Hi,
I'm searching for genes that are correlated with a specific phenotype through GWAS, but I'm currently struggling with the adjustment of the p-value to reduce the amount of false positive.
I'm using multiple phenotypes and for each phenotype I have got p-values for every SNP. Since it is a multivariate analysis, I wanted to merge the p-values of the different phenotypes and decided to do so by Fisher's method. However my phenotypes are correlated so the merged p-values are artificially lower so a simple bonferonni correction for my significance threshold doesn't seem correct. To counteract for this, I wanted to use a permutated dataset, in which I switch the labels.
However for me it is unclear to do so. Is there somebody who could advise me how to do this or send some good papers explaining this?
Greetings,
V
