Fst analysis in the main geographic regions.

<p>Fst analysis in the main geographic regions.</p

Loci and alleles with the strongest impact on the SpatialPCA eigenvalues 1 and 2.

<p>Loci and alleles with the strongest impact on the SpatialPCA eigenvalues 1 and 2.</p

Maps of: A) scores for the 41 locations in sPC1 obtained on the full dataset with adegenet; B) scores in sPC2 obtained as in A; C) posterior assignment probabilities of the 41 locations to either of two clusters obtained on the reduced dataset derived from sPC1 with Geneland; D) posterior assignment probabilities of the 41 locations to either of two clusters obtained on the reduced dataset derived from sPC2 with Geneland.

<p>In A and B white and black squares represent negative and positive scores, respectively, with square size proportional to the absolute value (inset in panel A). In each of panels C and D shades of grey indicate probabilities of assignment to one of two mutually exclusive clusters from 0 (dark grey) to 1 (white). Color versions of panels C and D are reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167065#pone.0167065.s007" target="_blank">S7 Fig</a>.</p

Representation of effective migration surfaces as obtained with EEMS on the reduced datasets derived from sPC 1 (A) and sPC2 (B).

<p>The coloured area covers only the user-defined polygon. The grid used by the program is shown in grey. Note that only 34 sampled demes appear (black dots, with size proportional to the n. of individuals), assigned to a grid vertex and not necessarily coinciding exactly with the original sampling location. Pooled locations were (numbered as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167065#pone.0167065.s011" target="_blank">S1 Table</a>): 6+7, 9+10, 13+14+15+16, 25+26, 30+32. Note the different colour scales between the two maps. In both maps brown belts correspond to low migration values, i.e. barriers to gene flow.</p

Demographic and basic clinical characteristics of the study population.^a^, ^b

<p>Demographic and basic clinical characteristics of the study population.<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149052#t001fn002" target="_blank">^a</a>^, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149052#t001fn003" target="_blank">^b</a></p

Dendrogram and heatmap representations of the results of the cluster analysis “MB & OB” (a), “MB & VB” (b), “OB & VB” (c), and “MB & OB & VB” (d).

<p>For every case, hierarchical clustering using Euclidean metric and average linkage was employed and both patients and genes were clustered. For ease of demonstration, only the dendrograms for clustering of the patients are shown in the heatmaps. As the figure shows, the algorithm and the gene sets implemented successfully clustered distinct expressions of Behçet’s disease. The gene sets used for clustering were constituted from the DEGs identified during the corresponding class comparisons (i.e., MB <i>vs</i> OB, MB <i>vs</i> VB, OB <i>vs</i> VB, and MB <i>vs</i> OB <i>vs</i> VB).</p

Matches between the loci identified in the genome-wide association and the genome-wide linkage studies of Behçet’s disease and the loci of the differentially expressed genes documented in the present study.^a

<p>Matches between the loci identified in the genome-wide association and the genome-wide linkage studies of Behçet’s disease and the loci of the differentially expressed genes documented in the present study.<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149052#t005fn002" target="_blank">^a</a></p

The top 20 most differentially expressed genes in the class comparison analysis.^a

<p>The top 20 most differentially expressed genes in the class comparison analysis.<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149052#t003fn001" target="_blank">^a</a></p

Dendrogram and heatmap representations of the results of the initial cluster analysis “MB & VB” (a) and “MB & OB & VB” (b).

<p>For both cases, hierarchical clustering using Euclidean metric and average linkage was employed and both patients and genes were clustered. For sake of simplicity, only the dendrograms for clustering of the patients are shown in the heatmaps. Take note of the position of VB1 (study ID 2, GSM428038) in the MB branch of the dendrograms. Based on these clustering results, VB1 was excluded prior to further analysis. Also, as can be seen in the figure, the cluster analysis successfully clustered distinct expressions of Behçet’s disease and with the exception of VB1, the expression profiling based clustering results were in accordance with the manifestation based clustering of Behçet’s disease patients. The gene sets used for clustering were constituted from the DEGs identified during the corresponding class comparisons (i.e., MB <i>vs</i> VB and MB <i>vs</i> OB <i>vs</i> VB).</p

Clustered heatmap representations of <i>TNFAIP3</i> and <i>CD69</i> expressions in class comparisons MB <i>vs</i> C (a), OB <i>vs</i> C (b), and VB <i>vs</i> C (c).

<p><i>CD69</i>, cluster of differentiation 69; <i>TNFAIP3</i>, tumor necrosis factor, α-induced protein 3.</p