title: Clinical pertinence metric enables hypothesis-independent genome-phenome analysis for neurologic diagnosis.
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.
authors.affiliation: SimulConsult Inc, Chestnut Hill, MA, USA jcn@simulconsult.com.
authors.forename: Michael M
authors.initials: MM
authors.lastname: Segal
authors.affiliation: Center for Brain Development, University of California, San Diego, La Jolla, CA, USA.
authors.forename: Mostafa
authors.initials: M
authors.lastname: Abdellateef
authors.affiliation: Division of Clinical Genetics and Metabolic Disorders, Pediatric Department, Tawam Hospital, Al-Ain, United Arab Emirates.
authors.forename: Ayman W
authors.initials: AW
authors.lastname: El-Hattab
authors.affiliation: Real Time Genomics Inc, San Bruno, CA, USA.
authors.forename: Brian S
authors.initials: BS
authors.lastname: Hilbush
authors.affiliation: Real Time Genomics Inc, San Bruno, CA, USA Department of Genetics, Stanford University, CA, USA.
authors.forename: Francisco M
authors.initials: FM
authors.lastname: De La Vega
authors.affiliation: Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA.
authors.forename: Gerard
authors.initials: G
authors.lastname: Tromp
authors.affiliation: Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA.
authors.forename: Marc S
authors.initials: MS
authors.lastname: Williams
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA.
authors.forename: Rebecca A
authors.initials: RA
authors.lastname: Betensky
authors.affiliation: Center for Brain Development, University of California, San Diego, La Jolla, CA, USA.
authors.forename: Joseph
authors.initials: J
authors.lastname: Gleeson
mesh_terms: D003198:Computer Simulation; D005190:Family; D056726:Genetic Association Studies; D005820:Genetic Testing / Q000379:methods*; D014644:Genetic Variation; D006801:Humans; D009422:Nervous System Diseases / Q000175:diagnosis* / Q000235:genetics* / Q000503:physiopathology; D010363:Pattern Recognition, Automated; D010641:Phenotype
keywords: diagnosis; diagnostic decision support; whole exome sequencing