biostudies-literatureUnknown16Garnica VCDesigning and identifying biologically meaningful weather-based predictors of plant disease is challenging due to the temporal variability of conducive conditions and interdependence of weather factors. Confounding effects of plant genotype further obscure true environmental signals within observed disease responses. To address these limitations, this study leveraged window-pane analysis with feature engineering and stability selection, to identify weather-based variables associated with latent environmental factors ( λ^ ) of a factor analytic model explaining genotype-by-environment (GEI) effects on disease severity in multi-environment trials. Using Stagonospora nodorum blotch of wheat as a case study and a two-stage feature engineering procedure, hourly weather data, i.e., air temperatuFrontiers in plant science1637130https://www.ebi.ac.uk/biostudies/studies/S-EPMC12460299biostudies-literatureLeveraging window-pane analysis with environmental factor loadings of genotype-by-environment interaction to identify high-resolution weather-based variables associated with plant disease.PMC12460299Garnica VCOjiambo PSfalseLeveraging window-pane analysis with environmental factor loadings of genotype-by-environment interaction to identify high-resolution weather-based variables associated with plant disease.Designing and identifying biologically meaningful weather-based predictors of plant disease is challenging due to the temporal variability of conducive conditions and interdependence of weather factors. Confounding effects of plant genotype further obscure true environmental signals within observed disease responses. To address these limitations, this study leveraged window-pane analysis with feature engineering and stability selection, to identify weather-based variables associated with latent environmental factors ( λ^ ) of a factor analytic model explaining genotype-by-environment (GEI) effects on disease severity in multi-environment trials. Using Stagonospora nodorum blotch of wheat as a case study and a two-stage feature engineering procedure, hourly weather data, i.e., air temperatu2025-01-01T00:00:00Z20252026-06-03T23:53:56.565Z2026-05-03T03:11:49.149ZS-EPMC124602994101972710.3389/fpls.2025.1637130