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Genetic Cluster Number Of Genetic Clusters

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2.5 Number of genetic clusters
To infer genetic cluster number (K) in our sample set, we used two Bayesian approaches based on the clustering method which differed in that they: a) incorporate or not a null allele model, and b) use a non-spatial or spatial algorithm. We selected this approach because Bayesian models capture genetic population structure by describing the genetic variation in each population using a separate joint posterior probability distribution over loci. First, we used STRUCTURE v.2.3.3 (Falush et al., 2003; Pritchard et al., 2000), which does not incorporate a null allele model, but uses a non-spatial model based on a clustering method and it is able to quantify the individual genome proportion from each inferred population. A previous run had been carried out to define what ancestry models (i.e. no admixture model and admixture model) and allele frequency models (i.e. correlated and uncorrelated allele frequency models) fit our dataset. All these previous runs were conducted with locality information prior to improving the detection of structure when this could be weak (Hubisz et al., 2009). Run parameters of previous simulations included five runs with 50,000 iterations following a burn-in period of 5,000 iterations for K = 1–10 as number of tested clusters. Before choosing models to run our dataset we evaluated Evanno’s index ΔK (Evanno et al., 2005), to identify whether different models yielded different K values, implemented in STRUCTURE HARVESTER

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