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November 2009 Genome-Wide Significance Levels and Weighted Hypothesis Testing
Kathryn Roeder, Larry Wasserman
Statist. Sci. 24(4): 398-413 (November 2009). DOI: 10.1214/09-STS289

Abstract

Genetic investigations often involve the testing of vast numbers of related hypotheses simultaneously. To control the overall error rate, a substantial penalty is required, making it difficult to detect signals of moderate strength. To improve the power in this setting, a number of authors have considered using weighted p-values, with the motivation often based upon the scientific plausibility of the hypotheses. We review this literature, derive optimal weights and show that the power is remarkably robust to misspecification of these weights. We consider two methods for choosing weights in practice. The first, external weighting, is based on prior information. The second, estimated weighting, uses the data to choose weights.

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Kathryn Roeder. Larry Wasserman. "Genome-Wide Significance Levels and Weighted Hypothesis Testing." Statist. Sci. 24 (4) 398 - 413, November 2009. https://doi.org/10.1214/09-STS289

Information

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

zbMATH: 1329.62435
MathSciNet: MR2779334
Digital Object Identifier: 10.1214/09-STS289

Rights: Copyright © 2009 Institute of Mathematical Statistics

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Vol.24 • No. 4 • November 2009
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