I'm using permutation tests, based on random forest importance measures, to perform variables selection in my dataset. Using $99$ permutations, my permutation tests $p$-values are comprised between $0.01$ and $1$.
As I am using this procedure with more than $300$ covariates, I considered the issue of multiple testing. However, with all the $p$-value adjustment procedures I considered (BH, q-value, etc.) none of my $p$-values remained significant. Is there an easy way to deal with multiple testing when using permutation tests?