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Actionable Genes, Core Databases, and Locus‐Specific Databases
Author(s) -
Pinard Amélie,
Miltgen Morgane,
Blanchard Arnaud,
Mathieu Hélène,
Desvignes JeanPierre,
Salgado David,
Fabre Aurélie,
Arnaud Pauline,
Barré Laura,
Krahn Martin,
Grandval Philippe,
Olschwang Sylviane,
Zaffran Stéphane,
Boileau Catherine,
Béroud Christophe,
CollodBéroud Gwenaëlle
Publication year - 2016
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.23112
Subject(s) - database , locus (genetics) , context (archaeology) , gene , biology , phenotype , genetics , computational biology , identification (biology) , computer science , paleontology , botany
ABSTRACT Adoption of next‐generation sequencing (NGS) in a diagnostic context raises numerous questions with regard to identification and reports of secondary variants (SVs) in actionable genes. To better understand the whys and wherefores of these questioning, it is necessary to understand how they are selected during the filtering process and how their proportion can be estimated. It is likely that SVs are underestimated and that our capacity to label all true SVs can be improved. In this context, Locus‐specific databases (LSDBs) can be key by providing a wealth of information and enabling classifying variants. We illustrate this issue by analyzing 318 SVs in 23 actionable genes involved in cancer susceptibility syndromes identified through sequencing of 572 participants selected for a range of atherosclerosis phenotypes. Among these 318 SVs, only 43.4% are reported in Human Gene Mutation Database (HGMD) Professional versus 71.4% in LSDB. In addition, 23.9% of HGMD Professional variants are reported as pathogenic versus 4.8% for LSDB. These data underline the benefits of LSDBs to annotate SVs and minimize overinterpretation of mutations thanks to their efficient curation process and collection of unpublished data.