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

Abstract

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.

Diagnostic yield of exome and genome sequencing after non-diagnostic multi-gene panels in patients with single-system diseases

“…Given that our cohort primarily consisted of adults with single-system involvement, our outcomes differ from those of a study that employed automated re-analysis for GS cases. The latter revealed positive findings in 31% (5 out of 16) of undiagnosed pediatric cases, with two of them linked to variants found in genes initially omitted from the original panel due to incomplete initial phenotyping [35].…”

“…Kidney genetic diseases have more specific phenotypes and more clear gene-disease associations than other diseases included in this study so the clinical MGP were comprehensive and included most of the known genes expressed in the kidney [20]. Conversely, we included fewer cases from the Neurology department which usually encompass phenotypes known to have higher solve rate after ES/GS [35,37]. The study highlighted the importance of targeted, phenotype-specific EGBP to maintain clinical sensitivity while minimizing the burden of analyzing a larger number of variants in genes that might not be related to the main phenotype.…”