Motivation: The importance of testing associations allowing for interactions has been demonstrated by Marchini et al. (2005) . A fast method detecting associations allowing for interactions has been proposed by Wan et al. (2010a) . The method is based on likelihood ratio test with the assumption that the statistic follows the v 2 distribution. Many single nucleotide polymorphism (SNP) pairs with significant associations allowing for interactions have been detected using their method. However, the assumption of v 2 test requires the expected values in each cell of the contingency table to be at least five. This assumption is violated in some identified SNP pairs. In this case, likelihood ratio test may not be applicable any more. Permutation test is an ideal approach to checking the P-values calculated in likelihood ratio test because of its non-parametric nature. The P-values of SNP pairs having significant associations with disease are always extremely small. Thus, we need a huge number of permutations to achieve correspondingly high resolution for the P-values. In order to investigate whether the P-values from likelihood ratio tests are reliable, a fast permutation tool to accomplish large number of permutations is desirable. Results: We developed a permutation tool named PBOOST. It is based on GPU with highly reliable P-value estimation. By using simulation data, we found that the P-values from likelihood ratio tests will have relative error of >100% when 50% cells in the contingency with P-values smaller than 1:6 Â 10 À12 in the WTCCC datasets in 1 week. The source code and sample data are available at http://bioinfor-
PBOOST implemented parallelization of the permutation test on GPU REF .
PBOOST: a GPU-based tool for parallel permutation tests in genome-wide association studies
Bioinformatics
Bioinformatics
Computer Science Medicine