Plant Genome. 2019 Mar;12(1). doi: 10.3835/plantgenome2018.05.0025.
Maize ( L.) kernel oil provides high-quality nutrition for animal feed and human health. A certain number of maize breeding programs seek to enhance oil concentration and composition. Genomic selection (GS), which entails selection based on genomic estimated breeding values (GEBVs), has proven to be efficient in breeding programs. Here, we estimate the robustness of predictions for the oil traits of maize kernels in biparental recombination inbred lines (RILs) using a GS model built based on an association population. Most statistical models, including ridge regression-best linear unbiased prediction (RR-BLUP), showed high prediction accuracy in the training population through a cross validation procedure. The training population size was more important than marker density and a statistical model for prediction performance. Using the optimized GS model, prediction of the biparental RIL population showed medium-high prediction accuracy (0.68) compared with prediction using only oil associated markers ( = 0.43). The potential to apply the GS model to another RIL population that is genetically less related to the training population was also examined, showing promising prediction accuracy in the top selected lines. Our results proved that genomic prediction using existing data is robust for the prediction of polygenic traits with moderate to high heritability.
玉米（L.）仁油为动物饲料和人类健康提供高质量的营养。许多玉米育种计划旨在提高油浓度和组成。基于基因组估计育种值（GEBVs）的选择的基因组选择（GS）已被证明在育种计划中是有效的。在这里，我们使用基于关联群体构建的 GS 模型来估计双亲和重组自交系（RILs）中玉米仁油性状的预测稳健性。大多数统计模型，包括岭回归最佳线性无偏预测（RR-BLUP），通过交叉验证程序在训练群体中显示出较高的预测准确性。训练群体大小比标记密度和统计模型对预测性能更重要。使用优化的 GS 模型，与仅使用油相关标记的预测（ = 0.43）相比，对双亲和 RIL 群体的预测显示出中等至较高的预测准确性（0.68）。还检查了将 GS 模型应用于与训练群体遗传关系较弱的另一个 RIL 群体的潜力，在顶级选择的线路中显示出有希望的预测准确性。我们的结果证明，使用现有数据进行基因组预测对于预测具有中等到高度遗传力的多基因性状是稳健的。
