Predicting hybrid performance in rice using genomic best linear unbiased prediction
Predicting hybrid performance in rice using genomic best linear unbiased prediction
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使用基因组最佳线性无偏预测预测水稻的杂交性能
DOI：
10.1073/pnas.1413750111
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发表时间：
2014-08-26
影响因子：
11.1
通讯作者：
Zhang, Qifa
中科院分区：
文献类型：
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作者：
Xu, Shizhong;Zhu, Dan;Zhang, Qifa
基因组选择是数量性状标记辅助选择的一种升级形式，它与传统的标记辅助选择的不同之处在于利用整个基因组中的标记来预测遗传值，而跳过了QTL检测步骤。对于多基因控制的性状，基因组选择有望比传统的标记辅助选择更有效。用于纯种改良的基因组选择是基于标记信息的，因此由于在测量表型之前进行早期选择而导致成本节约。当应用于杂交育种时，预期基因组选择甚至更有效，因为杂交种的基因型由其近交亲本预先确定。杂交育种已成为提高作物产量的重要手段。在这里，我们提出并应用了一种先进的方法来预测混合性能，其中所有潜在的杂交种的子集被用作训练样本来预测所有潜在的杂交种的性状值。该方法被称为基因组最佳线性无偏预测。应用于杂交种的技术被称为基因组杂交育种。我们使用了278个随机选择的杂交种，来自210个重组自交系的水稻作为训练样本，预测所有21,945个潜在的杂交种。前100个选择的平均产量比所有潜在杂交种的平均产量增加16%。标记引导的杂交种产量预测的新策略作为一种新技术的概念证明，可能会彻底改变杂交育种。
Genomic selection is an upgrading form of marker-assisted selection for quantitative traits, and it differs from the traditional marker-assisted selection in that markers in the entire genome are used to predict genetic values and the QTL detection step is skipped. Genomic selection holds the promise to be more efficient than the traditional marker-assisted selection for traits controlled by polygenes. Genomic selection for pure breed improvement is based on marker information and thus leads to cost-saving due to early selection before phenotypes are measured. When applied to hybrid breeding, genomic selection is anticipated to be even more efficient because genotypes of hybrids are predetermined by their inbred parents. Hybrid breeding has been an important tool to increase crop productivity. Here we proposed and applied an advanced method to predict hybrid performance, in which a subset of all potential hybrids is used as a training sample to predict trait values of all potential hybrids. The method is called genomic best linear unbiased prediction. The technology applied to hybrids is called genomic hybrid breeding. We used 278 randomly selected hybrids derived from 210 recombinant inbred lines of rice as a training sample and predicted all 21,945 potential hybrids. The average yield of top 100 selection shows a 16% increase compared with the average yield of all potential hybrids. The new strategy of marker-guided prediction of hybrid yields serves as a proof of concept for a new technology that may potentially revolutionize hybrid breeding.