利用多态学和预测建模来实现适应气候变化的作物育种:从基因组到田地
Adnan Amin1, Wajid Zaman1, SeonJoo Park1
1Department of Life Sciences, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Genes
|July 29, 2025
概括
气候变化威胁到农业,需要适应性强的作物. 与预测建模相结合的多omics数据加快了育种,提高了植物应激耐受性和农业可持续性.
科学领域:
- 植物科学 植物科学
- 农业科学 农业科学
- 计算生物学 计算生物学
背景情况:
- 气候变化对全球农业构成重大威胁,需要适应气候的作物品种.
- 多种omics技术 (基因组学,转录组学,蛋白质组学,代谢组学,现象组学) 提高对植物应激反应的理解.
- 先进的预测建模,包括机器学习,有助于基因型到表型的预测.
研究的目的:
- 审查整合农作物弹性多学科数据的战略.
- 检查数字表型和环境数据在育种中的作用.
- 讨论多omics引导作物改进的挑战和未来方向.
主要方法:
- 关于多主题数据整合策略的综合文献综述.
- 对数据协调的计算工具和框架的分析.
- 检查数字表型和环境数据贡献.
主要成果:
- 多omics与预测建模的整合加快了对压力耐受性的繁殖.
- 数字表型和环境数据对于剖析基因型与环境相互作用至关重要.
- 在数据集成和共享方面确定了技术,生物和伦理方面的挑战.
结论:
- 多种OMIC指导的育种为开发适应气候变化的作物提供了变革性的潜力.
- 解决数据协调,可解释性和协作方面的挑战是关键.
- 可扩展的基础设施和公平的数据共享对于各种农业生态环境至关重要.
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