Harnessing Multi-Omics and Predictive Modeling for Climate-Resilient Crop Breeding: From Genomes to Fields

Area of Science:

• Plant Science
• Agricultural Science
• Computational Biology

Background:

• Climate change poses significant threats to global agriculture, necessitating climate-resilient crop varieties.
• Multi-omics technologies (genomics, transcriptomics, proteomics, metabolomics, phenomics) enhance understanding of plant stress responses.
• Advanced predictive modeling, including machine learning, aids in genotype-to-phenotype predictions.

Purpose of the Study:

• To review strategies for integrating multi-omics data for crop resilience.
• To examine the role of digital phenotyping and environmental data in breeding.
• To discuss challenges and future directions in multi-omics-guided crop improvement.

Main Methods:

• Comprehensive literature review of multi-omics data integration strategies.
• Analysis of computational tools and frameworks for data harmonization.
• Examination of digital phenotyping and environmental data contributions.

Main Results:

• Multi-omics integration with predictive modeling accelerates breeding for stress tolerance.
• Digital phenotyping and environmental data are crucial for dissecting genotype-by-environment interactions.
• Identified technical, biological, and ethical challenges in data integration and sharing.

Conclusions:

• Multi-omics-guided breeding offers transformative potential for developing climate-resilient crops.
• Addressing challenges in data harmonization, interpretability, and collaboration is key.
• Scalable infrastructures and equitable data sharing are vital for diverse agroecological contexts.