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 A. Betensky, Joseph Gleeson

Research output: Contribution to journalArticlepeer-review


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
Issue number7
StatePublished - Jun 4 2015


  • diagnosis
  • diagnostic decision support
  • whole exome sequencing

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health
  • Clinical Neurology


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