Application of ISSR Markers to Analyze Molecular Relationships in Iranian Jasmine (Jasminum spp.) Accessions

Abstract

There are many species of jasmines in different regions of Iran in natural or cultivated form, and there is no information about their genetic status. Therefore, inter-simple sequence repeat (ISSR) analysis was used to evaluate genetic variations of the 53 accessions representing eight species of Jasminum collected from different regions of Iran. A total of 21 ISSR primers were used which generated 981 bands of different sizes. Mean percentage of polymorphic bands was 90.64 %. Maximum resolving power, polymorphic information content average, and marker index values were 21.55, 0.35, and 14.42 for primers of 3, 4, and 3 respectively. The unweighted pair group method with arithmetic mean dendrogram based on Jaccard's coefficients indicated that 53 accessions were divided into two major clusters. The first major cluster was divided into two subclusters; the subcluster A included Jasminum grandiflorum L., J. officinale L., and J. azoricum L. and the subcluster B consisted of three forms of J. sambac L. (single, semi-double, and double flowers). The second major cluster was divided into two subclusters; the first subcluster (C) included J. humile L., J. primulinum Hemsl., J. nudiflorum Lindl. and the second subcluster (D) consisted of J. fruticans L. At the species level, the highest percentage of polymorphism (34.05 %), numbers of effective alleles (1.16), Shannon index (0.151), and Nei's genetic diversity (0.098) were observed in J. officinale. The lowest values of percentage polymorphism (0.011), number of effective alleles (1.009), Shannon index (0.007), and Nei's genetic diversity (0.005) were obtained for J. nudiflorum. Based on pairwise population matrix of Nei's unbiased genetic identity, the highest identity (0.85) was found between J.officinale and J. azoricum and the lowest identity (0.69) was between J. grandiflorum and J. perimulinum. Based on analysis of molecular variance, the amount of genetic variations among the eight populations was 83 %. This study demonstrated that the ISSR is an useful tool in jasmine genomic diversity studies and to detect their relationships.

Genetic diversity and structure of Elymus tangutorum accessions from western China as unraveled by AFLP markers

“…This values also agree with AFLP studies done with other forage grass such as Dactylis glomerata [47] and Phalaris aquatica [48]. The Marker index (MI) and resolving power (RP), two importantly alternative parameters in selecting informative markers for diversity studies [49, 50], had also displayed high efficiency of AFLP markers in unveiling polymorphisms in E. tangutorum germplasm. A very strong and positive correlation observed among PIC, RP and MI value in present work indicates the use of any of three parameters to screen the informative primer combinations [38]; although the relationship between the informativeness of molecular markers for identifying germplasms and these marker parameters is not totally clear [46].…”

Assessment of genetic diversity, population structure and relationships in Indian and non-Indian genotypes of finger millet (Eleusine coracana (L.) Gaertn) using genomic SSR markers

“…However, in the present study, we have obtained highest CCC value of 0.9216 which is associated with acceptable genetic similarity matrix. Similarly, Ghasemi Ghehsareh et al ( 2015 ) also obtained highest CCC value of 0.9968 in 53 genotypes representing eight species collected from Iran using microsatellite markers.…”

Genetic Diversity as Revealed by Intersimple Sequence Repeat Polymorphism in Narcissus Accessions to Identify the Tolerant Genotypes for Deficit Irrigation

“…Prevost and Wilkinson (1999) found that Rp can be used to evaluate the ability of each primer for differentiating among several accessions. In general, primers with a higher Rp value can better distinguish among accessions (Ghasemi Ghehsareh et al, 2014).…”

“…The ability of each primer to differentiate between accessions was assessed by calculating their resolving power [Rp (Deore and Samantaray, 2015;Gilbert et al, 1999)], marker index [MI (Ghasemi Ghehsareh et al, 2014;Powell et al, 1996)], and polymorphic information content [PIC (Deore and Samantaray, 2015;Ghasemi Ghehsareh et al, 2014;Weising et al, 2005)]. PIC was calculated as PIC = 1 -pi 2 -qi 2 , where p is the band rate and q is no band rate (Chadha and Gopalakaishna, 2007;Deore and Samantaray, 2015).…”

“…PIC was calculated as PIC = 1 -pi 2 -qi 2 , where p is the band rate and q is no band rate (Chadha and Gopalakaishna, 2007;Deore and Samantaray, 2015). MI was calculated as MI = PIC • effective multiplex ratio (EMR) and Rp = P Ib, Ib (band in formativeness) = 1 -(2 • [0.5 -p]), where p is the proportion of each accession comprising the band (Ghasemi Ghehsareh et al, 2014). GenAlEx 6.4 (Peakall and Smouse, 2006) was used to calculate the percentage of polymorphic fragments, and the parameters of PIC, EMR, MI, and Rp were calculated by Excel (2010 for Windows; Microsoft Corp., Redmond, WA).…”