To provide a mechanism for prioritizing SNPs when supplementing SNP microarrays for addiction, we used the genomic information network (GIN) technique introduced by Saccone and colleagues [4]. The GIN method assigns each SNP a numeric prioritization score indicating the biological relevance for addiction: the higher the score, the greater the priority. Figure S3 shows the network model we used for addiction, which is a modification of the nicotine dependence model used by Saccone and colleagues. The score incorporates a number of factors, including SNP/gene functional properties (such as coding and promoter regions), and evolutionary conserved regions (ECRs, provided by ECRbase [21], http://ecrbase.dcode.org). The original GIN method introduced by Saccone and colleagues incorporated LD into the prioritization score through the use of LD proxies. This is more appropriate when prioritizing SNPs for replication after an initial GWAS. In our current implementation, where we are selecting SNPs to supplement arrays for the discovery phase of a GWAS, we have eliminated the LD component in order to avoid redundancy among the selected SNPs.
