We assumed that results from different GWAS should share a significant intersection of addiction vulnerable SNPs which would be genetic factors underlying drug addiction in general, regardless of addictive drug types and population demographics [32]. We thus implemented a "meta-signature" approach following the "meta-signature" method that Oncomine used to identify common gene-expression signatures [47]. Briefly, (i) Five GWAS as described in the previous paragraph were selected for meta-signature study; (ii) Significant thresholds (T) were chosen to define positive SNPs in the 5 selected GWAS; (iii) Positive SNPs were selected in each GWAS result; (iv) Positive SNPs were sorted by the number of GWAS positive findings in which they are present; (v) the numbers of positive SNPs with 1~5 supporting GWAS were tallied as (T1, T2, T3, T4, T5); (vi) 10,000 random permutations were performed, in which the actual p-values were randomly assigned to SNPs within each GWAS, so that the positive SNPs in each GWAS change at random, but the number of positive SNPs remained the same. This simulation generated distributions about the number of positive SNPs with 1~5 supporting
