Marcio Balestre

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.

Evaluation of maize hybrids and environmental stratification by the methods AMMI and GGE biplot

The purpose of this study was to evaluate yield stability, adaptability and environmental stratification by the methods AMMI (Additive Main Effects and Multiplicative Interaction Analysis) and GGE (Genotype and Genotypes by Environment Interaction) biplot and to compare the efficiency of these methods. Data from the evaluation of 20 experimental single-cross and three commercial hybrids and 11 locations, in two growing seasons, 2005/2006 and 2006/2007 were used. Analyses of variance, adaptability, stability and environmental stratification were performed. A better combination of adaptability and stability was observed in the hybrids 10 and 16, according to the graphics of AMMI and GGE biplot methods, respectively. The number of locations could be reduced by 28% based on stratification. The predictive correlation of the AMMI and GGE methods was 0.88 and 0.86, respectively. The results showed that it is possible to reduce the number of evaluation sites; AMMI tended to be more accurate than GGE analysis.

Inclusion of Dominance Effects in the Multivariate GBLUP Model

New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components.

Genomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markers

BackgroundThe identification of lines resistant to ear diseases is of great importance in maize breeding because such diseases directly interfere with kernel quality and yield. Among these diseases, ear rot disease is widely relevant due to significant decrease in grain yield. Ear rot may be caused by the fungus Stenocarpella maydi; however, little information about genetic resistance to this pathogen is available in maize, mainly related to candidate genes in genome. In order to exploit this genome information we used 23.154 Dart-seq markers in 238 lines and apply genome-wide selection to select resistance genotypes. We divide the lines into clusters to identify groups related to resistance to Stenocarpella maydi and use Bayesian stochastic search variable approach and rr-BLUP methods to comparate their selection results.ResultsThrough a principal component analysis (PCA) and hierarchical clustering, it was observed that the three main genetic groups (Stiff Stalk Synthetic, Non-Stiff Stalk Synthetic and Tropical) were clustered in a consistent manner, and information on the resistance sources could be obtained according to the line of origin where populations derived from genetic subgroup Suwan presenting higher levels of resistance. The ridge regression best linear unbiased prediction (rr-BLUP) and Bayesian stochastic search variable (BSSV) models presented equivalent abilities regarding predictive processes.ConclusionOur work showed that is possible to select maize lines presenting a high resistance to Stenocarpella maydis. This claim is based on the acceptable level of predictive accuracy obtained by Genome-wide Selection (GWS) using different models. Furthermore, the lines related to background Suwan present a higher level of resistance than lines related to other groups.

Bayesian mapping of multiple traits in maize: the importance of pleiotropic effects in studying the inheritance of quantitative traits

Pleiotropy has played an important role in understanding quantitative traits. However, the extensiveness of this effect in the genome and its consequences for plant improvement have not been fully elucidated. The aim of this study was to identify pleiotropic quantitative trait loci (QTLs) in maize using Bayesian multiple interval mapping. Additionally, we sought to obtain a better understanding of the inheritance, extent and distribution of pleiotropic effects of several components in maize production. The design III procedure was used from a population derived from the cross of the inbred lines L-14-04B and L-08-05F. Two hundred and fifty plants were genotyped with 177 microsatellite markers and backcrossed to both parents giving rise to 500 backcrossed progenies, which were evaluated in six environments for grain yield and its components. The results of this study suggest that mapping isolated traits limits our understanding of the genetic architecture of quantitative traits. This architecture can be better understood by using pleiotropic networks that facilitate the visualization of the complexity of quantitative inheritance, and this characterization will help to develop new selection strategies. It was also possible to confront the idea that it is feasible to identify QTLs for complex traits such as grain yield, as pleiotropy acts prominently on its subtraits and as this “trait” can be broken down and predicted almost completely by the QTLs of its components. Additionally, pleiotropic QTLs do not necessarily signify pleiotropy of allelic interactions, and this indicates that the pervasive pleiotropy does not limit the genetic adaptability of plants.

Stability and adaptability of upland rice genotypes

The aim of this study was to identify upland rice genotypes with high stability and adaptability by the GGE biplot method based on the predicted genotypic and phenotypic values. Of the 20 genotypes evaluated, 14 were lines developed by the cooperative program for rice improvement of Minas Gerais and six were controls. The GGE biplot analysis showed that cultivar BRS Pepita and MG1097 were closest to the ideal genotype. In the comparison of the fixed with the random models (% G + GE, prediction error sum of squares and correlation), it was observed that the use of phenotypic means in all comparative parameters indicated a lower predictive potential under simulated imbalance than the use of predicted genotypic values. The conclusion was drawn that BRS Pepita and MG1097 are ideal genotypes for southern Minas Gerais and that the predictive power of the phenotypic means underlying the study of stability and adaptability is lower than of genotypic means.

Quantitative trait loci associated with resistance to gray leaf spot and grain yield in corn

A incorporacao de resistencia genetica a doencas e o aumento na produtividade de graos estao entre os principais objetivos dos programas de desenvolvimento de hibridos. A identificacao de locos de caracteres quantitativos (QTL) por meio de analises estatisticas associadas a marcadores moleculares possibilita a rapida obtencao de hibridos resistentes e produtivos. Nesta pesquisa, objetivou-se identificar locos de caracteres quantitativos (QTL) associados com resistencia a cercosporiose e com producao de graos em germoplasma de milho tropical. Foram utilizadas duas linhagens contrastantes em niveis de reacao a doenca (genotipos pertencentes a GENESEEDS - Ltda), seu hibrido F1 e a populacao segregante F2. Essas plantas foram fenotipadas quanto a resistencia a doenca e quanto a producao de graos e genotipadas com 94 marcadores de microssatelites. A associacao dos marcadores ao QTL foi realizada por meio de analises de marcas individuais, utilizando as metodologias de regressao linear e analise da maxima verossimilhanca. Observou-se que o tipo de efeito predominante no controle genetico da resistencia a cercosporiose foi o aditivo e para o controle genetico da producao de graos foi de dominância. Os marcadores microssatelites mais promissores para serem utilizados em estudos de selecao assistida para resistencia a cercosporiose sao umc2082 na posicao 4.03 e umc1117 na posicao 4.04 e para producao de graos, umc1042 e umc1058 nas posicoes 2.07 e 4.11.

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.

Identification of QTLs for Resistance to Sclerotinia sclerotiorum in Carioca Common Bean by the Moving Away Method.

The aim of this study was to use multiple DNA markers for detection of QTLs related to resistance to white mold in an F2 population of common bean evaluated by the straw test method. The DNA from 186 F2 plants and from the parents was extracted for genotypic evaluation using SSR, AFLP, and SRAP markers. For phenotypic analysis, 186 F2:4 progenies and ten lines were evaluated, in a 14 × 14 triple lattice experimental design. The adjusted mean values of the F2:4 progenies were used for identification of QTLs by Bayesian shrinkage analysis. Significant differences were observed among the progenies for reaction to white mold. In identification of QTLs, 17 markers identified QTLs for resistance—13 SSRs and 4 AFLPs. The moving away method under the Bayesian approach proved to be efficient in the identification of QTLs when a genetic map is not used due to the low density of markers. The ME1 and BM211 markers are near the QTLs, with the effect of increasing resistance to white mold, and they have high heritability. They are thus promising for marker-assisted selection.

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.