title: GeneVetter: a web tool for quantitative monogenic assessment of rare diseases.
abstract: When performing DNA sequencing to diagnose affected individuals with monogenic forms of rare diseases, accurate attribution of causality to detected variants is imperative but imperfect. Even if a gene has variants already known to cause a disease, rare disruptive variants predicted to be causal are not always so, mainly due to imperfect ability to predict the pathogenicity of variants. Existing population-scale sequence resources such as 1000 Genomes are useful to quantify the 'background prevalence' of an unaffected individual being falsely predicted to carry causal variants. We developed GeneVetter to allow users to quantify the 'background prevalence' of subjects with predicted causal variants within specific genes under user-specified filtering parameters. GeneVetter helps quantify uncertainty in monogenic diagnosis and design genetic studies with support for power and sample size calculations for specific genes with specific filtering criteria. GeneVetter also allows users to analyze their own sequence data without sending genotype information over the Internet. Overall, GeneVetter is an interactive web tool that facilitates quantifying and accounting for the background prevalence of predicted pathogenic variants in a population.
  
  
  METHODS
  GeneVetter is available at http://genevetter.org/
  
  
  BACKGROUND
  mgsamps@med.umich.edu or hmkang@umich.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Division of Nephrology, Department of Pediatrics and Communicable Diseases, University of Michigan School of Medicine, Ann Arbor, MI, USA and.
authors.forename: Christopher E
authors.initials: CE
authors.lastname: Gillies
authors.affiliation: Division of Nephrology, Department of Pediatrics and Communicable Diseases, University of Michigan School of Medicine, Ann Arbor, MI, USA and.
authors.forename: Catherine C
authors.initials: CC
authors.lastname: Robertson
authors.affiliation: Division of Nephrology, Department of Pediatrics and Communicable Diseases, University of Michigan School of Medicine, Ann Arbor, MI, USA and.
authors.forename: Matthew G
authors.initials: MG
authors.lastname: Sampson
authors.affiliation: Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
authors.forename: Hyun Min
authors.initials: HM
authors.lastname: Kang
mesh_terms: D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D006801:Humans; D020407:Internet; D009404:Nephrotic Syndrome / Q000235:genetics; D035583:Rare Diseases / Q000235:genetics*; D017422:Sequence Analysis, DNA; D012984:Software