Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for clinicians

Segal, Michael M.; George, Renee; Waltman, Peter; El-Hattab, Ayman W.; James, Kiely N.; Stanley, Valentina; Gleeson, Joseph

In diagnosis of rare genetic diseases we face a decision as to the degree to which the sequencing lab offers one or more diagnoses based on clinical input provided by the clinician, or the clinician reaches a diagnosis based on the complete set of variants provided by the lab. We tested a software approach to assist the clinician in making the diagnosis based on clinical findings and an annotated genomic variant table, using cases already solved using less automated processes.

For the 81 cases studied (involving 216 individuals), 70 had genetic abnormalities with phenotypes previously described in the literature, and 11 were not described in the literature at the time of analysis (“discovery genes”). These included cases beyond a trio, including ones with different variants in the same gene. In 100% of cases the abnormality was recognized. Of the 70, the abnormality was ranked #1 in 94% of cases, with an average rank 1.1 for all cases. Large CNVs could be analyzed in an integrated analysis, performed in 24 of the cases. The process is rapid enough to allow for periodic reanalysis of unsolved cases.

A clinician-friendly environment for clinical correlation can be provided to clinicians who are best positioned to have the clinical information needed for this interpretation.


  • thumnail for 13023_2020_Article_1461.pdf 13023_2020_Article_1461.pdf application/pdf 497 KB Download File

Also Published In

Orphanet Journal of Rare Diseases

More About This Work

Published Here
September 22, 2023


Rare disease diagnosis, Diagnostic decision support system, Artificial intelligence, Genomic analysis, Copy number variation