These kinase inhibitor KPT-330 differences could affect the impact of some of the findings. Finally, none of the present findings are corrected for multiple comparisons. To our knowledge, this is the first study to specifically examine the findings of the NICSNP Consortium. These single-point (i.e., SNP) and haplotype analyses tended to confirm the suggestion by the NICSNP Consortium and others that nicotinic receptor variation affects vulnerability to nicotine dependence (Hutchison et al., 2007; Li et al., 2005; Saccone et al., 2007; Zeiger et al., 2008). However, we did not find any significant single-point associations at the candidate genes; most significant SNP signals came from the NICSNP candidate gene study. As noted earlier, this finding may be secondary to methodological or power issues.

Using logistic regression analysis, our sample size of 515, and a significance level of .05, we estimated that the present study had 80% power to detect an association accounting for 1.6% of the overall trait variance and 60% power to detect an association accounting for 1.0% of the variance (Gauderman & Morrison, 2006). Therefore, the present study was inadequately powered to detected risk SNPs of low frequency or low relative risk. In contrast, the NICSNP studies were case�Ccontrol analyses that included only subjects who had smoked 100 cigarettes and compared subjects with FTND scores of 0 with those who had scores of at least 4. In addition, secondary to the complex method by which Saccone et al.

(2007) corrected for multiple comparisons in their study and the hypothesized a priori likelihood of association, otherwise meritorious findings with respect to nicotine dependence may have been ignored. Hence, some of the differences between our two studies may not be as stark as they seem. In the present study, our most significant associations were cholinergic genes. Each of these findings has prior support in the literature: CHRNA2 (Faraone et al., 2004; Sullivan et al., 2004), CHRNA7 (Faraone et al., 2004; Greenbaum et al., 2006), CHRNB1 (Lou et al., 2006), and CHRNA1 (Faraone et al., 2004). The present findings extend those earlier findings by specifying the haplotypes for each of the loci. This contribution should be of aid to others seeking to define more exactly the Brefeldin_A nature of the genetic variation at these loci affecting vulnerability. However, because we wished to follow closely the NICSNP Consortium’s procedures and a number of our SNPs failed, our haplotypes do not completely cover all the genes in question. Furthermore, our haplotype analyses do not fully capture the effect of rare variation on risk. This rare variance, as epitomized by CHRNA7 SNP rs12114756 (see Table 2), may make significant contributions to the overall effects contributed by these loci.