I am working on a GWAS dataset containing 920 individuals with genotype information on ~1.5M SNPs (genotyped on Illumina 2.5omni chip; no imputed SNPs). I am testing several different phenotypes in this dataset, but everytime I run an association analysis (logistic regression), my QQ plots look deflated, i.e. the P values are systematically less significant than the expected distribution. I have tested several different phenotypes, and get very similar results every time.
I have tried the following solutions:
- adjustment for PCA coordinates
- increasing minor allele frequency threshold to only include SNPs with maf>0.05
- LD-based pruning down to 240.000 SNPs
None of which remove the deflation.
I searched for literature on this, and while some studies report this type of QQ plot, none seem to actually address the reason for the deflation. From what I can tell, deflation is known to occur in studies using imputed SNPs or data on copy number variation, but should not be seen in a dataset such as mine.
Does anyone know why my plots look like this, and whether this reflects a problem in data?