Molecular diagnosis of muscular diseases in outpatient clinics: A Canadian perspective.

OBJECTIVE: To evaluate the diagnostic yield of an 89-gene panel in a large cohort of patients with suspected muscle disorders and to compare the diagnostic yield of gene panel and exome sequencing approaches. METHODS: We tested 1,236 patients from outpatient clinics across Canada using a gene panel and performed exome sequencing for 46 other patients with sequential analysis of 89 genes followed by all mendelian genes. Sequencing and analysis were performed in patients with muscle weakness or symptoms suggestive of a muscle disorder and showing at least 1 supporting clinical laboratory. RESULTS: We identified a molecular diagnosis in 187 (15.1%) of the 1,236 patients tested with the 89-gene panel. Diagnoses were distributed across 40 different genes, but 6 (DMD, RYR1, CAPN3, PYGM, DYSF, and FKRP) explained about half of all cases. Cardiac anomalies, positive family history, age <60 years, and creatine kinase >1,000 IU/L were all associated with increased diagnostic yield. Exome sequencing identified a diagnosis in 10 (21.7%) of the 46 patients tested. Among these, 3 were attributed to genes not included in the 89-gene panel. Despite differences in median coverage, only 1 of the 187 diagnoses that were identified on gene panel in the 1,236 patients could have been potentially missed if exome sequencing had been performed instead. CONCLUSIONS: Our study supports the use of gene panel testing in patients with suspected muscle disorders from outpatient clinics. It also shows that exome sequencing has a low risk of missing diagnoses compared with gene panel, while potentially increasing the diagnostic yield of patients with muscle disorders.

Clinical validity of phenotype-driven analysis software PhenoVar as a diagnostic aid for clinical geneticists in the interpretation of whole-exome sequencing data.

PURPOSE: We sought to determine the diagnostic yield of whole-exome sequencing (WES) combined with phenotype-driven analysis of variants in patients with suspected genetic disorders. METHODS: WES was performed on a cohort of 51 patients presenting dysmorphisms with or without neurodevelopmental disorders of undetermined etiology. For each patient, a clinical geneticist reviewed the phenotypes and used the phenotype-driven analysis software PhenoVar (http://phenovar.med.usherbrooke.ca/) to analyze WES variants. The prioritized list of potential diagnoses returned was reviewed by the clinical geneticist, who selected candidate variants to be confirmed by segregation analysis. Conventional analysis of the individual variants was performed in parallel. The resulting candidate variants were subsequently reviewed by the same geneticist, to identify any additional potential diagnoses. RESULTS: A molecular diagnosis was identified in 35% of the patients using the conventional analysis, and 17 of these 18 diagnoses were independently identified using PhenoVar. The only diagnosis initially missed by PhenoVar was rescued when the optional "minimal phenotypic cutoff" filter was omitted. PhenoVar reduced by half the number of potential diagnoses per patient compared with the conventional analysis. CONCLUSION: Phenotype-driven software prioritizes WES variants, provides an efficient diagnostic aid to clinical geneticists and laboratories, and should be incorporated in clinical practice.

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

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.