Accurate classification of MLH1/MSH2 missense variants with Multivariate Analysis of Protein Polymorphisms-Mismatch Repair (MAPP-MMR)

Elizabeth C. Chao, Jonathan L. Velasquez, Mavee S.L. Witherspoon, Laura S. Rozek, David Peel, Pauline Ng, Stephen B. Gruber, Patrice Watson, Gad Rennert, Hoda Anton-Culver, Henry Lynch, Steven M. Lipkin

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

Abstract

Lynch syndrome, also known as hereditary nonpolyposis colon cancer (HNPCC), is the most common known genetic syndrome for colorectal cancer (CRC). MLH1/MSH2 mutations underlie approximately 90% of Lynch syndrome families. A total of 24% of these mutations are missense. Interpreting missense variation is extremely challenging. We have therefore developed multivariate analysis of protein polymorphisms-mismatch repair (MAPP-MMR), a bioinformatic algorithm that effectively classifies MLH1/MSH2 deleterious and neutral missense variants. We compiled a large database (n > 300) of MLH1/MSH2 missense variants with associated clinical and molecular characteristics. We divided this database into nonoverlapping training and validation sets and tested MAPP-MMR. MAPP-MMR significantly outperformed other missense variant classification algorithms (sensitivity, 94%; specificity, 96%; positive predictive value [PPV] 98%; negative predictive value [NPV], 89%), such as SIFT and PolyPhen. MAPP-MMR is an effective bioinformatic tool for missense variant interpretation that accurately distinguishes MLH1/MSH2 deleterious variants from neutral variants.

Original languageEnglish (US)
Pages (from-to)852-860
Number of pages9
JournalHuman mutation
Volume29
Issue number6
DOIs
StatePublished - Jun 1 2008

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

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