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包括标记物x环境相互作用改善了红三叶草 (Trifolium pratense L.) 的基因组预测
Leif Skøt1, Michelle M Nay2, Christoph Grieder2
1Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom.
Frontiers in plant science
|June 25, 2024
概括
包括标志物x环境 (MxE) 相互作用在内的基因组预测模型提高了红三叶草繁殖的准确性. 在预测诸如干物质产量等重要特征方面,MxE模型的表现优于单一环境和跨环境模型.
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.
- 农业科学 农业科学
背景情况:
- 基因组预测通常忽视基因型x环境相互作用,限制其在多环境试验中的应用.
- 标记物x环境 (MxE) 相互作用模型已经出现,以更好地捕捉基因型x环境效应.
研究的目的:
- 评估红三叶草 (Trifolium pratense L.) 中不同基因组预测模型的有效性.
- 将单个环境,跨环境和MxE交互模型进行比较,以预测产量和质量特征.
- 评估人口结构对预测准确性的影响.
主要方法:
- 在EUCLEG项目中,在五个欧洲地点进行了实地试验.
- 我们比较了三个基因组预测模型:SingleEnv,AcrossEnv和MxE.
- 预测能力 (PA) 被评估为年度干物质产量 (DMY),原蛋白 (CP) 和开花日期 (DOF).
主要成果:
- 与SingleEnv和AcrossEnv模型相比,MxE模型在各个特征中表现出更好的预测能力.
- 干物质产量 (DMY) 显示出最高的预测能力,PA范围从0.40到0.87.
- 粗蛋白 (CP) 的预测很差,而开花日期 (DOF) 的预测是中度至高的PAs (0.67-0.87).
- 人口结构影响了预测能力,特别是对于DMY和DOF.
结论:
- 结合标志物x环境相互作用显著提高了红三叶草多环境试验中的基因组预测准确性.
- MxE模型为识别稳定和环境特异性的遗传标记提供了一个有希望的方法.
- 这些发现对优化育种策略和加速料作物的遗传收益有影响.
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