Results from the Bayesian clustering analysis in STRUCTURE, using k=2. Each population represented by a vertical line with different colors representing the assignments probabilities to each of the clusters.
<p>We inferred population structure using a Bayesian
clustering algorithm as implemented in the software STRUCTURE (Pritchard et al.
2000, Falush et al. 2003). The most probable number of genetic clusters based
on the log probability of the data was inferred following the method of Evanno
et al. (2005). We varied the number of genetic clusters (K) from 1 to 10 and
ran 10 independent simulations for each k with a burn-in period of 50,000
iterations, followed by 50,000 Markov chain Monte Carlo steps. For all
simulations, the admixture model was used. The distribution
of log- likelihoods for the number of genetic clusters (K) from the Bayesian
clustering analysis with STRUCTURE peaked at estimates of k=2. In this k, 52.9
% of individuals could be assigned to one of the clusters with more than 50%
probability. The
∆k method by Evanno et al (2005) favored k=2 than the others, with a seven–fold
higher value of ∆k. The assignment of this cluster with respect to population
is illustrated in Fig. 3. The most individuals collected from Gonbad and
Esfahan regions were assigned to cluster 1; whereas, individuals assigned with
high probability to cluster 2 were found in Moghan, Sabzvar and Zarghan
regions. Most individuals from Kalposh and Miandoab regions were assigned in
cluster 2.</p><p></p><p></p
