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.
|Original language||English (US)|
|Issue number||SUPPL. 1|
|State||Published - 2009|
All Science Journal Classification (ASJC) codes
- Multiple testing