Improving genomic prediction for plant disease using environmental covariates

Authors: BRAULT, CHARLOTTE - University Of Minnesota; CONLEY, EMILY - University Of Minnesota; Read, Andrew; GREEN, ANDREW - North Dakota State University; GLOVER, KARL - South Dakota State University; COOK, JASON - Montana State University; GILL, HARSIMARDEEP - University Of Minnesota; Fiedler, Jason; ANDERSON, JAMES - University Of Minnesota

Submitted to: Plant Methods
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/5/2025
Publication Date: 8/20/2025
Citation: Brault, C., Conley, E., Read, A.C., Green, A., Glover, K., Cook, J., Gill, H.S., Fiedler, J.D., Anderson, J.A. 2025. Improving genomic prediction for plant disease using environmental covariates. Plant Methods. 21:Article 114. https://doi.org/10.1186/s13007-025-01418-0.
DOI: https://doi.org/10.1186/s13007-025-01418-0

Interpretive Summary: Fusarium head blight is a major fungal disease that poses a major threat in wheat and barley. Plant breeding to improve the genetic resistance of commercial cultivars is an important tool to defeat the disease and decrease risk for producers. In this study, we investigated the impact of environmental conditions on the disease severity in the long-running Uniform Regional Scab Nursery and evaluated formal environmental affects for its ability to improve prediction models for breeding. Exploring several different parameters for model-building, we fine-tuned a system that was able to accurately predict performance of new breeding material. Properly leverage of this public information with historical weather data increased our prediction accuracy, which further assists individual breeding programs to improve lines faster and ultimately provide producers with superior varieties that can natively combat this disease in the field.

Technical Abstract: Background Fusarium Head Blight (FHB) is a destructive fungal disease affecting wheat and barley, leading to significant yield losses and reduced grain quality. Susceptibility to FHB is influenced by genetic factors, environmental conditions, and genotype-by-environment interactions (G×E), making it challenging to predict disease resistance across diverse environments. This study investigates GxE in a multi-environment trial dataset spanning 30 years from a collaborative nursery established in 1995 to assess resistant genotypes from spring wheat breeding programs across the northern U.S. Results Traditionally, GxE has been analyzed as a reaction norm over an environment index. Here, we computed the environment index as a linear combination of environmental covariables specific to each environment, and we derived an environment relationship matrix. Three methods were compared, all aimed at predicting untested genotypes in untested environments: the widely used Finlay-Wilkinson regression (FW), the joint-genomic regression analysis (JGRA) method, and mixed models incorporating an environmental relationship matrix. These were benchmarked against a baseline genomic selection model (GS) without environmental covariates. Predictive abilities were assessed within and across environments. The results revealed that the JGRA marker effect method was more accurate than GS in within- and across-environment predictions, although the differences were small. The predictive ability slightly decreased when the target environment was less related to the training environments. Mixed models performed similarly to JGRA within-environment, but JGRA outperformed the other methods for across-environment predictions. Additionally, JGRA identified significant genetic markers associated with baseline FHB resistance and environmental sensitivity. Furthermore, location-specific genomic estimated breeding values were predicted, providing insights into genotype stability across varying locations. Conclusion These findings highlight the value of incorporating environmental covariates to increase predictive ability and improve the selection of resistant genotypes for diverse, untested environments. By leveraging this approach, breeders can effectively exploit G×E interactions to improve disease management at no additional cost.