Comparison of coefficients and distance measurements in passion fruit plants based on molecular markers and physicochemical descriptors

Abstract

ABSTRACT. We investigated seven distance measures and 14 similarity coefficients in a set of observations of variables of the 'yellow' passion fruit plant (Passiflora edulis Sims), submitted to multivariate analyses (distance, projection and grouping). Fourteen genotypes were characterized, based on DNA amplification with 16 random amplified polymorphic DNA primers and the assessment of nine fruit physical-chemical descriptors. The distance measurements and the similarity coefficients were compared by the Spearman correlation test, projection in two-dimensional space and grouping efficiency, using five grouping methods; the genotype ranking varied with the different techniques. Coler-Rodger distance measures, Euclidean distance square measures and Yule similarity coefficients proved to be inadequate for projection in two-dimensional space or for group- ing matrices. Regardless of the origin of the distance matrix, the unweighted pair group method with arithmetic mean grouping method proved to be the most adequate. The various distance measurements, similarity coefficients and grouping methods gave different values of distortion, cophenetic correlation and stress; they influence the characterization of genetic variability and this should be taken into account for studies of yellow passion fruit plants.

Genetic diversity in yellow passion fruit (Passiflora edulis Sims) based on RAPD

“…Thus, the variability observed between the genotypes of P. edulis was visually represented by a dendrogram and not by scatterplots. The results observed for the stress values and use of UPGMA agreed with the previous, related to the effectiveness of different methods of multivariate statistics in the characterization of variability in P. edulis (Cerqueira-Silva et al 2009b).…”

“…The following multivariate analysis procedures were carried out: i) estimation of the genetic similarity (gd ij = 1 -gs ij , where similarity = gs ij and dissimilarity = gd ij ), based on the coefficient of Dice (1945); ii) genotype grouping by the unweighted pair-group method with arithmetic mean (UPGMA), selected for the lowest values of distortion and stress, as well as the highest cophenetic correlation for the species, as applied by Cerqueira-Silva et al (2009b); iii) two-dimensional data projection and; iv) evaluation of cluster quality and two-dimensional data projection based on distortion, stress and correlation estimates.…”

Multivariate analysis of morphological characteristics of two species of passion flower with ornamental potential and of hybrids between them

“…Although the estimation of genetic parameters provides such information, extremely useful for plant breeders, studies of this kind for the genetic improvement of passifloras as ornamental plants have not been developed, whereas there are many researchs on the cultivated species (Viana et al, 2004;Moraes et al, 2005;Gonçalves et al, 2007;Silva et al, 2009). In passion fruit plants, multivariate analysis is applied especially in genetic divergence studies (Crochemore et al, 2003;Primot et al, 2005;Cerqueira-Silva et al, 2009;Viana et al, 2010).…”

Genetic variability in wild genotypes of Passiflora cincinnata based on RAPD markers

“…Multivariate statistical analysis was performed: i) estimate on the complement of genetic similarity (dg ij = 1 -sg ij ; similarity = sg ij and dissimilarity = dg ij ) based on the coefficient of Dice (1945); ii) grouping of genotypes by using the method of arithmetic averages (UPGMA), the latter being selected from other hierarchical methods (Ward, Gower, more distant neighbor, and nearest neighbor) for presenting the lowest distortion and stress values, as well as the highest values of cophenetic correlations for the species (Cerqueira- Silva et al, 2009); iii) the projection of data in the two-dimensional space, and iv) assessing the quality of clusterings and the projection into the two-dimensional space by estimating distortion values, stress and correlation.…”