Location: Plant, Soil and Nutrition Research
Project Number: 8062-21000-052-019-A
Project Type: Cooperative Agreement
Start Date: Sep 1, 2025
End Date: Aug 31, 2026
Objective:
The proposed work is a joint effort to develop genomic and bioinformatics approaches to accelerate the breeding of crops and to discover genetic mechanisms across the grain and fruit crops that can be used to enhance the disease resistance, stress tolerance, efficiency, quality and yield.
The three goals are:
1. Leverage knowledge developed in crops and model systems to create functionally based machine learning and genomic selection models that work across all US crops.
a. Develop transferable genomics models that predict gene expression and protein activities machine learning calibrated to genomic data.
2. Identify genes and regulation patterns that will enable grain and fruit production to be more nutritious, sustainable, and higher yielding.
a. Develop systems for plant breeders and geneticists to deploy haplotype and gene activity models across species.
b. Identify the genes and regulatory patterns used by grain and fruit to be more productive and control nutritional qualities.
3. Identify the genes, markers, germplasm, and treatments to reduce plant disease severity, block insect transmission and increase tolerance to abiotic stresses.
a. Develop grain and fruit crop germplasm with improved resistance to diseases and tolerance to abiotic stresses.
b. Develop treatments and biotechnology tools for grain and fruit crops to manage diseases and abiotic stresses.
Approach:
1. Genomic and phenomic datasets will be generated for grain and fruits under a range of biotic and abiotic conditions and tissues. These will be integrated with and compared to other plant datasets to train machine learning models that accurately predict RNA expression and protein levels.
2. Software systems will be developed to efficiently share genomic haplotypes and inferences of gene activity levels using machine learning and AI to plant geneticists and breeders. This will involve developing databases (e.g., PHG – Practical Haplotype Graph, T3 – The Triticeae Toolbox), algorithms (e.g., TASSEL, GeneCAD, PlantCAD), and breeder/ geneticist accessible software libraries (e.g., rPHG, rTASSEL).
3. Breeding lines, edited plants and field-deployable biotechnology tools will be developed combining disease resistance, blocked insect transmission and/or stress tolerance with agronomic and quality traits. Test the efficacy of treatments (such as double-stranded RNAs and small, secreted peptides) aimed at reducing disease severity and increasing abiotic stress tolerance. This will include biotechnology approaches, high-throughput phenotyping, QTL analysis, and rapid cycle plant breeding.