title: An informatics approach to analyzing the incidentalome.
abstract: OBJECTIVE
  Next-generation sequencing has transformed genetic research and is poised to revolutionize clinical diagnosis. However, the vast amount of data and inevitable discovery of incidental findings require novel analytic approaches. We therefore implemented for the first time a strategy that utilizes an a priori structured framework and a conservative threshold for selecting clinically relevant incidental findings.
  
  
  METHODS
  We categorized 2,016 genes linked with Mendelian diseases into "bins" based on clinical utility and validity, and used a computational algorithm to analyze 80 whole-genome sequences in order to explore the use of such an approach in a simulated real-world setting.
  
  
  RESULTS
  The algorithm effectively reduced the number of variants requiring human review and identified incidental variants with likely clinical relevance. Incorporation of the Human Gene Mutation Database improved the yield for missense mutations but also revealed that a substantial proportion of purported disease-causing mutations were misleading.
  
  
  CONCLUSIONS
  This approach is adaptable to any clinically relevant bin structure, scalable to the demands of a clinical laboratory workflow, and flexible with respect to advances in genomics. We anticipate that application of this strategy will facilitate pretest informed consent, laboratory analysis, and posttest return of results in a clinical context.
authors.affiliation: Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. jsberg@med.unc.edu
authors.forename: Jonathan S
authors.initials: JS
authors.lastname: Berg
authors.forename: Michael
authors.initials: M
authors.lastname: Adams
authors.forename: Nassib
authors.initials: N
authors.lastname: Nassar
authors.forename: Chris
authors.initials: C
authors.lastname: Bizon
authors.forename: Kristy
authors.initials: K
authors.lastname: Lee
authors.forename: Charles P
authors.initials: CP
authors.lastname: Schmitt
authors.forename: Kirk C
authors.initials: KC
authors.lastname: Wilhelmsen
authors.forename: James P
authors.initials: JP
authors.lastname: Evans
mesh_terms: D000465:Algorithms; D000483:Alleles; D030541:Databases, Genetic; D005787:Gene Frequency; D055106:Genome-Wide Association Study / Q000379:methods*; D023281:Genomics / Q000379:methods*; D006801:Humans; D009154:Mutation