We have designed a SNP database (available at http://zork.wustl.edu/nida/neurosnp.html) to systematically determine how to supplement these commercial microarrays for addiction. Our database includes a SNP prioritization score based on the genomic information network (GIN) method introduced by S. Saccone and colleagues [4]. This method was originally designed to systematically incorporate a priori biological hypotheses into the prioritization of SNPs after a genome-wide association study. The method begins with a set of SNPs that are ranked by their association p-values, and then increases the rank of a SNP when it is determined to be biologically relevant to the phenotype according to an a priori set of conditions, such as being in a biologically relevant gene, and additionally, perhaps, being a missense mutation. The score is a measure of biological relevance to addiction, and can be used independently of association p-values to prioritize which SNPs are selected to supplement commercial microarrays. The score incorporates SNP/gene functional properties (such as coding and promoter regions), human/mouse evolutionary conservation, and a quantitative trait locus (QTL) mapping method that utilizes mouse models to identify genes associated
