Dep. of Statistics, Oklahoma State Univ., 301 MSCS, Stillwater, OK, 74078.
Dep. of Plant and Soil Sciences, Oklahoma State Univ., 371 Agriculture Hall, Stillwater, OK, 74078.
Plant Genome. 2019 Nov;12(3):1-15. doi: 10.3835/plantgenome2018.11.0090.
Prediction performance for winter wheat grain yield and end-use quality traits. Prediction accuracies evaluated by cross-validations are significantly overestimated. Nonparametric algorithms outperform the parametric alternatives in cross-year predictions. Strategically designing training population improves response to selection. Response to selection varies across growing seasons and environments. Considering the practicality of applying genomic selection (GS) in the line development stage of a hard red winter (HRW) wheat (Triticum aestivum L.) variety development program (VDP), the effectiveness of GS was evaluated by prediction accuracy and by the response to selection across field seasons that demonstrated challenges for crop improvement under significant climate variability. Important breeding targets for wheat improvement in the southern Great Plains of the United States, including grain yield, kernel weight, wheat protein content, and sodium dodecyl sulfate (SDS) sedimentation volume as a rapid test for predicting bread-making quality, were used to estimate the effectiveness of GS across harvest years from 2014 (drought) to 2016 (normal). In general, nonparametric algorithms reproducing kernel Hilbert space (RKHS) and random forest (RF) produced higher accuracies in both same-year cross-validations (CVs) and cross-year predictions for the purpose of line selection. Further, the stability of GS performance was greatest for SDS sedimentation volume but least for wheat protein content. To ensure long-term genetic gain, our study on selection response suggested that across this sample of environmental variability, and though there are cases where phenotypic selection (PS) might be still preferred, training conducted under drought or in suboptimal conditions could provide an encouraging prediction outcome when selection decisions were made in normal conditions. However, it is not advisable to use training information collected from a normal season to predict trait performance under drought conditions. Finally, the superiority of response to selection was most evident if the training population (TP) can be optimized.
冬小麦籽粒产量和用途品质性状的预测表现。通过交叉验证评估的预测精度存在显著高估。非参数算法在跨年度预测中优于参数替代算法。策略性地设计训练群体可提高对选择的响应。对选择的响应因生长季节和环境而异。考虑到基因组选择 (GS) 在硬红冬 (HRW) 小麦 (Triticum aestivum L.) 品种开发计划 (VDP) 的系开发阶段的实际应用，通过预测精度和跨田间季节的选择响应来评估 GS 的有效性，这些田间季节表现出在显著气候变异性下进行作物改良的挑战。美国大平原南部小麦改良的重要育种目标，包括籽粒产量、千粒重、小麦蛋白质含量和十二烷基硫酸钠 (SDS) 沉淀体积，作为预测面包制作品质的快速测试方法，用于估计 2014 年 (干旱) 至 2016 年 (正常) 收获年的 GS 有效性。一般来说，用于系选择的非参数算法再现核希尔伯特空间 (RKHS) 和随机森林 (RF) 在同年交叉验证 (CV) 和跨年度预测中都产生了更高的精度。此外，GS 性能的稳定性对 SDS 沉淀体积最大，但对小麦蛋白质含量最小。为了确保长期遗传增益，我们对选择响应的研究表明，在这种环境变异性样本中，尽管有时表型选择 (PS) 可能仍然更受欢迎，但在干旱或次优条件下进行的训练可以在正常条件下做出选择决策时提供令人鼓舞的预测结果。但是，不建议使用从正常季节收集的训练信息来预测干旱条件下的性状表现。最后，如果可以优化训练群体 (TP)，则对选择的响应优势最为明显。
