The problem of multiple testing and its solutions for genome-wide studies. Even if
there is no real change, the traditional p = 0.05 can cause 5% of the investigated
tests being reported significant. Multiple testing corrections have been developed
to solve this problem. Here the authors describe the one-step (Bonferroni), multi-step
(step-down and step-up) and graphical methods. However, sometimes a correction for
multiple testing creates more problems, than it solves: the universal null hypothesis
is of little interest, the exact number of investigations to be adjusted for can not
determined and the probability of type II error increases. For these reasons the authors
suggest not to perform multiple testing corrections routinely. The calculation of
the false discovery rate is a new method for genome-wide studies. Here the p value
is substituted by the q value, which also shows the level of significance. The q value
belonging to a measurement is the proportion of false positive measurements when we
accept it as significant. The authors propose using the q value instead of the p value
in genome-wide studies.
