Location: Cereal Crops Improvement Research
Project Number: 3060-21000-046-044-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 5, 2025
End Date: Aug 31, 2027
Objective:
Fusarium head blight (FHB) or scab is a major disease of spring wheat, causing yield loss and end-use quality degradation. Most of the currently grown cultivars in the Upper Midwest are rated as susceptible or moderately susceptible, requiring fungicide spraying during flowering to limit the damage. The most economical way of combating the disease is to grow varieties with genetic resistance. Molecular breeding tools such as marker-assisted selection (MAS) and genomic selection (GS) are promising methods used to predict disease resistance and performance to accelerate the development of commercially viable FHB resistant spring wheat cultivars. A challenge with these methods are that predicted trait metrics need to be generated rapidly every year to inform selection/crossing decisions in the next generation, usually in off-season nurseries over the winter. The overall goals of the project are to 1) improve currently used molecular breeding schemes and 2) annually provide on-time predicted trait metrics to all the northern US spring wheat breeding programs. Not all the programs are at the same stage of usage of these tools, and this project will provide infrastructure to ensure that breeders are supported through the development and deployment of these tools.
Approach:
The USDA-ARS Uniform Regional Nursery (URN) has been a long-running experiment where advanced breeding germplasm is evaluated in many sites throughout the spring wheat growing region. The four main spring wheat breeders manage the URN locations in their regions, and each location evaluates 10-20 public entries per year. This data, along with per-program information is submitted to the genomic selection coordinator supported by this project for combined analysis to identify trait-associated markers and develop models for trait prediction. These initial models have shown great progress but also highlight areas for improvement.
For the first year of this project, we will identify genomic predictors on the mid-density USDA-3K genotyping array that exhibit poor technical performance and develop add-in content to fill in missing linkage block and MAS markers. Additionally, we will look to improve the models by incorporating environmental effects along with genotyping data and investigate the usefulness of the major known disease-resistance gene regions and track genetic gain in the historic trials.