Categorização de dados quantitativos para estudos de diversidade genética

• Autor(a) principal: Barroso, Natália Caixeta
• Data de Publicação: 2010
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: http://locus.ufv.br/handle/123456789/4037
• topic: Categorização; Diversidade genética; Medidas de dissimilaridade; Análise de agrupamento; Categorization; Genetic diversity; Dissimilarity measures; Cluster analysis; CNPQ::CIENCIAS AGRARIAS

The genetic diversity study is an important tool in the identification of genetically divergent individuals, wich, can increase the effect of heterosis in the progeny when combinaded. A statistical technique usually applied in this type of study is the cluster analysis. However, before applying this technique, it must be obtained a similarity matrix (or distance) between the genotypes. These distances can be calculated in several ways, which different proposals are found in the literature for quantitative variables, binary and multicategoric. The transformation of quantitative variables in multicategoric can be used to facilitate their characterization with preliminary useful information. There are quite a few methods to make such changes, but they need to be better understood so that the loss of information occurred in such changes does not damage significantly the results of the analysis. Therefore the purposes of this study are: to determine which of these variables categorization methods are efficient; to research the influence of the choice of different coefficients of dissimilarity in cluster analysis, made from simulated data by using quantitative variables and multicategoric; and to investigate whether some hierarchical methods group efficiently the simulated data. For that, there were made 50 simulations of ten quantitative variables to twenty genotypes of a species of reference as corn, each one with four replications. These data were converted in multicategoric using the following methods: equitable division of amplitude, equitable percentage, square rule, Sturges rule and normal distribution. A number of classes had to be established for the first two methods, which were used four and five classes for both. Were used to create distance matrices, in the original data and multicategoric, the dissimilarity measures: Euclidean distance, the average Euclidean, squared Euclidean distance, Mahalanobis distance and weighted distance. Subsequently, the grouping was done by the method of nearest neighbor and the average linkage between groups (UPGMA). The efficiency of these was verified by the statistics of efficiency cophenetic correlation coefficient, stress and distortion degree between the phenetic and cophenetic matrices. The results showed that the cluster method UPGMA was superior to method of nearest neighbor for all distance measures used. Euclidean distances and average Euclidean showed similar performance in all cluster analysis done. Moreover, these two measures got the best performance in all groups performed. All methods of data categorization achieved a satisfactory performance when grouped by UPGMA, except the method of equal percentage with four and five classes. However, the data which have their classes estimated by the square rule had the most similar dendrogram when compared to the obtained using the original data, and therefore, this is the recommended method to perform the categorization of data.