Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for clinicians
Abstract Background 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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-07-01
|
| Series: | Orphanet Journal of Rare Diseases |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13023-020-01461-1 |
| _version_ | 1828757485421330432 |
|---|---|
| author | Michael M. Segal Renee George Peter Waltman Ayman W. El-Hattab Kiely N. James Valentina Stanley Joseph Gleeson |
| author_facet | Michael M. Segal Renee George Peter Waltman Ayman W. El-Hattab Kiely N. James Valentina Stanley Joseph Gleeson |
| author_sort | Michael M. Segal |
| collection | DOAJ |
| description | Abstract Background 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. Results 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. Conclusions 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. |
| first_indexed | 2024-12-11T00:26:58Z |
| format | Article |
| id | doaj.art-602ee97181f24459813280ff8f4b5023 |
| institution | Directory Open Access Journal |
| issn | 1750-1172 |
| language | English |
| last_indexed | 2024-12-11T00:26:58Z |
| publishDate | 2020-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Orphanet Journal of Rare Diseases |
| spelling | doaj.art-602ee97181f24459813280ff8f4b50232022-12-22T01:27:30ZengBMCOrphanet Journal of Rare Diseases1750-11722020-07-0115111010.1186/s13023-020-01461-1Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for cliniciansMichael M. Segal0Renee George1Peter Waltman2Ayman W. El-Hattab3Kiely N. James4Valentina Stanley5Joseph Gleeson6SimulConsult IncDepartment of Neurosciences, University of California San DiegoRockefeller UniversityDepartment of Clinical Sciences, College of Medicine, University of SharjahDepartment of Neurosciences, University of California San DiegoDepartment of Neurosciences, University of California San DiegoDepartment of Neurosciences, University of California San DiegoAbstract Background 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. Results 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. Conclusions 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.http://link.springer.com/article/10.1186/s13023-020-01461-1Rare disease diagnosisDiagnostic decision support systemArtificial intelligenceGenomic analysisCopy number variation |
| spellingShingle | Michael M. Segal Renee George Peter Waltman Ayman W. El-Hattab Kiely N. James Valentina Stanley Joseph Gleeson Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for clinicians Orphanet Journal of Rare Diseases Rare disease diagnosis Diagnostic decision support system Artificial intelligence Genomic analysis Copy number variation |
| title | Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for clinicians |
| title_full | Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for clinicians |
| title_fullStr | Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for clinicians |
| title_full_unstemmed | Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for clinicians |
| title_short | Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for clinicians |
| title_sort | clinician centric diagnosis of rare genetic diseases performance of a gene pertinence metric in decision support for clinicians |
| topic | Rare disease diagnosis Diagnostic decision support system Artificial intelligence Genomic analysis Copy number variation |
| url | http://link.springer.com/article/10.1186/s13023-020-01461-1 |
| work_keys_str_mv | AT michaelmsegal cliniciancentricdiagnosisofraregeneticdiseasesperformanceofagenepertinencemetricindecisionsupportforclinicians AT reneegeorge cliniciancentricdiagnosisofraregeneticdiseasesperformanceofagenepertinencemetricindecisionsupportforclinicians AT peterwaltman cliniciancentricdiagnosisofraregeneticdiseasesperformanceofagenepertinencemetricindecisionsupportforclinicians AT aymanwelhattab cliniciancentricdiagnosisofraregeneticdiseasesperformanceofagenepertinencemetricindecisionsupportforclinicians AT kielynjames cliniciancentricdiagnosisofraregeneticdiseasesperformanceofagenepertinencemetricindecisionsupportforclinicians AT valentinastanley cliniciancentricdiagnosisofraregeneticdiseasesperformanceofagenepertinencemetricindecisionsupportforclinicians AT josephgleeson cliniciancentricdiagnosisofraregeneticdiseasesperformanceofagenepertinencemetricindecisionsupportforclinicians |