Molecular Plant Breeding 2015, Vol.6, No.19, 1
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The ISSR analysis performed was tested for marker
reproducibility/genotyping errors, by repeating the
experiment 3 times and scoring only the sharp and
consistent bands obtained (Pompanon et al., 2005).
Moreover, the initial DNA material was obtained from
10 randomly collected leaves in each replication. Although
we did not calculate error rate for the studied samples,
we performed Hickory test for ISSR data analysis that
is Bayesian approach based method and run the
program for 3 times for consistency. Moreover, we
used no DNA sample in ISSR-PCR for each primer.
3.3Data analyses
3.3.1 Genetic diversity and population structure
ISSR bands obtained were coded as binary characters
(presence = 1, absence = 0). The genetic diversity
parameters like, Nei’s gene diversity (H), Shannon
information index (I), number of effective alleles, and
percentage of polymorphism (Freeland et al., 2011;
Weising et al., 2005), were determined. Nei’s genetic
distance was used for clustering (Weising et al., 2005;
Freeland et al., 2011). Neighbor Joining (NJ) and
Ward clustering as well as PCoA (Principal coordinate
analysis) were used for population grouping, after
1000 times bootstrapping/ and or permutations (Podani,
2000; Freeland et al., 2011).The Mantel test was
performed to check correlation between geographical
and the genetic distances of the studied populations
(Podani, 2000). PAST ver. 2.17 (Hamer et al
.
, 2012)
and, DARwin ver. 5 (2012) programs were used for
these analyses.
AMOVA (Analysis of molecular variance) test (with
1000 permutations) as implemented in GenAlex 6.4
(Peakall and Smouse, 2006), and Nei
,
Gst analysis of
GenoDive ver.2 (2013) (Meirmans and Van Tienderen
2004), were used to reveal significant genetic difference
among the studied species (Sheidai et al., 2014).
The population genetic differentiation was studied by
G'st_est = standardized measure of genetic differentiation
(Hedrick, 2005), and D_est = Jost measure of
differentiation (Jost, 2008) and CVA (Canonical variate
analysis) method (Podani, 2000).
In order to overcome potential problems caused by the
dominance of ISSR markers, a Bayesian program,
Hickory (ver. 1.0) (Holsinger and Lewis, 2003), was
used to estimate parameters related to genetic structure
(theta B value) (Tero et al., 2003).
Bayesian based model STRUCTURE analysis (Pritchard
et al., 2000), with 10
5
permutations and admixture
method was used to study the genetic structure of
populations (Sheidai et al., 2014). For STRUCTURE
analysis, data were scored as dominant markers
(Falush et al., 2007).
The optimum number of genetic groups (k) was
determined by 1- Evanno test (Evanno et al
.
, 2005)
performed on STRUCTURE result and 2- K-Means
clustering method (Sheidai et al., 2014).
3.3.2 Gene flow
Gene flow was determined by different approaches. 1-
Calculating Nm an estimate of gene flow from Gst by
PopGene version 1.32 (1997) as: Nm = 0.5(1 - Gst)/Gst.
This approach considers equal amount of gene flow
among all populations. 2- STRUCTURE analysis
based on admixture model and Bayesian approach
(Pritchard et al., 2000), and 3- population assignment
test based on maximum likelihood as performed in
Genodive ver. in GenoDive ver. 2. (2013).
Frichot et al. (2013) latent factor mixed models(LFMM)
was used to check if ISSR markers show correlation
with environmental features of the studied populations.
The analysis was done by LFMM program Version:
1.2 (2013).
References
Brach A.R., and Song H., 2006,
Taxon, 55(1): 188-192
Bureš P., Wang Y., Horova L., and Suda J., 2004, Genome size variation in
central European species of
Cirsium
(Compositae) and their natural
hybrids, Annals of Botany, 94: 353-363
Evanno G., Regnaut S., Goudet J., 2005, Detecting the number of clusters of
individuals using the software STRUCTURE: a simulation study,
Molecular Ecology, 14, 2611-2620
Falush D., Stephens M., and Pritchard J.K., 2007, Inference of population
structure using multilocus genotype data: dominant markers and null
alleles. Molecular Ecology Notes, 7: 574-578
Frankham R., Ballou J.D., and DBriscoe D.A., 2002, Introduction to
conservation genetics. Cambridge University Press, Cambridge, UK.
Freeland J.R., Kirk H., and Peterson S.D., 2011, Molecular Ecology (2
nd
ed),
UK: Wiley-Blackwell, pp. 449
Frichot E., Schoville S.D., Bouchard G., and Francois O., 2013, Testingfor
Associations between Loci and Environmental Gradients Using Latent
Factor Mixed Models. Molecular Biology and Evolution, 30: 1687–1699
Hamer Øm., Harper D.A.T.,and Ryan P.D., 2012, PAST: Paleontological Statistics
software package for education and data analysis, Palaeontol Elect 4: 9
Hedrick P., 2005, Genetics of Populations, 3rd edn, Jones and Bartlett
