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      Emprego de modelos gráficos na seleção de genitores de milho para hibridização e mapeamento genético Translated title: Use of graphic models in maize parental selection for hybridization and mapping

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          Abstract

          A dissimilaridade genética estimada por meio de marcadores moleculares, quando acompanhada de informações fenotípicas, é importante para a seleção de genótipos para o melhoramento e o mapeamento genético. Desta forma, os objetivos deste estudo foram: i) estimar a dissimilaridade genética entre 30 linhagens de milho contrastantes para a tolerância ao encharcamento; ii) selecionar genitores para mapeamento e melhoramento genético; iii) comparar diferentes métodos de visualização gráfica das distâncias. Foram utilizados 21 iniciadores de RAPD. A dissimilaridade genética foi estimada por meio do complemento do coeficiente de similaridade de Dice, posteriormente foi construído um dendrograma pelo método de agrupamento da distância média e calculado o coeficiente de correlação cofenética entre a matriz de dissimilaridade e o dendrograma gerado. O complemento da matriz de similaridade foi submetido também à análise de componentes principais e de escala multidimensional. Para ambas as análises, foi testada a eficiência das projeções, por meio da correlação entre as distâncias originais e as representadas nos gráficos. As técnicas de agrupamento não revelaram um bom ajuste entre as distâncias apresentadas graficamente e a matriz original de distâncias, com correlações de 0,70, 0,53 e 0,75 para o dendrograma, componentes principais e análise de escala multidimensional, respectivamente. Dentre as técnicas de agrupamento empregadas, a que atendeu de forma mais precisa aos objetivos do trabalho foi a análise multidimensional, uma vez que esta, além de apresentar a maior correlação com a matriz original de distâncias, preservou as distâncias entre todos os pares de genótipos. Além disso, esta técnica é a mais indicada quando o objetivo do trabalho é a definição de cruzamentos, pois ela permite uma observação mais fácil das distâncias entre todos os pares de genótipos.

          Translated abstract

          Associating phenotypic to molecular data can be a powerful tool for the selection of parental genotypes for breeding and mapping purposes. Thus, the objectives of the study were: i) to estimate the genetic dissimilarity among 30 maize inbred lines (15 tolerant and 15 sensitive to flooding); ii) to select potential parents for mapping and breeding; iii) to compare the efficiency of different graphical models in displaying the calculated distances. A total of 21 RAPD primers were used for the estimation of genetic dissimilarity. The genetic dissimilarity was obtained according to the complement of Dice similarity coefficient, clustering procedure was performed by the average linkage method and the cophenetic coefficient was obtained. The complement of Dice similarity coefficient was subjected to principal components and multidimensional scale analyses, and the output efficiency was tested by the correlation between the original distances and those presented in the graphs. The clustering techniques did not reveal a perfect agreement with the original matrix, with correlations of 0.70, 0.53 and 0.75 for the dendrogram, principal components and multidimensional scale analyses, respectively. Among the tested techniques employed, multidimensional scale analyses gave more precise outputs, since this technique showed higher agreement with the original distance matrix, and preserved distances between all genotype pairs. Besides, this technique is the most indicated when the objective is to plan crosses, since it displays the distances between genotype pairs.

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          Introduction to Quantitative Genetics

          This is an introductory textbook with the emphasis on general principles rather than on practical applications. It covers a range of topics in genetics, including mutation, and this edition seeks to include the developments of the 20 years since the first edition and to provide more material on plants. Though the mathematics does not go beyond simple algebra (neither calculus nor matrix methods are used), the author does assume a knowledge of statistics, particularly of the analysis of variance and of correlation and regression. separately, at the end of the relevant chapter. Solutions are provided.
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              Multivariate Data Analysis with Readings

              Key terms what is muitivariate analysis, impact of computer revolution and multivariate analysis defined. Multiple regression analysis, multiple discriminant analysis and multivariate analysis of variance. Canonical correlation analysis, factor analysis, cluster analysis, muldidimensional scaling, canjoint analysis, structural equation modeling, a mathematical representation in LISREL, notation. Path analysis: a method of computing structural coefficients. Overall goodness-of Fit measures for structural equation modeling. Application of multivariate data analysis.
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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Journal
                cr
                Ciência Rural
                Cienc. Rural
                Universidade Federal de Santa Maria (Santa Maria )
                1678-4596
                October 2005
                : 35
                : 5
                : 986-994
                Affiliations
                [1 ] Universidade Federal de Pelotas Brazil
                [2 ] Universidade Federal de Pelotas Brazil
                Article
                S0103-84782005000500002
                10.1590/S0103-84782005000500002
                b68c8cc2-a671-42e0-a0cd-170ef674c430

                http://creativecommons.org/licenses/by/4.0/

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                Product

                SciELO Brazil

                Self URI (journal page): http://www.scielo.br/scielo.php?script=sci_serial&pid=0103-8478&lng=en
                Categories
                AGRONOMY

                Horticulture
                Zea mays,flooding tolerance,clustering techniques,resistência ao encharcamento,técnicas de agrupamento

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