Location: Cereal Crops Improvement Research
Project Number: 3060-21000-046-046-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jun 1, 2026
End Date: May 31, 2028
Objective:
The purpose of this research is to drive a transformative advance in genomic, epigenomic, and translational breeding for small grains, focusing on wheat, barley, and oat. The project seeks to fortify U.S. cereal crop resilience, accelerate genetic gain, and deliver breeder-ready decision-support systems that are worthy of the next generation of American agriculture. This effort will capitalize on the explosive growth of AI, machine learning, and computational biology. We aim to develop centralized analytics and harmonized data resources that allow breeders to evaluate their germplasm with unprecedented precision. Core emphases include breeder-oriented solutions, genomics of disease resistance, and epigenetic discovery pipelines for key cereal crops.
Approach:
Cereal crops are entering a period of heightened vulnerability—facing intensifying disease pressure (crown rust, stem rust, viruses, root-rot pathogens) and major abiotic constraints (drought, heat, salinity). The U.S. wheat, barley, and oat sectors urgently need genomics-enabled precision breeding to stay competitive and secure a healthier agricultural future. This project will operate as a major pillar of the Oat Data Analytics Core (ODAC), a stakeholder-driven innovation hub dedicated to data harmonization, multi-omics integration, and breeder-focused analytics. ODAC will unify sequencing, expression, diversity, and pangenomics datasets from cereals to deliver a stable, interoperable digital infrastructure for the breeding community. The approach is three pronged that ensures data analytics, gene discovery and epigenetic variation.
Decision-Support and Multi-Omics Data Integration for cereals small grains: Through this project, we will forge proactive partnerships with plant breeders, in the cereal community to translate unmet needs into technical specifications and work collaboratively with AI and other computational specialists to create the required functionalities in a harmonized way across all the programs. We will provide breeder solutions through the prediction models integrating SNPs, haplotypes, epigenetic marks, and stress-response features. These models will interface with T3, Breeding Insight, and custom USDA-ARS pipelines to generate custom solutions for the breeders and stakeholders. The marginalized data pipelines and multi-omics datasets (for example pan-genome and pan-transcriptome) will be integrated at scale. We will be extending the oat-developed frameworks wherever genetic architecture and datasets allow. The long-term goal is to develop the digital ecosystem to support rapid cultivar improvement.
Epigenome Atlas and Stress-Responsive Epigenomic Framework: We will produce a comprehensive epigenome atlas elite lines, breeding parents, and diversity panels across small grain cereals oats, barley and wheat predominantly. The comprehensive landscape will be strengthened through the use Combining DNA methylation, expression, and chromatin accessibility will allow us to identify master regulators, stress-responsive regulatory elements, and epigenetically modulated resistance loci. This resource will position the U.S. small grains community at the global forefront of epigenomic breeding innovation.
Cross-Species Immune Receptor Atlas in Wheat, Barley, and Oat: We will characterize disease-resistance genes with a special focus on NLR and TIR-domain evolution. The key components include mapping cereal specific expansions/contractions of immune receptor families, structural-variant analysis around NLR clusters, integrating long-read pangenomes for NLR discovery and characterization and using genomics-assisted introgression to deploy stress-tolerance genes into breeding materials.