Sandhu Karansher S, Mihalyov Paul D, Lewien Megan J, Pumphrey Michael O, Carter Arron H
Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States.
Dewey Scientific, Pullman, WA, United States.
Front Plant Sci. 2021 Feb 12;12:613300. doi: 10.3389/fpls.2021.613300. eCollection 2021.
Genomics and high throughput phenomics have the potential to revolutionize the field of wheat ( L.) breeding. Genomic selection (GS) has been used for predicting various quantitative traits in wheat, especially grain yield. However, there are few GS studies for grain protein content (GPC), which is a crucial quality determinant. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. The objectives of this research were to compare performance of single and multi-trait GS models for predicting GPC and grain yield in wheat and to identify optimal growth stages for collecting secondary traits. We used 650 recombinant inbred lines from a spring wheat nested association mapping (NAM) population. The population was phenotyped over 3 years (2014-2016), and spectral information was collected at heading and grain filling stages. The ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12% for GPC and 20% for grain yield by including secondary traits in the models. Spectral information collected at heading was superior for predicting GPC, whereas grain yield was more accurately predicted during the grain filling stage. Green normalized difference vegetation index had the largest effect on the prediction of GPC either used individually or with multiple indices in the GS models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.
基因组学和高通量表型组学有潜力彻底改变小麦育种领域。基因组选择（GS）已被用于预测小麦的各种数量性状，尤其是籽粒产量。然而，针对籽粒蛋白质含量（GPC）这一关键品质决定因素的GS研究较少。在GS模型中纳入二级相关性状已被证明可提高准确性。本研究的目的是比较单性状和多性状GS模型预测小麦GPC和籽粒产量的性能，并确定收集二级性状的最佳生长阶段。我们使用了来自春小麦巢式关联作图（NAM）群体的650个重组自交系。该群体在3年（2014 - 2016年）间进行了表型分析，并在抽穗期和灌浆期收集了光谱信息。使用二级性状、单变量、协变量和多变量GS模型对周期内和跨周期预测中GPC和籽粒产量的预测能力进行了评估。我们的结果表明，通过在模型中纳入二级性状，GPC的GS准确性平均提高了12%，籽粒产量提高了20%。抽穗期收集的光谱信息对预测GPC更优，而灌浆期对籽粒产量的预测更准确。绿色归一化差值植被指数在GS模型中单独使用或与多个指数一起使用时，对GPC预测的影响最大。纳入二级性状提高了GPC和籽粒产量的预测能力，这表明在小麦育种中提高单位时间和成本的遗传增益具有潜力。
