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Analysis of population structure and genetic diversity of Iranian Wild Salicornia (Salicornia iranica Akhani) population

Authors :
Mohammad Aghaei
Abbas Hassani
Hosein Nazemiyeh
Babak Abdollahi Mandoulkani
Mohammad Saadatian
Source :
Journal of Genetic Engineering and Biotechnology, Vol 20, Iss 1, Pp 1-15 (2022)
Publication Year :
2022
Publisher :
Elsevier, 2022.

Abstract

Abstract Background Salicornia is a halophyte plant capable of being irrigated with seawater, which can be used as an alternative food. Given this, it is necessary to study the potentials of this plant’s morphological diversity in the natural environment. In this study, 33 wild populations of Salicornia were collected from different geographical areas around Urmia Lake during the flowering stage, and 55 morphological traits and 25 ISSR loci of the plant were analyzed. Based on morphological and molecular traits and the cluster analysis, Salicornia populations were divided into four and two groups, respectively. Results Overall, the high percentage of polymorphic loci (65.69%), the average number of effective alleles per locus (1.63), and the Shannon data index (0.540) indicate that ISSR markers was used to identify genetic diversity. Molecular data cluster analysis divided the studied populations into two main groups, which included 12.12% and 87.88% of the populations, respectively. Based on the effective analysis of the population’s genetic structure and the precise classification of individuals into suitable sub-populations, the value of K=2 was calculated. Conclusions The research findings indicated that the populations of Salicornia have a considerable diversity in morphological traits. Furthermore, markers UBC823, B, A7, and K, as well as markers with the Shannon index, effective allele, and large heterozygosis values, are the most effective markers in comparison with other markers used in this study. The findings of this study will aid in parental selection studies for breeding programs of Salicornia in future.

Details

Language :
English
ISSN :
20905920
Volume :
20
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Journal of Genetic Engineering and Biotechnology
Publication Type :
Academic Journal
Accession number :
edsdoj.516651c556e47028dcc90df928f453b
Document Type :
article
Full Text :
https://doi.org/10.1186/s43141-022-00337-0