ArticleJournal of child neurology2015
Clinical pertinence metric enables hypothesis-independent genome-phenome analysis for neurologic diagnosis.
Article in Journal of child neurology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Defining an approach to empower clinical geneticists to do genomic reanalysis.BMC medical genomics · 2026Article
- User testing of a diagnostic decision support system with machine-assisted chart review to facilitate clinical genomic diagnosis.BMJ health & care informatics · 2021Article
- Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for clinicians.Orphanet journal of rare diseases · 2020Article
- Molecular diagnosis in recessive pediatric neurogenetic disease can help reduce disease recurrence in families.BMC medical genomics · 2020Article
- Experience with Integrating Diagnostic Decision Support Software with Electronic Health Records: Benefits versus Risks of Information Sharing.EGEMS (Washington, DC) · 2017Article
- Evidence-based decision support for pediatric rheumatology reduces diagnostic errors.Pediatric rheumatology online journal · 2016Article
- Genome interpretation: Clinical correlation is recommended.Applied & translational genomics · 2015Article
- Girls with Seizures Due to the c.320A>G Variant in ALG13 Do Not Show Abnormal Glycosylation Pattern on Standard Testing.JIMD reports · 2015Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
We describe an "integrated genome-phenome analysis" that combines both genomic sequence data and clinical information for genomic diagnosis. It is novel in that it uses robust diagnostic decision support and combines the clinical differential diagnosis and the genomic variants using a "pertinence" metric. This allows the analysis to be hypothesis-independent, not requiring assumptions about mode of inheritance, number of genes involved, or which clinical findings are most relevant. Using 20 genomic trios with neurologic disease, we find that pertinence scores averaging 99.9% identify the causative variant under conditions in which a genomic trio is analyzed and family-aware variant calling is done. The analysis takes seconds, and pertinence scores can be improved by clinicians adding more findings. The core conclusion is that automated genome-phenome analysis can be accurate, rapid, and efficient. We also conclude that an automated process offers a methodology for quality improvement of many components of genomic analysis.
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Registered trials
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