Journal Article

Leveraging historical trials to predict Fusarium head blight resistance in spring wheat breeding programs

Fiedler, Jason D., Gill, Harsimardeep S., Conley, Emily J., Anderson, James A., Cook, Jason P., Read, Andrew C., Glover, Karl D., Green, Andrew J., Brault, Charlotte

2025

Overview

Fusarium head blight (FHB) is a fungal disease posing a major threat to wheat production. Plant breeding that leverages genotyping is an effective method to improve the genetic resistance of cultivars. Started in 1995, the uniform regional scab nursery (URSN) consists of germplasm from several public breeding programs in the Northern US region. Its main objective is to showcase new sources of resistance and enable germplasm exchange among the cooperators; however, the data from the URSN have not been studied. Phenotypic and genotypic data from this nursery were gathered, as well as from two current breeding programs in the US Midwest. Genomic prediction on eight traits related to FHB and agronomic traits was applied, and the effects of statistical method, marker density, training set size, genetic structure, and genetic architecture of the trait were studied. Using the URSN population, reproducing kernel Hilbert space was the best method in various prediction settings, with an average accuracy of 0.63, marker density could be as low as 500 without decreasing the prediction accuracy, and training set optimization was useful for two traits. Furthermore, genotypic values were predicted in breeding programs using the URSN population as a training set with various prediction scenarios. Predicting unrelated populations led to a significant decrease in accuracy but with encouraging values for some traits and populations. Ultimately, when progressively decreasing the number of lines from breeding populations in the training set, the advantage of adding the URSN population was more pronounced, with an increase in accuracy up to 0.19. Core Ideas Genomic predictive ability ranged from 0.49 to 0.72 for predicting Fusarium head blight (FHB) and agronomical traits in wheat. Training set size had more impact on accuracy than marker density, which could be reduced to between 500 and 1000 markers. Training set optimization with a sparse selection index increased the accuracy of genomic prediction for two traits. Adding an unrelated population in the training set allows a reduction in phenotyping effort. Plain Language Summary Genomic prediction was used as a tool to improve the genetic resistance of spring wheat to Fusarium head blight, using data from a historical nursery and from two current breeding programs. Parameters related to genomic predictive ability were thoroughly studied. Predictive ability within the historical nursery was medium to high for all the eight traits studied and all methods gave similar results. Marker density did not affect predictive ability compared to training set size. Training set optimization had mixed results depending on the trait. We tested several prediction scenarios useful in a breeding context by harnessing the historical dataset in the training set for predicting breeding lines. While the lack of genetic relatedness decreased the accuracy of genomic prediction, we showed that breeding programs could benefit from these historical data by incorporating their information into training models, thus reducing the phenotyping effort.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
• Subject: Accuracy / Barley / Cultivars / Disease resistance / Disease Resistance - genetics / fungi / Fusarium / Fusarium - pathogenicity / Fusarium - physiology / Fusarium head blight / genetic resistance / Genetic structure / genome / genomics / Genotype / Genotyping / Germplasm / germplasm exchange / Midwestern United States / Original / Phenotype / Plant Breeding / Plant Diseases - genetics / Plant Diseases - microbiology / Polymorphism / prediction / Predictions / Special Section: Tribute to Ron Phillips: Crop Genetics, Genomics and Biotechnology / spring wheat / Statistical analysis / Triticum - genetics / Triticum - microbiology