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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. © 2012 by The American Society of Human Genetics. All rights reserved.
Author(s): Wason JMS, Dudbridge F
Publication type: Article
Publication status: Published
Journal: American Journal of Human Genetics
Year: 2012
Volume: 90
Issue: 5
Pages: 760-773
Print publication date: 04/05/2012
Online publication date: 03/05/2012
ISSN (print): 0002-9297
ISSN (electronic): 1537-6605
Publisher: Cell Press
URL: https://doi.org/10.1016/j.ajhg.2012.03.007
DOI: 10.1016/j.ajhg.2012.03.007
PubMed id: 22560088
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