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利用人工智能和机器学习来识别与作物种子质量特征相关的定量特征位置 (QTL)

My Abdelmajid Kassem¹

¹Plant Genomics and Bioinformatics Lab, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA.

Plants (Basel, Switzerland)

|June 13, 2025

概括

人工智能 (AI) 和机器学习 (ML) 正在彻底改变种子质量的定量特征位置 (QTL) 映射. 这些先进的方法改善了特征预测和基因发现,加速了作物育种,以获得更好的产量和营养.

科学领域:

遗传学和基因组学遗传学和基因组学
植物育种植物育种
计算生物学计算生物学

背景情况:

种子的质量特征对于作物生产率和营养价值至关重要.
传统的定量特征位点 (QTL) 绘制方法面临复杂的遗传架构和环境相互作用的挑战.
人工智能 (AI) 和机器学习 (ML) 为特征预测和标记特征关联提供了先进的解决方案.

研究的目的:

在QTL映射和种子特征预测中提供AI/ML应用的综合概述.
突出关键的AI/ML方法及其在各种作物物种中的有效性.
探索多omics数据的集成,以提高QTL分辨率.

主要方法:

审查AI/ML技术,包括LASSO回归,随机森林,梯度提升,弹性网,卷积神经网络 (CNN) 和图形神经网络 (GNN).
使用ML模型对大豆种子矿物营养积累的案例研究.
探索人工智能驱动的基因组学,转录组学,代谢组学和现象学数据的整合.

主要成果:

人工智能/ML模型有效地识别了与种子特征相关的显著单核酸多态 (SNP),如在大豆中所示.
与大豆中的基于树的方法相比,LASSO和ElasticNet模型显示出更高的预测准确性.
AI/ML已经成功地在小麦,生菜,大米和棉花等各种作物中增强了QTL检测.

结论:

人工智能/ML显著提高了QTL映射和种子特征预测的准确性和效率.
将多学科数据与AI/ML集成,在遗传分析中提供更高分辨率.
人工智能具有加速基因组辅助育种和开发改进的作物品种的变革潜力.

关键词:

在 QTL 映射中使用 QTL 地图.人工智能的人工智能是人工智能.深度学习是一种深度学习.功能选择功能选择基因组预测基因组预测机器学习是机器学习.多主题整合多主题整合.现象学现象学现象学种子质量的种子质量.

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