A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. Application to a large winter wheat dataset. Development of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection.
