Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China.
Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China; Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai 200240, PR China.
J Dairy Sci. 2019 Jun;102(6):5219-5229. doi: 10.3168/jds.2018-15561. Epub 2019 May 2.
Information about genetic parameters is population specific and it is crucial for designing animal breeding programs and predicting response to selection. This study was carried out to estimate the genetic parameters for 23 body conformation traits of 45,517 Chinese Holstein reared in Eastern China from 1995 to 2017 with the Bayesian inference method using a linear animal mixed model. The methods to integrate these traits included (1) using the composite index from the Dairy Association of China and (2) applying principal component analysis and factor analysis to explore the relationship between the conformation traits. Estimates of heritability using the composite index were low (0.04; feet and legs) to moderate (0.23; body capacity). Strong genetic correlations were observed between the individual body conformation traits. Both principal components (1 to 7; eigenvalues ≥ 1) and latent factors (1 to 7; eigenvalues ≥ 1) explained 60.37% of total variability. Principal component 1 and factor 1 accounted for the traits that are usually associated with milk production. Moderate to low heritability were estimated through multi-trait analysis for principal components (from 0.07 to 0.21) and latent factors (from 0.07 to 0.23). Genetic correlations among the 2 multivariate techniques are typically lower compared with the one existing among the measured traits. Results from these analyses suggest the possibility of using both principal component analysis and factor analysis in morphological evaluation, simplifying the information given by the body conformation traits into new variables that could be useful for the genetic improvement of the Chinese Holstein population. This information could also be used to avoid analyzing large number of correlated traits, thereby improving precision and reducing computation burdens to analyze large and complex data.
有关遗传参数的信息是特定于群体的，对于设计动物育种计划和预测选择反应至关重要。本研究使用贝叶斯推断方法，通过线性动物混合模型，对 1995 年至 2017 年在中国东部饲养的 45517 头中国荷斯坦奶牛的 23 个体型特征进行了遗传参数估计。整合这些特征的方法包括：（1）使用中国奶牛协会的综合指数，（2）应用主成分分析和因子分析来探索体型特征之间的关系。使用综合指数的遗传力估计值较低（0.04；脚和腿）至中等（0.23；体容量）。个体体型特征之间存在较强的遗传相关性。两个主成分（1 到 7；特征值≥1）和潜在因子（1 到 7；特征值≥1）解释了 60.37%的总变异性。主成分 1 和因子 1 代表了通常与产奶量相关的特征。通过多性状分析，主成分（0.07 到 0.21）和潜在因子（0.07 到 0.23）的遗传力中等至较低。与测量特征之间存在的遗传相关性相比，这两种多变量技术之间的遗传相关性通常较低。这些分析结果表明，在形态评估中可以同时使用主成分分析和因子分析，将体型特征提供的信息简化为新的变量，这对中国荷斯坦牛种群的遗传改良可能是有用的。这些信息还可以用于避免分析大量相关特征，从而提高精度并减少分析大型和复杂数据的计算负担。
