ABSTRACT. 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 F 2 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 4231©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 12 (4): 4230-4242 (2013) Production of doubled haploid lines in tropical maize 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.

ABStRACt. The present study compared different similarity and dissimilarity coefficients and their influence in maize inbred line clustering. Ninety maize S 0:1 inbred lines were used and genotyped with 25 microsatellite markers (simple sequence repeat). The simple matching, Rogers and Tanimoto, Russel and Rao, Hamann, Jaccard, SorensenDice, 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 (r s = 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.

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.

ABSTRACT.This study aimed to analyze the robustness of mixed models for the study of genotype-environment interactions (G x E). Simulated unbalancing of real data was used to determine if the method could predict missing genotypes and select stable genotypes. Data from multienvironment trials containing 55 maize hybrids, collected during the 2005-2006 harvest season, were used in this study. Analyses were performed in two steps: the variance components were estimated by restricted maximum likelihood, using the expectation-maximization (EM) algorithm, and factor analysis (FA) was used to calculate the factor scores and relative position of each genotype in the biplot. Random unbalancing of the data was performed by removing 10, 30, and 50% of the plots; the scores were then re-estimated using the FA model. It was observed that 10, 30, and 50% unbalancing exhibited mean correlation values of 0.7, 0.6, and 0.56, respectively. Overall, the genotypes classified as stable in the biplot had smaller prediction error sum of squares (PRESS) value and prediction 14263 Factor analysis using mixed models ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (4): 14262-14278 (2015) amplitude of ellipses. Therefore, our results revealed the applicability of the PRESS statistic to evaluate the performance of stable genotypes in the biplot. This result was confirmed by the sizes of the prediction ellipses, which were smaller for the stable genotypes. Therefore, mixed models can confidently be used to evaluate stability in plant breeding programs, even with highly unbalanced data.

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