Fig. 1

2D plot of the Principal Component Analysis (PCA) computed on the 1046 × 112 ternary matrix. PCA is a dimensionality reduction technique that computes an orthogonal linear transformation of the data to a new 2D coordinate system so that the greatest variance is on the x-axis (PC1) and the second greatest variance on y-axis. Each dot represents a patient, that is plotted in the 2D space accordingly to its genetic profile expressed in the ternary matrix. PC1 and PC2 show the main sources of variance in our data, accounting for 22% of overall variance, that are represented by variants on MAPT and NOTCH3 genes, respectively. PCA plot and hierarchical clustering recognize clusters that correspond to the MAPT haplotypes on the x-axis, as shown by coloured labels in the picture legend. Similarly, the distribution along the y-axis matches haplotypes in the notch3 gene (not shown)