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Updated: Feb 10, 2026

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走向超越基因型数据的整体表型预测

Abdulqader Jighly^1,2, Reem Joukhadar^1,2, Rajeev K Varshney³

¹Qingdao Agricultural University, Qingdao, Shandong Province, P.R. China.

Journal of experimental botany

|February 8, 2026

概括

基因组选择 (GS) 使用遗传数据预测特征,但整合不同的数据类型可以显著提高准确性. 本综述探讨了五种策略,以提高表型预测超越仅仅基因组学.

科学领域:

植物和动物育种植物和动物育种
遗传学是一个遗传学.
生物信息学是一种生物信息学.

背景情况:

基因组选择 (GS) 通过从遗传数据中预测表型来彻底改变育种.
当前的GS模型仅解释了观察到的现型变异的一小部分.
需要整合不同的数据类型,以提高预测准确度.

研究的目的:

审查和分类将非基因组数据整合到基因组选择中的策略.
探索那些超越遗传信息,增强表型预测的方法.
提供多重数据集成在育种中的全面概述.

主要方法:

将数据整合策略分为五种类型:消除,促进,聚合,整合和调节.
审查利用环境,表型和其他生物数据的方法.
讨论先进的建模技术,包括深度学习 (例如,CNN).

主要成果:

五种不同的数据整合策略为表型预测提供了不同的好处.
促进,聚合,整合和调节方法显示出改善GS准确性的前景.
显式建模交互和转换数据用于高级模型是关键方法.

结论:

多数据表型预测为理解复杂的生物系统提供了一个整体的方法.
整合不同的数据类型可以显著提高育种计划中的预测准确性.
未来的研究应该专注于开发综合性预测模型,将基因组学和其他数据源结合起来.

关键词:

人工智能的人工智能是人工智能.农作物生长模型的模型环境类型设计基因组选择基因组选择基因型根据环境相互作用的基因型多个omics的多个omics.

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