Drug and Alcohol Dependence 104S (2009) S58–S63 Contents lists available at ScienceDirect Drug and Alcohol Dependence journal homepage: www.elsevier.com/locate/drugalcdep Review Smoking and smoking cessation in disadvantaged women: Assessing genetic contributions夽 George R. Uhl a,∗ , Tomas Drgon a , Chuan-Yun Li a,b , Catherine Johnson a , Qing-Rong Liu a a b Molecular Neurobiology Branch, NIH-IRP (NIDA), Baltimore, MD, USA National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing, China a r t i c l e i n f o Article history: Received 23 September 2008 Received in revised form 26 February 2009 Accepted 24 March 2009 Available online 12 May 2009 Keywords: Genetics Genome wide association Tobacco Dependence a b s t r a c t Abundant evidence from family, adoption and twin studies points to large genetic contributions to individual differences in vulnerability to develop dependence on one or more addictive substances, including tobacco. Twin data suggests that much of this genetic vulnerability is shared by individuals who are dependent on a variety of addictive substances. Interestingly, some twin data also supports substantial differences in the apparent heritability of nicotine dependence in women as environmental conditions become more permissive for their smoking. In addition, twin studies also support the idea that ability to quit smoking displays substantial heritability, and that this heritable influence overlaps partially with genetic influences on nicotine dependence. Candidate gene molecular genetic studies and genome wide association studies of substance dependence and ability to quit smoking each document apparent polygenic influences that identify lists of genes that display partial overlap, as expected from classical genetic studies. More of these genes are expressed in the brain than would be anticipated by chance. These lists of genes overlap significantly with those identified in molecular genetic studies of individual differences in cognitive abilities, frontal lobe brain volumes as well as personality and psychiatric phenotypes. Though most available genome wide association data do not separate results by gender, it may be notable that few of these genes lie on sex chromosomes. These data provide a substrate to improve understanding of nicotine dependence, the ability to quit smoking, the potential for less permissive environments to restrict the expression of genetic influences on smoking and the possibility that brain features that underlie phenotypes such as individual differences in cognitive abilities might interact with environmental features that are especially prominent for disadvantaged women to provide special circumstances that should be considered in prevention and treatment efforts to reduce smoking. Published by Elsevier Ireland Ltd. Contents 1. 2. 3. 4. 5. Introduction: classical genetics of substance dependence, nicotine dependence and smoking cessation phenotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . Molecular genetic observations for dependence on (and other phenotypes related to) substances including nicotine . . . . . . . . . . . . . . . . . . . . . . . . . . Molecular genetic observations for smoking cessation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Molecular genetics for other possibly relevant phenotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smoking in women in light of this evidence for genetic and environmental influences on vulnerability to smoking and ability to quit . . . . . . . . Role of funding source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix A. Supplementary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 夽 Supplementary details are provided with the online version of this paper at doi:10.1016/j.drugalcdep.2009.03.012. ∗ Corresponding author at: Molecular Neurobiology, Box 5180, Baltimore, MD 21224, USA. Tel.: +1 410 550 2843x146; fax: +1 410 550 1535. E-mail address: guhl@intra.nida.nih.gov (G.R. Uhl). 0376-8716/$ – see front matter. Published by Elsevier Ireland Ltd. doi:10.1016/j.drugalcdep.2009.03.012 S58 S59 S60 S61 S61 S62 S62 S62 S62 S62 1. Introduction: classical genetics of substance dependence, nicotine dependence and smoking cessation phenotypes Current models for the genetic architecture for dependence on addictive substances in the population are based on information from: (1) family, adoption and twin data that each support G.R. Uhl et al. / Drug and Alcohol Dependence 104S (2009) S58–S63 substantial heritability for addictions, (2) twin data (in which concordance in genetically identical monozygotic and genetically half-identical dizygotic twins are compared) that document that most of this heritable influence is not substance-specific, (3) linkage and association studies that fail to provide evidence for genes of major effect (e.g. for any single gene whose variants produce substantial reproducible differences in addiction vulnerability) for substance dependence. Support for the idea that vulnerability to addictions is a complex trait with strong genetic influences that are largely shared by abusers of different legal and illegal addictive substances (Uhl et al., 1995; Tsuang et al., 1998; True et al., 1999; Karkowski et al., 2000) comes from classical genetic studies. Family studies document that first degree relatives (e.g. sibs) of addicts display greater risk for developing substance dependence than more distant relatives (Uhl et al., 1995; Merikangas et al., 1998). Adoption studies find greater similarities between levels of substance abuse between adoptees vs biological relatives than adoptees vs members of the adoptive families (Uhl et al., 1995). In twin studies, differences in concordance between genetically identical and fraternal twins also support substantial heritability for vulnerability to addictions (Grove et al., 1990; Gynther et al., 1995; Tsuang et al., 1996; Woodward et al., 1996; Kendler and Prescott, 1998; Karkowski et al., 2000; Agrawal et al., 2004; Kendler et al., 2006). Twin data also allows quantitation of the amount, about half, of addiction vulnerability that is heritable. Twin data supports the idea that the environmental influences on addiction vulnerability that are not shared among members of twin pairs are much larger than those that are shared by members of twin pairs (e.g. e2 ≫ c2 in virtually every such study). Many of the environmental influences on human addiction vulnerability are thus likely to come from outside of the immediate family environment. We are also fortunate to have data from studies of identical vs fraternal twin pairs that evaluate the degree to which one twin’s dependence on a substance enhances the chance that his or her co-twin will become dependent on a substance of a different class. Results of these analyses document that much or even most of the genetic influences on addiction vulnerability are common to dependence on multiple different substances, though others do appear to be substance-specific (Tsuang et al., 1998; Agrawal et al., 2004; Kendler et al., 2006, 2008). Data from classical genetic studies of smoking reveal several especially interesting features. Overall heritability for vulnerability to become dependent on nicotine has been well documented in males and females sampled in a number of environments. But not all. Studies of twins raised in late 19th–early 20th century Swedish environments document the progressively greater emergence of apparent heritable influences on smoking in women over this time (Kendler et al., 2000). During this time, the initially strong social constraints against smoking in women were relaxed. Interestingly, heritability estimates in men did not change over this same time period. The allelic variants that predispose modern Swedish women to smoke are likely to be virtually identical to those present in their grandmothers who were environmentally constrained against smoking (Kendler et al., 2000). This work thus provides one of the most striking examples of influences that a strongly nonpermissive environment can have on the expression of an underlying genetic vulnerability in women. Conceivably, the disproportionate fraction of the cigarettes consumed in the United State by disadvantaged women (see other chapters in this volume) could be viewed as based, in part, on interactions between environmental features that might be more permissive for these individuals, allowing them to express underlying genetic predispositions. Metaanalyses do identify modest differences in heritability in comparing male vs female twin pairs, though recent studies identify nearly the same heritability in men and women (Li et al., 2003; S59 Broms et al., 2006) Other features that can contribute to socioeconomic status can also display heritability, as noted in other chapters in this volume. Heritability can also be demonstrated for a number of distinct smoking-related phenotypes. Diagnostic and Statistical Manual (DSM) criteria measure nicotine dependence in ways that are more analogous to those in which dependence on other addictive substances in measured. The Fagerstrom Test for Nicotine Dependence (FTND) assesses a battery of items that reflect more physiological nicotine dependence (Fagerstrom, 1978; Fagerstrom and Schneider, 1989; Pomerleau et al., 1989, 1994; Heatherton et al., 1991; Lessov et al., 2004). Bierut and colleagues have documented evidence for apparent heritability for comparisons between smokers with FTND dependence and smokers who do not display FTND dependence (although a significant number display DSM dependence) (Bierut et al., 2007). Finally, and importantly, the ability to quit smoking can display a remarkably robust heritable component, even though the exact questions that provide evidence about this phenotype differ between the studies that have studied heritability of success in quitting (Broms et al., 2006). Not all of the genetics of these heritable, smoking-related phenotypes are identical. There are likely to be substantial differences between the genetics of becoming dependent on nicotine and the genetics of ability to quit (Broms et al., 2006). This review aims to provide an introduction to the rapidly moving area of the molecular genetics that underlie some of these classical genetic observations. Additional perspectives can be found in a number of recent reviews that include those in this volume and (Caron et al., 2005; Benowitz, 2008; Lessov-Schlaggar et al., 2008). 2. Molecular genetic observations for dependence on (and other phenotypes related to) substances including nicotine One of the largest single smoking-related molecular genetic effects is found in data that compares heavy smokers with high FTND scores to smokers without evidence for dependence by FTND criteria. Markers in the chromosome 15 gene cluster that encodes the ␣3, ␣5 and ␤4 nicotinic acetylcholine receptors display different allelic frequencies between these heavy vs light smokers in each of several studies (Bierut et al., 2007; Saccone et al., 2007; Berrettini et al., 2008). Elsewhere, we have defined “primary” pharmacogenomics based on individual differences in “ADME” adsorption/distribution/metabolism/excretion features of substances, “secondary” pharmacogenomics based on individual differences in the sites that initially recognize drugs and “higher order” pharmacogenomics based on individual differences in “post receptor” sites that are also responsible for individual differences in drug actions (Uhl et al., 2008a,b,c). This chromosome 15 locus is thus likely to provide a good example of “secondary” pharmacogenomics, since (1) it is identified in relation to this quantity–frequency related phenotype, (2) it has not been identified in comparisons between FTND dependent and control, nonsmokers, and (3) it has not been associated as reproducibly with dependence on other substances (but see Grucza et al., 2008). Markers in this chromosomal location have now been associated with differences between light and heavy smokers (and/or with lung cancers whose cell types are intimately associated with smoking histories) in samples from Iceland, Spain, Australia and several US sites (Amos et al., 2008; Thorgeirsson et al., 2008). By contrast, no GWA or linkage study provides evidence for any other “oligogenic” effect of variants at any single locus on DSM or FTND nicotine dependence, per se. Comparisons of dependent smokers to controls with modest or no lifetime smoking identify polygenic effects of genes that fall into a number of gene classes (Table 1). Many of these loci, and the loci identified in comparing S60 G.R. Uhl et al. / Drug and Alcohol Dependence 104S (2009) S58–S63 Table 1 Summary of gene classes with addiction vulnerability gene variants identified in Uhl et al. (2008a,b,c) and Drgon et al. (2008, manuscript in preparation). Functional gene class Genes identified Cell adhesion related DNA/RNA handling Enzyme Ligand Protein handling/modification Receptor Signaling Structure Transcription regulation Transport Unknown 13 7 15 1 10 10 4 15 9 7 13 these samples have identified modest differences in associations in male and female samples, more study will be required to establish reproducible gender-specific effects of these and other allelic variants. 3. Molecular genetic observations for smoking cessation We have recently reported data from genome wide association in three samples of smokers who were successful, compared to those who were unsuccessful, in clinical trials conducted in Philadelphia, Washington, DC, Buffalo, Providence and Durham (Uhl et al., 2007, 2008a,b,c). These subjects for clinical trials were treated with nicotine replacement or with buproion, accompanied by standardized behavioral counseling. There is remarkably convergent data from comparisons of these three “smoking cessation success” GWA datasets. Nominally positive clustered SNPs from successful vs unsuccessful quitter comparisons from these samples cluster together on small chromosomal regions to extents much greater than chance (Uhl et al., 2008a,b,c). The Monte Carlo p values for the replication for these samples, taken two at a time, were 0.00054, 0.0016 and 0.00063, respectively. Among the smokers identified in the NIH samples described above, we were also able to compare data from individuals who reported lifetime nicotine dependence and current smoking when interviewed vs individuals who reported having been nicotine dependent at sometime in their lives but who achieved abstinence (Drgon et al., 2009). The “current smokers” started to smoke at age 17 (±4), smoked for 18 (±13) years, consumed 20 (±13) cigarettes/day and continued to smoke when interviewed, while the “quitters” starting smoking at 17 (±3) years of age, smoked an average of 20 (±13) cigarettes/day for 13 (±11) years but subsequently maintained abstinence for 16 (±12) years by the time of interviews (Lueders et al., 2002). Remarkably, the data from these “community quitter” comparisons identified chromosomal regions that were also identified by data from quit success in two of the three clinical trial samples in Uhl et al. (2008a,b,c) (p ≤ 0.0001). Genes that we have identified by clusters of nominally positive SNPs in both clinical trial and community based samples for ability to successfully quit smoking include ataxin 2-binding protein 1; CUB and Sushi multiple domain 1, Down syndrome cell adhesion molecule, protocadherin 15 and the retinoic acid receptor ␤. (See supplementary material available with the online version of this paper listing SNPs associated with successful vs unsuccessful quitters.) As for a number of the other comparisons noted here, a disproportionate number of these genes thus represent cell adhesion molecules. “chippers” to dependent smokers, identify the same chromosomal loci that have been identified in genome wide association studies of dependence on other addictive substances (Uhl et al., 2008a,b,c). Findings in the neurexin NRXN3 cell adhesion molecule gene (although in a slightly different region of this gene) (Bierut et al., 2007) are likely to support prior identification of variants in this gene in vulnerability to dependence on illegal substances (Liu et al., 2005). Further work will allow us to identify the extent to which findings in different portions of the same gene identify the same functional alleles, identify different functional alleles that display similar functional consequences, or even provide differing effects (Hishimoto et al., 2007). Many of these genes have also been identified in other studies of smokers. We have recently compared GWA data from almost 500 European American NIH research volunteers sampled in Bethesda, Maryland who never smoked to those who smoked substantial quantities (Drgon et al., 2009). Clustered nominally positive SNPs identified by comparisons between dependent and nondependent subjects provide highly significant overlap with the subset of 38,000 SNPs that were identified as nominally significant by Bierut and colleagues in comparisons of dependent smokers to nondependent smokers (Monte Carlo p < 0.0001). The degree to which these genes were also identified in 600,000–1 M GWA for dependence on at least one illegal substance vs ethnically matched control individuals provided p values reached statistical significance, but at a more modest level (p = 0.047). Work on candidate genes for substance dependence has also identified a list of the genes that were largely identified in relation to activities in the dopaminergic, opioid, cannabinoid and other circuits that provide targets for abused drugs and/or are activated by acute administration of many addictive substances, including nicotine, in animal studies (Table 2). While a few of Table 2 Genes and polymorphisms identified in a recent meta-analysis of 212 papers concerning candidate gene association studies for substance dependence (C.Y. Li et al., in preparation) based on their display of nominally significant odds ratios in meta-analyses. Summary ORs and 95% CI values were calculated using both the DerSimonian and Laird random-effects model and fixed-effects model. Genes that have also been associated with individual differences in ability to quit smoking in at least one study are highlighted (references include David et al., 2007; Ray et al., 2007; Munafo et al., 2008). Gene(s) Polymorphism Model N (studies) N (cases/controls) Random effects OR (95% CI) DRD2/ANKK1 BDNF OPRM1 CNR1 CCK DRD4 COMT FAAH HNMT OPRK1 OPRM1 SLC4A7 Taq1A rs6265 rs1799971 (AAT)n −45 C/T 48-bp repeat rs4680 rs324420 rs35953316 rs702764 C691G rs3278 A2 > A1 G>A A>G >14/other C>T 7/8 < other Val > Met P>T Thr > Ile A>G C>G G>A 20 9 9 8 6 6 3 3 3 3 3 3 6312/7424 2530/4126 2846/4072 2304/2144 860/2002 2324/1932 862/1594 498/1570 1540/1306 292/246 796/786 1410/906 1.38 (1.096–1.733) 1.38 (1.056–1.790) 1.31 (0.958–1.790) 0.75 (0.619–0.906) 1.34 (1.083–1.646) 1.48 (1.000–2.197) 0.76 (0.634–0.922) 1.32 (0.807–2.171) 0.72 (0.444–1.179) 0.62 (0.412–0.944) 0.61 (0.330–1.095) 2.28 (1.555–3.333) G.R. Uhl et al. / Drug and Alcohol Dependence 104S (2009) S58–S63 A number of candidate gene markers, generally those identified in studies of substance dependence, have also been examined in relation to ability to quit smoking. Several of the genes that are identified in Table 2 based on metaanalysis of candidate gene study results for addiction have also displayed initial or even reproducible associations with ability to quit smoking in at least some of the subgroups of studied individuals (boldfaced in Table 2). 4. Molecular genetics for other possibly relevant phenotypes Initial genome wide association analyses of traits and disorders that co-occur with addictions at frequencies that are higher than expected by chance provide data that support the ideas that influences of variants in some of the same genes impact vulnerability to substance dependence and these other phenotypes. We have recently reviewed data that provide molecular genetic support for shared genetic underpinnings with heritable, complex phenotypes that include cognitive functions, frontal lobe volumes, bipolar disorder and the personality trait, neuroticism (Uhl et al., 2008a,b,c). Shared genetic influences with individual differences in cognitive abilities and individual differences in frontal lobe brain volumes may be of especial interest. We have recently completed 500k–1 M genome wide association studies of these traits, and have analyzed our data in relation to 100k genome wide data for frontal lobe volume reported for Framingham study participants (Seshadri et al., 2007) and in relation to 500k genome wide data for a measure of cognitive function reported by Butcher et al. (2008), Uhl et al. (2008a,b,c) and Uhl et al. (submitted for publication). We have first noted that data for different cognitive function samples and different frontal lobe brain volume samples identify many of the same chromosomal regions, as we should anticipate, since each of these phenotypes displays substantial heritability in classical genetic datasets. Secondly, we noted that the data for cognitive function and that for frontal lobe brain volume identify the same chromosomal regions more than expected by chance, as we should again anticipate based on classical genetic studies (Butcher et al., 2008; Uhl et al., 2008a,b,c). Importantly, genome wide data from both of these phenotypes identifies the same chromosomal regions that are identified by genome wide data for addiction vulnerability and for ability to quit smoking to extents greater than expected by chance (Uhl et al., 2008a,b,c). Put another way, some of the genetic influences on developing dependence on nicotine and some of the genetic influences on ability to quit appear to be shared with genetic influences on cognitive ability and with genetic influences on brain volume. The shared genomic regions identified by these GWA datasets, as we note below, direct our attention to individual differences in brains and in core brain functions that provide individual differences in cognitive abilities in informing individual differences in vulnerability to addiction and abilities to quit. Such overlaps, of course, should not obscure the large roles that other genetic and environmental elements play in these phenotypes. 5. Smoking in women in light of this evidence for genetic and environmental influences on vulnerability to smoking and ability to quit The results and classical and molecular genetic studies reviewed above provide a number of potential interpretations that we pursue here. (1) The results of Swedish twin studies (Kendler et al., 2000) provide a relatively clear example of environmental determinants (2) (3) (4) (5) S61 that can overwhelm any genetic predispositions to smoke in women. No deterministic genetic influences on smoking (or, quite likely, on ability to quit) can thus be identified. Similarly, not all individuals develop nicotine dependence even in environments that sustain high overall rates of smoking (Johnson et al., 2002). It is thus likely that better understanding of environmental variables, including those that relate to educational attainment, other features of socioeconomic status and gender, will help us to improve understanding of the role of genetics in disadvantaged women. In addition, variants in specific genes, including variants in a nicotinic receptor gene cluster, appear to contribute to a set of genetic and environmental influences that allow “chippers” to smoke even relatively large total numbers of cigarettes for extended periods of time without experiencing marked symptoms of physiological dependence as assessed by FTND scores. Nevertheless, there are predispositions that derive from genetic and from nonfamily environment that result in greater vulnerability to becoming dependent on tobacco, as well as other predispositions that derive from genetic and from nonfamily environment that yield greater likelihood of success in achieving sustained abstinence when smokers try to quit. There are as yet no convincing evidence that the genetics of these predispositions in (1) and (2) differs strikingly between men and women. No large molecular genetic result has yet been identified on sex chromosomes. In many environments, men and women display similar heritabilities for developing nicotine dependence and for ability to quit smoking. It seems likely that at least some gender-selective genetic influences will ultimately be identified, even in current environments. However, most current data fit with the idea that the majority of genetic influences on nicotine dependence and ability to quit will be shared by men and women. The nature of many of the genes that are identified in molecular genetic studies point to the likelihood that many of the genetic influences on smoking-related phenotypes are likely to be mediated through their impact on individual differences in brains. The shared genomic regions in which allelic variants appear to provide of many of the genetic influences on dependence to a variety of substances also points in this direction, since drugs differ from each other in susceptibility to “primary pharmacogenomic” individual differences in absorption, distribution, metabolism and excretion as well as in “secondary pharmacogenomic” influences on receptor sites (Uhl et al., 2008a,b,c). The repeated, disproportionate identification of genes that encode cell adhesion molecules in molecular genetic studies, for example, appears to directly support the hypothesis that brain differences mediate much of the differential vulnerability to developing dependence on nicotine and differential ability to quit smoking. The overlap between molecular genetic results for vulnerability to substance dependence, cognitive ability and frontal brain volume (Uhl et al., 2008a,b,c) suggests that we cannot ignore the roles that common brain mechanisms that might be shared by these phenotypes might play in selected human populations. It is important to note that none of the datasets for these phenotypes provide an accurate quantitative assessment of the exact magnitude of these likely shared (vs nonshared) genetic influences. However, if we proceed without considering roles that individual differences in cognitive abilities and frontally linked executive functions might have in individual differences in response to prevention and treatment efforts, for example, we may do disservice to those individuals who have great need for the most appropriately targeted and tailored efforts. There is a parallel responsibility for careful framing of the discussion and careful education to minimize that chances that genetic infor- S62 G.R. Uhl et al. / Drug and Alcohol Dependence 104S (2009) S58–S63 mation is not misinterpreted or misused. Clearly, disadvantaged women who provide the focus for this special volume display a wide range of cognitive abilities, for example. Currently available data thus appears to support the idea that tailoring treatments and prevention strategies in ways that would maximize their benefit to disadvantaged women should be aided by recognition of important individual differences in genetic determinants for vulnerability to develop nicotine dependence, genetic determinants for ability to quit, genetic determinants for cognitive and executive function and environmental differences between these individuals. (6) This special issue focuses on the complex interactions between socioeconomic status, smoking and gender. Given the increasing concentration of higher smoking prevalence and lower likelihood of successful cessation in some members of low socioeconomic status groups (see other papers in this volume), there remain important questions about how genetic influences may (or may not) manifest themselves during periods of dramatic change in both the prevalence of smoking and in the apparent “permissiveness” of the general environment for smoking in many developed countries. It is an exciting time to be able to summarize and review the rapidly emerging data on the complex genetics of human addiction vulnerability, ability to quit and of related phenotypes. Genome wide association results for dependence on nicotine, as well as for several other classes of addictive substances, converge with each other in striking fashion that is highly unlikely to be due to chance. These data fit a genetic architecture for addiction and ability to quit smoking that is based on polygenic contributions from common allelic variants that also influence other brain-based phenotypes. Such a genetic architecture is quite consistent with data from family, adoption and twin classical genetic studies. We believe that increasing understanding of genetic contributions to nicotinerelated phenotypes will provide a new tool for studies that seek to elucidate environmental influences and gene × environment interactions. Together, this improved understanding will add significantly to our armamentarium for reducing nicotine dependence in all individuals. Role of funding source Funding for this study was provided by NIH, NIDA Intramural Research Program and Peking University (CYL). NIDA-IRP and Peking University had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. Contributors George R. Uhl conceived the study, wrote and edited the draft of the manuscript; Tomas Drgon participated in data collection, literature searches, statistical analysis and editing of the manuscript; Chuan-Yun Li participated in the data analysis; Catherine Johnson maintained and queried the genotype databases, performed statistical and Monte Carlo analyses, and edited the manuscript; and Qing-Rong Liu participated in data acquisition. All authors contributed to and have approved the final manuscript. Conflict of interest The authors, George R. Uhl, Tomas Drgon, Chuan-Yun Li, Catherine Johnson and Qing-Rong Liu report no biomedical financial interests or perceived or potential conflicts of interest. Appendix A. 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