Evaluating statistical significance in two-stage genomewide association studies.
Genomewide association studies are being conducted to unravel the genetic etiology of complex human diseases. Because of cost constraints, these studies typically employ a two-stage design, under which a large panel of markers is examined in a subsample of subjects, and the most-promising markers are then examined in all subjects. This report describes a simple and efficient method to evaluate statistical significance for such genome studies. The proposed method, which properly accounts for the correlated nature of polymorphism data, provides accurate control of the overall false-positive rate and is substantially more powerful than the standard Bonferroni correction, especially when the markers are in strong linkage disequilibrium.
Lin
D Y
DY
Department of Biostatistics, University of North Carolina, Chapel Hill, 27599-7420, USA. lin@bios.unc.edu
D003627:Data Interpretation, Statistical; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D006801:Humans; D015810:Linkage Disequilibrium; D012984:Software
D005819:Genetic Markers