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Calibration of Multiple In Silico Tools for Predicting Pathogenicity of Mismatch Repair Gene Missense Substitutions
Author(s) -
Thompson Bryony A.,
Greenblatt Marc S.,
Vallee Maxime P.,
Herkert Johanna C.,
Tessereau Chloe,
Young Erin L.,
Adzhubey Ivan A.,
Li Biao,
Bell Russell,
Feng Bingjian,
Mooney Sean D.,
Radivojac Predrag,
Sunyaev Shamil R.,
Frebourg Thierry,
Hofstra Robert M.W.,
Sijmons Rolf H.,
Boucher Ken,
Thomas Alun,
Goldgar David E.,
Spurdle Amanda B.,
Tavtigian Sean V.
Publication year - 2013
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.22214
Subject(s) - missense mutation , msh2 , biology , genetics , in silico , lynch syndrome , computational biology , dna mismatch repair , gene , mutation , dna repair
Classification of rare missense substitutions observed during genetic testing for patient management is a considerable problem in clinical genetics. The Bayesian integrated evaluation of unclassified variants is a solution originally developed for BRCA1/2 . Here, we take a step toward an analogous system for the mismatch repair ( MMR ) genes ( MLH1 , MSH2 , MSH6 , and PMS2 ) that confer colon cancer susceptibility in L ynch syndrome by calibrating in silico tools to estimate prior probabilities of pathogenicity for MMR gene missense substitutions. A qualitative five‐class classification system was developed and applied to 143 MMR missense variants. This identified 74 missense substitutions suitable for calibration. These substitutions were scored using six different in silico tools ( A lign‐ G rantham V ariation G rantham D eviation, multivariate analysis of protein polymorphisms [ MAPP ], M ut P red, P oly P hen‐2.1, S orting I ntolerant F rom T olerant, and X var), using curated MMR multiple sequence alignments where possible. The output from each tool was calibrated by regression against the classifications of the 74 missense substitutions; these calibrated outputs are interpretable as prior probabilities of pathogenicity. MAPP was the most accurate tool and MAPP + P oly P hen‐2.1 provided the best‐combined model ( R 2 = 0.62 and area under receiver operating characteristic = 0.93). The MAPP + P oly P hen‐2.1 output is sufficiently predictive to feed as a continuous variable into the quantitative B ayesian integrated evaluation for clinical classification of MMR gene missense substitutions.