Research Article

Multivariate analysis to determine the genetic distance among backcross papaya (Carica papaya) progenies

Published: May 14, 2012
Genet. Mol. Res. 11 (2) : 1280-1295 DOI: https://doi.org/10.4238/2012.May.14.2
Cite this Article:
(2012). Multivariate analysis to determine the genetic distance among backcross papaya (Carica papaya) progenies. Genet. Mol. Res. 11(2): gmr1563. https://doi.org/10.4238/2012.May.14.2
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Abstract

Morpho-agronomic and molecular (RAPD and ISSR markers) data were used to evaluate genetic distances between papaya backcross progenies in order to help identify agronomically superior genotypes. Thirty-two papaya progenies were evaluated based on 15 morpho-agronomic characteristics, 20 ISSR and 19 RAPD primers. Manhattan, Jaccard and Gower distances were used to estimate differences based on continuous and binary data and combined analyses, respectively. Except for production, there were significant differences in the continuous variables among the genotypes. The molecular analysis revealed 193 dominant markers (ISSR and RAPD), being 53 polymorphic loci. Among the various clusters that were generated, the one based on a combined analysis of morpho-agronomic and molecular data gave the highest cophenetic correlation (0.72) compared to individual analysis, consistently allocating the progenies into six groups. We found that the Gower algorithm was more coherent in the discrimination of the genotypes, demonstrating that a combination of molecular and agronomic data is valuable for studies of genetic dissimilarity in papaya.

Morpho-agronomic and molecular (RAPD and ISSR markers) data were used to evaluate genetic distances between papaya backcross progenies in order to help identify agronomically superior genotypes. Thirty-two papaya progenies were evaluated based on 15 morpho-agronomic characteristics, 20 ISSR and 19 RAPD primers. Manhattan, Jaccard and Gower distances were used to estimate differences based on continuous and binary data and combined analyses, respectively. Except for production, there were significant differences in the continuous variables among the genotypes. The molecular analysis revealed 193 dominant markers (ISSR and RAPD), being 53 polymorphic loci. Among the various clusters that were generated, the one based on a combined analysis of morpho-agronomic and molecular data gave the highest cophenetic correlation (0.72) compared to individual analysis, consistently allocating the progenies into six groups. We found that the Gower algorithm was more coherent in the discrimination of the genotypes, demonstrating that a combination of molecular and agronomic data is valuable for studies of genetic dissimilarity in papaya.