Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases

Damian Smedley; Sebastian Köhler; Johanna Christina Czeschik; Joanna Amberger; Carol Bocchini; Ada Hamosh; Julian Veldboer; Tomasz Zemojtel; Peter N Robinson

doi:10.1093/bioinformatics/btu508

Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases

Bioinformatics. 2014 Nov 15;30(22):3215-22. doi: 10.1093/bioinformatics/btu508. Epub 2014 Jul 30.

Authors

Damian Smedley¹, Sebastian Köhler¹, Johanna Christina Czeschik¹, Joanna Amberger¹, Carol Bocchini¹, Ada Hamosh¹, Julian Veldboer², Tomasz Zemojtel², Peter N Robinson³

Affiliations

¹ Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany.
² Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany.
³ Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany Mouse Informatics Group, The Wellcome Trust Sang

Abstract

Motivation: Whole-exome sequencing (WES) has opened up previously unheard of possibilities for identifying novel disease genes in Mendelian disorders, only about half of which have been elucidated to date. However, interpretation of WES data remains challenging.

Results: Here, we analyze protein-protein association (PPA) networks to identify candidate genes in the vicinity of genes previously implicated in a disease. The analysis, using a random-walk with restart (RWR) method, is adapted to the setting of WES by developing a composite variant-gene relevance score based on the rarity, location and predicted pathogenicity of variants and the RWR evaluation of genes harboring the variants. Benchmarking using known disease variants from 88 disease-gene families reveals that the correct gene is ranked among the top 10 candidates in ≥50% of cases, a figure which we confirmed using a prospective study of disease genes identified in 2012 and PPA data produced before that date. We implement our method in a freely available Web server, ExomeWalker, that displays a ranked list of candidates together with information on PPAs, frequency and predicted pathogenicity of the variants to allow quick and effective searches for candidates that are likely to reward closer investigation.

Availability and implementation: http://compbio.charite.de/ExomeWalker

Contact: : [email protected].

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Disease / genetics*
Exome*
Genes
Humans
Protein Interaction Mapping*
Proteins / genetics
Sequence Analysis, DNA / methods*

Substances

Proteins

Grants and funding

Wellcome Trust/United Kingdom