Technical Abstract: Fusarium head blight (FHB) resistance is quantitative and difficult to evaluate. Genomic selection (GS) could accelerate FHB resistance breeding. We used US cooperative FHB wheat nursery data to evaluate GS models for several FHB resistance traits including deoxynivalenol (DON) levels. For all traits we compared the models: ridge regression (RR), Bayesian LASSO (BL), reproducing kernel Hilbert spaces (RKHS) regression, random forest (RF) regression, and multiple linear regression (fixed effects; MLR). For DON, we evaluated additional prediction methods including bivariate RR models, phenotypes for correlated traits, and RF regression models combining markers and correlated phenotypes as predictors. Additionally, for all traits, we compared different marker sets including: genome-wide markers, FHB quantitative trait loci (QTL) targeted markers, and both sets combined. GS accuracies were always higher than MLR accuracies, RF and RKHS regression were often the most accurate methods, and for DON marker + trait RF regression was more accurate than all other methods. For all traits except DON, using QTL targeted markers alone led to lower accuracies than using genome-wide markers. This study indicates that cooperative FHB nursery data can be useful for GS, and prior information about correlated traits and QTL could be utilized to improve accuracies in some cases.