I sometimes have the situation where I have from several dozen to over 100 linear models to perform hypothesis tests on. They have the same predictor variables, but different response variables.
Let's say I have 100 models and each model has four p-values-- one for the intercept, one each for two main effects, and one for the interaction effect. If I want to calculate false discovery rates, should I calculate one set of FDRs based on the 400 p-values, or should I calculate a separate set of FDRs for each term in the model, based on the 100 p-values for that one term? I've been told by a more experienced colleague that it is the latter, but I don't understand why.
In case it matters, usually one of the main effects and its interactions with the other effects is of primary interest, and the other terms are included because they might influence the response and therefore must be taken into account.