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The human gene damage index as a gene-level approach to prioritizing exome variants

Itan, Yuval, Shang, Lei, Boisson, Bertrand, Patin, Etienne, Bolze, Alexandre, Moncada-Vélez, Marcela, Scott, Eric, Ciancanelli, Michael J., Lafaille, Fabien G., Markle, Janet G., Martinez-Barricarte, Ruben, de Jong, Sarah Jill, Kong, Xiao-Fei, Nitschke, Patrick, Belkadi, Aziz, Bustamante, Jacinta, Puel, Anne, Boisson-Dupuis, Stéphanie, Stenson, Peter D., Gleeson, Joseph G., Cooper, David Neil, Quintana-Murci, Lluis, Claverie, Jean-Michel, Zhang, Shen-Ying, Abel, Laurent and Casanova, Jean-Laurent 2015. The human gene damage index as a gene-level approach to prioritizing exome variants. Proceedings of the National Academy of Sciences of the United States of America 112 (44) , pp. 13615-13620. 10.1073/pnas.1518646112

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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). The GDI server, data, and software are freely available to noncommercial users from lab.rockefeller.edu/casanova/GDI.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: Q Science > QH Natural history > QH426 Genetics
Q Science > QR Microbiology > QR180 Immunology
Publisher: National Academy of Sciences
ISSN: 0027-8424
Date of Acceptance: 22 September 2015
Last Modified: 10 Jun 2019 13:41
URI: http://orca.cf.ac.uk/id/eprint/84079

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