Its a great example of the omnigenic model. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5536862/
Hi. I'm trying to run GWAS pipeline using plink, but the results I got look really off. The QQ-plot of the p-values is far above the diagonal.
The phenotype I used is the height. I'm pretty sure I followed the correct QC steps. The inclusion of covariates is probably also not the one causing this issue, since with and without covariates I got very similar QQ-plots which look wrong (attached below).
Does anyone have experience with this and know what's going wrong? Thanks!
1 answer
Height is the quintessential quantitative (in the sense of "hyper-polygenic") trait, with vast numbers of associated variants.
If you make a QQ plot of the recent summary from the GIANT consortium it looks like this:
and zooming in on just the first 10 orders of magnitude:
so do not expect height to look "well-calibrated"; in effect no variant in the genome is null.
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