Abstract
Accurate detection of pathogenic single nucleotide variants (SNVs) is a key challenge in whole exome and whole genome sequencing studies. To date, several in silico tools have been developed to predict deleterious variants from this type of data. However, these tools have limited power to detect new pathogenic variants, especially in non-coding regions. In this study, we evaluate the use of a new metric, the Shannon Entropy of Locus Variability (SELV), calculated as the Shannon entropy of the variant frequencies reported in genome-wide population studies at a given locus, as a new predictor of potentially pathogenic variants in non-coding nuclear and mitochondrial DNA and also in coding regions with a selective pressure other than that imposed by the genetic code, e.g splice-sites. For benchmarking, SELV was compared to predictors of pathogenicity in different genomic contexts. In nuclear non-coding DNA, SELV outperformed CDTS (AUC(SELV) = 0.97 in ROC curve and PR-AUC(SELV) = 0.96 in Precision-recall curve). For non-coding mitochondrial variants (AUC(SELV) = 0.98 in ROC curve and PR-AUC(SELV) = 1.00 in Precision-recall curve) SELV outperformed HmtVar. Moreover, SELV was compared against two state-of-the-art ensemble predictors of pathogenicity in splice-sites, ada-score, and rf-score, matching their overall performance both in ROC (AUC(SELV) = 0.95) and Precision-recall curves (PR-AUC = 0.97), with the advantage that SELV can be easily calculated for every position in the genome, as opposite to ada-score and rf-score. Therefore, we suggest that the information about the observed genetic variability in a locus reported from large scale population studies could improve the prioritization of SNVs in splice-sites and in non-coding regions.