在解决特征基因组对现象 (G2P) 维度问题上的育种观点,使用基于集体的基因组预测
Mark Cooper1,2, Shunichiro Tomura3,4, Melanie J Wilkinson3,4
1Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia. mark.cooper@uq.edu.au.
概括
基于预测的育种从多样化的基因组对基因组 (G2P) 模型中受益,特别是在使用合奏时. 这种方法通过考虑复杂的特征架构和繁殖背景来增强遗传收益.
科学领域:
- 遗传学和植物育种 植物育种
- 计算生物学 计算生物学
- 农业科学 农业科学
背景情况:
- 全基因组预测 (WGP) 方法受到特征基因组对基因组 (G2P) 维度和繁殖背景的影响.
- "没有免费午餐"理论表明,没有单一的WGP方法是普遍最佳的.
- 了解复杂的特征遗传结构对于推进育种方法和加速遗传收益至关重要.
研究的目的:
- 在WGP的整体框架内研究各种G2P模型的应用.
- 探索人工智能和机器学习 (AI-ML) 如何增强基于预测的育种的G2P模型多样性.
- 为了证明混合作物生长模型-G2P (CGM-G2P) 模型如何改善基于集合的预测和选择轨迹.
主要方法:
- 使用各种G2P模型的集合来捕捉特征遗传架构.
- 整合AI-ML算法来引入新的G2P模型多样性.
- 开发和应用混合CGM-G2P模型,将作物生长模拟与G2P预测相结合.
- 设计多环境试验,通过环境相互作用暴露特征.
主要成果:
- 多种G2P模型的集合提供了一个框架来研究WGP的特征遗传架构.
- 人工智能-ML算法为基于集合的WGP提供了有价值的多样性.
- 混合CGM-G2P模型提供了基于集体的增强预测和对收益率表现的理解的潜力.
- 多环境试验可以揭示有影响力的CGM-G2P维度用于选择.
结论:
- 使用各种G2P模型的集体基预测,包括AI-ML和混合CGM-G2P方法,为基于预测的育种中的遗传收益提供了新的途径.
- 通过这些方法更深入地了解特征遗传结构,可以加速作物改进.
- 玉米TEONAM实验作为利用G2P合奏来改进基于预测的育种策略的模型.
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