在使用随机回归模型对纵向干物质产量数据进行多年料育种试验中评估基因型的适应性和稳定性
Claudio Carlos Fernandes Filho1, Sanzio Carvalho Lima Barrios2, Mateus Figueiredo Santos2
1Sugarcane Technology Center, Piracicaba, SP 13400-970, Brazil.
G3 (Bethesda, Md.)
|February 14, 2025
概括
本研究引入了一种随机回归模型 (RRM),用于选择使用纵向干物质产量数据的多年料基因型. RRM有效地分析季节性表现,帮助育种者选择适合不同环境的优质品种.
科学领域:
- 植物育种 植物育种
- 定量遗传学 是一种定量遗传学.
- 料农业学是一种料农业学.
背景情况:
- 永久性料干物质产量 (DMY) 的基因型选择依赖于纵向数据,捕获时间趋势对于跨季度识别高性能品种至关重要.
- 了解基因型特定的性能随着时间的推移对于食物种成功的育种计划至关重要.
研究的目的:
- 介绍和评估一种随机回归模型 (RRM) 方法,用于根据纵向DMY数据选择基因型.
- 建议使用RRM衍生反应规范来估计适应性和稳定性的方法.
- 评估RRM在多个繁殖试验和多年生物种中分析基因型表现的实用性.
主要方法:
- 将随机回归模型 (RRM) 应用于三种多年生植物的10项育种试验的纵向DMY数据:草,草和草.
- 使用曲线下的面积 (AUC) 估计的基因型适应性和使用反应规范变化系数 (CV) 的稳定性.
- 分析了RRM近似的协差结构,确定了一个自回归模式.
主要成果:
- 随机回归模型 (RRM) 始终将纵向DMY数据的 (共差) 结构与自回归模式相近.
- RRM提供了关于基因型季节性和多年料繁殖中的性能趋势的宝贵见解.
- 反应规范的解释使得育种者能够根据其特定的季节性反应选择基因型.
结论:
- 随机回归模型 (RRM) 是推用于分析长度特征在多年料繁殖试验.
- RRM通过提供解释反应规范和了解季节性表现的框架来促进基因型选择.
- 拟议的适应性和稳定性估计方法增强了用于料遗传改进的RRM方法.
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