and EA samples (EA; p(EEG vs. EEG+SNP)< 0.001, p(SNP vs. EEG+SNP <.001) (AA; p(EEG vs. EEG+SNP)< 0.001, p(SNP vs. EEG+SNP <0.001). Results were confirmed in a follow up analysis in the AA and EA age groups (AA: p(early adolescence ,EEG vs. EEG+SNP)<.001, p(late adolescence s, EEG vs. EEG+SNP)< 0.001, p(adults, EEG vs. EEG+SNP)< 0.001 )(EA: p(late adolescence s, EEG vs. EEG+SNP)= 0.002 )(Table 1, Supplementary Figure 1). The EA age groups combined models reached significance in the early and late adolescence age range but did not outperform the EEG based model accuracy (Table 1, Supplementary Figure 1). Gender stratified analyses unveiled higher model accuracy in the AA female group over the male in all three features categories (EEG, SNPs and the combined EEG+SNP model) (whole sample; p(EEG male vs. EEG female)< 0.001, AA; p(SNP male vs. SNP female)= 0.008, p(EEG+SNP male vs. EEG+SNP female)< 0.001) while in the EA group only the combined model EA: p(EEG+SNP male vs. EEG+SNP female)< 0.001 (Figure 3). Overall, out of all the combined models of EEG+SNP features, the AA & EA female groups achieved the highest accuracy of 79.33% (specificity=71.02%, sensitivity = 87.67%, AUC=0.99, F=0.81), 78.91% (specificity=76.82%, sensitivity = 81%, AUC=0.9, F=0.79), respectively, and
