2.4 Statistical Data Analysis
All 33 SSR loci were evaluated for their adherence to the Hardy-Weinberg
equilibrium and the presence of null alleles using the heterozygous
deficiency method in Genepop v4.7 (Brookfield, 1996). The presence or
absence of linkage disequilibrium between loci was confirmed by testing
for genotypic linkage disequilibrium. The genetic parameters for each
population were assessed by calculating various parameters, including
allelic richness (A), mean observed heterozygosity
(H O), percentage of polymorphic loci (P), and
unbiased expected heterozygosity (H E). All
genetic diversity estimates were calculated using GenAlEx 6.5.01
software (Peakall and Smouse, 2006). Further, the fixation index
(F ST) values were calculated using FSTAT version
2.9.3 and Arlequin 3.5 (Excoffier and Lischer, 2010) to investigate the
species/population differentiation. The AMOVA was performed in Arlequin
3.5 to determine the proportion of genetic variance explained by the
differences within and among species/populations. Furthermore, a
model‐based program, STRUCTURE 2.3.4 (Pritchard et al., 2000),
was used to infer the number of distinct genetic clusters and to assign
individuals to a specific genetic cluster using default parameters. The
program was executed with 10 independent runs for each value of K
ranging from 1 to 10, each with 1,000,000 Markov chain Monte Carlo
replications, following a 100, 000 burn-in period. The admixture
ancestry model and a correlated allele frequency model were used for all
runs. The STRUCTURE HARVESTER online application was utilized to
determine the estimated numbers of genetic components (K values) (Evannoet al., 2005; Verkuil et al., 2012). Clustering patterns
and population structure inferences were determined throughout the K
using the web tool CLUMPAK (Jakobsson and Rosenberg, 2007; Kopelmanet al., 2015). Both inter- and intra‐specific genetic structures
of the different populations were assessed using multivariate principal
component analyses (PCA) via multivariate principal component analyses
(PCA) through the dudi.pca() function of “adegenet” R package (Jombart
and Ahmed, 2011). The UPGMA clustering analysis of all populations was
performed based on Nei’s (1972) unbiased genetic distance using the
PowerMarker software (Liu and Muse, 2005), and the resulting tree was
visualized with TREEVIEW ver. 1.52. The Venn diagram was tabulated using
the number of private alleles identified by genetic analysis for
cultivated (inbred, landraces, and feral rice), wild, and weedy types.
MIGRATE v. 4.4.4 (Beerli, 2008) was used to estimate effective
population size Ne (θ/4μ) and asymmetric gene flow M (m/μ) between pairs
of different Oryza groups found in Sri Lanka. The analyses were
conducted using Bayesian inference under the structured Coalescent
model. First, two shorter runs (10 short chains of 10,000 sampled, 500
records and three final chains of 100,000, 5,000 recorded) were
performed. Then, a long final run (10 short chains of 10,000 sampled,
500 recorded, and three final chains of 500,000 sampled and 25,000
recorded) was performed, and results from this final run were reported.