:10.1016/j.livsci.2013.05.017

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…Overall, it has been shown that genomic selection (De Los Campos et al, 2013; Gianola et al, 2009; Meuwissen et al, 2001; Pérez et al, 2010) would improve the accuracy of genomic estimated breeding values not only for meat tenderness (Magnabosco et al, 2016; Stone et al, 2005) but also for carcass traits (Elzo et al, 2013; Fernandes Júnior et al, 2016; Stone et al, 2005; Yamaji et al, 2013). Thus, it enables a significant increase in the rate of genetic gain (Van Eenennaam et al, 2011).…”

Section: Resultsmentioning

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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