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简短通讯:利用东非奶牛遗传学项目的数据对杂交牛群体进行基因组选择

Short communication: Genomic selection in a crossbred cattle population using data from the Dairy Genetics East Africa Project.

作者信息

Brown A, Ojango J, Gibson J, Coffey M, Okeyo M, Mrode R

机构信息

Animal and Veterinary Sciences, Scotland's Rural College, Easter Bush, Midlothian EH25 9RG, Scotland, United Kingdom.

International Livestock Research Institute (ILRI) Box 30709, Nairobi, Kenya.

出版信息

J Dairy Sci. 2016 Sep;99(9):7308-7312. doi: 10.3168/jds.2016-11083. Epub 2016 Aug 8.

DOI:10.3168/jds.2016-11083
PMID:27423951
Abstract

Due to the absence of accurate pedigree information, it has not been possible to implement genetic evaluations for crossbred cattle in African small-holder systems. Genomic selection techniques that do not rely on pedigree information could, therefore, be a useful alternative. The objective of this study was to examine the feasibility of using genomic selection techniques in a crossbred cattle population using data from Kenya provided by the Dairy Genetics East Africa Project. Genomic estimated breeding values for milk yield were estimated using 2 prediction methods, GBLUP and BayesC, and accuracies were calculated as the correlation between yield deviations and genomic breeding values included in the estimation process, mimicking the situation for young bulls. The accuracy of evaluation ranged from 0.28 to 0.41, depending on the validation population and prediction method used. No significant differences were found in accuracy between the 2 prediction methods. The results suggest that there is potential for implementing genomic selection for young bulls in crossbred small-holder cattle populations, and targeted genotyping and phenotyping should be pursued to facilitate this.

摘要

由于缺乏准确的系谱信息,在非洲小农户系统中无法对杂交牛进行遗传评估。因此,不依赖系谱信息的基因组选择技术可能是一种有用的替代方法。本研究的目的是利用东非奶牛遗传项目提供的肯尼亚数据,检验在杂交牛群体中使用基因组选择技术的可行性。使用GBLUP和BayesC这两种预测方法估计了产奶量的基因组估计育种值,并将准确性计算为产量偏差与估计过程中包含的基因组育种值之间的相关性,模拟年轻公牛的情况。评估的准确性在0.28至0.41之间,具体取决于所使用的验证群体和预测方法。两种预测方法在准确性上没有发现显著差异。结果表明,在杂交小农户牛群体中对年轻公牛实施基因组选择具有潜力,应进行有针对性的基因分型和表型分析以促进这一过程。

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