稀少的测试设计,以优化资源分配在多环境麻育种试验
Nelson Lubanga1, Beatrice E Ifie1, Reyna Persa2
1Insitute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK.
The plant genome
|February 6, 2025
概括
在子育种中稀少的测试可以降低多环境试验 (METs) 的表型化成本. 实施基因型与环境相互作用 (G × E) 的模型可以提高预测能力,这表明培训需要更少的重叠基因型.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 遗传学 是一个遗传学.
背景情况:
- 开发改进的作物品种需要多环境试验 (METs) 来评估不同条件下的基因型性能.
- 在MET中,高的表型化成本限制了在所有目标环境中对众多基因型的评估.
研究的目的:
- 调查麻瓜育种计划中稀疏测试策略的有效性,以减少MET的表型化费用.
- 评估结合基因组数据和基因型与环境相互作用 (G × E) 的预测模型,以优化稀疏测试设计.
主要方法:
- 利用了435种麻基因型的种群,在尼日利亚的五个环境中评估了干物质和新鲜根产量.
- 开发基于非重叠 (NOL) 和完全重叠 (OL) 基因型分配的稀疏测试设计.
- 评估了三种预测模型:一种仅包含表型,两种包含基因组数据,有或没有G × E建模.
主要成果:
- 所有模型都表现出更高的预测能力和更低的平均平方误差 (MSE) 与更大的训练数据集.
- 模拟G × E显著提高了给定训练集大小的预测能力和减少了MSE.
- 增加的OL基因型导致预测能力下降和MSE增加,表明需要在训练套件中最小的OL基因型.
结论:
- 稀少的测试,特别是在纳入G × E时,提供了一种可行的方法来降低木MET中的表型化成本.
- 在稀疏测试中优化培训人群的规模和分布可以提高预测能力和成本效益.
- 将作物生长模型 (CGM) 与基因组预测相结合,为进一步提高预测准确性提供了未来的潜力.
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