Effective evaluation of millions of crop genetic stocks is an essential component of exploiting genetic diversity to achieve global food security. By leveraging genomics and data analytics, genomic prediction is a promising strategy to efficiently explore the potential of these gene banks by starting with phenotyping a small designed subset. Reliable genomic predictions have enhanced selection of many macroscopic phenotypes in plants and animals. However, the use of genomicprediction strategies for analysis of microscopic phenotypes is limited. Here, we exploited the power of genomic prediction for eight maize traits related to the shoot apical meristem (SAM), the microscopic stem cell niche that generates all the above‐ground organs of the plant. With 435 713 genomewide single‐nucleotide polymorphisms (SNPs), we predicted SAM morphology traits for 2687 diverse maize inbreds based on a model trained from 369 inbreds. An empirical validation experiment with 488 inbreds obtained a prediction accuracy of 0.37–0.57 across eight traits. In addition, we show that a significantly higher prediction accuracy was achieved by leveraging the U value (upper bound for reliability) that quantifies the genomic relationships of the validation set with the training set. Our findings suggest that double selection considering both prediction and reliability can be implemented in choosing selection candidates for phenotyping when exploring new diversity is desired. In this case, individuals with less extreme predicted values and moderate reliability values can be considered. Our study expands the turbocharging gene banks via genomic prediction from the macrophenotypes into the microphenotypic space.

中文翻译：

---

玉米微表型的基因组预测为优化选择和挖掘多样性提供了见识。

有效评估数百万种作物遗传资源是利用遗传多样性实现全球粮食安全的重要组成部分。通过利用基因组学和数据分析，基因组预测是一种有前途的策略，可以通过对一个小的设计子集进行表型化来有效地探索这些基因库的潜力。可靠的基因组预测增强了动植物中许多宏观表型的选择。但是，使用基因组预测策略分析微观表型是有限的。在这里，我们利用基因组预测功能对与芽顶分生组织（SAM）相关的八个玉米性状进行了研究，芽顶分生组织是产生植物所有地上器官的微观干细胞生态位。有了435713个全基因组范围的单核苷酸多态性（SNP），我们基于369个自交系训练的模型，预测了2687个不同玉米自交系的SAM形态特征。488个自交系的实证验证实验对八个性状的预测准确度为0.37–0.57。此外，我们表明，通过利用U值（可靠性的上限），用于量化验证集与训练集的基因组关系。我们的发现表明，在探索新的多样性时，可以同时考虑预测和可靠性的双重选择可以在选择表型的候选选择中实现。在这种情况下，可以考虑具有极低的预测值和中等可靠性值的个人。我们的研究通过从大表型到微表型空间的基因组预测扩展了涡轮增压基因库。