title: Gene-based bin analysis of genome-wide association studies.
abstract: BACKGROUND
  With the improvement of genotyping technologies and the exponentially growing number of available markers, case-control genome-wide association studies promise to be a key tool for investigation of complex diseases. However new analytical methods have to be developed to face the problems induced by this data scale-up, such as statistical multiple testing, data quality control and computational tractability.
  
  
  RESULTS
  We present a novel method to analyze genome-wide association studies results. The algorithm is based on a Bayesian model that integrates genotyping errors and genomic structure dependencies. p-values are assigned to genomic regions termed bins, which are defined from a gene-biased partitioning of the genome, and the false-discovery rate is estimated. We have applied this algorithm to data coming from three genome-wide association studies of Multiple Sclerosis.
  
  
  CONCLUSIONS
  The method practically overcomes the scale-up problems and permits to identify new putative regions statistically associated with the disease.
authors.affiliation: Merck Serono International S,A, 9 chemin des Mines, 1202 Geneva, Switzerland.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Omont
authors.forename: Karl
authors.initials: K
authors.lastname: Forner
authors.forename: Marc
authors.initials: M
authors.lastname: Lamarine
authors.forename: Gwendal
authors.initials: G
authors.lastname: Martin
authors.forename: François
authors.initials: F
authors.lastname: Képès
authors.forename: Jérôme
authors.initials: J
authors.lastname: Wojcik