在果中进行综合多环境基因组预测
Michaela Jung1,2, Carles Quesada-Traver2, Morgane Roth3
1Fruit Breeding, Agroscope, Mueller-Thurgau-Strasse 29, 8820 Waedenswil, Switzerland.
Horticulture research
|March 5, 2025
概括
多环境基因组预测模型改善了不同气候的果基因型的选择. 统计和深度学习方法提高预测能力,帮助适应环境变化.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 植物育种 植物育种
背景情况:
- 基因组预测模型整合了基因和环境数据来进行基因型选择.
- 多环境基因组预测对于作物适应各种条件至关重要.
- 在果育种中的应用受到复杂的数据集和模型的限制.
研究的目的:
- 应用先进的统计和深度学习模型用于果多环境基因组预测.
- 评估基因组和环境组数据的整合,以预测11个果特征.
- 评估基因型与环境相互作用和非添加效应的影响.
主要方法:
- 使用了包含基因型与环境相互作用 (GxE) 的统计模型.
- 采用深度学习方法进行基因组预测.
- 综合基因组 (添加剂,非添加剂) 和环境数据.
- 将模型与基准进行比较 (G-BLUP).
主要成果:
- 使用GxE的统计模型对9个特征的预测能力提高了高达0.08%.
- 替代的核心 (高斯,深) 有效地取代了G-BLUP.
- 深度学习模型在三个寡原性特征中实现了最高的预测能力 (高达0.10的改进).
- 与基准值相比,非添加剂和环境效应的影响最小.
结论:
- 统计模型有效地捕捉了果中的基因型与环境相互作用.
- 深度学习模型有效地整合了多样化的基因组和环境数据.
- 这项研究促进了气候适应性果品种选择的多环境基因组预测.
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