R.G. Von Pinho

Genotypic stability and adaptability in tropical maize based on AMMI and GGE biplot analysis.

We evaluated the phenotypic and genotypic stability and adaptability of hybrids using the additive main effect and multiplicative interaction (AMMI) and genotype x genotype-environment interaction (GGE) biplot models. Starting with 10 single-cross hybrids, a complete diallel was done, resulting in 45 double-cross hybrids that were appraised in 15 locations in Southeast, Center-West and Northeast Brazil. In most cases, when the effects were considered as random (only G effects or G and GE simultaneously) in AMMI and GGE analysis, the distances between predicted values and observed values were smaller than for AMMI and GGE biplot phenotypic means; the best linear unbiased predictors of G and GE generally showed more accurate predictions in AMMI and GGE analysis. We found the GGE biplot method to be superior to the AMMI 1 graph, due to more retention of GE and G + GE in the graph analysis. However, based on cross-validation results, the GGE biplot was less accurate than the AMMI 1 graph, inferring that the quantity of GE or G + GE retained in the graph analysis alone is not a good parameter for choice of stabilities and adaptabilities when comparing AMMI and GGE analyses.

Prediction of maize single-cross performance by mixed linear models with microsatellite marker information.

We evaluated the potential of the best linear unbiased predictor (BLUP) along with the relationship coefficient for predicting the performance of untested maize single-cross hybrids. Ninety S(0:2) progenies arising from three single-cross hybrids were used. The 90 progenies were genotyped with 25 microsatellite markers, with nine markers linked to quantitative trait loci for grain yield. Based on genetic similarities, 17 partial inbred lines were selected and crossed in a partial diallel design. Similarity and relationship coefficients were used to construct the additive and dominance genetic matrices; along with BLUP, they provided predictions for untested single-crosses. Five degrees of imbalance were simulated (5, 10, 20, 30, and 40 hybrids). The correlation values between the predicted genotypic values and the observed phenotypic means varied from 0.55 to 0.70, depending on the degree of imbalance. A similar result was observed for the specific combining ability predictions; they varied from 0.61 to 0.70. It was also found that the relationship coefficient based on BLUP provided more accurate predictions than similarity-in-state predictions. We conclude that BLUP methodology is a viable alternative for the prediction of untested crosses in early progenies.

Potential use of molecular markers for prediction of genotypic values in hybrid maize performance

We evaluated the potential of genetic distances estimated by microsatellite markers for the prediction of the performance of single-cross maize hybrids. We also examined the potential of molecular markers for the prediction of genotypic values and the applicability of the Monte Carlo method for a correlation of genetic distances and grain yield. Ninety S(0:2) progenies derived from three single-cross hybrids were analyzed. All 90 progenies were genotyped with 25 microsatellite markers, including nine markers linked to quantitative trait loci for grain yield. The genetic similarity datasets were used for constructing additive genetic and dominance matrices that were subsequently used to obtain the best linear unbiased prediction of specific combining ability and general combining ability. The genetic similarities were also correlated with grain yield, specific combining ability and heterosis of the hybrids. Genetic distances had moderate predictive ability for grain yield (0.546), specific combining ability (0.567) and heterosis (0.661). The Monte Carlo simulation was found to be a viable alternative for a correlation of genetic distances and grain yield. The accuracy of genotypic values using molecular data information was slightly higher than if no such information was incorporated. The estimation of the relationship using molecular markers proved to be a promising method for predicting genetic values using mixed linear models, especially when information about pedigree is unavailable.

Bayesian inference to study genetic control of resistance to gray leaf spot in maize.

Gray leaf spot (GLS) is a major maize disease in Brazil that significantly affects grain production. We used Bayesian inference to investigate the nature and magnitude of gene effects related to GLS resistance by evaluation of contrasting lines and segregating populations. The experiment was arranged in a randomized block design with three replications and the mean values were analyzed using a Bayesian shrinkage approach. Additive-dominant and epistatic effects and their variances were adjusted in an over-parametrized model. Bayesian shrinkage analysis showed to be an excellent approach to handle complex models in the study of genetic control in GLS, since this approach allows to handle overparametrized models (main and epistatic effects) without using model-selection methods. Genetic control of GLS resistance was predominantly additive, with insignificant influence of dominance and epistasis effects.

Prediction of maize hybrid performance using similarity in state and similarity by descent information.

We evaluated the efficiency of the best linear unbiased predictor (BLUP) and the influence of the use of similarity in state (SIS) and similarity by descent (SBD) in the prediction of untested maize hybrids. Nine inbred lines of maize were crossed using a randomized complete diallel method. These materials were genotyped with 48 microsatellite markers (SSR) associated with the QTL regions for grain yield. Estimates of four coefficients of SIS and four coefficients of SBD were used to construct the additive genetic and dominance matrices, which were later used in combination with the BLUP for predicting genotypic values and specific combining ability (SCA) in unanalyzed hybrids under simulated unbalance. The values of correlations between the genotypic values predicted and the means observed, depending on the degree of unbalance, ranged from 0.48 to 0.99 for SIS and 0.40 to 0.99 using information from SBD. The results obtained for the SCA ranged from 0.26 to 0.98 using the SIS and 0.001 to 0.990 using the SBD information. It was also observed that the predictions using SBD showed less biased than SIS predictions demonstrating that the predictions obtained by these coefficients (SBD) were closer to the observed value, but were less efficient in the ranking of genotypes. Although the SIS showed a bias due to overestimation of relatedness, this type of coefficient may be used where low values are detected in the SBD in the group of parents because of its greater efficiency in ranking the candidates hybrids.

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.

Heat-resistant protein expression during germination of maize seeds under water stress.

Low water availability is one of the factors that limit agricultural crop development, and hence the development of genotypes with increased water stress tolerance is a challenge in plant breeding programs. Heat-resistant proteins have been widely studied, and are reported to participate in various developmental processes and to accumulate in response to stress. This study aimed to evaluate heat-resistant protein expression under water stress conditions during the germination of maize seed inbreed lines differing in their water stress tolerance. Maize seed lines 91 and 64 were soaked in 0, -0.3, -0.6, and -0.9 MPa water potential for 0, 6, 12, 18, and 24 h. Line 91 is considered more water stress-tolerant than line 64. The analysis of heat-resistant protein expression was made by gel electrophoresis and spectrophotometry. In general, higher expression of heat-resistant proteins was observed in seeds from line 64 subjected to shorter soaking periods and lower water potentials. However, in the water stress-tolerant line 91, a higher expression was observed in seeds that were subjected to -0.3 and -0.6 MPa water potentials. In the absence of water stress, heat-resistant protein expression was reduced with increasing soaking period. Thus, there was a difference in heat-resistant protein expression among the seed lines differing in water stress tolerance. Increased heat-resistant protein expression was observed in seeds from line 91 when subjected to water stress conditions for longer soaking periods.