Amin Adnan, Zaman Wajid, Park SeonJoo
Department of Life Sciences, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Genes (Basel). 2025 Jul 10;16(7):809. doi: 10.3390/genes16070809.
The escalating impacts of climate change pose significant threats to global agriculture, necessitating a rapid development of climate-resilient crop varieties. The integration of multi-omics technologies-such as genomics, transcriptomics, proteomics, metabolomics, and phenomics-has revolutionized our understanding of the intricate molecular networks that govern plant stress responses. Coupled with advanced predictive modeling approaches such as machine learning, deep learning, and multi-omics-assisted genomic selection, these integrated frameworks enable accurate genotype-to-phenotype predictions that accelerate breeding for augmented stress tolerance. This review comprehensively synthesizes the current strategies for multi-omics data integration, highlighting computational tools, conceptual frameworks, and challenges in harmonizing heterogeneous datasets. We examine the contribution of digital phenotyping platforms and environmental data in dissecting genotype-by-environment interactions critical for climate adaptation resilience. Further, we discuss technical, biological, and ethical challenges, encompassing computational bottlenecks, trait complexity, data standardization, and equitable data sharing. Finally, we outline future directions that prioritize scalable infrastructures, interpretability, and collaborative platforms to facilitate the deployment of multi-omics-guided breeding in diverse agroecological contexts. This integrative approach possesses transformative potential for the development of resilient crops, ensuring agricultural sustainability amidst increasing environmental volatility.
气候变化影响的不断升级对全球农业构成了重大威胁，因此需要迅速培育适应气候变化的作物品种。基因组学、转录组学、蛋白质组学、代谢组学和表型组学等多组学技术的整合，彻底改变了我们对控制植物应激反应的复杂分子网络的理解。再加上机器学习、深度学习和多组学辅助基因组选择等先进的预测建模方法，这些整合框架能够实现从基因型到表型的准确预测，从而加速培育具有更强抗逆性的品种。本综述全面综合了当前多组学数据整合的策略，重点介绍了计算工具、概念框架以及协调异构数据集方面的挑战。我们研究了数字表型平台和环境数据在剖析对气候适应复原力至关重要的基因型与环境相互作用方面的贡献。此外，我们还讨论了技术、生物学和伦理方面的挑战，包括计算瓶颈、性状复杂性、数据标准化和公平数据共享。最后，我们概述了未来的发展方向，重点是可扩展的基础设施、可解释性和协作平台，以促进在不同农业生态环境中部署多组学指导的育种。这种综合方法对培育抗逆作物具有变革潜力，可确保在环境波动加剧的情况下实现农业可持续发展。
