Improving genomic prediction for plant disease using environmental covariates

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.