Increased diagnostic yield from negative whole genome‐slice panels using automated reanalysis

We evaluated the diagnostic yield using genome‐slice panel reanalysis in the clinical setting using an automated phenotype/gene ranking system. We analyzed whole genome sequencing (WGS) data produced from clinically ordered panels built as bioinformatic slices for 16 clinically diverse, undiagnosed cases referred to the Pediatric Mendelian Genomics Research Center, an NHGRI‐funded GREGoR Consortium site. Genome‐wide reanalysis was performed using Moon™, a machine‐learning‐based tool for variant prioritization. In five out of 16 cases, we discovered a potentially clinically significant variant. In four of these cases, the variant was found in a gene not included in the original panel due to phenotypic expansion of a disorder or incomplete initial phenotyping of the patient. In the fifth case, the gene containing the variant was included in the original panel, but being a complex structural rearrangement with intronic breakpoints outside the clinically analyzed regions, it was not initially identified. Automated genome‐wide reanalysis of clinical WGS data generated during targeted panels testing yielded a 25% increase in diagnostic findings and a possibly clinically relevant finding in one additional case, underscoring the added value of analyses versus those routinely performed in the clinical setting. Whole genome sequencing (WGS) data, produced from clinically ordered panels built as bioinformatic slices for 16 clinically diverse, undiagnosed cases, were reanalyzed using a machine‐learning‐based tool for variant prioritization and yielded additional potentially clinically significant variants in five out of 16 cases including a complex variant not detectable by exome.