PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes
1 Department of Medical Genetics, McGill University Health Centre, Montreal, Canada
2 Department of Paediatrics, division of medical genetics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Canada
3 Department of Computer Science, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Canada
4 Département de Biologie, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Canada
5 Medical geneticist, Biochemical Genetics Fellow, McGill University Health Centre, The Montreal Children’s Hospital, 2300 Tupper Street, Room A-604, Montreal H3H 1P3, Qc, Canada
6 Medical geneticist, Department of Paediatrics, Medical director molecular genetics laboratory, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Canada
BMC Medical Genomics 2014, 7:22 doi:10.1186/1755-8794-7-22Published: 12 May 2014
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.