I did a QTL analysis with a panel of 7M SNPs, and want to analyze the enrichment of the significant qtl-SNPs in different genic regions (promoters, gene bodies, TFBS, etc.).
A straightforward way to do it would be with an odds ratio (OR), using as 'background' the full 7M SNPs, but I prefer to compare with a background with a similar MAF distribution to that of the qtl-SNPs. For that, I'm thinking in two alternatives:
- Take a single random sample of all SNPs with size 10n (n is the number of qtl-SNPs), keeping the same MAF distribution of the qtl-SNPs results, as well as the same proportion of qtl-SNPs in the sample compared to the full 7M panel. Then, test enrichments using ONLY the random sample (with OR and Fisher's exact test).
- Do a bootstrapping, taking multiple random samples (excluding qtl-SNPs), each of size n, and keeping as well the same MAF distribution. For each iteration, get the OR between the full qtl-SNPs and the random sample. Then, the mean of the OR will be the point estimate and the quantiles the confidence intervals. For this approach, the problem would be to compute a p-value.
Which approach seems to fit better? If the second one, how to quantify the significance? Can you suggest a better approach?