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The model selection stage has become a central theme in applying the additive main effects and multiplicative interaction (AMMI) model to determine the optimal number of bilinear components to be retained to describe the genotype-by-environment interaction (GEI). In the Bayesian context, this problem has been addressed by using information criteria and the Bayes factor. However, these procedures are computationally intensive, making their application unfeasible when the model’s parametric space is large. A Bayesian analysis of the AMMI model was conducted using the Reversible Jump algorithm (RJMCMC) to determine the number of multiplicative terms needed to explain the GEI pattern. Three a priori distributions were assigned for the singular value scale parameter under different justifications, namely: i) the insufficient reason principle (uniform); ii) the invariance principle (Jeffreys’ prior) and iii) the maximum entropy principle. Simulated and real data were used to exemplify the method. An evaluation of the predictive ability of models for simulated data was conducted and indicated that the AMMI analysis, in general, was robust, and models adjusted by the Reversible Jump method were superior to those in which sampling was performed only by the Gibbs sampler. In addition, the RJMCMC showed greater feasibility since the selection and estimation of parameters are carried out concurrently in the same sampling algorithm, being more attractive in terms of computational time. The use of the maximum entropy principle makes the analysis more flexible, avoiding the use of procedures for correcting prior degrees of freedom and obtaining improper posterior marginal distributions.

Water deficit stress is the abiotic factor with the highest impact on crop yield. The objective of this study was to evaluate grain yield (GY), the anthesis-silking interval (ASI) and drought tolerance indices in maize hybrids. We evaluated GY and the ASI of 86 hybrids under two moisture levels (normal irrigation and water stress) for three consecutive years. The stress susceptibility index, water stress tolerance, drought resistance coefficient, drought resistance index, stress tolerance index and harmonic mean were evaluated. There were significant hybrid x environment interactions for GY and the ASI. Differences in the ASI among environments ranged from 0 to 5 days. The hybrids P3862, 1I873, 1I923, 1I862 and 1J1211 showed high GY, associated with the highest drought tolerance indices. The stress tolerance index and harmonic mean indices can be used to identify higher-yielding maize hybrids in environments with and without water restriction.

The identification of genotypes presenting wide adaptability and stability is pivotal in breeding programs. To identify such genotypes, it is necessary to use sophisticated analytical tools to establish the genotypes × environments interaction (GEI) pattern across multi-environment trials and select for genotypic stability and adaptability. The aim of the present study was to estimate GEI using Bayesian analysis of Additive Main Effects and Multiplicative Interaction (AMMI) models for both balanced and unbalanced data sets and estimate the predictive ability of model. Two studies were assessed to showcase this approach; in the first, 10 commercial maize (Zea mays) single-cross hybrids and 45 double-cross hybrids were evaluated at 15 different locations. In the second study, 28 hybrids were evaluated in 35 different environments distributed over two different harvest seasons (first and second harvests) with unbalanced data sets within and between harvests. The Bayesian analysis of the AMMI models was robust in dealing with the unbalanced data. This approach is promising for the identification of interaction patterns and the estimation of GEI. The genotypes and environments could be grouped according to their interaction patterns even using the unbalanced data sets, showing that Bayesian analysis of AMMI models could be applied effectively for multi-environment trials. The prediction for missing hybrids was satisfactory in a simulated unbalanced design and captured the GEI and patterns in the data. This allowed the direct comparison of genotypes from the first and second harvests and the estimation of selection gain.

The identification of causal regions associated with resistance to Fusarium verticillioides can be useful to understand resistance mechanisms and further be used in breeding programs. In this study, a genome-wide association study (GWAS) was conducted to identify candidate markers associated with resistance to the ear rot caused by the fungus F. verticillioides. A total of 242 maize inbred lines were genotyped with 23,153 DArT-seq markers. A total of 12 DArTs were associated with ear rot resistance. Some DArTs were localized close to genes with functions directly related to ear rot resistance, such as a gene responsible for the innate immune response that belongs to the class of NBS-LRR receptors. Some markers were also found to be closely associated with genes that synthesize transcription factors (nactf11 and nactf61), genes responsible for the oxidation-reduction process and peroxidase activity. These results are encouraging since some candidate markers can present functional relationship with ear rot resistance in maize.

The diversity analysis and screening of major genes using genomewide association studies (GWAS) for quantitative traits have been exploited in several grass crops, but in maize it has been neglected. This study was conducted to evaluate the genetic diversity of a collection of lines and the potential of genomic analysis as an initial screening tool. For this purpose, a collection of 470 maize inbred lines were genotyped with 23,153 Dart-seq markers. The genomewide analysis of ear weight was used aiming to perform an initial screening in our germplasm. The diversity analysis allowed the lines to be separated into distinct groups, and most of the lines were in agreement with previous pedigree information. The GWAS revealed ten regions potentially associated with the ear weight trait, one of which was related to drought stress. The results of a multiple marker analysis confirmed four of these regions, presenting a predictive accuracy of 0.6. Based on the molecular data and the results of GWAS, it is possible to infer the potential use of this genetic background for breeding.

This work was carried out to predict the combining abilities, both general and specific, for performance traits and bodily yields of Serrasalmidae. Ninety‐six 30‐day‐old juveniles were purchased from two commercial fish farms, 12 of each of the following eight genetic groups: pacu, pirapitinga, tambaqui, tambacu, tambatinga, patinga, paqui and piraqui. Six fish from each genetic group were grown in 500‐L fibreglass tanks (two tanks per genetic group) until they were 495 days old. At the end of the growth period fish were weighed, subjected to morphometric analysis and processed to obtain their bodily yields. Two nuclear markers and one mitochondrial marker were used to confirm the identity of the animals. Combining abilities were obtained using the method proposed by Griffing in 1956 (Australian Journal of Biological Science, 4, 463–493) adapted to a mixed models analysis, environmental effects were estimated by the empirical best linear unbiased estimator method and genetic effects (general and specific combining abilities) were estimated with the empirical best linear unbiased predictor. Predictions of the combining abilities of advanced hybrids were obtained by the mixed models mixture method with normal distributions. Tambaqui showed higher general and specific combining abilities than the other groups for most of the variables, making it the most important genetic group. General combining ability makes a greater contribution to phenotypic variance than specific combining ability for most variables, indicating a predominance of genes with an additive effect in the control of evaluated traits.

To build up efficient strategies in plant breeding programs, it is requested a certain level of knowledge about the genotype–by-environments interaction (GEI) effects over the crop to be improved. One efficient way to gather this information is using linear mixed models using a parsimonious structure of GEI pattern such as factor analytic (FA) structure. In this work, we applied a multivariate analysis using the FA structure on a dataset composed of 11 cotton genotypes (Gossypium hirsutum) which were evaluated in seven environments in Mozambique, to identify stable and wide/specific adapted genotypes. Using the FA structure, it has been possible to select genotypes with specific and broad adaptability for the environmental network. The results indicated that FK37, Flash, BA525 and BA919 were the most productive genotypes, reaching the highest scores for first Factor. Nevertheless, the FK37 genotype presented Factor 2 close to zero classifying it as the most stable followed by Flash cultivar. The genotypes ISA205, QM301 and Albar SZ9314 were considered the less productive, although QM301 presented good stability. These findings suggest that the FK37 genotype might be recommended for Mozambique, since it had good yield and potential stability when compared to the most cultivated varieties, CA324 and Albar SZ9314.

Made available in DSpace on 2018-11-26T17:40:17Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-11-01. Added 1 bitstream(s) on 2021-07-15T15:01:38Z : No. of bitstreams: 1 S0103-90162017000600443.pdf: 524774 bytes, checksum: 6cef05bd83bbdc4deac7ae9e9e4a8129 (MD5) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) This study aimed to impute the genetic makeup of individual fishes of Serrasalmidae family on the basis of body weight and morphometric measurements. Eighty-three juveniles, belonging to the genetic groups Pacu, Pirapitinga, Tambaqui, Tambacu, Tambatinga, Patinga, Paqui and Piraqui, were separated into 16 water tanks in a recirculation system, with two tanks per genetic group, where they remained until they reached 495 days of age. They were then weighed and analyzed according to the following morphometric parameters: Standard Length (SL), Head Length (HL), Body Height (BH), and Body Width (BW). The identity of each fish was confirmed with two SNPs and two mitochondrial markers. Two analyses were performed: one for the validating the imputation and another for imputing a genetic composition of animals considered to be advanced hybrids (post F1). In both analyses, we used linear mixed models with a mixture of normal distributions to impute the genetic makeup of the fish based on phenotype. We applied the mixed models method, whereby the environmental effects were estimated by the Empirical Best Linear Unbiased Estimator (EBLUE) and genetic effects are considered random, obtaining the Empirical Best Linear Unbiased Predictor (EBLUP) from the general (GCA) and the specific (SCA) combining ability effects. The results showed that validation of the genetic makeup imputation based on body weight can be used because of the strong correlation between the observed and imputed genotype. The fish classified as advanced hybrids had a genetic composition with a high probability of belonging to known genotypes and there was consistency in genotype imputation according to the different characteristics used. Goiano Fed Inst, Dept Anim Sci, Rod Goiana,Km 01, BR-75901970 Rio Verde, Go, Brazil Univ Fed Lavras, Dept Exact Sci, CP 3037, BR-37200000 Lavras, MG, Brazil Univ Fed Lavras, Dept Anim Sci, Lavras, MG, Brazil Univ Fed Lavras, Dept Food Sci, Lavras, MG, Brazil Sao Paulo State Univ, Sch Sci, Biol Sci Dept, Av Engn Luiz Edmundo Carrijo Coube 1401, BR-17033360 Bauru, SP, Brazil Sao Paulo State Univ, FCAV, Biol Sci Dept, Via Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, Brazil Sao Paulo State Univ, Sch Sci, Biol Sci Dept, Av Engn Luiz Edmundo Carrijo Coube 1401, BR-17033360 Bauru, SP, Brazil Sao Paulo State Univ, FCAV, Biol Sci Dept, Via Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, Brazil

The additive main effects and multiplicative interaction (AMMI) model is frequently applied in plant breeding for studying the genotype × environment (G × E) interaction. One of the main difficulties related to this method of analysis is the incorporation of inference to the bilinear terms that compose the biplot representation. This study aimed to incorporate credible intervals for the genotypic and environmental scores in the AMMI model by using an informative prior for the genotype effect. This approach differs from the Bayesian methods that have been presented so far, which assume the same restrictions as the fixed effects model. The method was exemplified by using data from a study with 55 maize hybrids in nine different environments for which variable being studied was the yield of unhusked ears. Our results demonstrated that the credible intervals allowed for the identification of genotypes and environments that did not contribute to the G × E interaction. In addition, it facilitated recognition of homogeneous subgroups of genotypes and environments (with respect to the effect of the interaction) and the adaptability of genotypes to specific environments of great interest to breeders. The posterior distributions of singular vectors were bimodal but with the same density peaks in absolute value. This reflects the arbitrary choice of signs of the main component that was used in different mathematical algorithms. Although our data set was based on unrelated single cross hybrids, the choice of genotypes as random effects enabled the Bayesian AMMI to accommodate the additive and nonadditive relationship matrices. Additionally, the flexibility of the analysis facilitated working with unbalanced data and the incorporation of heterogeneity of variances.

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.

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.

In many species, low levels of polymorphism prevent the assembly of linkage maps that are used to identify genetic markers related to the expression of quantitative trait loci (QTLs). This study compared two methods of locating QTLs in association studies that do not require a previous estimation of linkage maps. Method I (MI) was a Bayesian multiple marker regression and Method II (MII) combined multiple QTL mapping and "moving away from markers". In this method, markers are not directly regressed to the phenotype, but are used as pivots to search for QTLs along the genome. To compare methods, we simulated 300 individuals from an F2 progeny with two levels of marker loss (20 and 80%). A total of 165 markers and seven QTLs were spread along 11 chromosomes (roughly emulating the genetic structure of the common bean, Phaseolus vulgaris). A real data example with 186 progenies of a F2:4 generation of the species was analyzed using 59 markers (17 simple sequence repeats, 31 amplified fragment length polymorphisms, and 11 sequence-related amplified polymorphisms). MII was more precise than MI for both levels of marker loss. For real data, MII detected 17 candidate positions for QTLs, whereas MI did not detect any. MII is a powerful method that requires further studies with actual data and other designs such as crossover, and genome-wide studies.

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.

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.

The aim of this study was to perform genome-wide selection using a set of Dart-seq markers associated to the additive-dominant genomic best linear unbiased prediction (GBLUP) model to predict maize grain yield in different crop seasons and locations. Genotyping was performed with Dart-seq markers from 447 lines coming from a germplasm bank of a private maize breeding company. Crossing these lines provided 838 single-cross hybrids evaluated in six locations in the winter crop season of 2013 and 797 single-cross hybrids evaluated in four locations in the summer crop season of 2013/2014. Four k-fold levels were applied on the full panel of 23,153 Dart genotyping-by-sequencing markers and samples of 50% of the available markers. The different crop seasons were used as training and validation populations to estimate the predictive accuracy. The magnitude of the correlations between predicted and observed hybrids ranged from 0.82 to 0.89 in the winter crop season and from 0.56 to 0.76 in the summer crop season. The correlations between combinations tested in different crop seasons and locations were encouraging (0.53). Predictive ability was highly influenced by the genetic background and also by the interaction between crop seasons. The coincidences between the genomic values of the summer crop and winter crop, in terms of discard, were 89 and 90%. This result shows the possibility of using genomic prediction in breeding programs for initial discard of low-yielding genotypes. The GBLUP method was able to generate high correlations between predicted and observed hybrids, even at high levels of missing in k-fold and in different locations and crop seasons.

Plant breeding strategies are constantly refined to increase efficiency during the selection process. Consequently, the aims of this study were to examine genetic control and efficiency of selection based on the per se performance of maize lines and based on hybrid performance in reaction to white spot, common rust, and ear rot diseases in maize breeding programs. We also aimed to examine the relationship between the per se performance of the lines and the single hybrids for resistance to these diseases. To do so, we evaluated the reaction to these diseases in 106 lines and 661 hybrids in different locations in Brazil. Subsequently, the relative importance of the additive and non-additive genetic effects on expression of these traits, the association between the parameters considered upon carrying out the selection in these generations, and gain from selection were estimated. The results indicated that additive effects predominate for resistance to white spot and ear rot diseases, whereas for common rust, dominance effects predominate. However, evaluating the hybrids through diallel analysis was effective in selection for resistance to these diseases. The diseases and the generations evaluated were interrelated; the increase in the level of resistance had a more significant correlated response when selection was directed by the reaction to white spot, and there was a greater genetic similarity between the parameters considered in selection directed by hybrid performance.

The aim of this study was to use a multi-environment trial approach from a mixed model point of view for factor analysis (FA) of the stability and adaptability of hybrids. Twenty-eight hybrids were analyzed in 35 environments across four seasons/years (summer season 2010, winter season 2011, summer season 2011 and winter season 2012). Several of these hybrids were analyzed during the first seasons and were not evaluated in later seasons or vice versa. Therefore, the dataset used in this study simulated the dynamics of a genetic breeding program with removal and inclusion of genotypes over the years. A biplot of the factor scores and loadings showed that the environments were more similar within seasons than between seasons, thereby suggesting that a given site may behave differently year after year. The season was more effective in discovery mega-environments. The FA models may be directly interpreted as GGE biplot analysis since the first factor score had a perfect fit ( $$r^{2} = 0.99$$ ) with the empirical best linear unbiased predictors of the genotypes. Given the assumption of normality for the factor scores, confidence ellipses could be created to directly compare the genotypes in the biplot. Stability and adaptability could be analyzed in unbalanced experiments with the removal and inclusion of genotypes over time. This approach allowed certain trends in a breeding program to be measured by directly comparing hybrids developed for the first or winter season. The biplot interpretation was direct and intuitive, and it has the same properties as the GGE biplot obtained by singular value decomposition.

Background The large amount of data used in genomic analysis has allowed geneticists to achieve some understanding of the genetic architecture of complex traits. Although the information gathered by molecular markers has permitted gains in predictive accuracy and gene discovery, epistatic effects have been ignored based on exhaustive searches requesting estimates of its effects on the whole genome. In this work, we propose the reversible-jump technique to estimate epistasis in the genome without drastically altering the model dimension. To this end, we used a real maize dataset based on 256 F2:3 progenies plus a simulation data set based on 300 F2 individuals. In the simulation scenario, six QTL presenting main effects (additive and dominance) were combined with seven other epistatic effects totaling 13 QTL controlling the trait. Results Our model explored 18,624 candidate epistases, but even in this vast space, only one spurious interaction was found. The three epistases selected by our model, named here as 18x26, 56x68 and 59x93, were very close to simulated ones (19x25, 54x72, 59x91 and 59x94). In the real dataset, we estimate 33,024 epistatic effects, and several minor epistatic combinations were found to explain a significant proportion of the genetic variance. The broad participation of epistasis in the real dataset may indicate the presence of pervasive epistasis acting on maize grain yield. Conclusions The power of selecting true epistasis in thousands of possible combinations suggests the attractiveness of our model to handle genomic data Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3342-6) contains supplementary material, which is available to authorized users.

The development of sequencing technologies has enabled the discovery of markers that are abundantly distributed over the whole genome. Knowledge about the marker locations in reference genomes provides further insights in the search for causal regions and the prediction of genomic values. The present study proposes a Bayesian functional approach for incorporating the marker locations into genomic analysis using stochastic methods to search causal regions and predict genotypic values. For this, three scenarios were analyzed: F2 population with 300 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 12,150 SNP markers that were distributed through ten linkage groups; F∞ populations with 320 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 10,020 SNP markers that were distributed through ten linkage groups; and data related to Eucalyptus spp. to measure the model performance in a real LD setting, with 611 individuals whose phenotypes were simulated from QTLs distributed through a panel of 36,812 SNPs with known positions. The performance of the proposed method was compared with those of other genome selection models, namely, RR-BLUP, Bayes B and Bayesian Lasso. The Bayesian functional model presented higher or similar predictive ability when compared with those classical regressions methods in simulated and real scenarios on different LD structures. In general, the Bayesian functional model also achieved higher computational efficiency, using 12 SNPs per MCMC round. The model was efficient in the identification of causal regions and showed high flexibility of analysis, as it is easily adaptable to any genomic selection model.

One of the main challenges in plant breeding programs is the efficient quantification of the genotype-by-environment interaction (GEI). The presence of significant GEI may create difficulties for breeders in the selection and recommendation of superior genotypes for a wide environmental network. Among the diverse statistical procedures developed for this purpose, we highlight those based on mixed models and factor analysis that are called factor analytic (FA) models. However, some inferential issues are related to the factor analytic model, such as Heywood cases that make the model non-identifiable. Moreover, the representation of the loads and factors in the conventional biplot does not involve any measurement of uncertainty. In this work, we propose dealing with the FA model using the Bayesian framework with direct sampling of factor loadings via spectral decomposition; this guarantees identifiability in the estimation process and eliminates the need for the rotationality of factor loadings or imposition of any ad hoc constraints. We used simulated and real data to illustrate the method's application in multi-environment trials (MET) and to compare it with traditional FA mixed models on controlled unbalancing. In general, the Bayesian FA model was robust under different simulated unbalanced levels, presenting the superior predictive ability of missing data when compared to competing models, such as those based on FA mixed models. In addition, for some scenarios, the classical FA mixed model failed in estimating the full FA model, illustrating the parametric problems of convergence in these models. Our results suggest that Bayesian factorial models might be successfully used in plant breeding for MET analysis.

This study seeks to quantify the importance of epistatic effects on the heterosis of maize using complex pedigrees in a single model of the so-called bi-, tri-, and tetra-alleles in an analysis with and without information from microsatellite markers. To this end, 51 inbred lines were sampled from different backgrounds, obtaining 6 double cross hybrids, 14 triple cross hybrids, and 58 single cross hybrids, for a total of 78 hybrids. Seventy-nine microsatellite markers were used in the genotyping of the 51 lines. These markers were distributed throughout the 10 linkage groups in maize. This information was used to construct an information matrix on kinship. The mixed models and restricted maximum likelihood approaches were used to estimate additive, dominant and epistatic effects. It was observed that the dominant by dominant epistasis was the most important effect related to genetic control of the heterosis in maize. Also, our study demonstrated that it is possible to exploit a large amount of information when we jointly analyze simple, double, and three-way cross hybrids under the same model. Using this approach, it is possible to dissect heterosis into several components and to adopt the best crossbreeding strategy based on the importance of each component. Additionally, it was possible to verify that the use of molecular markers improves the accuracy of calculating the epistatic and dominance effects. Thus, using the current state-of-art in quantitative genetics and statistical methods the concept of crossbreeding can be expanded to frontiers that are far beyond the traditional general and specific combining ability.

Este trabalho foi realizado com o objetivo de avaliar os efeitos da aplicação de fungicida, no controle da Cercosporiose e da Mancha-Branca do milho, e suas consequências na produtividade de grãos e na incidência de grãos ardidos, além de estabelecer a relação existente entre a produtividade de grãos e a severidade dessas doenças. Dois experimentos distintos (com Azoxystrobina + Cyproconazole e tratamento controle) foram conduzidos em três locais, no ano agrícola de 2007/2008. Utilizaram-se 12 híbridos comerciais de milho, que foram avaliados em delineamento de blocos casualizados, com três repetições. Foram realizadas cinco avaliações da severidade das doenças Cercosporiose e Mancha-Branca, por meio de escala de notas, variando de 1 (altamente resistente) a 9 (altamente susceptível). Estimou-se a área abaixo da curva de progresso da doença (AACPD). A aplicação de fungicida é eficiente no controle de doenças foliares e na redução da incidência de grãos ardidos, além de proporcionar 12% de aumento na produtividade de grãos, em relação à do tratamento controle. As doenças foliares Cercosporiose e Mancha-Branca reduzem a produtividade de grãos de milho e essa redução é maior quando as doenças ocorrem mais precocemente. A Cercosporiose provoca maior redução na produtividade de grãos, quando comparada com a Mancha-Branca.

The additive main effects and multiplicative interaction (AMMI) and the genotype main effects and genotype x environment interaction (GGE) models stand out among the linear-bilinear models used in genotype x environment interaction studies. Despite the advantages of their use to describe genotype x environment (AMMI) or genotype and genotype x environment (GGE) interactions, these methods have known limitations that are inherent to fixed effects models, including difficulty in treating variance heterogeneity and missing data. Traditional biplots include no measure of uncertainty regarding the principal components. The present study aimed to apply the Bayesian approach to GGE biplot models and assess the implications for selecting stable and adapted genotypes. Our results demonstrated that the Bayesian approach applied to GGE models with non-informative priors was consistent with the traditional GGE biplot analysis, although the credible region incorporated into the biplot enabled distinguishing, based on probability, the performance of genotypes, and their relationships with the environments in the biplot. Those regions also enabled the identification of groups of genotypes and environments with similar effects in terms of adaptability and stability. The relative position of genotypes and environments in biplots is highly affected by the experimental accuracy. Thus, incorporation of uncertainty in biplots is a key tool for breeders to make decisions regarding stability selection and adaptability and the definition of mega-environments.

The objective of our study was to characterize and determine the patterns of genetic control in relation to tolerance and efficiency of nitrogen use by means of a complete diallel cross involving contrasting inbred progenies of tropical maize based on a univariate approach within the perspective of a multivariate mixed model. Eleven progenies, previously classified regarding the tolerance and responsiveness to nitrogen, were crossed in a complete diallel cross. Fifty-five hybrids were obtained. The hybrids and the progenies were evaluated at two different nitrogen levels, in two locations. The grain yield was measured as well as its yield components. The heritability values between the higher and lower nitrogen input environment did not differ among themselves. It was observed that the general combining ability values were similar for both approaches univariate and multivariate, when it was analyzed within each location and nitrogen level. The estimate of variance of the specific combining ability was higher than general combining ability estimate and the ratio between them was 0.54. The univariate and multivariate approaches are equivalent in experiments with good precision and high heritability. The nonadditive genetic effects exhibit greater quantities than the additive genetic effects for the genetic control of nitrogen use efficiency. Made available in DSpace on 2015-01-28T08:54:35Z (GMT). No. of bitstreams: 1 apiInheritance.pdf: 1282983 bytes, checksum: 9bc16bc6124dbbf8f6b693f04d904e79 (MD5) Previous issue date: 2015-01-27

The main objective of a maize breeding program is to generate hybrid combinations that are more productive than those pre-existing in the market. However, the number of parents, and consequently the number of crosses, increases so rapidly that the phenotypic evaluation of all the possible combinations becomes economically and technically infeasible. In this context, predicting the performance of the most promising genotypes may increase the genetic gains with increased selection intensity and reduced breeding cycles. Thus, the present study aimed to use the total effects of associated markers method to predict genomic breeding values (GBVs) via cross-validation and by using different imbalance levels (10, 30, 50, and 70%). A set of 51 genotyped strains was used with 79 microsatellite markers and 273 hybrids that were generated by a partial diallel. A total of 186 and 272 hybrids were analyzed in the experiments within the southern and central regions of Brazil, respectively. The GBVs were, thus, predicted for each location in both the regions, and for training in one region and validation in another region. The correlation between the predicted and observed GBVs ranged from 0.48 to 0.91, depending on the imbalance level and the region analyzed. Overall, the results obtained in the present study were promising, particularly considering that a small number of markers were used and that the training and predictions occurred in the very distinct regions of southern and central Brazil.

We aimed to identify simple sequence repeat (SSR) markers linked to quantitative trait loci (QTLs) associated with white mold resistance in a segregating population derived from a cross between common bean cultivars Jalo and Small White, in the Southern State of Minas Gerais. Parents were crossed to obtain the F2 generation of 190 plants. From these, F2:3 and F2:4 progenies were obtained for phenotypic evaluation. DNA was extracted from F2 plants and parents for genotyping with SSR primers. For phenotypic assessment by the straw test, we used 190 F2:3 progenies and six lines in a triple-lattice design of 14 x 14 m. Nine trials were conducted for phenotyping by the oxalic acid method to evaluate 177 F2:4 progenies, in addition to the two parents and two common treatments represented by the Jalo and Corujinha lines. The experimental design for the oxalic acid method was completely randomized with three replicates. Adjusted means of evaluations related to F2:3 and F2:4 were used to identify QTLs by using the moving away method from the marker under Bayesian analysis. The markers GATS91, BM197, and X60000 stood out with high heritability and with effects on disease reduction by the straw test; therefore, these markers are promising for selection. The markers BM183, BM189, and SSR-IAC143 were associated with the QTLs identified by oxalic acid, providing greater effects in white mold resistance with high heritability. Based on the oxalic acid and straw test methods, the most prominent marker was PVBR189.

The analysis of multiples traits has been largely employed in animal and forestry crop breeding; however, in annual crops such as common bean this approach is practically lacking. This study examined the selective accuracy and estimates of genetic parameters in common bean using single- and multi-trait analyses in several designs of genetic correlations and heritabilities by means of simulated- and real-traits. We also compared the efficiency of the Bayesian and REML methods as regarding its selective accuracy and estimates of genetic parameters. In real data experiments, 100 endogamic families of common bean were assessed for grain yield (GY) and grain type (GT) in three environments using a 10 × 10 triple lattice design. Single- and multi-trait analyses were applied for these traits using REML-based BLUP analysis and Bayesian methods. In simulated experiments, traits corresponding to GY and yield components in common bean were simulated under several levels of genetic correlations and evaluated under different levels of heritability in order to verify in which genetic/experimental design it is advantageous to apply single- or multi-trait analyses. The results obtained in this study indicated that GY and GT present low genetic correlation and, therefore, multi-trait analysis may be unjustifiable for these traits. The Bayesian and REML multi-trait analyses were equivalent in point and interval estimates of genetic parameters and predictive accuracy, both for the field and simulated experiments. In addition, it was found that multi-trait analysis is justified in situations where the traits present low/moderate heritability and medium/high genetic correlations among them. It also was found that GY in common bean may be outstandingly favored in accuracy when evaluated jointly with its components. Thus, one can conclude that the simultaneous analysis of GY and yield components in common bean may provide superior gains accuracy and selection efficiency, being the response to selection better obtained by use of the multi-trait heritability.

O objetivo deste trabalho foi avaliar os efeitos da aplicacao de fungicida no controle da Cercosporiose e Mancha-Branca do milho e suas implicacoes na produtividade de graos e na incidencia de graos ardidos, alem de estabelecer a relacao existente entre a produtividade de graos e a severidade dessas doencas. Dois experimentos distintos (com Azoxystrobina + Cyproconazole e tratamento controle) foram conduzidos dentro de tres locais no ano agricola de 2007/2008. Utilizou-se 12 hibridos comerciais de milho que foram avaliados em delineamento de blocos casualizados, com tres repeticoes. Foram realizadas cinco avaliacoes da severidade das doencas Cercosporiose e Mancha-Branca, por meio de escala de notas variando de 1 (altamente resistente) a 9 (altamente suscetivel). A aplicacao de fungicida e eficiente no controle de doencas foliares e reducao da incidencia de graos ardidos alem de proporcionar 12% de aumento na produtividade de graos em relacao ao tratamento controle. As doencas foliares Cercosporiose e Mancha-Branca reduzem a produtividade de graos de milho e essa reducao e maior quando as doencas ocorrem mais precocemente. A Cercosporiose provoca maior reducao na produtividade de graos, quando comparada com a Mancha-Branca.

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.

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.

The objective of this study was to evaluate stability and adaptability of the grain yield of commercial intervarietal maize hybrids by the GGE (Genotype and Genotype by Environment Interaction) biplot and AMMI (Additive Main Effects and Multiplicative Interaction) analyses. Two intervarietal hybrids (BIO 2 and BIO4) were evaluated together with single, double and three-way cross hybrids. The performance of the intervarietal hybrid BIO 4 was superior to all double and three-way cross hybrids and outmatched the single-cross hybrids by 43%. In terms of stability, BIO 2 was more stable than BIO4, which is desirable, but biological stability, which is not necessarily desirable, was also observed, since the yield was below the environmental mean. The graphical GGE biplot analysis was superior to the AMMI1 since a greater portion of the sum of squares of GE and G+GE was captured and the predictive accuracy was higher. On the other hand, the AMMI2 graph outperformed the GGE biplot in predictive accuracy and explanation of G + GE and GE, although the difference in accuracy was smaller than between GGE2 and AMMI1.