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 noncoding regions. We sought for non-coding variants to 
determine the clinical validity of common CADD scores.
METHODS: We evaluated 12,391 unique SNVs in 624 patient samples submitted for 
germ-line mutation testing in a cancer-related gene panel. Stratifying by 
genomic region, we compared the distributions of CADD scores of rare SNVs, SNVs 
common in our patient population, and the null distribution of all possible 
SNVs.
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 the rare intronic variants with the highest scores. The 
receiver-operating characteristics (ROC) area under the curve (AUC) for 
noncoding 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.Genet Med 18 12, 1269-1275.