The objective of this study was to present heritability estimates and accuracy of genomic prediction using different methods for meat quality traits in Nelore cattle. Approximately 5000 animals with phenotypes and genotypes of 412,000 SNPs, were divided into two groups: (1) training population: animals born from 2008 to 2013 and (2) validation population: animals born in 2014. A single-trait animal model was used to estimate heritability and to adjust the phenotype. The methods of GBLUP, Improved Bayesian Lasso and Bayes Cπ were performed to estimate the SNP effects. Accuracy of genomic prediction was calculated using Pearson's correlations between direct genomic values and adjusted phenotypes, divided by the square root of heritability of each trait (0.03–0.19). The accuracies varied from 0.23 to 0.73, with the lowest accuracies estimated for traits associated with fat content and the greatest accuracies observed for traits of meat color and tenderness. There were small differences in genomic prediction accuracy between methods.

这项研究的目的是通过使用不同方法对内罗尔牛的肉质性状进行遗传估计和基因组预测的准确性。将约5000只具有412,000个SNP的表型和基因型的动物分为两组：（1）训练种群：2008年至2013年出生的动物；和（2）验证种群：2014年出生的动物。估计遗传力并调整表型。进行GBLUP，改进贝叶斯套索和贝叶斯Cπ的方法来估计SNP效应。基因组预测的准确性是使用直接基因组值和调整后的表型之间的皮尔森相关性除以每个性状的遗传力的平方根（0.03-0.19）来计算的。精度从0.23到0.73，估计与脂肪含量有关的性状的最低准确度，而对肉色和嫩度的性状所观察到的最大准确度。两种方法之间的基因组预测准确性差异很小。