Peixoto Marco Antônio, Leach Kristen A, Jarquin Diego, Flannery Patrick, Zystro Jared, Tracy William F, Bhering Leonardo, Resende Márcio F R
Laboratório de Biometria, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States.
Front Plant Sci. 2024 Apr 25;15:1293307. doi: 10.3389/fpls.2024.1293307. eCollection 2024.
Sweet corn breeding programs, like field corn, focus on the development of elite inbred lines to produce commercial hybrids. For this reason, genomic selection models can help the prediction of hybrid crosses from the elite lines, which is hypothesized to improve the test cross scheme, leading to higher genetic gain in a breeding program. This study aimed to explore the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction in a within-site across-year and across-site framework. A total of 506 hybrids were evaluated in six environments (California, Florida, and Wisconsin, in the years 2020 and 2021). A total of 20 traits from three different groups were measured (plant-, ear-, and flavor-related traits) across the six environments. Eight statistical models were considered for prediction, as the combination of two genomic prediction models (GBLUP and RKHS) with two different kernels (additive and additive + dominance), and in a single- and multi-trait framework. Also, three different cross-validation schemes were tested (CV1, CV0, and CV00). The different models were then compared based on the correlation between the estimated breeding values/total genetic values and phenotypic measurements. Overall, heritabilities and correlations varied among the traits. The models implemented showed good accuracies for trait prediction. The GBLUP implementation outperformed RKHS in all cross-validation schemes and models. Models with additive plus dominance kernels presented a slight improvement over the models with only additive kernels for some of the models examined. In addition, models for within-site across-year and across-site performed better in the CV0 than the CV00 scheme, on average. Hence, GBLUP should be considered as a standard model for sweet corn hybrid prediction. In addition, we found that the implementation of genomic prediction in a sweet corn breeding program presented reliable results, which can improve the testcross stage by identifying the top candidates that will reach advanced field-testing stages.
甜玉米育种计划与大田玉米一样，专注于培育优良自交系以生产商业杂交种。因此，基因组选择模型有助于预测优良自交系的杂交组合，据推测这可以改进测交方案，从而在育种计划中获得更高的遗传增益。本研究旨在通过在同一地点跨年和跨地点框架下的杂交预测，探索在甜玉米育种计划中实施基因组选择的潜力。在六个环境（2020年和2021年的加利福尼亚州、佛罗里达州和威斯康星州）中对总共506个杂交种进行了评估。在这六个环境中测量了来自三个不同组的总共20个性状（与植株、果穗和风味相关的性状）。考虑了八种统计模型用于预测，即两种基因组预测模型（GBLUP和RKHS）与两种不同核函数（加性和加性 + 显性）的组合，并在单性状和多性状框架下。此外，测试了三种不同的交叉验证方案（CV1、CV0和CV00）。然后根据估计育种值/总遗传值与表型测量值之间的相关性对不同模型进行比较。总体而言，性状间的遗传力和相关性各不相同。所实施的模型对性状预测显示出良好的准确性。在所有交叉验证方案和模型中，GBLUP的实施效果优于RKHS。对于一些所研究的模型，具有加性加显性核函数的模型比仅具有加性核函数的模型略有改进。此外，同一地点跨年和跨地点的模型在CV0方案中的平均表现优于CV00方案。因此，GBLUP应被视为甜玉米杂交预测的标准模型。此外，我们发现甜玉米育种计划中基因组预测的实施呈现出可靠的结果，这可以通过识别将进入高级田间测试阶段的顶级候选品种来改进测交阶段。
