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利用自动机器学习进行环境数据驱动的基因分析和玉米杂交品种的基因组预测

Kunhui He^1,2, Tingxi Yu^1,2, Shang Gao^1,2

¹State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, Beijing, 100081, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|March 6, 2025

概括

这项研究开发了一个自动机器学习框架,集成环境和基因组数据,以改善玉米的育种. 结合环境参数和特征相关标记物,提高了适应气候品种的基因组预测准确度.

科学领域:

植物遗传学和育种.
农业科学农业科学
计算生物学是一种计算生物学.

背景情况:

基因型,环境和基因型对环境 (G×E) 相互作用显著影响作物表型.
准确的基因分析和基因组预测对于开发改进的作物品种至关重要,尤其是在不断变化的环境条件下.

研究的目的:

构建和验证一个自动化的机器学习框架,整合环境和基因组数据,以进行增强的玉米遗传分析和基因组预测.
识别与表型可塑性和环境稳定性相关的遗传标记.
评估环境因素对作物表型的影响.

主要方法:

利用了一个大规模的,多环境的杂交玉米数据集.
开发的减少维度的环境参数 (RD_EPs) 与发展阶段保持一致.
进行了全基因组关联研究,以确定表型可塑性 (PP-TAM),环境稳定性 (主要TAM) 和G×E相互作用 (G×E-TAM) 的特征关联标记 (TAM).
训练有素的基因组预测模型,包括TAM和RD_EP.

主要成果:

鉴定了539个PP-TAM,223个主要TAM和92个G×E-TAM,表明了表型可塑性和G×E相互作用的独特遗传基础.
建立了RD_EPs和特征之间的线性关系,量化了环境对表型的影响.
与TAM和RD_EP训练的基因组预测模型显示,与全基因组标记方法相比,预测准确度增加了14.02%至28.42%.

结论:

环境数据集成显著改善了玉米的遗传分析和基因组选择精度.
开发的机器学习框架为识别适应气候的玉米品种提供了可扩展的方法.
了解G×E相互作用是培育适应各种环境的适应性作物的关键.

关键词:

环境数据环境数据基因分析遗传分析基因组选择基因组选择基因型与环境的相互作用机器学习是机器学习.

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