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J Med Genet. 2014 Nov;51(11):766-72. doi: 10.1136/jmedgenet-2014-102633. Epub 2014 Oct 3.

Clinical interpretation of CNVs with cross-species phenotype data.

Author information

  • 1Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany.
  • 2Foundation Institute Molecular Biology and Bioinformatics, Freie Universitaet Berlin, Berlin, Germany.
  • 3Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany.
  • 4Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • 5Department of Human Genetics, Radboud University Medical Centre, Nijmegen, The Netherlands.
  • 6Lawrence Berkeley National Laboratory, Berkeley, California, USA.
  • 7The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK.
  • 8Department of Medical Informatics and Epidemiology and OHSU Library, Oregon Health & Science University, Portland, USA.
  • 9Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany Max Planck Institute for Molecular Genetics, Berlin, Germany Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universitaet Berlin, Berlin, Germany.

Abstract

BACKGROUND:

Clinical evaluation of CNVs identified via techniques such as array comparative genome hybridisation (aCGH) involves the inspection of lists of known and unknown duplications and deletions with the goal of distinguishing pathogenic from benign CNVs. A key step in this process is the comparison of the individual's phenotypic abnormalities with those associated with Mendelian disorders of the genes affected by the CNV. However, because often there is not much known about these human genes, an additional source of data that could be used is model organism phenotype data. Currently, almost 6000 genes in mouse and zebrafish are, when knocked out, associated with a phenotype in the model organism, but no disease is known to be caused by mutations in the human ortholog. Yet, searching model organism databases and comparing model organism phenotypes with patient phenotypes for identifying novel disease genes and medical evaluation of CNVs is hindered by the difficulty in integrating phenotype information across species and the lack of appropriate software tools.

METHODS:

Here, we present an integrated ranking scheme based on phenotypic matching, degree of overlap with known benign or pathogenic CNVs and the haploinsufficiency score for the prioritisation of CNVs responsible for a patient's clinical findings.

RESULTS:

We show that this scheme leads to significant improvements compared with rankings that do not exploit phenotypic information. We provide a software tool called PhenogramViz, which supports phenotype-driven interpretation of aCGH findings based on multiple data sources, including the integrated cross-species phenotype ontology Uberpheno, in order to visualise gene-to-phenotype relations.

CONCLUSIONS:

Integrating and visualising cross-species phenotype information on the affected genes may help in routine diagnostics of CNVs.

KEYWORDS:

Copy number variation; data integration; human phenotype ontology; model organism phenotype; phenogram

PMID:
25280750
PMCID:
PMC4501634
DOI:
10.1136/jmedgenet-2014-102633
[PubMed - indexed for MEDLINE]
Free PMC Article
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