精确的基因组预测谷物产量和玉米杂交的谷物水分含量,使用多环境数据
Jingxin Wang1,2, Liwei Liu3,4, Kunhui He1,2
1State Key Laboratory of Crop Gene Resources and Breeding, National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Journal of integrative plant biology
|February 17, 2025
概括
结合基因型与环境相互作用和气候数据的基因组预测模型显著提高了玉米谷物水分和产量的预测准确性. 这种方法通过提高多环境试验中的选择精度来加速作物育种.
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:
- 基因组预测 (GP) 模型可以加速作物育种,但将基因型对环境 (GE) 相互作用效应与多环境气候数据相结合仍未得到充分探索.
- 对玉米的谷物水分含量 (GMC) 和谷物产量 (GY) 等特征的准确预测对于在各种环境中运行的育种计划至关重要.
研究的目的:
- 评估基因组最佳线性无偏预测 (GBLUP) 模型的有效性,这些模型包含了GE相互作用效应,使用多环境气候数据来预测玉米混合动力性能.
- 确定影响GMC和GY的关键气候因素,并评估在GE相互作用模型中减少计算负担的方法.
主要方法:
- 在两年内,在34个环境中使用了19个气候因素对玉米杂交进行了跨区域的GP研究.
- 基因组最佳线性无偏预测 (GBLUP) 模型进行了比较,包括GBLUP-GE19CF (包含19个气候因素),GBLUP-GE9CF (九个关键因素) 和GBLUP-GEPCA (主要组成部分).
- 用10倍交叉验证和一致性分析验证了预测.
主要成果:
- 与传统的GBLUP或BayesB模型相比,在完整数据集上训练的GBLUP-GE19CF模型实现了更高的预测准确度 (GMC为0.731,GY为0.331).
- 将气候数据减少到九个关键因素或九个主要组件 (GBLUP-GE9CF,GBLUP-GEPCA) 产生了类似的预测准确度,同时降低了计算负载.
- 增加的培训环境提高了预测准确性,一致性分析证实了GBLUP-GE19CF模型的可靠性.
结论:
- 将转基因相互作用效应与相关气候数据相结合,显著提高了玉米GMC和GY的基因组预测准确性.
- 简化气候数据 (关键因素或主要组件) 可以保持预测准确性并降低计算成本.
- 这些发现为优化多环境选择的玉米育种计划中的GP模型提供了实用策略.
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