title: Improving the signal-to-noise ratio in genome-wide association studies.
abstract: Genome-wide association studies employ hundreds of thousands of statistical tests to determine which regions of the genome may likely harbor disease-causing alleles. Such large-scale testing simultaneously requires stringent control over type I error and maintenance of sufficient power to detect true associations. These contradictory goals have led some researchers beyond Bonferroni correction of P-values to an exploration of methods to improve the detection of a few true effects in the presence of many unassociated loci. This article reviews how Genetic Analysis Workshop 16 Group 5 investigators proposed to adjust for multiple tests while simultaneously using information about the structure of the genome to improve the detection of true positives.
authors.affiliation: Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio 45229, USA. Lisa.martin@cchmc.org
authors.forename: Lisa J
authors.initials: LJ
authors.lastname: Martin
authors.forename: Guimin
authors.initials: G
authors.lastname: Gao
authors.forename: Guolian
authors.initials: G
authors.lastname: Kang
authors.forename: Yixin
authors.initials: Y
authors.lastname: Fang
authors.forename: Jessica G
authors.initials: JG
authors.lastname: Woo
mesh_terms: D001172:Arthritis, Rheumatoid / Q000453:epidemiology / Q000235:genetics; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D015810:Linkage Disequilibrium; D017720:Molecular Epidemiology; D020641:Polymorphism, Single Nucleotide