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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.

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., Competing Interests: The authors declare no conflict of interest.

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.

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.

The identification of genes related to heat tolerance is fundamental for the development of high-quality seeds that are tolerant to heat stress condition. The objective of this study was to evaluate maize lineages and the gene expression involved in high temperature tolerance during germination using physiological tests, proteomics, and transcriptome analysis. Seeds from six maize lineages (30, 44, 54, 63, 64, and 91) with different levels of tolerance to high temperatures were used. Lineages 54 and 91 were observed to be more tolerant to high temperature conditions. The highest expression of α-amylase was observed in maize seeds from lineages 30 and 91 that were subjected to controlled deterioration. The highest expression of α-amylase was observed in maize seeds from lineages 30 and 91 that were subjected to controlled deterioration; with the controlled deterioration, the highest level of gene expression did not occur in the most tolerant materials; the association of lower expression of genes involved in heat-resistant protein systems was observed in seeds from lineage 44, which were more susceptible to high temperatures, and the highest gene expression of LEA D-34, ZmAN13, and AOX-1 was observed in seeds from lineage 64 when submitted to controlled deterioration.

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.

Doubled haploid technology has been used by various private companies. However, information regarding chromosome duplication methodologies, particularly those concerning techniques used to identify duplication in cells, is limited. Thus, we analyzed and characterized artificially doubled haploids using microsatellites molecular markers, pollen viability, and flow cytometry techniques. Evaluated material was obtained using two different chromosome duplication protocols in maize seeds considered haploids, resulting from the cross between the haploid inducer line KEMS and 4 hybrids (GNS 3225, GNS 3032, GNS 3264, and DKB 393). Fourteen days after duplication, plant samples were collected and assessed by flow cytometry. Further, the plants were transplanted to a field, and samples were collected for DNA analyses using microsatellite markers. The tassels were collected during anthesis for pollen viability analyses. Haploid, diploid, and mixoploid individuals were detected using flow cytometry, demonstrating that this technique was efficient for identifying doubled haploids. The microsatellites markers were also efficient for confirming the ploidies preselected by flow cytometry and for identifying homozygous individuals. Pollen viability showed a significant difference between the evaluated ploidies when the Alexander and propionic-carmin stains were used. The viability rates between the plodies analyzed show potential for fertilization.

The production of maize doubled haploid (DH) lines is a technique commonly used by private companies, but not by Brazilian public institutions. Research on this technique is essential to develop and improve the production of DH lines grown under tropical conditions. We assessed the ability of a gynogenetic haploid inducer system to induce haploids in a tropical environment, assessed the induction rate of haploids identified using the R-navajo morphological marker, checked for interference from the generation of hybrid donors on haploid induction, measured the ability of flow cytometry, and simple sequence repeat marker techniques to identify doubled haploids. Seeds from the inducer Krasnodar Embryo Marker Synthetic (KEMS) line were sown in Ponta Grossa, PR, and Cravinhos, SP, and the plants were crossed to produce six hybrids and their F2 generations. The seeds were separated according to the R-navajo morphological marker indicator of haploidy (purple endosperm and white embryo) and germinated in a controlled environment. Chromosomal duplication was performed in seedlings selected as putative haploids. We performed subsequent confirmation of ploidy and the success of duplication using flow cytometry and SSR marker techniques. We concluded that DH lines can be obtained from hybrids crossed with the inducer KEMS line. The generation of inbred hybrids did not affect the induction rate or chromosomal duplication in haploids. The use of flow cytometry and SSR markers was effective in verifying chromosomal duplication in haploids.

We quantified and characterized the expression of heat-resistant proteins during seed development of maize lines with distinct levels of tolerance to high drying temperature. A corn field was planted for multiplication of seeds of different lines, two tolerant and two non-tolerant to high drying temperatures. Harvest of the seeds was carried out at various stages of development and they were then subjected to tests of moisture content, germination, first count of germination, accelerated aging, and cold test. The seeds were stored in a freezer for later analysis of expression of heat-resistant proteins by means of real-time PCR, electrophoresis, and spectrophotometry. We observed that heat-resistant proteins are expressed in a differential manner in seeds from different lines and at different stages of development. The expression of heat-resistant proteins was earlier in lines tolerant to high drying temperatures. Greater germination and vigor values was found for seeds collected at the last stage of development.

Brazilian legislation establishes a labeling limit for products that contain more than 1% material from genetically modified organisms (GMOs). We assessed the sensitivity of the lateral flow strip test in detection of the GMO corn varieties Bt11 and MON810 and the specificity and sensitivity of PCR techniques for their detection. For the strip test, the GMO seeds were mixed with conventional seeds at levels of 0.2, 0.4 and 0.8% for Bt11, and 0.4, 0.8 and 1.6% for MON810. Three different methodologies were assessed and whole seeds, their endosperm and embryonic axis were used. For the PCR technique, the GMO seeds of each of the two varieties were mixed with conventional seeds at levels of 20, 10, 5, 2, 1, and 0.5%. The seeds were ground and the DNA extracted. For detection of the GMO material, specific primers were used for MON810 and Bt11 and maize zein as an endogenous control. The sensitivity of the strip test varied for both maize varieties and methodologies. The test was positive for Bt11 only at 0.8%, in contrast with the detection limit of 0.4% indicated by the manufacturer. In the multiplex PCR, the primers proved to be specific for the different varieties. These varieties were detected in samples with one GMO seed in 100. Thus, this technique proved to be efficient in detecting contaminations equal to or greater than 1%.

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.

We examined the usefulness of the best linear unbiased prediction associated with molecular markers for prediction of untested maize double-cross hybrids. Ten single-cross hybrids from different commercial backgrounds were crossed using a complete diallel design. These 10 single-cross hybrids were genotyped with 20 microsatellite markers. The best linear unbiased prediction associated with microsatellite information gave relatively good prediction ability of the double-cross hybrid performance, with correlations between observed phenotypic values and genotypic prediction values varying from 0.27 to 0.54. Taking into account the predictions of specific combing ability, the correlation between observed and predicted specific combining ability varied from 0.50 to 0.88. Based on these results, we infer that it is feasible to predict maize double-cross hybrids with different unbalance degrees without including any prior information about parental inbreed lines or single-cross hybrid performance.

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.

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.

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.

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.

The present study examined the importance of mean (m+a' and d) components in the performance of single-cross hybrids for the formation of new populations and determined the contribution of the mixed model (best linear unbiased predictor of random effects, BLUP) method associated with molecular markers for the choice of crosses to obtain interpopulation hybrids. Ten single-cross commercial hybrids of different companies were used for this purpose, producing all possible double-cross hybrids through a complete diallel. The hybrids were evaluated in 15 locations in the agricultural year 2005/2006, using randomized complete block design with three repetitions. In three of these locations, estimates of m+a' and d were obtained. DNA was extracted from the single-cross hybrids and 20 SSR primers were used, nine of which were linked to QTL for yield. There was no correlation between m+a' of the single-cross hybrids with general combining ability (r = -0.15) inferring that populations with lines with high means do not always produce good hybrids. Also, it was observed that the correlation between the genetic distances with specific combining ability varied from 0.31 to 0.80 in the inter-group hybrids, while in the intra-group hybrids these estimates were low and non-significant. The heritability value obtained by BLUP was high and greater than that obtained by ordinary least squares (h(2) = 0.95 and 0.86), confirming the greater selection accuracy by the BLUP method. There were no differences between the accuracy values obtained with microsatellite information and without this information, inferring that there was no advantage of progenitor information on balanced data. It can be concluded that the estimate m+a' should not be used as a deciding parameter about the potential for extracting lines from a given population. The heritability and accuracy values obtained by BLUP allow the inference that it is possible to predict success in the choice of progenitors to obtain interpopulation hybrids.

The present study compared different similarity and dissimilarity coefficients and their influence in maize inbred line clustering. Ninety maize S0:1 inbred lines were used and genotyped with 25 microsatellite markers (simple sequence repeat). The simple matching, Rogers and Tanimoto, Russel and Rao, Hamann, Jaccard, Sorensen-Dice, Ochiai, and Roger's modified distance coefficients were compared by consensus index, projection efficiency in a two-dimensional space and by Spearman's correlation. Changes were found in high genetic similarity groupings with different coefficients using the consensus index. Russel and Rao and Jaccard coefficients had the greatest stress values with 75.67 and 40.16%, respectively, indicating that these coefficients should not be used. Genotype ranking changed, mainly in the comparison of the Roger's modified distance in relation to some coefficients (rs = 0.75). Russel and Rao's and Jaccard's coefficients should be avoided for their low accuracy. Moreover, genotype clustering by different similarly coefficients, without a close consideration of these coefficients could affect the research results.

quantitative trait locus (QTL) mapping and identification of traits of agronomic importance is important in the process of molecular marker-assisted selection in breeding programs. The molecular map of maize is well saturated and QTL and simple sequence repeat (SSR) markers have been identified, whereas few markers linked to seed quality traits are included. The present study aimed to identify QTL and the gene action and to quantify the effects of these regions in the phenotypic variation related to maize tolerance to high seed drying temperature. SSR markers and 129 segregating families of F2 plants of the cross of intolerant and tolerant lines were used in regression and composite interval mapping methods. Three maize QTL associated with tolerance to high seed drying temperature were identified and mapped to chromosomes 6 and 8, explaining 39% of the phenotypic variation of the trait with additive, dominance and overdominance gene action. These markers seem to be effectively associated with the evaluated trait, since all were mapped near genes whose expression products were associated with seed desiccation tolerance.