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. CONCLUSION: The method practically overcomes the scale-up problems and permits to identify new putative regions statistically associated with the disease.
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. CONCLUSION: The method practically overcomes the scale-up problems and permits to identify new putative regions statistically associated with the disease.
Authors: Robert J Klein; Caroline Zeiss; Emily Y Chew; Jen-Yue Tsai; Richard S Sackler; Chad Haynes; Alice K Henning; John Paul SanGiovanni; Shrikant M Mane; Susan T Mayne; Michael B Bracken; Frederick L Ferris; Jurg Ott; Colin Barnstable; Josephine Hoh Journal: Science Date: 2005-03-10 Impact factor: 47.728
Authors: Roger Horton; Laurens Wilming; Vikki Rand; Ruth C Lovering; Elspeth A Bruford; Varsha K Khodiyar; Michael J Lush; Sue Povey; C Conover Talbot; Mathew W Wright; Hester M Wain; John Trowsdale; Andreas Ziegler; Stephan Beck Journal: Nat Rev Genet Date: 2004-12 Impact factor: 53.242
Authors: E Birney; D Andrews; M Caccamo; Y Chen; L Clarke; G Coates; T Cox; F Cunningham; V Curwen; T Cutts; T Down; R Durbin; X M Fernandez-Suarez; P Flicek; S Gräf; M Hammond; J Herrero; K Howe; V Iyer; K Jekosch; A Kähäri; A Kasprzyk; D Keefe; F Kokocinski; E Kulesha; D London; I Longden; C Melsopp; P Meidl; B Overduin; A Parker; G Proctor; A Prlic; M Rae; D Rios; S Redmond; M Schuster; I Sealy; S Searle; J Severin; G Slater; D Smedley; J Smith; A Stabenau; J Stalker; S Trevanion; A Ureta-Vidal; J Vogel; S White; C Woodwark; T J P Hubbard Journal: Nucleic Acids Res Date: 2006-01-01 Impact factor: 16.971