PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes

• Published in: BMC medical genomics 2014-05, Vol.7 (1), p.22-22, Article 22
• Main Authors: Trakadis, Yannis J, Buote, Caroline, Therriault, Jean-François, Jacques, Pierre-Étienne, Larochelle, Hugo, Lévesque, Sébastien
• Format: Article
• Language: English
• Subjects: Congenital Abnormalities - diagnosis; Congenital Abnormalities - genetics; Genetic aspects; Genetics; Genome, Human - genetics; Genomes; Genomics; Genomics - methods; Genotype & phenotype; Health sciences; Humans; Medical research; Medicine, Experimental; Mutation; Ontology; Patients; Pharmacogenetics; Phenotype; Physiological aspects; Programming languages; Reproducibility of Results; Software; Syndrome
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/1755-8794-7-22

We propose a phenotype-driven analysis of encrypted exome data to facilitate the widespread implementation of exome sequencing as a clinical genetic screening test.Twenty test-patients with varied syndromes were selected from the literature. For each patient, the mutation, phenotypic data, and genetic diagnosis were available. Next, control exome-files, each modified to include one of these twenty mutations, were assigned to the corresponding test-patients. These data were used by a geneticist blinded to the diagnoses to test the efficiency of our software, PhenoVar. The score assigned by PhenoVar to any genetic diagnosis listed in OMIM (Online Mendelian Inheritance in Man) took into consideration both the patient's phenotype and all variations present in the corresponding exome. The physician did not have access to the individual mutations. PhenoVar filtered the search using a cut-off phenotypic match threshold to prevent undesired discovery of incidental findings and ranked the OMIM entries according to diagnostic score. When assigning the same weight to all variants in the exome, PhenoVar predicted the correct diagnosis in 10/20 patients, while in 15/20 the correct diagnosis was among the 4 highest ranked diagnoses. When assigning a higher weight to variants known, or bioinformatically predicted, to cause disease, PhenoVar's yield increased to 14/20 (18/20 in top 4). No incidental findings were identified using our cut-off phenotypic threshold. The phenotype-driven approach described could render widespread use of ES more practical, ethical and clinically useful. The implications about novel disease identification, advancement of complex diseases and personalized medicine are discussed.