Selection of maize inbred lines and gene expression for resistance to ear rot.

In recent years, there has been a large incidence of fungi causing "ear rot" in maize in Brazil, the main fungus being Fusarium verticillioides. The most efficient and competitive alternative for control of this disease consists of using maize hybrids resistant to this pathogen. Thus, the aims of this study were to analyze the genetic variability of maize inbred lines in regard to resistance to ear rot to observe if there is a maternal effect to resistance to ear rot, to study genetic control of the traits evaluated in hybrids originating from inbred lines of the maize breeding program at the Agriculture Department of Universidade Federal de Lavras (Lavras, MG, Brazil), and characterize the gene expression pattern related to the plant defense mechanism against F. verticillioides. High genetic availability was observed for resistance to this disease among the inbred lines evaluated. Considering combined diallel analysis, it was observed that the mean square of general combining ability (GCA) was not significant for the characteristic under study. However, specific combining ability (SCA) was significant, which indicates the predominance of non-additive effects involved in control of the characteristic for the population evaluated. A maternal effect was not observed for the characteristic of ear rot resistance in this study. Inbred lines 22, 58, and 91 showed potential for use in breeding programs aiming at resistance to F. verticillioides. Only two genes, LOX8 and Hsp82, had a satisfactory result that was able to be related to a plant defense mechanism when there is ear rot infection, though expression of these genes was observed in only one susceptible genotype. Thus, the genes LOX8 and Hsp82 are potential molecular markers for selection of maize inbred lines resistant to F. verticillioides.

Maize hybrid stability in environments under water restriction using mixed models and factor analysis.

In several crops, the water deficit is perhaps the main limiting factor in the search for high yields. The objective of this study was to evaluate the phenotypic stability of maize hybrids in environments with and without water restriction using the analytical factor (AF) approach. We evaluated 171 maize hybrids in 14 environments, divided into environments with (A1, A2, A3, A4, A5, A6, and A7) and without (A8, A9, A10, A11, A12, A13, and A14) water restriction, over a period of 7 years. Each year, 36 hybrids were evaluated. A square lattice design (6 x 6) was used, with common treatments between years. The characteristics of grain yield (GY), male flowering (MF) and female flowering (FF), plant height (PH), and ear height (EH) were evaluated. Phenotypic adaptability and stability of the hybrids were also verified. Hybrids G66, G99, G86, and G26 were the most stable and showed potential for use in environments with and without water restriction. The AF models showed to be useful for evaluating hybrids over many years, allowing selection of better hybrids with adaptability, specific and general stability, and correlation of hybrids with their production components, in addition to allowing identification of mega-environments that permit stability in the response of the adapted hybrids.

Assessing non-additive effects in GBLUP model.

Understanding non-additive effects in the expression of quantitative traits is very important in genotype selection, especially in species where the commercial products are clones or hybrids. The use of molecular markers has allowed the study of non-additive genetic effects on a genomic level, in addition to a better understanding of its importance in quantitative traits. Thus, the purpose of this study was to evaluate the behavior of the GBLUP model in different genetic models and relationship matrices and their influence on the estimates of genetic parameters. We used real data of the circumference at breast height in Eucalyptus spp and simulated data from a population of F2. Three commonly reported kinship structures in the literature were adopted. The simulation results showed that the inclusion of epistatic kinship improved prediction estimates of genomic breeding values. However, the non-additive effects were not accurately recovered. The Fisher information matrix for real dataset showed high collinearity in estimates of additive, dominant, and epistatic variance, causing no gain in the prediction of the unobserved data and convergence problems. Estimates presented differences of genetic parameters and correlations considering the different kinship structures. Our results show that the inclusion of non-additive effects can improve the predictive ability or even the prediction of additive effects. However, the high distortions observed in the variance estimates when the Hardy-Weinberg equilibrium assumption is violated due to the presence of selection or inbreeding can converge at zero gains in models that consider epistasis in genomic kinship.

Genetic progress estimation strategy for upright common bean plants using recurrent selection.

Common bean producers in Brazil tend to grow plants as upright as possible. Because the control of this trait involves a large number of genes, recurrent selection (RS) is the best approach for successful plant improvement. Because plant architecture (PA) is evaluated using scores and usually has high heritability, RS for PA is performed through visual selection in generation S0. The aim of the present study was to evaluate selection progress and investigate whether this progress varies with the number of selected progenies or the generation evaluated. In addition, the effect of RS for the upright (PA) trait on progeny grain yield (GY) was investigated. Data of progenies S0:3 and S0:4 of the fifth, eighth, and twelfth cycles were used. A combined analysis of variance was performed using the adjusted means of the 47 best progenies from each generation and cycle, using two control cultivars as reference. A joint analysis of the two generations used during the evaluation of progenies for the different cycles was also performed. The genetic progress (GP) was estimated by fitting a linear regression equation to the relationship between the adjusted mean of each cycle and the number of cycles. We found that RS was efficient and the estimated GP of the evaluated progenies was 4.5%. Based on the GY heritability estimates, in more advanced generation selection for GY can be successfully performed on progenies. Thus, the selection already done for PA in F2 could be associated to the most productive progenies.

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.