Harnessing Multi-Omics and Predictive Modeling for Climate-Resilient Crop Breeding: From Genomes to Fields
Adnan Amin, Wajid Zaman, SeonJoo Park
Abstract
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.