4.5 Article

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

期刊

JOURNAL OF ANIMAL BREEDING AND GENETICS
卷 140, 期 1, 页码 1-12

出版社

WILEY
DOI: 10.1111/jbg.12740

关键词

Bayesian approach; beef cattle; genomic prediction; informative SNPs; WBSF

向作者/读者索取更多资源

This study evaluates the advantage of preselecting SNP markers using the Markov blanket algorithm in genomic prediction for carcass and meat quality traits in Nellore cattle. The results show that using the Markov blanket SNP markers can result in lower prediction accuracy compared to using all SNPs, but in certain cases, still achieve high prediction accuracy. The use of Markov blanket-identified SNP markers can improve genomic selection efficiency and management decisions.
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 C pi 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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据