Multi-environment trials of crop plants are typically interpreted using genotype means across all or a subset of locations.  Other methods of interpreting genotype by environment interaction have been proposed that better model the interaction and genotype performance over environments than cell means alone.  Some GXE analyses such as AMMI and factor analysis discern patterns in the variation itself.   Others such as the Eberhart-Russell model rank environment performance based on genotype scores.  Our objective is to improve the information derived from the massive dataset generated by the Washington Wheat Variety trials (<15 locations per year) to better target varieties to specific environments and to predict breeding values of those varieties. Our objective was to conduct GXE analysis using methods described above and to incorporate additional information about environments (climate variables) and about gentoypes (pedigree data) into those analyses.  Methods include analysis of data using traditional ANOVA over environments, Eberhart-Russel stability analysis, AMMI, and factor analysis.  Prediction of BLUE scores for each gentoype will be based on genotype means and pedigree relationships.  Climate variables will be incorporated as covariates and through analysis using function analysis.