玉米谷物产量的表型可塑性:对环境梯度反应的遗传和环境见解
Fatma Ozair1, Alper Adak2, Seth C Murray2
1Interdisciplinary Graduate Program in Genetics and Genomics (Department of Biochemistry and Biophysics), Texas A&M University, College Station, Texas, USA.
The plant genome
|August 8, 2025
概括
了解基因型与环境之间的相互作用是作物改进的关键. 这项研究使用光热比率 (PTR) 预测玉米产量稳定性,发现它对基因组预测模型有效.
科学领域:
- 植物遗传学 植物遗传学
- 农业科学 农业科学
- 生物信息学是一种生物信息学.
背景情况:
- 基因型与环境 (G × E) 相互作用对于理解复杂特征的表型变异至关重要,特别是在作物改进中.
- 平均回归模型和环境指数值有助于量化表型可塑性和跨环境梯度的G × E趋势.
- 考虑特征稳定性可以改善全基因组关联研究 (GWAS) 和基因组预测 (GP) 模型,特别是在大型数据集中.
研究的目的:
- 用光热比率 (PTR) 指数分析玉米中混合谷物产量 (GY) 的G × E相互作用.
- 为了确定与GY和GY稳定性相关的定量特征位置 (QTLs).
- 评估一个修改后的基因组预测模型,包括PTR估计的反应规范参数,以提高效率和预测能力.
主要方法:
- 在29个不同的环境中分析了来自全国混合玉米基因组到田间项目的现场数据.
- 从混合谷物产量 (GY) 的PTR指数中获得反应规范参数.
- 利用10万个单核酸多态 (SNPs) 进行QTL发现,并实施修改后的回归-对-平均的GP模型.
主要成果:
- 与GY最相关的PTR时间窗口因测试者而异,并因地区而异.
- 发现了GY的96个QTL和GY稳定的6个QTL.
- 使用PTR参数修改的GP模型几乎与传统模型一样准确,但192倍更快,更好地预测未经测试的环境性能.
结论:
- 结合PTR等环境措施可以提高玉米的多环境基因组预测.
- 基于PTR的GP模型提供了一种更有效的方法来剖析跨环境的基因型性能.
- 这种方法有望通过更准确的基因组选择加速作物改进.
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