Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 112, Issue 44, Pages 13615-13620Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1518646112
Keywords
mutational damage; gene-level; gene prioritization; variant prioritization; next generation sequencing
Categories
Funding
- March of Dimes Grant [1-FY12-440]
- National Institute of Allergy and Infectious Diseases [5R37AI095983, 5R01AI088364, 5U01AI088685, P01AI061093]
- Rockefeller University
- INSERM
- University Paris Descartes
- St. Giles Foundation
- National Center for Advancing Translational Sciences, National Institutes of Health Clinical and Translational Science Award program [UL1 TR000043]
- New York Stem Cell Foundation
- Canadian Institutes of Health Research
- European Molecular Biology Organization
- German Research Foundation
- Stony-Wold Herbert Fund
- European Research Council under the European Union's Seventh Framework Programme (FP)/ERC Grant [281297]
- Cardiff University
- Qiagen
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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).
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