title: Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies.
abstract: BACKGROUND
  Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.
  
  
  RESULTS
  Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs.
  
  
  CONCLUSIONS
  The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.
authors.affiliation: Victor Chang Cardiac Research Institute, 384 Victoria St, Darlinghurst, 2010, NSW, Australia. e.teber@victorchang.edu.au
authors.forename: Erdahl T
authors.initials: ET
authors.lastname: Teber
authors.forename: Jason Y
authors.initials: JY
authors.lastname: Liu
authors.forename: Sara
authors.initials: S
authors.lastname: Ballouz
authors.forename: Diane
authors.initials: D
authors.lastname: Fatkin
authors.forename: Merridee A
authors.initials: MA
authors.lastname: Wouters
mesh_terms: D030541:Databases, Genetic; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics*; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide