33,731 significantly correlated SNP-expression pairs was identified, involving 22,178 SNPs and 3,640 transcripts [53-56]. Then, for the addiction vulnerable SNPs, We estimated the magnitude of the contributions of different molecular mechanisms to the effects of addiction susceptibility. We further compared observed values to those that would be obtained by chance based on 10,000 Monte Carlo simulations. Briefly, the positive SNPs were randomly selected from all tag SNPs, but the number of positive SNPs remained the same. Then, for each SNP list, we performed the identical pipelines to estimate the contributions of different molecular mechanisms to the effects of addiction susceptibility. Perl and R scripts were implemented to integrate the datasets, annotate addiction vulnerable SNPs and perform statistical tests.
