Genomic prediction for resistance to Fusarium ear rot and fumonisin contamination in maize

Abstract

AbstractFusarium 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 (GS) 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 2014–2015 to predict FER and FUM of 198 or 248 different lines evaluated at three locations in 2016. Single‐stage univariate and multivariate genomic best linear unbiased predictor (GBLUP) models and two‐stage GBLUP, Bayes Cπ, 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 (GWAS) in the training sets; otherwise, GBLUP models were best. These results suggest that GS can help improve resistance to FER and FUM contamination in an applied breeding program.