The determination of diversity and relationships among forty rice (Oryza sativa L.) accessions by comparative analysis of morphological and RAPD data. (40)

D.K Ojo, S.A Ogunbayo, K.A. Sanni, R. G. Guei


Morphological and molecular characterization of forty rice (Oryza sativa L.) accessions using 14 agro-botanical traits recommended by IRRI and 10 RAPD primers, respectively, was done in a field experiment in an augmented randomized complete block design. The aim of the work was to study variations and to select lines that can be used as potential parents in a future breeding programme. A principal components plot and, a dendrogram generated by cluster analysis using the unweighted pair group method with arithmetic mean (UPGMA) based on distance measures assessed for mean values of the morphological variables were used to group the accessions. Genetic relatedness among accessions based on RAPD molecular marker data (57 RAPD bands) was also presented in the bands form of a dendrogram generated by cluster analysis using (UPGMA). The relative effectiveness of the RAPD markers compared to botanical descriptors in assessing diversity among the accessions was investigated. A dendrogram was created by the UPGMA for morphological data and the 40 accessions classified into six morphological groups, whereas PCA re-ordered the accessions into four broad groups that had within cluster similarities and inter-cluster differences in morphological variation. Reaction products (bands) of the RAPD analysis were highly polymorphic, more discriminatory and informative as they were able to differentiate more pairs of accessions than the botanical descriptors. IITA rice accessions TOX 3052-46-3-3-2-1 and TOX 3027-44-1-E4-2-2 and Brazilian accessions (CL SELECCION 3B and 450) performed better in terms of yield, total tillers and number of filled tillers than accessions used as checks and could be selected for a future breeding programme.


Cluster analysis; genetic diversity; genetic relatedness; Oryza sativa L.; polymorphism; RAPD; principal component analysis

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