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Proc Natl Acad Sci U S A. 2015 Nov 3;112(44):13615-20. doi: 10.1073/pnas.1518646112. Epub 2015 Oct 19.

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

Author information

1
St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065; yitan@rockefeller.edu casanova@rockefeller.edu.
2
St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065;
3
Human Evolutionary Genetics Unit, Institut Pasteur, 75015 Paris, France; Centre National de la Recherche Scientifique, CNRS URA 3012, Paris, France;
4
St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065; Group of Primary Immunodeficiencies, Faculty of Medicine, University of Antioquia UdeA, Medellín, Colombia;
5
Neurogenetics Laboratory, Department of Neurosciences, University of California, San Diego, CA 92093-0662;
6
Bioinformatics Platform, University Paris Descartes, 75015 Paris, France;
7
Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U.1163, Necker Hospital for Sick Children, 75015 Paris, France; Paris Descartes University, Imagine Institute, 75015 Paris, France;
8
St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065; Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U.1163, Necker Hospital for Sick Children, 75015 Paris, France; Paris Descartes University, Imagine Institute, 75015 Paris, France; Center for Study of Primary Immunodeficiencies, Necker Hospital for Sick Children, Paris, France;
9
St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065; Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U.1163, Necker Hospital for Sick Children, 75015 Paris, France; Paris Descartes University, Imagine Institute, 75015 Paris, France;
10
Institute of Medical Genetics, Cardiff University, Cardiff CF14 4XN, United Kingdom;
11
Laboratory of Pediatric Brain Disease, The Rockefeller University, New York, NY 10065; New York Genome Center, New York, NY 10013; Howard Hughes Medical Institute, New York, NY 10065;
12
APHM & Structural and Genomic Information Laboratory, UMR7256, CNRS Aix-Marseille University, 13288 Marseille, France;
13
St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065; Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U.1163, Necker Hospital for Sick Children, 75015 Paris, France; Paris Descartes University, Imagine Institute, 75015 Paris, France; Howard Hughes Medical Institute, New York, NY 10065; Pediatric Hematology-Immunology Unit, Necker Hospital for Sick Children, 75015 Paris, France yitan@rockefeller.edu casanova@rockefeller.edu.

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.

KEYWORDS:

gene prioritization; gene-level; mutational damage; next generation sequencing; variant prioritization

PMID:
26483451
PMCID:
PMC4640721
DOI:
10.1073/pnas.1518646112
[Indexed for MEDLINE]
Free PMC Article

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