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

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...

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Published in:Proceedings of the National Academy of Sciences - PNAS 2015-11, Vol.112 (44), p.13615-13620
Main Authors: 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 N., Quintana-Murci, Lluis, Claverie, Jean-Michel, Zhang, Shen-Ying, Abel, Laurent, Casanova, Jean-Laurent
Format: Article
Language:English
Subjects:
Biological Sciences
DNA damage
Evolution
Exome
Genes
Genetic Diseases, Inborn - genetics
Humans
Mutation
Proteins
ROC Curve
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Summary: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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1518646112