Partially automated whole-genome sequencing reanalysis of previously undiagnosed pediatric patients can efficiently yield new diagnoses.

To investigate the diagnostic and clinical utility of a partially automated reanalysis pipeline, forty-eight cases of seriously ill children with suspected genetic disease who did not receive a diagnosis upon initial manual analysis of whole-genome sequencing (WGS) were reanalyzed at least 1 year later. Clinical natural language processing (CNLP) of medical records provided automated, updated patient phenotypes, and an automated analysis system delivered limited lists of possible diagnostic variants for each case. CNLP identified a median of 79 new clinical features per patient at least 1 year later. Compared to a standard manual reanalysis pipeline, the partially automated pipeline reduced the number of variants to be analyzed by 90% (range: 74%-96%). In 2 cases, diagnoses were made upon reinterpretation, representing an incremental diagnostic yield of 4.2% (2/48, 95% CI: 0.5-14.3%). Four additional cases were flagged with a possible diagnosis to be considered during subsequent reanalysis. Separately, copy number analysis led to diagnoses in two cases. Ongoing discovery of new disease genes and refined variant classification necessitate periodic reanalysis of negative WGS cases. The clinical features of patients sequenced as infants evolve rapidly with age. Partially automated reanalysis, including automated re-phenotyping through CNLP, has the potential to identify molecular diagnoses with reduced expert labor intensity.

Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation.

By informing timely targeted treatments, rapid whole-genome sequencing can improve the outcomes of seriously ill children with genetic diseases, particularly infants in neonatal and pediatric intensive care units (ICUs). The need for highly qualified professionals to decipher results, however, precludes widespread implementation. We describe a platform for population-scale, provisional diagnosis of genetic diseases with automated phenotyping and interpretation. Genome sequencing was expedited by bead-based genome library preparation directly from blood samples and sequencing of paired 100-nt reads in 15.5 hours. Clinical natural language processing (CNLP) automatically extracted children's deep phenomes from electronic health records with 80% precision and 93% recall. In 101 children with 105 genetic diseases, a mean of 4.3 CNLP-extracted phenotypic features matched the expected phenotypic features of those diseases, compared with a match of 0.9 phenotypic features used in manual interpretation. We automated provisional diagnosis by combining the ranking of the similarity of a patient's CNLP phenome with respect to the expected phenotypic features of all genetic diseases, together with the ranking of the pathogenicity of all of the patient's genomic variants. Automated, retrospective diagnoses concurred well with expert manual interpretation (97% recall and 99% precision in 95 children with 97 genetic diseases). Prospectively, our platform correctly diagnosed three of seven seriously ill ICU infants (100% precision and recall) with a mean time saving of 22:19 hours. In each case, the diagnosis affected treatment. Genome sequencing with automated phenotyping and interpretation in a median of 20:10 hours may increase adoption in ICUs and, thereby, timely implementation of precise treatments.