Maize (Zea mays 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 (r = 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.

中文翻译：

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利用现有历史数据进行双亲种群选择的基因组预测：玉米籽油研究。

玉米（玉米）L.）仁油为动物饲料和人类健康提供高质量的营养。一定数量的玉米育种计划试图提高油的浓度和组成。基因组选择（GS）需要基于基因组估计育种值（GEBV）进行选择，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模型估计双亲重组自交系（RIL）中玉米籽粒油性状预测的鲁棒性。大多数统计模型，包括岭回归-最佳线性无偏预测（RR-BLUP），都通过交叉验证程序在训练人群中显示出较高的预测准确性。训练人口规模比标记密度和预测性能统计模型更为重要。使用优化的GS模型，r = 0.43）。还研究了将GS模型应用于另一种与训练人群在遗传上关系不大的RIL人群中的潜力，在最上面选择的系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中等至高遗传力的多基因性状的预测是可靠的。