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For any genome-wide analysis, reporting individual p-values can be misleading, because the p-value does not correct for the large number of tests performed. The q-value is an analog of the p-value that incorporates multiple testing correction. The q-value is defined as the minimum false discovery rate at which an observed score is deemed significant. Thus, the q-value attempts to control the percentage of false positives among a collection of scores. This contrasts with a traditional Bonferroni correction (or E-value), which controls the probability of one or more false positives in a collection of scores.

A good introduction to false discovery rate estimation and the q-value is "Statistical significance for genomewide studies" by Storey and Tibshirani (PNAS 2003). Software for computing q-values from a collection of p-values is available at: