Evaluation of CADD Scores in Curated Mismatch Repair Gene Variants Yields a Model for Clinical Validation and Prioritization

K. Joeri van der Velde, Joel Kuiper, Bryony A. Thompson, John-Paul Plazzer, Gert van Valkenhoef, Mark de Haan, Jan D. H. Jongbloed, Cisca Wijmenga, Tom J. de Koning, Kristin M. Abbott, Richard Sinke, Amanda B. Spurdle, Finlay Macrae, Maurizio Genuardi, Rolf H. Sijmons, Morris A. Swertz*, InSiGHT Grp

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

29 Citations (Scopus)
103 Downloads (Pure)

Abstract

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 = 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. Published 2015 Wiley Periodicals, Inc.*

Original languageEnglish
Pages (from-to)712-719
Number of pages8
JournalHuman Mutation
Volume36
Issue number7
DOIs
Publication statusPublished - Jul-2015

Keywords

  • Lynch syndrome
  • variant classification
  • pathogenicity prediction
  • cumulative link model
  • GENOME
  • SUSCEPTIBILITY
  • CLASSIFICATION
  • PATHOGENICITY
  • DIAGNOSIS
  • CANCER

Cite this