AUTHOR=Requena David , Maffucci Patrick , Bigio Benedetta , Shang Lei , Abhyankar Avinash , Boisson Bertrand , Stenson Peter D. , Cooper David N. , Cunningham-Rundles Charlotte , Casanova Jean-Laurent , Abel Laurent , Itan Yuval 

TITLE=CDG: An Online Server for Detecting Biologically Closest Disease-Causing Genes and its Application to Primary Immunodeficiency

JOURNAL=Frontiers in Immunology

VOLUME=Volume 9 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2018.01340

DOI=10.3389/fimmu.2018.01340

ISSN=1664-3224

ABSTRACT=High-throughput genomic technologies yield about 20,000 variants in the protein-coding exome of each individual. A commonly used approach to select candidate disease-causing variants is to test whether the associated gene has been previously reported to be disease-causing. In the absence of known disease-causing genes, it can be challenging to associate candidate genes with specific genetic diseases.
To facilitate the discovery of novel gene-disease associations, we calculated the putative biologically-closest known genes and their associated diseases for 13,005 human genes not currently reported to be disease-associated. We used these data to construct the Closest Disease-Causing Genes (CDG) server, which can be used to infer the closest genes with an associated disease for a user-defined list of genes or diseases. We demonstrate the utility of the CDG server in 5 immunodeficiency patient exomes across different diseases and modes of inheritance, where CDG dramatically reduced the number of candidate genes to be evaluated. This resource will be a considerable asset for ascertaining the potential relevance of genetic variants found in patient exomes to specific diseases of interest. The CDG database and online server are freely available to non-commercial users at: http://lab.rockefeller.edu/casanova/CDG.