The Human Gene Damage Index as a Gene-level Approach to Prioritizing Exome Variants

ABSTRACT: The protein-coding exome of a patient with a monogenic disease contains about 20,000 variants, only one or two of which are disease causing. We found that 58% of rare variants in the protein-coding exome of the general population are located in only 2% of the genes. Prompted by this observ...

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Autores:
Itan, Yuval
Shang, Lei
Bertrand, Boisson
Patin, Etienne
Bolze, Alexandre
Moncada Vélez, Marcela
Scott, Eric
Ciancanelli, Michael
Lafaille, Fabien
Markle, Janet
Martinez Barricarte, Ruben
Jill de Jong, Sarah
Fei Kong, Xiao
Nitschke, Patrick
Belkadi, Aziz
Bustamante, Jacinta
Puel, Anne
Boisson-Dupuis, Stéphanie
Stenson, Peter D.
Gleeson, Joseph G.
Cooper, David N.
Quintana Murci, Lluis
Claverie, Jean Michel
Zhang, Shen Ying
Abel, Laurent
Casanova, Jean-Laurent
Tipo de recurso:
Article of investigation
Fecha de publicación:
2015
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/34974
Acceso en línea:
https://hdl.handle.net/10495/34974
Palabra clave:
Exome
Exoma
Mutation
Mutación
DNA Damage
Daño del ADN
Rights
openAccess
License
https://creativecommons.org/licenses/by-nc-nd/4.0/
Description
Summary:ABSTRACT: The protein-coding exome of a patient with a monogenic disease contains about 20,000 variants, only one or two of which are disease causing. We found that 58% of rare variants in the protein-coding exome of the general population are located in only 2% of the genes. Prompted by this observation, we aimed to develop a gene-level approach for predicting whether a given human protein-coding gene is likely to harbor disease-causing mutations. To this end, we derived the gene damage index (GDI): a genome-wide, gene-level metric of the mutational damage that has accumulated in the general population. We found that the GDI was correlated with selective evolutionary pressure, protein complexity, coding sequence length, and the number of paralogs. We compared GDI with the leading gene-level approaches, genic intolerance, and de novo excess, and demonstrated that GDI performed best for the detection of false positives (i.e., removing exome variants in genes irrelevant to disease), whereas genic intolerance and de novo excess performed better for the detection of true positives (i.e., assessing de novo mutations in genes likely to be disease causing).