Multivariate analyses of carcass traits for Angus cattle fitting reduced rank and factor analytic models
Multivariate analyses of carcass traits for Angus cattle fitting reduced rank and factor analytic models
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DOI：
10.1111/j.1439-0388.2007.00637.x
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发表时间：
2007-04-01
影响因子：
2.6
通讯作者：
Meyer, K.
中科院分区：
文献类型：
--
作者：
Meyer, K.
本文报道了安格斯牛胴体性状的多变量分析，包括胴体上记录的6个性状和活体超声扫描测量的8个辅助性状。分析进行了限制性最大似然法，拟合了一些降低秩和因子分析模型的遗传协方差矩阵。估计的特征值和特征向量的不同顺序的拟合进行了对比和遗传方差和相关性的估计的影响进行了检查。结果表明，最多需要8个主成分（PC）模型的14个性状之间的遗传协方差结构。选择指数的计算表明，这些PC的前七个是足够的，以获得估计的胴体性状的育种值，而不会损失在预期的准确性评估。这意味着直接估算主基因的育种值可以使胴体性状遗传评价中的拟合效应数减半。
Multivariate analyses of carcass traits for Angus cattle, consisting of six traits recorded on the carcass and eight auxiliary traits measured by ultrasound scanning of live animals, are reported. Analyses were carried out by restricted maximum likelihood, fitting a number of reduced rank and factor analytic models for the genetic covariance matrix. Estimates of eigenvalues and eigenvectors for different orders of fit are contrasted and implications for the estimates of genetic variances and correlations are examined. Results indicate that at most eight principal components (PCs) are required to model the genetic covariance structure among the 14 traits. Selection index calculations suggest that the first seven of these PCs are sufficient to obtain estimates of breeding values for the carcass traits without loss in the expected accuracy of evaluation. This implied that the number of effects fitted in genetic evaluation for carcass traits can be halved by estimating breeding values for the leading PCs directly.