Fusarium head blight (FHB) caused by Fusarium graminearum, is a serious disease of wheat (Triticum aestivum L.) causing yield and quality losses. Inadequate disease control strategies make breeding for FHB resistant wheat varieties a favorable approach. The aim of this study was to evaluate the potential of genomic selection (GS) in predicting FHB response and improving wheat resistance to the disease. A Canadian Winter Wheat Diversity Panel (CWWDP; n=450) was genotyped using the Illumina iSelect wheat 90K SNP beadchip, from which ~8K polymorphic markers were used for various analyses. The panel was phenotyped for FHB related traits, such as plant height, days to anthesis, disease incidence, disease severity, FHB Index, fusarium damaged kernels (FDK),and deoxynivalenol content, in FHB nurseries at Elora and Ottawa, Ontario in2016, 2017 and 2018. Population structure analysis identified seven subpopulations in the panel, which were differentiated predominantly by market class. Variance of uncorroborated prediction accuracy from multiple runs of GS modelling for a FHB related trait was used to optimise the size of the training population and testing panel. Three quarters of the panel genotypes in the training set and one quarter in the testing set consistently produced the lowest variance and thus this ratio of training to testing set size was used for GS modelling. Uncorroborated prediction accuracies for the two models, BayesCPi and RKHS, did not differ significantly for different FHB related traits. Prediction accuracies ranged from 0.6 for plant height, and days to anthesis to0.4 for FDK, and FHB Index. GS models yielded higher prediction accuracies for high heritability traits than for the quantitative traits with lower heritability estimates. GS modelling within the subpopulations was performed to investigate changes in prediction accuracies. For a highly quantitative trait as FHB response in wheat, GS modelling provides opportunity to account in background genetic effects better than other association analyses. Even with low prediction accuracy of FHB related traits, the wheat breeding programs can utilize GS to remove undesirable genotypes at an early breeding stage.