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4

Permutation as suggested by @StupidWolf's comment is essential to understand what's going on. If permutation makes this pattern go away, then you have a problem with your model specification, there's something uncorrected. If your data are weird, well, that's just how they are. But this argues to me that something else is going on confounding your ...


3

The first approach only address the question of how likely are you to end up with the observed over-representation given the MAF distribution. My suggestion is to use the second approach, but I am not sure if you would call it bootstrap. Bootstrap in general means sampling with replacement to estimate the uncertainty of a parameter, in this case, OR. So even ...


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