优化单步模型,以预测玉米杂交品种中富蒙尼辛抗性,并考虑基因型对环境的相互作用
Jeniffer Santana Pinto Coelho Evangelista1,2, Kaio Olimpo das Graças Dias2, Maria Marta Pastina3
1Agronomy Department, University of Florida, Gainesville, FL, United States.
Frontiers in genetics
|July 17, 2025
概括
使用B矩阵进行基因组选择,可以更好地预测热带玉米中富蒙尼辛污染的情况. 这种方法通过结合基因组和血统数据来提高准确性,特别是与更大的培训集.
科学领域:
- 植物育种 植物育种
- 遗传学 是一个遗传学.
- 农业科学 农业科学
背景情况:
- 疾病的爆发,比如虫污染 * Fusarium verticillioides *,是热带农业的重大挑战.
- 富蒙尼辛 (FUMO) 菌毒素对人类和动物健康构成风险,需要针对耐药品种采取有效的育种策略.
- 基因与环境相互作用 (G × E) 复杂化了诸如FUMO污染等多基因特征的遗传控制.
研究的目的:
- 评估单步B矩阵模型对预测热带玉米中富蒙尼辛污染的有效性.
- 用基因组,血统和组合数据比较模型的预测能力,包括一般组合能力 (GCA) 和特定组合能力 (SCA) 效应.
- 在不平衡的数据集中优化超参数选择以进行交叉验证,以提高预测准确度.
主要方法:
- 应用单步B矩阵方法,整合基因组和血统数据,用于预测建模.
- 开发和应用交叉验证策略,以优化标记衍生的方差分数 (w).
- 在两个交叉验证场景中对13个预测模型进行评估 (CV1:未经测试的混合物,CV2:部分测试的混合物).
主要成果:
- 与仅使用基因组或血统数据相比,B矩阵方法在五种线性模型中始终提高了预测能力.
- 较大的训练集尺寸在CV1 (未经测试的混合体) 下显示出更高的预测准确性.
- 在CV2 (部分测试的混合体) 下,预测准确度的提高主要归因于优异的协差结构,而不是训练集大小.
结论:
- B矩阵模型提供了一种可靠的方法,用于预测热带玉米育种计划中的富莫尼辛污染.
- 优化超参数选择 (w) 对于最大限度地提高预测准确性至关重要,特别是在不平衡的数据集中.
- 这种综合方法为旨在管理GCA,SCA和G × E相互作用的育种计划提供了有价值的见解.
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