Convergent, replicated observations from these 3 GWA studies of smoking cessation are consistent with the substantial heritabilities for successful vs unsuccessful abstinence that are supported by classical genetic studies.4,7,34–37
The overlap between some of these quit-success genes with substance-dependence vulnerability genes provides an interesting list that we discuss in the following paragraphs (eTables 1 and 2). Nevertheless, this overlap between quit-success and substance-dependence genes is more modest than the overlaps that we have previously identified in GWA studies of dependence on different addictive substances.18,19,22,23
Cadherin 13 (CDH13
) (Entrez Gene 1012) is a glycosyl-phosphatidylinositol–anchored cell adhesion molecule identified herein and also in multiple comparisons between substance-dependent vs control individuals. The CDH13
gene is expressed in neurons in interesting brain regions,38
can inhibit neurite extension,39
and can activate several signaling pathways,40–43
rendering it a strong candidate for roles in brain mechanisms important for both developing and quitting addictions.
Neurexin 3 (NRXN3
) (Entrez Gene 9369) is a single-transmembrane domain cell adhesion molecule that has also been identified in association- and linkage-based comparisons between controls and individuals dependent on legal and illegal substances.21,44
Localization of this molecule to presynaptic aspects of classical synapses45
and to interesting brain regions provides sites at which NRXN3
gene variants could affect addictive processes.
Down syndrome cell adhesion molecule (DSCAM
) (Entrez Gene 1826) is a single-transmembrane domain cell adhesion molecule that is expressed strongly in the brain46
in ways that are required for appropriate neuronal connections to form in memory-associated circuits in model organisms.47,48
expression in fruit flies changes memories for rewarded and punished behaviors.48
Cyclic guanosine monophosphate (cGMP)–dependent protein kinase 1 (PRKG1
) (Entrez Gene 5592) is expressed in hippocampal and other neurons.49,50
Nitric oxide dramatically modulates brain cGMP systems; PRKG1
thus provides a major target for the products of nitric oxide synthases. Mnemonic and addictive functions can each be altered by changes in cGMP-dependent protein kinase and/or nitric oxide synthases.51–53
The genes for triple functional domain/PTPRF interacting protein (Entrez Gene 7204), Cub and Sushi multiple domains 1 (Entrez Gene 64478), sarcoglycan zeta (Entrez Gene 137868), and receptor protein tyrosine phosphatase D (Entrez Gene 5789) are other cell adhesion genes that appear to contain variants that influence both vulnerability to addiction and success in quitting. Although the functions of none of these genes is well understood, each of these genes is highly expressed in the hippocampus (http://www.brain-map.org
Several of the genes identified by both quit-success and addiction vulnerability GWA approaches are likely to alter protein disposition and half-lives. Peptidase activities are regulated by inhibitory proteins that include the products of serpin peptidase inhibitor A1 (Entrez Gene 5265) and A2 (Entrez Gene 390502) genes, which are identified by results from addiction vulnerability and smoking cessation GWA data. These genes are expressed in the cerebral cortex, hippocampus, and other brain regions of interest. Cellular protein trafficking is likely to be altered by variants in the SorCS1 sortilin-related Vps10p domain containing receptor 1, which is expressed in interesting brain regions that include the cortex and hippocampus.
Signaling within neurons is likely to be altered by variants in a number of the genes that are identified for both quit success and addiction vulnerability. Variants in the A kinase anchor protein 13 gene (AKAP13) (Entrez Gene 11214) are likely to alter cyclic adenosine monophosphate–related signaling pathways, whereas variants in the chimerin 2 gene (CHN2) (Entrez Gene 1124) are also likely to alter signaling. Both of these genes exhibit abundant brain expression in interesting regions.
The neuronal PAS domain protein 3 gene (NPAS3) (Entrez Gene 64067) is expressed in developing and adult brains in ways that suggest that its variants could play important roles.
The gene for ataxin 2–binding protein 1 (A2BP1
) (Entrez Gene 54715) is highly expressed in neurons in brain regions that include the hippocampus (http://www.brain-map.org
). The A2BP1
gene binds to a UGCAUG splicing enhancer element that borders a substantial number of neuron-specific exons and thus acts to regulate splicing processes that form mature messenger RNAs.54
gene itself contains a number of splicing variants that are likely to alter its functions.
In addition to these genes that are identified in repeated comparisons between successful vs unsuccessful abstainers and in repeated comparisons between substance-dependent vs control individuals, we have also identified other interesting genes (and intragenic regions) in at least 2 samples of successful vs unsuccessful abstainers that are not consistently identified in comparisons of substance-dependent vs control individuals. These genes represent a large fraction of the total number of genes identified herein, consistent with classic genetic data that suggest modest overlap between the genetics of developing a dependence on tobacco and the genetics of successful cessation. Although detailed discussion of each of these genes is beyond the scope of the present report, the gene for calsyntenin 2 (CLSTN2
) (Entrez Gene 64084) falls into this group and was also identified by recent association and linkage studies of individual differences in memory and executive function.30,55
gene is heavily expressed in brain regions that include the hippocampus (http://www.brain-map.org
) and appears to function as a single trans-membrane domain cell adhesion molecule.
It is important to consider several limitations for these convergent, replicated genome-wide data for smoking cessation. The sample sizes available for this work provide moderate power to detect gene variants. False-negative results are likely, because we require positive data from each of 2 samples and also require positive results from several SNPs that cluster within small chromosomal regions. We focus only on data from autosomal regions herein, allowing us to combine data from male and female smokers in ways that will miss potentially important contributions from genes on sex chromosomes. These subjects volunteered for demanding clinical protocols, potentially rendering them not totally representative of all smokers or of all smokers who seek to quit.56
We used a preplanned analytic approach that represents only 1 of many current approaches to analyzing GWA data (the analysis and technical limits of GWA studies are discussed in the supplemental text
available online and at http://www.nhlbi.nih.gov/resources/listserv/number35.htm
Despite these limitations, the replicated positive results obtained herein and the failure of control experiments to support alternative hypotheses provide substantial confidence in roles for most of the genes reported.
Comparing data from successful vs unsuccessful abstainers from those treated with bupropion, NRT, or placebo provides samples that allow us to ask whether molecular genetic results are consistent with the idea that each gene variant identified in this work provides identical influences on responses to NRT and bupropion. There is a pharmacological rationale for differential effects because bupropion’s relatively high affinity as a dopamine transporter blocker contrasts with nicotine’s relatively high affinity for nicotinic acetylcholine receptors58
(but bupropion also has moderate affinities for nicotinic acetylcholine receptors59
). The results reported herein provide initial evidence that the genetics underlying successful smoking cessation with bupropion are not identical to the genetics of successful smoking cessation with NRT. There should be caution because the NRT- and bupropion-selective genes are identified in a single analysis that is not undergirded by twin study data. Nevertheless, these results (1) provide sets of clustered, nominally positive SNPs that converge with the SNPs where allele frequencies distinguish nondependent smokers from dependent smokers (Monte Carlo P
and (2) identify the CYP2B7P1/CYP2B6
gene cluster in which cluster products are important for generating bupropion’s pharmacologically active metabolite hydroxybupropion.58
Replication of these data will allow use of such SNPs to individualize therapeutic approaches by selecting better candidates for NRT vs bupropion therapies and allow us to consider whether each gene’s effect is additive or might depend on allelic variants at other loci (epistasis).
The data reported herein provide molecular genetic support for the idea that a smoker’s ability to abstain from nicotine has polygenic genetic components. The data support modest overlap between these heritable components and the genes that contribute to vulnerability to dependence on addictive substances. Several of the genes identified in this work are consistent with emerging views of the importance of mnemonic mechanisms for the development of and remission from addictions.3
The convergent, replicated results presented herein are supplemented by initial data that nominate differential genetic influences on quit success depending on which smoking cessation treatment is used. Taken together, the current data provide promise that we may soon use molecular genetics to match the type and/or intensity of antismoking treatments with the smokers most likely to benefit from them.