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SUPPLEMENTARY MATERIALS AND METHODS
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Population structure analyses
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The pruned dataset of modern SNPs was used to calculate pairwise population Fst genetic
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distances using the Arlequin package v. 3.5.1.2 and a graphical representation of the obtained
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distance matrix was drawn by means of metric multidimensional scaling (MDS) (Cox and
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Cox, 2001). Apportionment of genetic variance among the observed groups of populations
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was then investigated with a locus-by-locus Analysis of the Molecular Variance (AMOVA)
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(Excoffier et al., 1992).
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Admixture analysis
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The model-based maximum likelihood (ML) clustering algorithm implemented in the
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Admixture software (Alexander et al., 2009) was applied to explore potential population
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structure at the loci included in the pruned dataset. ML estimates for SNPs allele frequencies
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in a predefined number (K) of hypothetical ancestral populations, as well as the probabilistic
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assignment of each individual to these clusters, were obtained. Population structure was
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investigated at K=2 to K=5. The matrices of obtained ancestry fractions for each K value were
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imported into R version 2.15.1 (http://www.r-project.org/) and used to generate stacked
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barplots. A cross-validation (CV) procedure was applied to identify the K for which the model
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has the best predictive accuracy.
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Multivariate analyses
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The pruned dataset of SNPs was also used for applying multivariate analyses. Principal
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Components Analysis (PCA) was performed using the R adegenet package. Moreover, to
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provide further support to the identified population groups, evaluation of cluster membership
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probabilities for each individual was achieved by means of Discriminant Analysis of Principal
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Components (DAPC) (Jombart et al., 2010). In fact, this procedure is particularly well suited
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for depicting diversity patterns observable among pre-defined groups of observations. DAPC
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was repeated with different randomized groups for different numbers of retained PCs, whose
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optimal number (20) was identified as that optimizing the mean α-score (i.e. the closest to
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one) obtained as the difference between observed and random discriminations. Retained PCs
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were passed to a Linear Discriminant Analysis that constructed discriminant functions as
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linear combinations of the original variables in order to show the largest between-group
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variance and the smallest within-group variance. Given the low number of clusters identified
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by the other population structure analyses, all discriminant functions were retained and used
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to compute individuals’ membership probabilities.
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DAPC also allows to deal with “supplementary individuals”, that are observation which do
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not actually participate to the model construction, but which can be predicted using a model
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fitted on a different dataset. Data from the archaic species were thus not included in DAPC,
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but were transformed using the centering and scaling of the modern data and, according to the
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same discriminant coefficients as for the contributing individuals, were subsequently
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represented onto the obtained discriminant functions.
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Clustering analysis
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Scores from the 40 most informative PCs obtained by PCA, accounting for about 85% of the
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observed variation, were used to perform a cluster analysis via the Model Based Clustering
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algorithm (Fraley and Raftery, 2002) implemented in the R mclust package. This algorithm
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explores a set of ten different models for Expectation-Maximization (EM), each one being
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characterized by a different parameterization of the covariance matrix, for different number of
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clusters, finally choosing the best one according to the highest Bayesian Information Criterion
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(BIC). This procedure enabled the definition of parameters of both the maximum-BIC model
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and the corresponding classification (i.e. the affiliation of each individual to one of the
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inferred clusters).
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REFERENCES
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Alexander DH, Novembre J, Lange K (2009). Fast model-based estimation of ancestry in
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unrelated individuals. Genome Res 19: 1655-1664.
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Cox TF, Cox MAA (2001). Multidimensional Scaling. Chapman & Hall: London.
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Excoffier L, Smouse PE, Quattro JM (1992). Analysis of molecular variance inferred from
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metric distances among DNA haplotypes: application to human mitochondrial DNA
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restriction data. Genetics 131: 479-491.
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Fraley C, Raftery AE (2002). Model-based Clustering, Discriminant Analysis and Density
Estimation. J Amer Statist Assoc 97: 611-631.
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Jombart T, Devillard S, Balloux F (2010). Discriminant analysis of principal components: a
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new method for the analysis of genetically structured populations. BMC Genetics 11: 94.
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