Department of Agronomy, Iowa State University, 716 Farm House Ln, Ames, IA, 50011-1051, USA.
General Biology Department, Federal University of Viçosa, Peter H Rolfs Avenue, Viçosa, MG, 36570-900, Brazil.
Plant Genome. 2020 Mar;13(1):e20014. doi: 10.1002/tpg2.20014. Epub 2020 Mar 19.
Genomic prediction (GP) might be an efficient way to improve haploid induction rate (HIR) and to reduce the laborious and time-consuming task of phenotypic selection for HIR in maize (Zea mays L.). In this study, we evaluated GP accuracies for HIR and other agronomic traits of importance to inducers by independent and cross-validation. We propose the use of GP for cross prediction and parental selection in the development of new inducer breeding populations. A panel of 159 inducers from Iowa State University (ISU set) was genotyped and phenotyped for HIR and several agronomic traits. The data of an independent set of 53 inducers evaluated by the University of Hohenheim (UOH set) was used for independent validation. The HIR ranged from 0.61 to 20.74% and exhibited high heritability (0.90). High cross-validation prediction accuracy was observed for HIR (r = 0.82), whereas for other traits it ranged from 0.36 (self-induction rate) to 0.74 (days to anthesis). Prediction accuracies across different sets were higher when the larger panel (ISU set) was used as a training population (r = 0.54). The average HIR of the 12,561 superior predicted progenies (μ ) ranged from 1.00-18.36% and was closely related to the corresponding midparent genomic estimated breeding value (GEBV). A predicted genetic variance (V ) of reduced magnitude was observed in the twenty crosses with highest midparent GEBV or μ for HIR. Our results indicate that although GP is a useful tool for parental selection, decisions about which cross combinations should be pursued need to be based on optimal trade-offs between maximizing both μ and V .
基因组预测（GP）可能是提高单倍体诱导率（HIR）的有效方法，并且可以减少对玉米（Zea mays L.）中 HIR 的表型选择的费力和耗时的任务。在这项研究中，我们通过独立和交叉验证评估了 GP 对 HIR 和其他重要农艺性状的准确性。我们建议将 GP 用于新诱导剂育种群体的交叉预测和亲本选择。爱荷华州立大学（ISU 集）的 159 个诱导剂的面板进行了基因型和表型分析，用于 HIR 和几个农艺性状。由德国霍恩海姆大学（UOH 集）评估的 53 个诱导剂的独立数据集用于独立验证。HIR 的范围为 0.61 至 20.74%，表现出高遗传力（0.90）。HIR 的交叉验证预测准确性较高（r=0.82），而其他性状的范围为 0.36（自诱导率）至 0.74（开花期）。当使用较大的面板（ISU 集）作为训练群体时，不同数据集的预测准确性更高（r=0.54）。12561 个优秀预测后代的平均 HIR（μ）范围为 1.00-18.36%，与相应的中亲基因组估计育种值（GEBV）密切相关。在 HIR 的中亲 GEBV 或μ最高的二十个杂交中，观察到预测遗传方差（V）的幅度减小。我们的结果表明，尽管 GP 是亲本选择的有用工具，但关于应该追求哪些杂交组合的决策需要基于最大化μ和 V 之间的最佳权衡。
