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HGCS: an online tool for prioritizing disease-causing gene variants by biological distance

Yuval Itan1*, Mark Mazel1, Benjamin Mazel1, Avinash Abhyankar2, Patrick Nitschke3, Lluis Quintana-Murci45, Stephanie Boisson-Dupuis167, Bertrand Boisson1, Laurent Abel167, Shen-Ying Zhang167 and Jean-Laurent Casanova16789

Author Affiliations

1 St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA

2 New York Genome Center, New York, NY, USA

3 Platforme Bioinformatique, Université Paris Descartes, Paris, France

4 Unit of Human Evolutionary Genetics, Institut Pasteur, Paris, France

5 Centre Nationale de la Recherche Scientifique, CNRS URA 3012, Paris, France

6 Laboratory of Human Genetics of Infectious Diseases, Necker Branch, Inserm UMR 1163, Paris, France

7 Paris Descartes University, Imagine Institute, Paris, France

8 Pediatric Immunology-Hematology Unit, Necker Hospital for Sick Children, Paris, France

9 Howard Hughes Medical Institute, New York, NY, USA

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BMC Genomics 2014, 15:256  doi:10.1186/1471-2164-15-256

Published: 3 April 2014

Abstract

Background

Identifying the genotypes underlying human disease phenotypes is a fundamental step in human genetics and medicine. High-throughput genomic technologies provide thousands of genetic variants per individual. The causal genes of a specific phenotype are usually expected to be functionally close to each other. According to this hypothesis, candidate genes are picked from high-throughput data on the basis of their biological proximity to core genes — genes already known to be responsible for the phenotype. There is currently no effective gene-centric online interface for this purpose.

Results

We describe here the human gene connectome server (HGCS), a powerful, easy-to-use interactive online tool enabling researchers to prioritize any list of genes according to their biological proximity to core genes associated with the phenotype of interest. We also make available an updated and extended version for all human gene-specific connectomes. The HGCS is freely available to noncommercial users from: http://hgc.rockefeller.edu/ webcite.

Conclusions

The HGCS should help investigators from diverse fields to identify new disease-causing candidate genes more effectively, via a user-friendly online interface.