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November 2009 On Combining Data From Genome-Wide Association Studies to Discover Disease-Associated SNPs
Ruth M. Pfeiffer, Mitchell H. Gail, David Pee
Statist. Sci. 24(4): 547-560 (November 2009). DOI: 10.1214/09-STS286

Abstract

Combining data from several case-control genome-wide association (GWA) studies can yield greater efficiency for detecting associations of disease with single nucleotide polymorphisms (SNPs) than separate analyses of the component studies. We compared several procedures to combine GWA study data both in terms of the power to detect a disease-associated SNP while controlling the genome-wide significance level, and in terms of the detection probability (DP). The DP is the probability that a particular disease-associated SNP will be among the T most promising SNPs selected on the basis of low p-values. We studied both fixed effects and random effects models in which associations varied across studies. In settings of practical relevance, meta-analytic approaches that focus on a single degree of freedom had higher power and DP than global tests such as summing chi-square test-statistics across studies, Fisher’s combination of p-values, and forming a combined list of the best SNPs from within each study.

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Ruth M. Pfeiffer. Mitchell H. Gail. David Pee. "On Combining Data From Genome-Wide Association Studies to Discover Disease-Associated SNPs." Statist. Sci. 24 (4) 547 - 560, November 2009. https://doi.org/10.1214/09-STS286

Information

Published: November 2009
First available in Project Euclid: 20 April 2010

zbMATH: 1329.62434
MathSciNet: MR2779343
Digital Object Identifier: 10.1214/09-STS286

Rights: Copyright © 2009 Institute of Mathematical Statistics

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Vol.24 • No. 4 • November 2009
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