Genomic prediction from observed and imputed high-density ovine genotypes
Genomic prediction from observed and imputed high-density ovine genotypes
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根据观察和估算的高密度绵羊基因型进行基因组预测
DOI：
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发表时间：
2017
影响因子：
4.1
通讯作者：
J. V. D. van der Werf
中科院分区：
文献类型：
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作者：
N. Moghaddar;A. Swan;J. V. D. van der Werf
背景利用高密度(HD)标记基因进行基因组预测，由于单核苷酸多态与控制性状的数量性状基因座之间存在更强的连锁不平衡，因此有望获得更高的预测精度，特别是对绵羊和肉牛这样的多品种和杂交群体。本研究的目的是评估基于HD基因(600k，观察和推算)的澳大利亚绵羊品种的生产性状基因组预测精度与基于50k标记基因的预测相比是否有可能提高。方法采用基因组最佳线性无偏预测(GBLUP)和贝叶斯方法(BayesR)作为预测方法，使用大型多品种/杂交绵羊参考集的全部或子集作为预测方法。结果表明，当基于整个数据集进行预测时，根据基因组估计的繁殖值与基于子代测试估计的繁殖值之间的皮尔逊相关系数，对纯种美利诺、边界莱斯特、无角陶赛特和白萨福克种公猪的预测精度进行了评估。结果表明，从HD基因预测纯种动物的准确率有较小的绝对提高(0.0%~8.0%，所有性状平均2.2%)。对于与参考集遗传亲缘关系较低的动物，预测准确率有较大的提高(1.0~12.0%，平均5.2%)，而跨品种预测的准确率在0.0%~5.0%之间。平均而言，与GBLUP相比，BayesR没有观察到明显的优势。
Background Genomic prediction using high-density (HD) marker genotypes is expected to lead to higher prediction accuracy, particularly for more heterogeneous multi-breed and crossbred populations such as those in sheep and beef cattle, due to providing stronger linkage disequilibrium between single nucleotide polymorphisms and quantitative trait loci controlling a trait. The objective of this study was to evaluate a possible improvement in genomic prediction accuracy of production traits in Australian sheep breeds based on HD genotypes (600k, both observed and imputed) compared to prediction based on 50k marker genotypes. In particular, we compared improvement in prediction accuracy of animals that are more distantly related to the reference population and across sheep breeds.MethodsGenomic best linear unbiased prediction (GBLUP) and a Bayesian approach (BayesR) were used as prediction methods using whole or subsets of a large multi-breed/crossbred sheep reference set. Empirical prediction accuracy was evaluated for purebred Merino, Border Leicester, Poll Dorset and White Suffolk sire breeds according to the Pearson correlation coefficient between genomic estimated breeding values and breeding values estimated based on a progeny test in a separate dataset.ResultsResults showed a small absolute improvement (0.0 to 8.0% and on average 2.2% across all traits) in prediction accuracy of purebred animals from HD genotypes when prediction was based on the whole dataset. Greater improvement in prediction accuracy (1.0 to 12.0% and on average 5.2%) was observed for animals that were genetically lowly related to the reference set while it ranged from 0.0 to 5.0% for across-breed prediction. On average, no significant advantage was observed with BayesR compared to GBLUP.