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Letter
Nature Genetics 38, 209 - 213 (2006)
Published online: 15 January 2006; | doi:10.1038/ng1706


The PDF version of this article was corrected on 19 February 2006. Please see the PDF for details.

Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies

Andrew D Skol, Laura J Scott, Gonçalo R Abecasis & Michael Boehnke

Department of Biostatistics and Center for Statistical Genetics, University of Michigan, 1420 Washington Heights, Ann Arbor, Michigan 48109-2029, USA.

Correspondence should be addressed to Michael Boehnke boehnke@umich.edu

Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease1, 2, 3, 4. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pisamples) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pisamples) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone2. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pisamples 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pimarkers 0.01).


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Nature Genetics
ISSN: 1061-4036
EISSN: 1546-1718
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