1. In this cohort study, a combination of exome sequencing, microarray analysis, and phenotypic data improved the detection of rare pediatric diseases.
2. Several factors were identified that impact the probability of successful diagnosis, including the recruitment of a parent-offspring trio.
Evidence Rating Level: 2 (Good)
Study Rundown: Genomic sequencing has enabled significant progress in identifying new molecular causes for rare monogenic diseases. This has greatly benefited the pediatric population due to their early disease presentation, high clinical impact, and potential for lifelong improvement in outcomes. The Deciphering Developmental Disorders (DDD) study was a large-scale genomic study that included more than 13,500 families and combined exome sequencing, microanalyses, and standardized clinical phenotypes. The current study reported on computational analytic strategies to characterize numerous novel molecular diagnoses in the DDD study population. An average of 1.0 candidate variant was identified per parent-offspring trio and 2.5 variants per singleton proband. In total, 44% of these reported variants were found in the Developmental Disorders Gene2Phenotype (DD2P) database and associated with known developmental disorders. High concordance was reported between clinical diagnoses and predicted classification of pathogenic variants. The study’s generalizability was limited by a lack of longitudinal phenotype data and impact of diagnoses on clinical management. However, the study provided evidence for combining clinical assessment, genomics, and bioinformatics in diagnosing rare pediatric diseases.
Click here to read the study in NEJM
In-Depth [retrospective cohort]: The current study reported on the computational analytic approaches to identify thousands of novel molecular diagnoses in the DDD study population. Consultant clinical geneticists recruited a total of 13,610 probands. Eligibility criteria included the presence of neurodevelopmental disorders, congenital anomalies, dysmorphic features, unusual behavioral phenotypes, abnormal growth, or genetic disorders with unknown molecular basis. Three genomic tests were performed: exome sequencing of probands and families, exon-focused array comparative genomic hybridization of probands, and genome-wide single nucleotide polymorphism genotyping of probands. Candidate variants were compared against the DDG2P database to characterize pathogenicity. Among the 13,449 probands included in this analysis, 19,285 potentially pathogenic sequences, and structural variants were identified. High concordance between clinical and predicted pathogenic variants were reported, resulting in a sensitivity of this hybrid approach of 99.5%, a specificity of 85.0%, a positive predictive value of 96.5%, and a negative predictive value of 97.9%. Diagnoses were achieved in 41% of probands, 76% of which were a de novo variant. A key factor facilitating the probability of diagnosis is recruitment in a parent-offspring trio (odds ratio, 4.70; 95% confidence interval, 4.16 to 5.31). Factors negatively impacting the probability of diagnosis were extremely premature births, in-utero exposure to antiepileptics, maternal diabetes, and African ancestry. In summary, this study provided evidence that a hybrid approach combining clinical assessment, genome sequencing, and bioinformatics could aid in diagnosing rare monogenic developmental disorders in pediatric patients.
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