The molecular genetic observations reported here are consistent with substantial heritabilities for nicotine dependence vs nondependence and for successful abstinence vs unsuccessful abstinence, as suggested by classical genetic studies [3,6-8,10,11]. The current data support the idea that nicotine dependence shares substantial heritable features with dependence on other addictive substances. These molecular results also support the idea that some of the genetics of nicotine dependence overlaps with the genetic underpinnings of successful abstinence while some is independent.
Several genes contain SNPs whose allelic frequencies distinguish nicotine dependent from control individuals. We have focused on the 30 genes for which the differences between dependent and control individuals enhance the convergence of results previously obtained from four other abuser vs control whole genome association studies. The identification of allelic associations within so many genes that encode cell adhesion and extracellular matrix molecules support important roles for neuronal connectivities and memory-like functions in individual differences in vulnerabilities to addictions [32]. Data for each of these 30 genes provides new information about vulnerability to nicotine dependence. However, the approach that we use here does bias against genes that may contribute to vulnerability to nicotine alone. Failure to be included on this list should not be taken to exclude involvement in nicotine dependence of genes, such as those that encode nicotine metabolizing enzymes, that have been associated with nicotine dependence in previous studies [33].
Nominally-significant linkage of a number of genomic markers to smoking phenotypes has been identified. Five reports on data from the Framingham Heart Study (smoking rate) [16], (> 0 cigarettes/day) [34], (>0.0138 pack/years) [12], two reports on data from the Collaborative Study on the Genetics of Alcohol [17,35], (cigarettes/day for 1 year) [13], ("habitual smoking > 20 cigarettes/day for > 6 months) [36], two reports on data from a sample recruited in Christchurch, New Zealand (Fagerstrom) [18,19], two reports on data from a sample recruited in Richmond, Virginia (Fagerstrom) [18,19], as well as single reports on linkage data from Mission Indians (smoking daily > 1 mo; smoking > 10 cigarettes/day > 1 year) [14], Oregon Smoking in Families Study (Fagerstrom and nicotine dependence measures) [20], and Yale Anxiety Clinic pedigree members (> 20 cigarettes/day for >1 year or > 10 cigarettes/day for > 10 years) [15] add to the list of markers with nominally-significant linkage to smoking phenotypes. Support for cadherin 13 is enhanced by the linkages to D16S422 and D16S684 identified by Straub and by Sullivan [18] in New Zealand samples (also, see below).
The genes that contain multiple clustered nominally-positive SNPs that distinguish successful quitters from those who could not abstain successfully also represent an interesting group. This list of genes includes several that contain SNPs whose allelic frequencies also distinguish nicotine dependent from control individuals. Cadherin 13 is a cell adhesion molecule identified in both comparisons and in the linkage results noted above. Cadherin 13 is glycosyl-phosphatidylinositol (GPI) anchored and likely to be localized to lipid raft membrane domains where it produces homophilic interactions with other CDH 13 molecules and heterophilic interactions with ligands that include adiponectin hexamers and low density lipoproteins [37-40]. Ligand interactions with CDH13 activate signaling pathways including those that alter intracellular Ca2+ and tyrosine kinase, Erk 1/2 kinase, RhoA/ROCK and Rac pathways and NFkB [37-40]. Cadherin 13 can inhibit neurite extension from select neuron populations both as a substratum and as a soluble recombinant protein [41]. Expression is documented in neurons located in interesting human brain regions including frontal cortex, amygdala and ventral midbrain [42].
The cyclic G dependent protein kinase gene is identified in both comparisons. This gene is widely and multifocally expressed in brain in cells including neurons [43]. Proper PRKG1 expression is important for proper brain development [44]. Variants in this gene can lead to marked differences in behaviors of drosophila [45]. Nitric oxide can dramatically modulate brain cGMP systems, suggesting that these systems may provide some of the primary targets for the products of nitric oxide synthases (NOS). Mnemonic and addictive functions can each be altered by changes in cGMP-dependent protein kinase and/or NOS [46-48].
In addition to CDH13 and PRKG1, 214 additional genes are identified by the clustered positive results that we nominate from comparisons of treatment-seeking individuals who successfully vs unsuccessfully abstain from smoking. Sixteen of these additional genes produce products related to cell adhesion, 32 genes' products relate to enzymatic activities, 37 encode receptors and/or G-protein mechanisms, 27 encode transcriptional regulators and others encode channels, gene products involved in mechanisms for Mendelian disorders, structural proteins, proteins involved with vesicle function, transporters, genes involved with DNA, RNA or protein processing and genes of unknown functions. These genes, taken together, should be considered nominees to contain variants that could play roles in the genetic underpinnings of successful abstinence from smoking. We can confidently exclude the probability that technical features contribute to the genes identified by the quitter vs nonquitter comparisons. With the modest sample sizes reported here, however, we cannot exclude contributions from random differences in haplotype distributions between these two groups. Further studies will be necessary to confidently identify which of the individual genes nominated in this study display replicable results.
The current observations contain significant limitations that should be considered in their interpretation. First, the modest sizes of the samples used for these studies provide moderate power, at best, to detect gene variants related to nicotine dependence and successful quitting. As noted in the power calculations, the number of false negative results is likely to be higher for allelic variants that produce small effects. Second, in conjunction with the modest sample sizes, we have also imposed stringent requirements for the genes listed in Table 1. Each of these genes is required to contain SNPs that display nominally significant abuser/control allele frequency differences in four prior samples, and also to display enhanced Monte Carlo p values when the current dataset is added to previously-obtained datasets. While these analyses reduce the probability that these genes will represent false positives, it is also likely to lead to many false-negative results. If we even allow genes whose Monte Carlo probabilities are not reduced by adding the current data to be included, most of the genes previously supported by the four prior datasets for other addictions [22,31,49] would also be included in Table 1 (data not shown).
Third, the current data for nicotine-dependent vs control comparisons uses well-characterized research volunteer European-American control samples that overlap substantially with those used for comparisons with European-American polysubstance abusers. While we have no evidence for any substantial occult differences between the underlying European-American research participants sampled in North Carolina and those sampled in Maryland, differences that cannot be detected by our extensive genomic control procedures are not inconceivable. In addition, these results are thus not totally independent from those in the substance abuser vs control comparisons to which the current nicotine dependence vs control data are compared. Since the control group used here overlaps with only one of the control groups used for the previous datasets, we believe that this potentially confounding influence is unlikely to have a large impact on the overall results.
Fourth, as noted above, the list of genes that distinguish successful from unsuccessful quitters should be considered as a list of nominees, in light of the modest power available for this comparison and the likely inclusion of false-positive results on this list. In spite of this caution, however, we do find that this list of these genes overlaps with the genes that distinguish nicotine-dependent from control individuals. We also note that these positional cloning results identify genes whose products can substantially impact animal models for relapse. We identify corticotrophin releasing hormone (CRH), for example. Stressors of several sorts elevate CRH and lead to dramatically elevated relapse in animal models [50]. We also identify a gene cluster that contains two melanocortin G protein coupled receptors. We have never consistently identified CRH or melanocortin receptor genes in our studies comparing addicts to controls. These CRH and melanocortin receptor genes are thus candidates to contribute to the genetic influences on quitting success that may be independent of the genetic influences on nicotine dependence. Fifth, there are modest to moderate differences in the gender and age of nicotine-dependent vs control research volunteers studied here. While we have focused only on data from autosomal regions in these analyses and sought its replication in studies of several other addict vs control samples in ways that are likely to minimize these influences, they may not be able to eliminate them. Both nicotine-dependent and control groups are also sufficiently old to have passed through the vast majority of the ages of risk of development of nicotine dependence. Nevertheless, it is conceivable that the modest age differences in the samples studied here might have contributed modestly to some of the observed results. Sixth, in order to enhance the likelihood that the genes identified in the dependent vs control comparisons represent true positive observations, we have focused on gene variants that are also identified in other comparisons between individuals who are dependent on other substances vs controls. This strategy may reduce the novelty of the list of genes reported here, though these findings do provide novel information concerning the possible roles of variants in these genes in vulnerability to nicotine dependence as opposed to dependence on other substances. We can compare current data to very recent reports that identify SNPs whose allelic frequencies differ between dependent vs nondependent smokers [51,52]. Three hundred thirty-one and 623 of the SNPs that distinguish nicotine dependent vs control individuals and 16 and 25 of the SNPs that distinguish successful vs unsuccessful quitters lie within 10 and 100 kb of one of these candidate genes. These SNPs thus provide modest additional support to findings reported at the ADRBK2, AVPR1A, BDNF, CCK, CHRNA10, CHRNA2, CHRNA4, CHRNA5, CHRNA6, CHRNA7, CHRNB2, CHRNG, CLCA1, CLTCL1, CNR1, CTNNA3, DBH, DDC, DRD1, DRD3, FBXL17, FMO1, FMO4, FTO, GABBR2, GABRA4, GABRB2, HTR1A, HTR5A, KCNJ6, NPY, NRXN1, OPRD1, OPRK1, PDYN, PENK, PIP5K2A, POMC, SLC6A3, SLC6A4, TRPC7 and VPS13A loci [51,52].
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