title: A general framework for two-stage analysis of genome-wide association studies and its application to case-control studies.
abstract: Two-stage analyses of genome-wide association studies have been proposed as a means to improving power for designs including family-based association and gene-environment interaction testing. In these analyses, all markers are first screened via a statistic that may not be robust to an underlying assumption, and the markers thus selected are then analyzed in a second stage with a test that is independent from the first stage and is robust to the assumption in question. We give a general formulation of two-stage designs and show how one can use this formulation both to derive existing methods and to improve upon them, opening up a range of possible further applications. We show how using simple regression models in conjunction with external data such as average trait values can improve the power of genome-wide association studies. We focus on case-control studies and show how it is possible to use allele frequencies derived from an external reference to derive a powerful two-stage analysis. An illustration involving the Wellcome Trust Case-Control Consortium data shows several genome-wide-significant associations, subsequently validated, that were not significant in the standard analysis. We give some analytic properties of the methods and discuss some underlying principles.
authors.affiliation: Medical Research Council Biostatistics Unit, Institute for Public Health, Cambridge, UK.
authors.forename: James M S
authors.initials: JM
authors.lastname: Wason
authors.forename: Frank
authors.initials: F
authors.lastname: Dudbridge
mesh_terms: D016022:Case-Control Studies; D003198:Computer Simulation; D005787:Gene Frequency; D059647:Gene-Environment Interaction; D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D016014:Linear Models; D015203:Reproducibility of Results