Indirect selection for resistance to ear rot and leaf diseases in maize lines using biplots.

Leaf disease and ear rot have caused reductions in maize yield in Brazil and other producer countries. Therefore, the aims of this study were to analyze the association between husked ear yield and the severity of maize white spot, gray leaf spot, helminthosporium, and ear rot caused by Fusarium verticillioides and Diplodia maydis using biplots in a mixed-model approach. The responses of 238 lines introduced to Brazil and four controls were evaluated using an incomplete block design with three replicates in two locations: Lavras and Uberlândia, Minas Gerais, Brazil. Two experiments were conducted in each location, one with F. verticillioides and the other with D. maydis. The mixed models elucidated the relationship between yield, leaf disease, and ear disease. Significant genotype x environment and genotype x pathogen interactions were observed. In conclusion, husked ear yield is more associated with ear rot than with the leaf diseases evaluated, justifying the indirect selection for resistance to kernel rot in maize-F. verticillioides and maize-D. maydis pathosystems by yield evaluation.

Genome-wide prediction of maize single-cross performance, considering non-additive genetic effects.

The prediction of single-cross hybrids in maize is a promising technique for optimizing the use of financial resources in a breeding program. This study aimed to evaluate Genomic Best Linear Unbiased Predictors models for hybrid prediction and compare them with the Bayesian Ridge Regression, Bayes A, Bayesian LASSO, Bayes C, Bayes B, and Reproducing Kernel Hilbert Spaces Regression models, with inclusion or absence of non-additive effects under three heritability scenarios. Data from a maize germplasm bank belonging to USDA were used to determine the effects of molecular markers, which were considered to be parametric, to build 400 single-cross hybrids between two line groups via simulation. The following parameters were used to compare the models: predictive ability, estimation of variance components, heritability of genetic effects present in all situations, and the sum of squares of the predicted errors. The models responded positively when dominance effects were included in non-additive models, with all models tending to show an increase in the values of heritability parameters under all scenarios. Differences occur between models depending on the heritability range considered. Estimates of additive and dominant effects were better than estimates of epistatic effects. Estimates increased in accuracy for all models when non-additive effects for maize cob weight were considered.