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

Kiely N. James, Michelle M. Clark, Brandon Camp, Cyrielle Kint, Peter Schols, Sergey Batalov, Benjamin Briggs, Narayanan Veeraraghavan, Shimul Chowdhury, Stephen F. Kingsmore
<span title="2020-08-11">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="" style="color: black;">npj Genomic Medicine</a> </i> &nbsp;
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
more &raquo; ... ants 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1038/s41525-020-00140-1</a> <a target="_blank" rel="external noopener" href="">pmid:32821428</a> <a target="_blank" rel="external noopener" href="">pmcid:PMC7419288</a> <a target="_blank" rel="external noopener" href="">fatcat:5nh7y3vennd57erp7ppthd7dai</a> </span>
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