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首页> 外文期刊>Advances in Animal and Veterinary Sciences >Principle Component Analysis of Breeding Values Estimated by Six Animal Models for Evaluating Some Productive and Reproductive Traits of Holstein Dairy Cattle
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Principle Component Analysis of Breeding Values Estimated by Six Animal Models for Evaluating Some Productive and Reproductive Traits of Holstein Dairy Cattle

机译:六种动物模型评价荷斯坦奶牛综合六动物模型估算的育种价值原理分析

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This study was conducted to estimate genetic parameters and breeding values (EBVs) for milk yield (MY), peak yield (PY), lactation length (LL), days open (DO), calving interval (CI), and services per conception (SC) of Holstein dairy cattle. The direct genetic, maternal genetic and maternal permanent environmental effects were separately evaluated. Furthermore, the principle components analysis (PCA) was applied to explore the relationships among the animal EBVs. Genetic parameters were estimated using the multi-trait restricted maximum likelihood methodology by incorporating six different models that either included or excluded maternal effects. The best model was selected based on the likelihood ratio test. In this context of the research, a total of 18221 cows were assessed for records between 2007 and 2018. Out of the six animal models, the fourth model was chosen as the best model, because it had the smallest -2 Log Likelihood value. The range of direct heritability values were 0.21-0.35, 0.02-0.30, 0.15-0.33, 0.04-0.18, 0.05-0.18, and 0.05-0.15 for MY, PY, LL, DO, CI, and SC, respectively. The estimated maternal heritabilities were lower than direct heritabilities informed by all models. However, models 4 and 6 showed the greatest increase in maternal heritability, for all traits. PCA reduced the standardized EBVs of traits into two components, explaining 75.04 % of the total genetic variance. The EBVs of MY, LL, DO, SC, and CI highly associated with PC1, whereas those of PY is closely connected with PC2. In conclusion, the selection indices could be planned based on two PCs instead of all traits.
机译:进行该研究以估计牛奶产量(我的),峰收率(PY),乳液长度(LL),天开(DO),Cal瓣间隔(CI)和服务的遗传参数和育种值(EBVS),以及每概念的服务(荷斯坦奶牛的SC)。分别评估直接遗传,母体遗传和母体永久环境效应。此外,应用了原理分析(PCA)来探索动物EBVs之间的关系。通过掺入包括或排除母体效应的六种不同模型,使用多种特征限制最大似然方法估计遗传参数。基于似然比测试选择了最佳模型。在该研究的背景下,共有18221韩元被评估2007年至2018年的记录。出于六种动物模型中,第四种模型被选为最佳模型,因为它具有最小的-2日志似然价值。直接遗传性值的范围分别为0.21-0.35,0.02-0.30,0.15-0.33,0.04-0.18,0.05-0.18,0.05-0.15分别用于我的,py,ll,do,ci和sc。估计的孕产妇遗产低于所有模型的直接秘伤性。然而,对于所有特征,模型4和6显示出母体遗传性的最大增加。 PCA将标准化的eBV的特性降低到两个组分中,解释了总遗传方差的75.04%。我,LL,DO,SC和CI的EBV与PC1高度相关,而PY的EBV与PC2密切相关。总之,可以基于两种PC而不是所有特征来计划选择指数。

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