I am working on some GWAS (Genome-Wide Association Studies) now. A genome scan was done for all the SNPs, with first 3 principal components adjusted (PCs are used for adjusting ethnicity effect) and the QQ plot looks fine (most p-values lay on the diagonal line with a few signals off the line).
However, when I adjusted for clinical covariates (treatment, clinical stage) and rerun the genome scan, the QQ plot looks weird. Usually, when you adjust for covariates, you expect to see the p-values lay under the diagonal line (or fewer hits than unadjusted model). In my model, after adjusting for clinical covariates, the QQ plot is way off the diagonal line and almost all the SNPs are false positives. I don't know how to explain this. One important thing is that some clinical covariates are very very significant in the clinical model (the model with no SNP information), and the p-value goes to 10E-11. I have 250 samples and 750K SNPs in my dataset.
Best Answer
I don't know which p-values are appropriate in your case since I do not know what your data is like. For the Pc1df p-values I do not know what "corrected for possible inflation" means (population stratificaion perhaps?)
P1df: corresponding list of P-values of 1-d.f. (additive or allelic) test for association bestween SNP and trait
P2df: corresponding list of P-values of 2-d.f. (genotypic) test for association bestween SNP and trait
Pc1df:P-values from the 1-d.f. test for association bestween SNP and trait; the statistics is corrected for possible inflation
source: http://www.genabel.org/GenABEL/scan.gwaa-class.html