Extensive data from classical genetic studies indicate that vulnerabilities to substance dependence are complex traits with strong genetic influences. These genetic influences are largely shared by abusers of different legal and illegal addictive substances 1234. Nicotine dependence can be defined by Fagerstrom Test for Nicotine Dependence 5 or by DSM (diagnostic and statistical manual) criteria. Defined in either fashion, twin studies of nicotine dependence support strong heritability in the range of 40 – 70% 6789.

Phenotypes relating to successful abstinence from smoking have also been studied in twin samples 1011. Success at achieving smoking abstinence displays heritability in the range of 40 – 60%. Some of this heritable component appears to overlap with heritable features of nicotine dependence and some does not.

Little current data indicates which specific genes contain variants that are likely to contribute to vulnerability to nicotine dependence and/or to success in abstaining from nicotine in formerly-dependent individuals. Linkage based genome scans for nicotine dependence, age of smoking onset and other nicotine dependence related phenotypes (see discussion) have identified a number of linkage peaks 121314151617181920. The most prominent linkage peaks from these efforts largely differ from sample to sample, however.

Identifying genomic markers for the allelic variants that contribute to nicotine dependence vulnerability should improve understanding of human addictions and aid efforts to match vulnerable individuals with the prevention and treatment strategies most likely to work for them. Adding information about genomic variations that help to distinguish successful quitters from non-successful quitters could have a significant impact on strategies to reduce the health burdens that cigarette smoking imposes.

To provide a basis for molecular genetic studies, we hypothesize that nicotine dependent participants in smoking cessation studies will display allele frequencies different from those identified in ethnically-matched control research volunteers. We postulate that we will find the most reliable results when the allele frequency differences between dependent and control individuals identify genes that we have previously identified in four prior genome wide association studies that compare allele frequencies in control individuals to those in individuals who are dependent on other abused substances. In addition, we hypothesize that successful cigarette quitters will display allele frequencies that differ from those found in ethnically-matched individuals who were unsuccessful at quitting. We thus address two research questions: 1) smokers vs non-smokers, with special interest in genes that overlap with genes identified in studies of dependence on other substances and 2) successful vs unsuccessful quitters.

We thus now report 520,000 SNP genome wide association in pools of DNAs prepared from nicotine dependent European-American smoking cessation trial participants and control individuals. We compare genotypes from the entire group of nicotine dependent research participants to genotypes from European-American research volunteers free from any substantial lifetime use of any addictive substance. We also compare groups that displayed successful abstinence vs those who failed to display abstinence (Fig 1.).

SNP allele frequency assessments display modest variability. Standard errors for the variation among the four replicate studies of each DNA pool were +/- 0.035. Standard error for the variation among the pools studied for each phenotype group was +/- 0.028. Previous validating studies for these arrays have also revealed good fits between individual and pooled genotyping, with 0.95 correlations between pooled and individually-determined genotype frequencies 2122232425262728293031. The observed pool-to-pool standard deviations from these datasets thus indicate 0.94 and 1.0 power to detect 5 and 10% allele frequency differences with α = 0.05 in nicotine dependent vs control comparisons. We have 0.45 and 0.95 power to detect 5 and 10% allele frequency differences in successful vs unsuccessful quitters. Additional false negative results are likely to derive from the additional stringent requirement that four other samples each provide supporting evidence for the nicotine dependent vs control comparisons noted here.

We first focused on the first of the two research questions: 1) smokers vs nonsmokers, with a special interest in the genes that have overlap with dependence on other substances. When we compare allele frequencies in 134 nicotine-dependent vs 320 control individuals, 88,937 of the 520,000 tested SNPs displayed t values that provide nominally-significant abuser vs control allele frequency differences at p < 0.005. These nominally-positive SNPs are positioned near clustered-positive SNPs from four other abuser-control comparisons to extents that are greater than expected by chance (Table 1). 4701 of these nominally-significant SNPs lie within 100 Kb of a cluster of nominally-positive SNPs from replicate African-American and European-American NIDA polysubstance abuser vs control comparisons. Monte Carlo p values for this convergence were 0.0002. Thus, only 2 of 10,000 Monte Carlo simulation trials that each began by selecting 88,937 random SNPs displayed so many nominally-significant results near the clustered positive results from the two NIDA samples. 2133 of the nominally-significant SNPs from the current nicotine dependent vs control comparison meet several criteria. They 1) lie near clusters of positive SNPs from both NIDA samples, 2) lie within annotated genes, 3) lie within genes that also supported by nominally-positive results from JGIDA methamphetamine abuser vs control comparisons and 4) lie within genes that are also supported by nominally-positive results from COGA alcohol dependent vs control comparisons. The Monte Carlo p value for the observed degree of convergence between the current and prior data is 0.018.

The results of the nicotine-dependence vs control comparisons from the current study provide substantial confirmation for a number of genes in several gene classes that have been nominated and confirmed in prior addict vs control studies. Seven previously nominated genes related to cell adhesion processes, CNTN6, LRRN1, SEMA3C, CSMD1, PTPRD, LRRN6C and CDH13 each receive additional support from 100,000 Monte Carlo simulation trials. The convergence between current and previously-obtained data suggest that allelic variants in these genes are thus likely to contribute to individual differences in vulnerability to a variety of addictive substances (Table 1). Four genes related to enzymatic activity, SIPA1L2, PDE1C, PDE4D and PRKG1 each receive similar support. Genes involved in protein processing, a transcriptional regulator, and genes involved in channel, transporter, structural, disease and other processes receive similar support. Three G-protein coupled receptors, the GRM7 metabotropic glutamate receptor, the orphan GPR154 and the HRH4 histamine receptor also receive such support. Each of these genes, taken individually, is thus supported by data from studies of individuals selected on the basis of their dependence on illegal substances (largely cannabis, stimulants and opiates), methamphetamine, alcohol and tobacco.

Controls for occult stratification among these subjects and poor technical quality in the nominally-positive SNPs identified here fail to provide alternative explanations for the positive results of comparisons between smokers and controls. Only 837 of the nominally-positive SNPs from the smoker-control comparisons display large allele frequency differences between European- and African-American control individuals. This number is smaller than the 2,223 SNPs that would be expected to have such properties if they were selected by chance. Only 158 of the nominally-positive SNPs from the smoker-control comparisons in these data lie among the SNPs that display the largest variation between pools in data from this and other studies using the same arrays. This number is also smaller than chance values. These comparisons thus fail to support the alternative hypotheses that either occult ethnic stratification in these samples or technical problems with assays for these SNPs provided the basis for the overall results reported here.

We next focused on the second research question: 2) successful vs unsuccessful quitters.

In comparing data from successful vs unsuccessful quitters, we identified 4,570 SNPs whose allele frequencies differ between these two groups with t values for these differences that yield nominal p values < 0.01. The nominally-positive SNPs from comparisons between successful vs unsuccessful quitters cluster together to extents much greater than expected by chance if their allelic frequencies were independent of each other (Monte Carlo p < 0.00001). 944 of the 4,570 nominally-positive SNPs lay in 224 clusters in which each positive SNP lay within 100 Kb of at least one other positive SNP. We would anticipate such clustering if many of these reproducibly-positive SNPs identified haplotypes that were present in different frequencies in our samples of successful vs unsuccessful quitters, but not if they represented chance independent observations. We defined clusters as chromosomal sites where 1) three or more reproducibly-positive SNPs were positioned within 0.1 Mb of each other and 2) reproducibly-positive SNPs assessed by two different array types were represented, so that all positive data did not come from just Nsp I or from Sty I arrays.

The nominally-positive SNPs from successful vs unsuccessful quitter comparisons that cluster together on small chromosomal regions also cluster together in regions that are annotated as genes to extents much greater than chance if they represented independent observations (Monte Carlo p < 0.00001 for both).

Neither controls for occult stratification nor for poor technical quality explain the nominally-positive SNPs from the successful vs unsuccessful quitter comparisons. The SNPs that display the largest allele frequency differences between European- and African-American controls and the SNPs that display the largest between-pool variances do not overlap with those that distinguish successful vs unsuccessful quitters at levels significantly larger than those anticipated by chance (131 vs 114 and 143 vs 114, respectively).

Haplotypes that were present at different frequencies in the successful vs unsuccessful quitters by chance, not based on ethnic stratification, could conceivably contribute to some of this clustering; we thus view the results reported here [see additional file 1] as nominally-positive genes. Nevertheless, the 221 genes identified by these clustered positive results represent a highly interesting set [see additional file 1]. Seventeen of these genes produce products related to cell adhesion, 39 genes' products relate to enzymatic activities, 37 encode receptors and/or G-protein mechanisms, 5 encode channels, 27 encode transcriptional regulators, 9 genes' products are involved in mechanisms for Mendelian disorders, 12 encode structural proteins, 4 encode proteins involved with vesicle function, 5 encode transporters, 32 encode genes involved with DNA, RNA or protein processing and 34 are genes about which so little is known that we cannot confidently place them in a functional class.

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 36781011. 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 1735, (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) 1819, two reports on data from a sample recruited in Richmond, Virginia (Fagerstrom) 1819, 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 37383940. 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 37383940. 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 464748.

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 223149 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 5152. 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 5152.

Repeated studies in carefully selected samples will be necessary to confirm many of these observations. Larger samples that study effects of single pharmacologic treatments may also identify genes whose influences are specific to particular treatments. The current data not only nominates candidate for replication in further samples, however. Taken as a whole, it provides molecular genetic support for the idea that ability to abstain from nicotine has polygenic genetic components that overlap, in part, with those that contribute to vulnerability to nicotine dependence. This work also supports overlaps between the polygenic molecular genetic determinants that predispose to nicotine vulnerability and those that predispose to addictions to other legal and illegal addictive substances. Each of these features thus provides support for further elucidation of genetic variants that are associated with smoking cessation success. Each of these results provides promise that we may be able to begin to use such data to help match treatments with those most likely to benefit from them in the relatively near future.

DSM – diagnostic and statistical manual, CEPH – Center for human polymorphisms COGA – Collaborative study on the genetics of alcoholism, JGIDA – Japanese genetics initiative on drug abuse

GRU conceived the study and initiated its key collaborations, designed the research, interpreted data, drafted and polished the manuscript

QRL synthesized hybridization probes from DNA pools and helped manage data

TD participated in DNA pool construction, array analysis, data management and manuscript preparation and polishing

CJ participated in data management and performed the statistical analysis

DW participated in DNA pool construction, array analyses and data management

JER oversaw the consenting and clinical assessments of subjects, participated in data analyses and interpretation and in manuscript preparation and polishing.

All authors read and approved the final manuscript