A significant contribution that longitudinal selleck chemicals 17-DMAG studies can make to our understanding of nicotine dependence is the importance of exposure data to the formation of nicotine dependence. For our analysis of nicotine dependence, we used three exposure models. The first exposure model, NDall, is the lowest level of exposure and may capture some of the variance associated with exposure to passive smoking or with behaviors that result in individuals differentially segregating with respect to exposure environments. However, since the FTND is typically applied only to smokers, including nonsmokers in the analysis may weaken our ability to detect a finding when using this scale. The latter two levels of exposure, having smoked at least once or having smoked 100 times, are more conventional and allow us to directly compare our results with the results from the rest of the field.

Overall, in this sampling of genes, the highest levels of statistical significance seem to be found when the ND1 level of exposure is used. Biologically, this may mean that the genetic variation at these nicotinic loci comes into play even after smoking just one cigarette. If so, these findings suggest the public health importance of preventing that first cigarette. However, examination of this conclusion in other longitudinal studies is necessary. As noted earlier, we did not correct any of our analyses for multiple comparisons. The reasons for this are straightforward. First, each of these genes was specifically nominated on the basis of prior studies. Second, in our stepwise analysis at each of these loci, the analyses were highly interrelated.

We attempted to mitigate the number of false positives by the stepwise transition from risk SNP to risk haplotype, followed by a consideration of exposure data. It is reassuring to see that overall consideration of this exposure data sharpened the findings. However, some of these effects could be random effects. A number of researchers have suggested ways to address these problems (Nyholt, 2004; Risch, 2000). However, none of these solutions globally address the difficulties found in this type of candidate gene analysis. Therefore, we suggest caveat emptor, and we note that the sine non qua of validation is replication (Risch, 2000). In contrast to Cilengitide the candidate gene analysis, we did not replicate any of the findings from the NICSNP high-density association study. In this regard, that only 22 of the 33 SNPs from the study’s list of most highly significant findings could be genotyped successfully by our commercial collaborator, Sequenom Inc., using information from the public domain. Hence, the results from the high-density association study cannot be regarded as having been fully tested in our sample.