Gordo D G M, Espigolan R, Tonussi R L, Júnior G A F, Bresolin T, Magalhães A F Braga, Feitosa F L, Baldi F, Carvalheiro R, Tonhati H, de Oliveira H N, Chardulo L A L, de Albuquerque L G
J Anim Sci. 2016 May;94(5):1821-6. doi: 10.2527/jas.2015-0134.
The objective of this study was to determine whether visual scores used as selection criteria in Nellore breeding programs are effective indicators of carcass traits measured after slaughter. Additionally, this study evaluated the effect of different structures of the relationship matrix ( and ) on the estimation of genetic parameters and on the prediction accuracy of breeding values. There were 13,524 animals for visual scores of conformation (CS), finishing precocity (FP), and muscling (MS) and 1,753, 1,747, and 1,564 for LM area (LMA), backfat thickness (BF), and HCW, respectively. Of these, 1,566 animals were genotyped using a high-density panel containing 777,962 SNP. Six analyses were performed using multitrait animal models, each including the 3 visual scores and 1 carcass trait. For the visual scores, the model included direct additive genetic and residual random effects and the fixed effects of contemporary group (defined by year of birth, management group at yearling, and farm) and the linear effect of age of animal at yearling. The same model was used for the carcass traits, replacing the effect of age of animal at yearling with the linear effect of age of animal at slaughter. The variance and covariance components were estimated by the REML method in analyses using the numerator relationship matrix () or combining the genomic and the numerator relationship matrices (). The heritability estimates for the visual scores obtained with the 2 methods were similar and of moderate magnitude (0.23-0.34), indicating that these traits should response to direct selection. The heritabilities for LMA, BF, and HCW were 0.13, 0.07, and 0.17, respectively, using matrix and 0.29, 0.16, and 0.23, respectively, using matrix . The genetic correlations between the visual scores and carcass traits were positive, and higher correlations were generally obtained when matrix was used. Considering the difficulties and cost of measuring carcass traits postmortem, visual scores of CS, FP, and MS could be used as selection criteria to improve HCW, BF, and LMA. The use of genomic information permitted the detection of greater additive genetic variability for LMA and BF. For HCW, the high magnitude of the genetic correlations with visual scores was probably sufficient to recover genetic variability. The methods provided similar breeding value accuracies, especially for the visual scores.
本研究的目的是确定在 Nellore 牛育种计划中用作选择标准的视觉评分是否是屠宰后测量的胴体性状的有效指标。此外，本研究评估了关系矩阵（和）的不同结构对遗传参数估计和育种值预测准确性的影响。共有 13524 头牛进行了体型（CS）、育肥早熟性（FP）和肌肉量（MS）的视觉评分，分别有 1753 头、1747 头和 1564 头牛测量了眼肌面积（LMA）、背膘厚度（BF）和热胴体重（HCW）。其中，1566 头牛使用包含 777962 个单核苷酸多态性（SNP）的高密度芯片进行了基因分型。使用多性状动物模型进行了六项分析，每项分析包括 3 个视觉评分和 1 个胴体性状。对于视觉评分，模型包括直接加性遗传效应和残差随机效应，以及当代组（由出生年份、一岁时的管理组和农场定义）的固定效应和一岁时动物年龄的线性效应。胴体性状使用相同的模型，将一岁时动物年龄的效应替换为屠宰时动物年龄的线性效应。在使用分子关系矩阵（）或结合基因组和分子关系矩阵（）的分析中，通过限制最大似然法（REML）估计方差和协方差分量。用这两种方法获得的视觉评分的遗传力估计值相似且中等（0.23 - 0.34），表明这些性状对直接选择有响应。使用矩阵时，LMA、BF 和 HCW 的遗传力分别为 0.13、0.07 和 0.17，使用矩阵时分别为 0.29、0.16 和 0.23。视觉评分与胴体性状之间的遗传相关性为正，使用矩阵时通常获得更高的相关性。考虑到死后测量胴体性状的困难和成本，CS、FP 和 MS 的视觉评分可作为选择标准来改善 HCW、BF 和 LMA。基因组信息的使用使得能够检测到 LMA 和 BF 更大的加性遗传变异。对于 HCW，与视觉评分的高遗传相关性可能足以恢复遗传变异。这些方法提供了相似的育种值准确性，特别是对于视觉评分。
