Document Detail


ExactFDR: exact computation of false discovery rate estimate in case-control association studies.
MedLine Citation:
PMID:  18662924     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Jérôme Wojcik; Karl Forner
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Publication Detail:
Type:  Journal Article     Date:  2008-07-28
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  24     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2008 Oct 
Date Detail:
Created Date:  2008-10-07     Completed Date:  2008-12-08     Revised Date:  2009-11-04    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  2407-8     Citation Subset:  IM    
Affiliation:
Department of Bioinformatics, Merck Serono Geneva Research Center, 1202 Geneva, Switzerland. Jerome.wojcik@merckserono.net
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Case-Control Studies
Computational Biology / methods*
Data Interpretation, Statistical
False Positive Reactions
Gene Expression Profiling / methods
Genetic Predisposition to Disease*
Genome, Human
Humans
Models, Genetic
Software*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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