Clinical pertinence metric enables hypothesis-independent genome-phenome analysis for neurologic diagnosis

Michael M. Segal, Mostafa Abdellateef, Ayman W. El-Hattab, Brian S. Hilbush, Francisco M. De La Vega, Gerard Tromp, Marc S. Williams, Rebecca Betensky, Joseph Gleeson

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)881-888
Number of pages8
JournalJournal of Child Neurology
Volume30
Issue number7
DOIs
StatePublished - Jan 1 2015

Fingerprint

Nervous System
Clinical Decision Support Systems
Genome
Neurologic Manifestations
Quality Improvement
Nervous System Diseases
Differential Diagnosis
Genes

Keywords

  • diagnosis
  • diagnostic decision support
  • whole exome sequencing

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health
  • Clinical Neurology

Cite this

Segal, M. M., Abdellateef, M., El-Hattab, A. W., Hilbush, B. S., De La Vega, F. M., Tromp, G., ... Gleeson, J. (2015). Clinical pertinence metric enables hypothesis-independent genome-phenome analysis for neurologic diagnosis. Journal of Child Neurology, 30(7), 881-888. https://doi.org/10.1177/0883073814545884

Clinical pertinence metric enables hypothesis-independent genome-phenome analysis for neurologic diagnosis. / Segal, Michael M.; Abdellateef, Mostafa; El-Hattab, Ayman W.; Hilbush, Brian S.; De La Vega, Francisco M.; Tromp, Gerard; Williams, Marc S.; Betensky, Rebecca; Gleeson, Joseph.

In: Journal of Child Neurology, Vol. 30, No. 7, 01.01.2015, p. 881-888.

Research output: Contribution to journalArticle

Segal, MM, Abdellateef, M, El-Hattab, AW, Hilbush, BS, De La Vega, FM, Tromp, G, Williams, MS, Betensky, R & Gleeson, J 2015, 'Clinical pertinence metric enables hypothesis-independent genome-phenome analysis for neurologic diagnosis', Journal of Child Neurology, vol. 30, no. 7, pp. 881-888. https://doi.org/10.1177/0883073814545884
Segal, Michael M. ; Abdellateef, Mostafa ; El-Hattab, Ayman W. ; Hilbush, Brian S. ; De La Vega, Francisco M. ; Tromp, Gerard ; Williams, Marc S. ; Betensky, Rebecca ; Gleeson, Joseph. / Clinical pertinence metric enables hypothesis-independent genome-phenome analysis for neurologic diagnosis. In: Journal of Child Neurology. 2015 ; Vol. 30, No. 7. pp. 881-888.
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