ABSTRACT
BACKGROUND: Valvar pulmonary stenosis (vPS) accounts for 8% to 12% of congenital heart disease cases. Multiple genetic syndromes are associated with vPS, most commonly Noonan syndrome, but the cause is unknown in most cases. We analyzed genomic data from a large cohort with vPS to determine the prevalence of genetic diagnosis. METHODS: The Pediatric Cardiac Genomics Consortium database was queried to identify probands with vPS without complex congenital heart disease or aneuploidy and with existing whole exome or genome sequencing. A custom analysis workflow was used to identify likely pathogenic or pathogenic variants in disease-associated genes. Demographic and phenotypic characteristics were compared between groups with and without molecular diagnoses. RESULTS: Data from 119 probands (105 trios) were included. A molecular diagnosis was identified in 22 (18%); 17 (14%) had Noonan syndrome or a related disorder. Extracardiac and neurodevelopmental comorbidities were seen in 67/119 (56%) of probands. Molecular diagnosis was more common in those with extracardiac and neurodevelopmental phenotypes than those without (18/67 versus 4/52, P=0.0086). CONCLUSIONS: Clinicians should have high suspicion for a genetic diagnosis in individuals with vPS, particularly if additional phenotypes are present. Our results suggest that clinicians should consider offering sequencing of at least the known congenital heart disease and RASopathy genes to all individuals with vPS, regardless of whether that individual has extracardiac or neurodevelopmental phenotypes present.
Subject(s)
Heart Defects, Congenital , Noonan Syndrome , Pulmonary Valve Stenosis , Exome , Heart Defects, Congenital/diagnosis , Heart Defects, Congenital/epidemiology , Heart Defects, Congenital/genetics , Humans , Noonan Syndrome/diagnosis , Noonan Syndrome/epidemiology , Noonan Syndrome/genetics , Prevalence , Pulmonary Valve Stenosis/diagnosis , Pulmonary Valve Stenosis/epidemiology , Pulmonary Valve Stenosis/geneticsABSTRACT
PURPOSE: To improve the accuracy of matching rare genetic diseases based on patient's phenotypes. METHODS: We introduce new methods to prioritize diagnosis of genetic diseases based on integrated semantic similarity (method 1) and ontological overlap (method 2) between the phenotypes expressed by a patient and phenotypes annotated to known diseases. RESULTS: We evaluated the performance of our methods by two sets of simulated data and one set of patient's data derived from electronic health records. We demonstrated that the two methods achieved significantly improved performance compared with previous methods in correctly prioritizing candidate diseases in all of the three sets. Our methods are freely available as a web application ( https://gddp. RESEARCH: cchmc.org/ ) to aid diagnosis of genetic diseases. CONCLUSION: Our methods can capture the diagnostic information embedded in the phenotype ontology, consider all phenotypes exhibited by a patient, and are more robust than the existing methods when phenotypes are incorrectly or imprecisely specified. These methods can assist the diagnosis of rare genetic diseases and help the interpretation of the results of DNA tests.
