Statistical Significance for Genome-Wide Experiments

With the increase in genome-wide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genome-wide data set are tested against some null hypothesis, where many features are expected to be significant. Here we propose an approach to statistical significance in the analysis of genome-wide data sets, based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true findings and the number of false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q-value is associated with each tested feature in addition to the traditional p-value. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.