The fungus Sclerotinia sclerotiorum infects hundreds of plant species including many crops. Resistance to this pathogen in canola (Brassica napus L. subsp. napus) is controlled by numerous quantitative trait loci (QTL). For such polygenic traits, genomic prediction may be useful for breeding as it can capture many QTL at once while also considering nonadditive genetic effects. Here, we test application of common regression models to genomic prediction of S. sclerotiorum resistance in canola in a diverse panel of 218 plants genotyped at 24,634 loci. Disease resistance was scored by infection with an aggressive isolate and monitoring over 3 wk. We found that including first-order additive × additive epistasis in linear mixed models (LMMs) improved accuracy of breeding value estimation between 3 and 40%, depending on method of assessment, and correlation between phenotypes and predicted total genetic values by 14%. Bayesian models performed similarly to or worse than genomic relationship matrix-based models for estimating breeding values or overall phenotypes from genetic values. Bayesian ridge regression, which is most similar to the genomic relationship matrix-based approach in the amount of shrinkage it applies to marker effects, was the most accurate of this family of models. This confirms several studies Abbreviations: AGG, Australian Grains Genebank; ASSYST, Associative expression and systems analysis of complex traits in oilseed rape/canola; AUDPC, area under the disease progress curve; BLUP, best linear unbiased predictor; DPI, days post inoculation; G-BLUP, genomic best linear unbiased predictor; GEBV, genomic estimated breeding value; h, square root of narrow-sense heritability; H 2 , broad-sense heritability; h 2 , narrow-sense heritability; LMM, linear mixed model; MCMC, Markov chain Monte Carlo; PSCL, proportion of soft or collapsed lesions; QTL, quantitative trait loci; RR-BLUP, ridge regression best linear unbiased predictor; SNP, single nucleotide polymorphism. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.