探索现象和基因组选择对草产量和水果质量特征的有效性
Joshua A Sleper1, Caiwang Zheng2, Liyike Ji1,3
1Plant Breeding Graduate Program, Horticultural Sciences Department, IFAS Gulf Coast Research and Education Center, University of Florida, Wimauma, Florida, USA.
The plant genome
|November 21, 2025
概括
草育种中的现象选择显示出预测产量的前景,在季节内优于基因组选择. 结合现象和基因组数据可以改善跨季节的预测,突出特征,数据捕获时间和复制的重要性.
科学领域:
- 植物育种 植物育种
- 农业科学 农业科学
- 基因组学和现象学
背景情况:
- 现象选择 (PS) 使用现象数据来预测基因型,但它的有效性与基因组选择 (GS) 和最佳应用策略相比需要进一步调查.
- 草 (Fragaria × ananassa Duchesne) 繁殖可以从先进的选择方法中获益,以提高产量和水果质量特征.
研究的目的:
- 将现象选择 (PS) 与草中的基因组选择 (GS) 的预测能力进行比较.
- 评估不同数据类型 (光谱带,植被指数) 和数据采集时间对PS疗效的影响.
- 为了确定强大的PS模型的克隆复制品的最佳数量.
主要方法:
- 在四个季节中对1122个草克隆的多谱成像.
- 开发和比较PS和GS模型的产量和水果质量特征.
- 分析包含植被指数 (VI) 和综合现象基因组数据的模型.
- 对单个时间点与每周数据的评估以及克隆复制的影响.
主要成果:
- 季节内的PS对产量更有预测性,而GS对水果质量更好.
- 使用植被指数 (VI) 的现象模型比仅使用光谱带的模型有效16%.
- 综合现象基因组模型实现了56-57%的季节性果实大小和产量预测能力.
- 单个时间点数据捕获了每周数据的预测能力的91%,这取决于时间点.
- 在训练组中更多的克隆复制品时,PS的预测能力显著增加.
结论:
- 现象选择是草育种中的一个有价值的工具,特别是用于产量预测.
- 模型性能受到特定特征,数据捕获时间和克隆复制的程度的影响.
- 整合现象和基因组数据为跨季特征预测提供了最强大的方法.
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