We first determined how many unique eQTL SNPs had been identified in the original eQTL mapping (with an FDR<0.05) and how many of these are trait-associated. Subsequently we permuted the expression phenotypes relative to the genotypes (thus keeping the correlation structure within the genotype data and the correlation structure within the expression data intact, yet assigning the genotypes of a sample to the expression data of a randomly chosen sample) and reran the eQTL mapping, sorting all tested eQTLs on highest significance. We then took an equal number of top associated, but permuted, eQTL SNPs and determined how many of these permuted eQTL SNPs are trait-associated. By performing 100 permutations we obtained an empiric distribution of the number of trait-associated SNPs expected by chance. We subsequently fitted a generalized extreme value distribution (EVD, using the EVD add-on package for R), permitting us to estimate realistic enrichment significance estimates (called EVD p throughout the manuscript).
