Copy number variation, data integration, human phenotype ontology, model organism phenotype, phenogram
Animals; Computational Biology; DNA Copy Number Variations/genetics*; DNA Copy Number Variations/physiology*; Databases, Genetic
Animals; Computational Biology; DNA Copy Number Variations/genetics*; DNA Copy Number Variations/physiology*; Databases, Genetic; Disease/genetics*; Humans; Mice; Phenotype*; Species Specificity; Zebrafish
Köhler, S., Schoeneberg, U., Czeschik, J.C., Doelken, S.C., Hehir-Kwa, J.Y., Ibn-Salem, J., Mungall, C.J., Smedley, D., Haendel, M.A., Robinson, P.N. (2014) Clinical interpretation of CNVs with cross-species phenotype data. Journal of Medical Genetics. 51(11):766-72.
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.