ExactFDR: exact computation of false discovery rate estimate in case-control association studies
Author(s) -
Jérôme Wojcik,
Karl Forner
Publication year - 2008
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btn379
Subject(s) - false discovery rate , permutation (music) , false positive paradox , estimator , false positives and false negatives , multiple comparisons problem , computer science , computation , algorithm , statistical hypothesis testing , statistics , data mining , mathematics , artificial intelligence , biology , biochemistry , physics , gene , acoustics
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.
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