在豆中耐的基因组预测
Salvador Osuna-Caballero1, Diego Rubiales1, Paolo Annicchiarico2
1Institute for Sustainable Agriculture, Spanish National Research Council (CSIC), Cordoba, Spain.
Frontiers in plant science
|August 7, 2024
概括
基因组选择有效地预测了豆类生耐药性. 将特征与FAI-BLUP模型结合起来,并考虑标记物与环境的相互作用,可以提高植物育种中复杂特征的预测准确度.
科学领域:
- 植物遗传学和育种.
- 基因组选择应用程序
- 农作物疾病耐药性 农作物疾病耐药性
背景情况:
- 在植物育种中,基因组选择 (GS) 对于复杂的特征至关重要.
- 豆 (Uromyces pisi) 耐药性是一种复杂的特征,需要先进的育种策略.
- 了解基因型与环境相互作用 (GEI) 对于准确的耐药性预测至关重要.
研究的目的:
- 评估基因组选择模型,以预测豆抗性.
- 评估标记物×环境 (M×E) 相互作用对预测准确性的影响.
- 为了比较单特征与多特征指数方法,用于复杂的特征预测.
主要方法:
- 使用了320个孔子接入和26,045个-DART标记.
- 采用基因组最佳线性无偏预测 (GBLUP) 模型,包括那些具有M×E相互作用的模型.
- 对比了各种交叉验证策略,并评估了像FAI-BLUP这样的特征指数方法.
主要成果:
- GBLUP模型,特别是与M×E相互作用的模型,显示出对耐性具有卓越的预测能力.
- 在贝叶斯拉索 (BL) 模型中的FAI-BLUP方法实现了最高的预测能力 (0.635).
- 在不同的环境中,M×E相互作用显著提高了预测准确性,但对于非表型的线条而言却没有.
结论:
- 基因组选择,特别是多特征FAI-BLUP和M×E相互作用建模,对于豆耐蚀性繁殖是有效的.
- GEI显著影响预测能力,突出了对强大的模型的需求.
- 先进的GS策略增强了抗病豆品种的发展.
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