基因组预测的可移植性在两种玉米料育种周期的稀疏因数设计上训练
Alizarine Lorenzi1,2, Cyril Bauland1, Sophie Pin1
1Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France.
概括
基因组选择使用稀疏的因数训练集有效地预测混合性能. 更新这些集跨世代显著提高了玉米育种计划的遗传收益.
科学领域:
- 植物育种 植物育种
- 基因组学就是基因组学.
- 定量遗传学 是一种定量遗传学.
背景情况:
- 基因组选择 (GS) 提供了一个强大的替代方案,以传统的表型在杂交育种.
- 为基因组预测模型优化训练集设计仍然是一个关键的挑战.
- 稀少的因数设计显示出在一代内预测单交叉杂交的前景.
研究的目的:
- 调查因数设计的优化,以预测线条通用组合能力 (GCAs) 和跨繁殖周期的混合值.
- 在玉米育种计划中评估更新基因组预测模型跨代的策略.
主要方法:
- 使用了双周期玉米相互基因组选择方案与多父母种群.
- 使用稀疏因数训练集来预测GCA和混合值.
- 使用选择的单交叉混合体或随机子集更新训练集的测试策略.
主要成果:
- 从因数设计中基于基因组预测的选择,实现了干物质产量的显著遗传收益.
- 稀少的因数训练集被证明是有效的预测线路GCAs和杂交价值跨繁殖周期.
- 更新新一代混合动力或选定的交叉训练集提高了预测能力.
结论:
- 稀少的因数设计是有效的校准混合基因组预测模型.
- 更新培训套件反复地提高了跨育种世代的预测准确性.
- 该CDmean标准有助于有效地选择个人进行培训设置更新.
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