Training set optimization under population structure in genomic selection.
Training set optimization under population structure in genomic selection.
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DOI：
10.1007/s00122-014-2418-4
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发表时间：
2015-01
影响因子：
5.4
通讯作者：
Sorrells, Mark E.
中科院分区：
文献类型：
--
作者：
Isidro, Julio;Jannink, Jean-Luc;Akdemir, Deniz;Poland, Jesse;Heslot, Nicolas;Sorrells, Mark E.
在优化训练集种群之前，必须评估种群结构。最大化训练集捕获的表型方差对于最佳性能很重要。 基因组选择中的训练集优化问题是影响预测模型准确性的重要因素，在动植物育种中受到广泛关注。在这项研究中，五种不同的TRS抽样算法，分层抽样，决定系数的平均值（CDmean），预测误差方差的平均值（PEVmean），分层CDmean（StratCDmean）和随机抽样，在不同水平的人口结构的存在下，预测精度进行了评估。在存在群体结构的情况下，期望通过TRS中的抽样方法捕获最多的表型变异。小麦数据集表现出温和的群体结构，和CDmean和分层的CDmean方法显示出最高的精度为所有的性状，除了测试重量和抽穗期。水稻数据集具有很强的群体结构，基于分层抽样的方法对所有性状都显示出最高的准确性。一般来说，CDmean最小化TRS中基因型之间的关系，最大化TRS和测试集之间的关系。这使得它适合作为长期选择的优化标准。我们的研究结果表明，用于优化TRS的最佳选择标准似乎取决于性状结构和群体结构的相互作用。本文的在线版本（doi：10.1007/s 00122 -014-2418-4）包含补充材料，可供授权用户使用。
Population structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance. The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure. The online version of this article (doi:10.1007/s00122-014-2418-4) contains supplementary material, which is available to authorized users.
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DOI：
10.1186/1297-9686-42-5
发表时间：
2010-02-19
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Habier D;Tetens J;Seefried FR;Lichtner P;Thaller G
通讯作者：
Thaller G
影响因子：
3.3
作者：
Habier, D.;Fernando, R. L.;Dekkers, J. C. M.
通讯作者：
Dekkers, J. C. M.
影响因子：
5.4
作者：
Guo, Zhigang;Tucker, Dominic M.;Gay, Gilles
通讯作者：
Gay, Gilles
影响因子：
3.3
作者：
Habier, D.;Fernando, R. L.;Dekkers, J. C. M.
通讯作者：
Dekkers, J. C. M.
影响因子：
3.3
作者：
Gianola, Daniel
通讯作者：
Gianola, Daniel