CADD score has limited clinical validity for the identification of pathogenic variants in non-coding regions in a hereditary cancer panel

PURPOSE: Several in silico tools have been shown to have reasonable research sensitivity and specificity for classifying sequence variants in coding regions. The recently-developed Combined Annotation Dependent Depletion (CADD) method generates predictive scores for single nucleotide variants (SNVs) in all areas of the genome, including non-coding regions. We sought to determine the clinical validity of non-coding variant CADD scores. METHODS: We evaluated 12,391 unique SNVs in 624 patient samples submitted for germline mutation testing in a cancer-related gene panel. We compared the distributions of CADD scores of rare SNVs, common SNVs in our patient population, and the null distribution of all possible SNVs stratifying by genomic region. RESULTS: The median CADD scores of intronic and nonsynonymous variants were significantly different between rare and common SNVs (p<0.0001). Despite these different distributions, no individual variants could be identified as plausibly causative among rare intronic variants with the highest scores. The ROC AUC for non-coding variants is modest, and the positive predictive value of CADD for intronic variants in panel testing was found to be 0.088. CONCLUSION: Focused in-silico scoring systems with much higher predictive value will be necessary for clinical genomic applications.