Multivariate analysis from SSR and morphological data in chickpea (Cicer arietinum L.) for breeding purposes

Abstract

AbstractIn order to enhance genetic potential of chickpea materials from the National University of Córdoba Breeding Programme and Germplasm collection (Argentina), a study for a comprehensive understanding of the amount and pattern of genetic variation within and between genotypes was carried out by applying a multivariate analysis form single simple repeats (SSR) and morphological data. Molecular data were also used to determine the discriminating power for genotype identification, and to find the optimal primer combination to ensure unambiguous identification. With the analysis of 15 SSR markers on 53 genotypes, a total of 58 alleles were detected with individual values ranging from one to nine alleles per locus. High values of discriminating power (Dj ⩾ 0.7, PIC ⩾ 0.7), and low values of confusion probability (Cj ⩽ 0.23) were obtained for at least four evaluated markers. The combination of TA113 + TA114 + H1B09 + TA106 primers was effective for discriminating the 53 chickpea genotypes with a cumulative confusion probability value (Ck) of 9.60 × 10−4. Except for some exceptions, individual chickpea genotypes within a cluster in the consensus tree were definitely more closely related with each other by the origin or pedigree. The results confirmed that both multivariate data analysis methods, ordination and clustering, were complementary. In most genotypes, discriminant principal component analysis classification was consistent with the original clusters defined by molecular data. Differences in results from molecular and morphological data indicate that they provide complementary and relevant information for establishing genetic relationships among chickpea materials and a better description and interpretation of the available variability in the germplasm collection.