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Bioinformatics Advance Access originally published online on July 28, 2008
Bioinformatics 2008 24(20):2407-2408; doi:10.1093/bioinformatics/btn379
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

ExactFDR: exact computation of false discovery rate estimate in case-control association studies

Jérôme Wojcik * and Karl Forner

Department of Bioinformatics, Merck Serono Geneva Research Center, 1202 Geneva, Switzerland

*To whom correspondence should be addressed.


  Abstract

Summary: Genome-wide association studies require accurate and fast statistical methods to identify relevant signals from the background noise generated by a huge number of simultaneously tested hypotheses. It is now commonly accepted that exact computations of association probability value (P-value) are preferred to {chi}2 and permutation-based approximations. Following the same principle, the ExactFDR software package improves speed and accuracy of the permutation-based false discovery rate (FDR) estimation method by replacing the permutation-based estimation of the null distribution by the generalization of the algorithm used for computing individual exact P-values. It provides a quick and accurate non-conservative estimator of the proportion of false positives in a given selection of markers, and is therefore an efficient and pragmatic tool for the analysis of genome-wide association studies.

Availability: A Java 1.6 (1.5-compatible) version is available on SourceForge: http://sourceforge.net/projects/exactfdr.

Contact: Jerome.wojcik{at}merckserono.net

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Alex Bateman

Received on February 26, 2008; revised on June 27, 2008; accepted on July 18, 2008

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