CLASSIFYING MEXICAN MAIZE ACCESSIONS USING HIERARCHICAL AND DENSITY SEARCH METHODS

Cluster analysis is commonly used for studying genetic diversity. Prob lems with hierarchical cluster analysis include how to combine differe nt types of variables (discrete and continuous), choosing distance mea surements, applying an appropriate clustering strategy, designating th e optimal number of clusters, and identifying variables with significa nt discriminatory power. Hierarchical clustering methods are only desc riptive and do not represent probabilities for classifying individuals into groups. The objectives of this study were to: (i) examine the pe rformance of different cluster strategies based on several criteria, ( ii) propose a classification method for germplasm accessions with stat istical properties, and (iii) examine how the results of the proposed classification method can be applied to form core subsets. Morphologic and agronomic attributes collected for 115 Mexican maize (Zea mays L. ) accessions, grouped in five races, from the Latin America Maize Proj ect (LAMP) were subjected to the hierarchical cluster algorithms UPGMA (arithmetic mean method), Centroid, Median, and the Ward method. Two other techniques were studied, Density and the Normix (Nor) density se arch methods, which were both restricted to continuous variables. The Nor method was applied to groups formed ''a priori'' by means of the h ierarchical methods UPGMA, Centroid, Median, and Ward and resulted in subgroups denoted as NorU, NorC, NorM, and NorW, respectively. The Nor W method formed five well defined groups of accessions and was an appr opriate strategy for grouping accessions into relatively homogeneous g roups. Strategies UPGMA, NorU, Centroid, NorC, Median, NorM, and Densi ty were not very effective for classifying accessions into homogeneous groups. Different subsets can be formed based on the characteristics of the five homogeneous groups formed by NorW.