title: Symptom-driven idiopathic disease gene identification.
abstract: OBJECTIVE
  Rare genetic variants are the major cause of Mendelian disorders, yet only half of described genetic diseases have been causally linked to a gene. In addition, the total number of rare genetic diseases is projected to be far greater than that of those already described. Whole-genome sequencing of patients with subsequent genetic and functional analysis is a powerful way to describe these gene anomalies. However, this approach results in tens to hundreds of candidate disease-causative genes, and the identification of additional individuals suffering from the same disorder can be difficult because of rarity and phenotypic heterogeneity.
  
  
  METHODS
  We describe a genetic network-based method to rank candidate genes identified in family-based sequencing studies, termed phenotype informed network (PIN) ranking. Furthermore, we present a case study as an extension of the PIN ranking method in which disease symptoms drive the network ranking and identification of the disease-causative gene.
  
  
  RESULTS
  We demonstrate, through simulation, that our method is capable of identifying the correct disease-causative gene in a majority of cases. PIN-rank is available at https://genomics.scripps.edu/pinrank/.
  
  
  CONCLUSIONS
  We have developed a method to prioritize candidate disease-causative genes based on symptoms that would be useful for both the prioritization of candidates and the identification of additional subjects.
authors.affiliation: Scripps Translational Science Institute, La Jolla, California, USA.|Department of Integrative Structural and Computational Biology, Scripps Research Institute, La Jolla, California, USA.
authors.forename: Bhuvan
authors.initials: B
authors.lastname: Molparia
authors.affiliation: Cypher Genomics, La Jolla, California, USA.
authors.forename: Phillip H
authors.initials: PH
authors.lastname: Pham
authors.affiliation: Scripps Translational Science Institute, La Jolla, California, USA.|Department of Integrative Structural and Computational Biology, Scripps Research Institute, La Jolla, California, USA.|Cypher Genomics, La Jolla, California, USA.|Scripps Health, La Jolla, California, USA.|Department of Molecular and Experimental Medicine, Scripps Research Institute, La Jolla, California, USA.
authors.forename: Ali
authors.identifier: http://orcid.org/0000-0003-0232-8053
authors.initials: A
authors.lastname: Torkamani
mesh_terms: D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D030541:Databases, Genetic; D056726:Genetic Association Studies / Q000379:methods; D030342:Genetic Diseases, Inborn / Q000175:diagnosis / Q000235:genetics; D015894:Genome, Human; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D006801:Humans; D010641:Phenotype