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首页> 外文期刊>Livestock Science >Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis
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Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis

机译:通过主要成分分析解开Nellore牛生长,视觉评分和生殖性状的生物生物型,通过主要成分分析

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Principal component analysis (PCA) is used to summarize important information from multivariate data in sets of new variables named principal components (PCs). In animal breeding, these new composite variables can be used to study the associations among multiple traits using the magnitude and direction of the PCA coefficients (in the eigenvectors) for each trait. Phenotypic data from 355 524 Nellore animals were used to estimate genetic parameters and explore the relationship among growth (weaning and post-weaning weight gain), visual score (weaning and yearling conformation, finishing precocity and muscling) and reproductive (scrotal circumference) traits using PCA. Genetic parameters were estimated by multi-trait analysis using a mixed linear animal model. The eigen-decomposition of the additive genetic (co)variance matrix (A(T) matrix) obtained using multi-trait analysis were used to calculate the PCs. In addition, PCA using the (co)variance matrix of the breeding values (EBVs) from single- and multi-trait analyses were investigated for comparison purposes. The direct heritability estimates for the weaning and yearling traits ranged from 0.17 (birth-to-weaning weight gain and conformation) to 0.21 (finishing precocity) and from 0.18 (weaning-to-yearling weight gain) to 0.46 (scrotal circumference), respectively. Genetic correlations estimated among all analyzed traits were positive (favorable) ranging from 0.15 (conformation at weaning and scrotal circumference) to 0.96 (finishing precocity and muscling at weaning). The first three PCs from multi-trait analysis using the eigen-decomposition of the A(T) matrix, explained 87.11% of the total additive genetic variance for the traits. The first PC (PC1) had negative and relatively similar coefficients for all traits, the second PC (PC2) contrasted the animals with early or late biotype, and the third PC (PC3) characterized a contrast between weaning and yearling traits. Our findings suggest that the PCA could be explored in breeding programs to select Nellore cattle to tailor selection towards specific PC, targeting, for instance, faster growth and precocious biotype.
机译:主成分分析(PCA)用于将来自多变量数据的重要信息总结为名为主组件(PC)的新变量集中。在动物繁殖中,这些新的复合变量可用于使用PCA系数(在特征向量)的多个特征之间的关联对每个特征来研究多个特征。 35555555555524中的表型数据用于估计遗传参数,并探索生长(断奶和断奶后重量增益)之间的关系,视觉得分(断奶和一岁兼容,精加工,精密和肌肉)和生殖(阴囊周长)的性状PCA。利用混合线性动物模型通过多特征分析估算遗传参数。使用多特征分析获得的添加剂遗传(CO)方差基质(A(T)矩阵)的特征分解用于计算PC。另外,研究了使用来自单个和多特征分析的繁殖值(EBV)的(CO)方差矩阵的PCA进行比较。断奶和一卷重性的直接可遗传性估计范围为0.17(出生的重量增值和构象)至0.21(精加工)和0.18(断奶重量增益)至0.46(阴囊周长) 。在所有分析的性分析中估计的遗传相关性为0.15(断奶和阴囊圆周的构象)至0.96(在断奶时精加工和肌肉)的阳性(有利)。来自多特征分析的前三个PC使用A(t)基质的特征分解,解释了该性状的总添加剂遗传方差的87.11%。第一PC(PC1)对所有特征具有负性和相对相似的系数,第二个PC(PC2)对比生物型或晚期或晚期的动物对比,并且第三个PC(PC3)表征断奶和一卷曲性状之间的对比度。我们的研究结果表明,PCA可以在育种计划中探索,以选择朝向特定PC定制选择的联合牛,瞄准例如更快的生长和预先诱导的生物型。

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