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Clinical interpretation of CNVs with cross-species phenotype data
  1. Sebastian Köhler1,2,
  2. Uwe Schoeneberg3,
  3. Johanna Christina Czeschik4,
  4. Sandra C Doelken1,
  5. Jayne Y Hehir-Kwa5,
  6. Jonas Ibn-Salem1,
  7. Christopher J Mungall6,
  8. Damian Smedley7,
  9. Melissa A Haendel8,
  10. Peter N Robinson1,2,9,10
  1. 1Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
  2. 2Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany
  3. 3Foundation Institute Molecular Biology and Bioinformatics, Freie Universitaet Berlin, Berlin, Germany
  4. 4Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
  5. 5Department of Human Genetics, Radboud University Medical Centre, Nijmegen, The Netherlands
  6. 6Lawrence Berkeley National Laboratory, Berkeley, California, USA
  7. 7The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
  8. 8Department of Medical Informatics and Epidemiology and OHSU Library, Oregon Health & Science University, Portland, USA
  9. 9Max Planck Institute for Molecular Genetics, Berlin, Germany
  10. 10Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universitaet Berlin, Berlin, Germany
  1. Correspondence to Dr Sebastian Köhler, Charité Universitätsmedizin, Institute for Medical Genetics and Human Genetics, Augustenburger Platz 1, Berlin 13353, Germany; sebastian.koehler{at}charite.de

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.

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

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