PANOMICS meets germplasm.

Area of Science:

• Plant breeding
• Genomics
• Systems Biology

Background:

• Genotyping-by-sequencing (GBS) enables genomic selection for crop improvement, with large datasets of genotypes and SNPs revealing extensive intraspecific genetic variation.
• Phenotypic plasticity arises not only from genetic variation but also from complex environmental interactions influencing gene regulation at multiple molecular levels.

Purpose of the Study:

• To propose a framework for future breeding strategies by integrating genomic selection with multi-omics analysis and deep learning.
• To enhance the understanding of environment-dependent phenotypic plasticity.
• To accelerate crop functional validation and precision breeding.

Main Methods:

• Combining genome selection with environment-dependent Pan-omics analysis and deep learning for improved prediction accuracy.
• Utilizing Pan-omics resolution at subtissue, cellular, and subcellular levels to understand marker functions.
• Integrating Pan-omics data with genome editing and speed breeding tools.

Main Results:

• The proposed framework aims to improve prediction accuracy for marker-dependent trait performance.
• Pan-omics analysis provides insights into the fundamental functions of genetic markers.
• Integration of Pan-omics with genome editing and speed breeding accelerates functional validation.

Conclusions:

• Future breeding strategies require integrating genomic selection with comprehensive, environment-aware Pan-omics data and advanced computational approaches like deep learning.
• Understanding gene regulation across molecular levels is essential for dissecting phenotypic plasticity.
• Accelerated functional validation through combined Pan-omics, genome editing, and speed breeding will drive precision agriculture.