Multivariate procedures are used for the extraction of variables from the correlation matrix of phenotypes in order to identify those traits that explain the largest proportion of phenotypic variation and to evaluate the relationship structure between these traits. The reproductive traits (days from calving to first estrus [CFE], days from calving to last service [CLS], calving interval [CI] and gestation length [GL]) and milk production traits (milk yield at 305 days of lactation [MY305], peak yield [PY] and milk yield per day of calving interval [MYCI]) of 5,217 Holstein females (primiparous and multiparous) were measured. Principal component analysis (PCA) and factor analysis of the correlation matrix were used to estimate the correlation between traits. Analysis grouped the seven traits into three principal components and four latent factors that retained approximately 81.5% and 88.9% of the total variation of the data, respectively. The production variables exhibited positive phenotypic correlation coefficients of high magnitude (>.67). The phenotypic correlation estimates between the productive and reproductive traits were low, ranging from .13 to .22. A strong association (.99) was observed between CLS and CI. Our results indicate that multivariate analysis was effective in generating correlations between the traits studied, grouping the seven traits in a smaller number of variables that retained approximately 81% of the total variation of the data.

中文翻译：

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使用多变量分析的荷斯坦牛繁殖性状与生产性状之间的关系。

多变量程序用于从表型的相关矩阵中提取变量，以便识别出解释表型变异最大比例的那些特征，并评估这些特征之间的关系结构。生殖特征（从产犊到发情的天数[CFE]，从产犊到最后服务的天数[CLS]，产犊间隔[CI]和孕期[GL]）和产奶性状（泌乳305天的产奶量[MY305] ]，测量了5,217头荷斯坦雌性（初产和复产）的峰值产蛋率[PY]和每天产犊间隔的牛奶产量[MYCI]。相关矩阵的主成分分析（PCA）和因子分析用于估计性状之间的相关性。分析将七个特征分为三个主要成分和四个潜在因素，分别保留了数据总变异的约81.5％和88.9％。生产变量表现出很高的表型正相关系数（> .67）。生产性状和生殖性状之间的表型相关性估计值较低，范围为.13至.22。在CLS和CI之间观察到强关联（0.99）。我们的结果表明，多变量分析可有效地在研究的性状之间产生相关性，将七个性状分组在较少数量的变量中，这些变量保留了数据总变异的约81％。生产变量表现出很高的表型正相关系数（> .67）。生产性状和生殖性状之间的表型相关性估计值较低，范围为.13至.22。在CLS和CI之间观察到强关联（0.99）。我们的结果表明，多变量分析可有效地在研究的性状之间产生相关性，将七个性状分组在较少数量的变量中，这些变量保留了数据总变异的约81％。生产变量表现出很高的表型正相关系数（> .67）。生产性状和生殖性状之间的表型相关性估计值较低，范围为.13至.22。在CLS和CI之间观察到强关联（0.99）。我们的结果表明，多变量分析可有效地在研究的性状之间产生相关性，将七个性状分组在较少数量的变量中，这些变量保留了数据总变异的约81％。