Genomic Prediction for Resistance to Fusarium Ear Rot and Fumonisin Contamination in Maize

Data and R scripts used for "Genomic Prediction for Resistance to Fusarium Ear Rot and Fumonisin Contamination in Maize" by Holland, Marino, Manching, and Wisser. Published in Crop Science (2020). Fusarium ear rot (FER) disease of maize (Zea mays L.) is caused by Fusarium verticillioides (Sacc.) Nirenberg, which produces fumonisin (FUM), a mycotoxin linked to human and animal health risks. Extensive field trials, laborious inoculations and ear evaluations, and expensive antibody assays are required to reliably assess resistances to FER and FUM contamination in breeding populations. To evaluate the potential utility of genomic selection to improve FER and FUM in maize, we genotyped 6086 single nucleotide polymorphisms (SNPs) on 449 S0:1 lines from a recurrent selection population. Two different partitions of the S0:1 evaluation data were made to test the ability of models trained on 251 or 201 lines evaluated at three locations in 2014-15 to predict FER and FUM of 198 or 248 different lines evaluated at three locations in 2016. Single-stage univariate and multivariate GBLUP models and two-stage GBLUP, Bayes C pi, Bayesian LASSO, and extreme gradient boosting models were compared for prediction. Maximum prediction accuracy for untested lines in a new year was 0.46 for FER and 0.67 for FUM. Bayesian models optimized for predicting traits influenced by major effect loci were best for FUM in one set, despite no evidence for significant individual SNP-trait associations from genome-wide association study in the training sets; otherwise GBLUP models were best. These results suggest that genomic selection can help improve resistance to FER and FUM contamination in an applied breeding program. This data supplement includes the raw trait data, pre- and post-filtered SNP data, and R codes used to conduct analyses.