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Improving genomic prediction for plant disease using environmental covariates

Brault, Charlotte1; Conley, Emily1; Read, Andrew2; Green, Andrew3; Glover, Karl4; Cook, Jason5; Gill, Harsimardeep1; Anderson, James1; Fiedler, Jason6

Published Aug 16, 2025 on Dryad. https://doi.org/10.5061/dryad.wstqjq2z0

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Aug 16, 2025 version files 219.94 MB

Abstract

Fusarium head blight (FHB) is a devastating fungal disease affecting wheat and barley, with susceptibility influenced by genotype, environment, and genotype-by-environment interactions (GxE). 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. 

Traditionally, GxE has been analyzed as a reaction norm over an environmental index. Here, we computed the environment index as a linear combination of environmental covariates 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.

These findings highlight the value of incorporating environmental covariates to increase predictive ability and improve the selection of resistant genotypes for diverse, untested environments.