Affiliations 

  • 1 Institute of Bioscience, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia. zimisuhara@gmail.com
  • 2 Department of Biochemistry, Faculty of Biotechnology and Molecular Sciences, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia. alireza.valdiani@gmail.com
  • 3 Department of Biochemistry, Faculty of Biotechnology and Molecular Sciences, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia. noorazmi@upm.edu.my
  • 4 Institute of Bioscience, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia. fqz@upm.edu.my
  • 5 Institute of Bioscience, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia. maziahm@upm.edu.my
Int J Mol Sci, 2015 Jun 24;16(7):14369-94.
PMID: 26114389 DOI: 10.3390/ijms160714369

Abstract

Genetic structure and biodiversity of the medicinal plant Ficus deltoidea have rarely been scrutinized. To fill these lacunae, five varieties, consisting of 30 F. deltoidea accessions were collected across the country and studied on the basis of molecular and morphological data. Molecular analysis of the accessions was performed using nine Inter Simple Sequence Repeat (ISSR) markers, seven of which were detected as polymorphic markers. ISSR-based clustering generated four clusters supporting the geographical distribution of the accessions to some extent. The Jaccard's similarity coefficient implied the existence of low diversity (0.50-0.75) in the studied population. STRUCTURE analysis showed a low differentiation among the sampling sites, while a moderate varietal differentiation was unveiled with two main populations of F. deltoidea. Our observations confirmed the occurrence of gene flow among the accessions; however, the highest degree of this genetic interference was related to the three accessions of FDDJ10, FDTT16 and FDKT25. These three accessions may be the genetic intervarietal fusion points of the plant's population. Principal Components Analysis (PCA) relying on quantitative morphological characteristics resulted in two principal components with Eigenvalue >1 which made up 89.96% of the total variation. The cluster analysis performed by the eight quantitative characteristics led to grouping the accessions into four clusters with a Euclidean distance ranged between 0.06 and 1.10. Similarly, a four-cluster dendrogram was generated using qualitative traits. The qualitative characteristics were found to be more discriminating in the cluster and PCA analyses, while ISSRs were more informative on the evolution and genetic structure of the population.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.