We applied pathway analysis to two substance dependence GWAS datasets, the “Nicotine addiction Genetics” (OZALC-NAG) [21], and the “Study of Addiction: Genetics and Environment” (SAGE) [22], analyzing a commonly used measure of smoking behavior [7], [9], [10], cigarettes per day (CPD )(Table 1), to identify enriched candidate sets of genes defined as Gene Ontology (GO) terms [23] and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [24]. We carried out the statistical analysis by executing the algorithm ALIGATOR [15] an overrepresentation method that analyzes genes exhibiting significance below a specified threshold. We analyzed these two studies independently, and selected those terms and pathways that were statistically significant (p-value<0.05) in both datasets, and further verified their role by analyzing the EA subjects from the Atherosclerosis Risk in Communities study (ARIC) [25], [26]. We applied this same approach by executing the algorithm MAGENTA [27] an implementation of the method Gene Set Enrichment Analysis (GSEA).
