Next-generation sequencing in clinical diagnostics is providing valuable genomic 
variant data, which can be used to support healthcare decisions. In silico tools 
to predict pathogenicity are crucial to assess such variants and we have 
evaluated a new tool, Combined Annotation Dependent Depletion (CADD), and its 
classification of gene variants in Lynch syndrome by using a set of 2,210 DNA 
mismatch repair gene variants. These had already been classified by experts from 
InSiGHT's Variant Interpretation Committee. Overall, we found CADD scores do 
predict pathogenicity (Spearman's ρ = 0.595, P < 0.001). However, we discovered 
31 major discrepancies between the InSiGHT classification and the CADD scores; 
these were explained in favor of the expert classification using population 
allele frequencies, cosegregation analyses, disease association studies, or a 
second-tier test. Of 751 variants that could not be clinically classified by 
InSiGHT, CADD indicated that 47 variants were worth further study to confirm 
their putative pathogenicity. We demonstrate CADD is valuable in prioritizing 
variants in clinically relevant genes for further assessment by expert 
classification teams.