Goss's bacterial wilt and leaf blight is one of the most important foliar diseases of maize ( L.). To date, neither large-effect resistance genes, nor practical chemical controls exist to manage the disease. Thus, the importance of discovering durable host resistance necessitates additional genetic mapping for this disease. Unfortunately, because of the biology of the pathogen and the highly significant genotype-by-environment interaction effect observed with Goss's wilt, consistent phenotyping across multiple years poses a hurdle for genetic studies and conventional breeding methods. The objective of this study was to perform a genome-wide association study (GWAS) to identify regions of the genome associated with Goss's wilt resistance as well as to use genomic prediction models to evaluate the utility of genomic selection (GS) in predicting Goss's wilt phenotypes in a panel of diverse maize lines. Using genome-wide association mapping, we were unable to identify any variants significantly associated with Goss's wilt. However, using genomic prediction we were able to train a model with an accuracy of 0.69. Taken together, this suggests that resistance to Goss's wilt is highly polygenic. In addition, when evaluating the accuracy of our prediction model under reduced marker density, it was shown that only 10,000 single nucleotide polymorphisms (SNPs), or ∼20% of our total marker set, was necessary to achieve prediction accuracies similar to the full marker set. This is the first report of genomic prediction for a bacterial disease of maize, and these results highlight the potential of GS for disease resistance in maize.
© 2019 The Author(s).