The top SNP findings summarized in Tables 2 and 3 were used in a multi-locus model to predict nicotine dependence. The initial model included main effects for all 30 top SNPs. A backwards elimination procedure was applied to eliminate all SNPs that were not significant in the multiple regression at p<0.15. This was done to eliminate loci that are moderately correlated with nearby loci due to linkage disequilibrium. Thirteen loci remained in the model after the elimination procedure. For the interaction model, both main effect and interaction terms from the top 30 SNPs were initially entered, except in cases where interaction terms were not significant in the single-locus model (DBH and KCNB1). After eliminating redundant SNPs through the backwards elimination procedure, 13 SNPs remained. Predicted logistic regression risk scores and concordance rates, employing the leave-one-out cross-validation procedure were computed for each of these multi-locus models, compared with the non-genetic base model (Equation 1). Results of these parameters for both models, as well as covariates-only (non-genetic) model are presented in Table 5. Incorporation of the top genetic main effects into the
