Hi

I'm having a problem with a whole-exome-sequenced dataset consisting of about 400 human subjects, 200 cases with a certain disease, and 200 controls without. The dataset has been through a rigorous quality control (standardised QC in plink with HWE, IBD, missingness, sex-check ++, along with HapMap population stratification and Eigenstrat/PCA-analysis). I´m using plink to do a basic association-analysis for all variants between cases and controls, and while the resulting QQ-plot for the common (MAF > 0.01) variants is OK, the plot for the rare (MAF < 0.01) variants is less so. Below are the three QQ-plots for all, common and rare variants along with lambda-values:

- QQ-plot all variants, lambda 1.83
http://postimg.org/image/oyr53wchr/
- QQ-plot common variants, lambda 1.04
http://postimg.org/image/6lqjtc20v/
- QQ-plot rare variants, lambda 2.43
http://postimg.org/image/ic4haputb/

The main problem seems to be the positive deviation (observed > expected) of the rare variants in the first part of the plot, causing the lambda to be very big, both for the QQ-plot for rare and all variants. I am wondering what could be the cause of this behaviour for the rare variants, and also what the implications this has for the prospects of doing analysis on rare and common variants together. 

I would be grateful if anybody has any experience in these matters and could provide some input. 

Many thanks. 
 
QC has likely nothing to do with your problem.  Here are two things you need to think about:

1. you're using a 2df test (genotypic?) for rare variants even though some of your cell counts are likely to be 0.
2. You're using a qq-plot designed to assess the distribution of a continuous variable when yours only has a finite number of possible values due to the small cell counts, making the qq-plot uninterpretable.

Don't over think the qq-plot in that case.