Design of training populations for selective phenotyping in genomic prediction
Design of training populations for selective phenotyping in genomic prediction
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DOI：
10.1038/s41598-018-38081-6
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发表时间：
2019-02-05
影响因子：
4.6
通讯作者：
Isidro-Sanchez, Julio
中科院分区：
文献类型：
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作者：
Akdemir, Deniz;Isidro-Sanchez, Julio
表型鉴定是当前植物育种的瓶颈，特别是因为在过去的20年里，下一代测序已经将基因鉴定成本降低了100.000多倍。因此，表型鉴定的成本需要在育种计划中进行优化。当将基因组选择方案的实施设计到育种周期中时，育种者需要选择最优的方法来(1)选择最大限度地提高基因组预测精度的训练种群，(2)在提高精度的同时降低表型成本。在本文中，我们比较了两种情况下的训练群体选择方法：第一，当目标是选择一个训练群体集(TRS)来预测相同群体(非目标)中的剩余个体时，第二，当首先定义测试集(TS)并对其进行基因分型时，然后针对TS(目标)对TRS进行优化。我们的结果表明，包括来自测试集(目标)的信息的优化方法显示出最高的准确性，表明来自TS的先验信息改善了基因组预测。此外，预测能力增强，特别是当种群规模较小时，这是在育种计划中降低表型成本的目标。
Phenotyping is the current bottleneck in plant breeding, especially because next-generation sequencing has decreased genotyping cost more than 100.000 fold in the last 20 years. Therefore, the cost of phenotyping needs to be optimized within a breeding program. When designing the implementation of genomic selection scheme into the breeding cycle, breeders need to select the optimal method for (1) selecting training populations that maximize genomic prediction accuracy and (2) to reduce the cost of phenotyping while improving precision. In this article, we compared methods for selecting training populations under two scenarios: Firstly, when the objective is to select a training population set (TRS) to predict the remaining individuals from the same population (Untargeted), and secondly, when a test set (TS) is first defined and genotyped, and then the TRS is optimized specifically around the TS (Targeted). Our results show that optimization methods that include information from the test set (targeted) showed the highest accuracies, indicating that apriori information from the TS improves genomic predictions. In addition, predictive ability enhanced especially when population size was small which is a target to decrease phenotypic cost within breeding programs.