title: Clinical phenotype-based gene prioritization: an initial study using semantic similarity and the human phenotype ontology.
abstract: BACKGROUND
  Exome sequencing is a promising method for diagnosing patients with a complex phenotype. However, variant interpretation relative to patient phenotype can be challenging in some scenarios, particularly clinical assessment of rare complex phenotypes. Each patient's sequence reveals many possibly damaging variants that must be individually assessed to establish clear association with patient phenotype. To assist interpretation, we implemented an algorithm that ranks a given set of genes relative to patient phenotype. The algorithm orders genes by the semantic similarity computed between phenotypic descriptors associated with each gene and those describing the patient. Phenotypic descriptor terms are taken from the Human Phenotype Ontology (HPO) and semantic similarity is derived from each term's information content.
  
  
  RESULTS
  Model validation was performed via simulation and with clinical data. We simulated 33 Mendelian diseases with 100 patients per disease. We modeled clinical conditions by adding noise and imprecision, i.e. phenotypic terms unrelated to the disease and terms less specific than the actual disease terms. We ranked the causative gene against all 2488 HPO annotated genes. The median causative gene rank was 1 for the optimal and noise cases, 12 for the imprecision case, and 60 for the imprecision with noise case. Additionally, we examined a clinical cohort of subjects with hearing impairment. The disease gene median rank was 22. However, when also considering the patient's exome data and filtering non-exomic and common variants, the median rank improved to 3.
  
  
  CONCLUSIONS
  Semantic similarity can rank a causative gene highly within a gene list relative to patient phenotype characteristics, provided that imprecision is mitigated. The clinical case results suggest that phenotype rank combined with variant analysis provides significant improvement over the individual approaches. We expect that this combined prioritization approach may increase accuracy and decrease effort for clinical genetic diagnosis.
authors.forename: Aaron J
authors.initials: AJ
authors.lastname: Masino
authors.forename: Elizabeth T
authors.initials: ET
authors.lastname: Dechene
authors.forename: Matthew C
authors.initials: MC
authors.lastname: Dulik
authors.forename: Alisha
authors.initials: A
authors.lastname: Wilkens
authors.forename: Nancy B
authors.initials: NB
authors.lastname: Spinner
authors.forename: Ian D
authors.initials: ID
authors.lastname: Krantz
authors.forename: Jeffrey W
authors.initials: JW
authors.lastname: Pennington
authors.forename: Peter N
authors.initials: PN
authors.lastname: Robinson
authors.affiliation: Department of Pediatrics, Cincinnati Children's Hospital and Medical Center, Cincinnati, OH, USA. peter.white@cchmc.org.
authors.forename: Peter S
authors.initials: PS
authors.lastname: White
mesh_terms: D000465:Algorithms; D064229:Biological Ontologies; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D004194:Disease / Q000235:genetics*; D059472:Exome / Q000235:genetics; D006801:Humans; D010641:Phenotype; D012660:Semantics; D012984:Software