Anyone hear familiar with the FDR ?

I have a sample of 80 people, some ill, some healthy. For each one I have a series of let's say for the sake of example, 300 different blood tests. I want to check for each blood test, if there are significant differences between healthy and ill. That means 300 t-tests (or non parametric equivalent). The problem is "over fitting". At the 5% significance level, I expect to receive 15 p values under 0.05, which will be type I errors.

I am trying to use FDR to overcome this problem, I work with the fdrtool package in R. One of the outputs is a vector of "q-values". Are these the values I should refer to instead of the p values ?

any other ideas how to handle such a situation ?

thanks

> fdrtool(p_value_t,statistic="pvalue",plot=TRUE,color.figure=TRUE)

Step 1... determine cutoff point

Step 2... estimate parameters of null distribution and eta0

Step 3... compute p-values and estimate empirical PDF/CDF

Step 4... compute q-values and local fdr

Step 5... prepare for plotting

$pval

[1] 0.86075075 0.02505751 0.23051528 0.38258383 0.20683905

$qval

[1] 0.29341273 0.04077265 0.12502865 0.15581219 0.12152287

$lfdr

[1] 1.0000000 0.1671567 0.2485930 1.0000000 0.1671567

$statistic

[1] "pvalue"

$param

cutoff N.cens eta0 eta0.SE

[1,] 0.3854334 1 0.3254326 0.2910758