Fig. 2

Principal component analysis for patterns among clusters of variants and symptoms. For novel gene discovery, the SNP density was determined 2 k bps ahead of transcription start sites. The Principle Component Analysis (PCA) was used to show similarity based on variances of all values (SNP density) of all samples. Each principle component (PC1, 2, 3…) with PC1 and PC2 indicating the most variance were included to show similarity/dissimilarity of samples from highest to lowest. A Fisher exact test was used to correlate clinical outcomes and generate a significant gene list clustered with hearing loss (HL) (a) or symptomatic infection (b). c A heatmap displaying hierarchical clustering performed on samples (horizontal axes) and genes (vertical) with high level SNP densities associated with outcomes. The color indicates SNP density values above (red) average and below (green) average (no color) after z-normalization (average = 0 and SD = 1)