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Drug and Alcohol Dependence 104S (2009) S58–S63

Contents lists available at ScienceDirect

Drug and Alcohol Dependence

journa l homepage: www.e lsev ier .com/ locate /drugalcdep

eview

moking and smoking cessation in disadvantaged women:ssessing genetic contributions�

eorge R. Uhla,∗, Tomas Drgona, Chuan-Yun Lia,b, Catherine Johnsona, Qing-Rong Liua

Molecular Neurobiology Branch, NIH-IRP (NIDA), Baltimore, MD, USANational Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing, China

r t i c l e i n f o

rticle history:eceived 23 September 2008eceived in revised form 26 February 2009ccepted 24 March 2009vailable online 12 May 2009

eywords:eneticsenome wide associationobaccoependence

a b s t r a c t

Abundant evidence from family, adoption and twin studies points to large genetic contributions to indi-vidual differences in vulnerability to develop dependence on one or more addictive substances, includingtobacco. Twin data suggests that much of this genetic vulnerability is shared by individuals who aredependent on a variety of addictive substances. Interestingly, some twin data also supports substantialdifferences in the apparent heritability of nicotine dependence in women as environmental conditionsbecome more permissive for their smoking. In addition, twin studies also support the idea that abilityto quit smoking displays substantial heritability, and that this heritable influence overlaps partially withgenetic influences on nicotine dependence. Candidate gene molecular genetic studies and genome wideassociation studies of substance dependence and ability to quit smoking each document apparent poly-genic influences that identify lists of genes that display partial overlap, as expected from classical geneticstudies. More of these genes are expressed in the brain than would be anticipated by chance. These lists ofgenes overlap significantly with those identified in molecular genetic studies of individual differences incognitive 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 fewof these genes lie on sex chromosomes. These data provide a substrate to improve understanding of nico-tine dependence, the ability to quit smoking, the potential for less permissive environments to restrictthe expression of genetic influences on smoking and the possibility that brain features that underlie phe-notypes such as individual differences in cognitive abilities might interact with environmental featuresthat 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.

ontents

1. Introduction: classical genetics of substance dependence, nicotine dependence and smoking cessation phenotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . S582. Molecular genetic observations for dependence on (and other phenotypes related to) substances including nicotine . . . . . . . . . . . . . . . . . . . . . . . . . . S593. Molecular genetic observations for smoking cessation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S604. Molecular genetics for other possibly relevant phenotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S615. Smoking in women in light of this evidence for genetic and environmental influences on vulnerability to smoking and ability to quit . . . . . . . . S61

Role of funding source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S62Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S62

Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S62Appendix A. Supplementary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

� Supplementary details are provided with the online version of this paper atoi:10.1016/j.drugalcdep.2009.03.012.∗ Corresponding author at: Molecular Neurobiology, Box 5180, Baltimore, MD

1224, USA. Tel.: +1 410 550 2843x146; fax: +1 410 550 1535.E-mail address: guhl@intra.nida.nih.gov (G.R. Uhl).

376-8716/$ – see front matter. Published by Elsevier Ireland Ltd.oi:10.1016/j.drugalcdep.2009.03.012

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1. Introduction: classical genetics of substance dependence,nicotine dependence and smoking cessation phenotypes

Current models for the genetic architecture for dependenceon addictive substances in the population are based on informa-tion from: (1) family, adoption and twin data that each support

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ubstantial heritability for addictions, (2) twin data (in whichoncordance in genetically identical monozygotic and geneticallyalf-identical dizygotic twins are compared) that document thatost of this heritable influence is not substance-specific, (3) link-

ge and association studies that fail to provide evidence for genesf major effect (e.g. for any single gene whose variants produceubstantial reproducible differences in addiction vulnerability) forubstance dependence.

Support for the idea that vulnerability to addictions is a com-lex trait with strong genetic influences that are largely shared bybusers of different legal and illegal addictive substances (Uhl et al.,995; Tsuang et al., 1998; True et al., 1999; Karkowski et al., 2000)omes from classical genetic studies. Family studies document thatrst degree relatives (e.g. sibs) of addicts display greater risk foreveloping substance dependence than more distant relatives (Uhlt al., 1995; Merikangas et al., 1998). Adoption studies find greaterimilarities between levels of substance abuse between adoptees vsiological relatives than adoptees vs members of the adoptive fam-

lies (Uhl et al., 1995). In twin studies, differences in concordanceetween genetically identical and fraternal twins also support sub-tantial heritability for vulnerability to addictions (Grove et al.,990; Gynther et al., 1995; Tsuang et al., 1996; Woodward et al.,996; Kendler and Prescott, 1998; Karkowski et al., 2000; Agrawalt al., 2004; Kendler et al., 2006). Twin data also allows quantitationf the amount, about half, of addiction vulnerability that is herita-le. Twin data supports the idea that the environmental influencesn addiction vulnerability that are not shared among members ofwin pairs are much larger than those that are shared by mem-ers of twin pairs (e.g. e2 � c2 in virtually every such study). Manyf the environmental influences on human addiction vulnerabil-ty are thus likely to come from outside of the immediate familynvironment.

We are also fortunate to have data from studies of identical vsraternal twin pairs that evaluate the degree to which one twin’sependence on a substance enhances the chance that his or hero-twin will become dependent on a substance of a different class.esults of these analyses document that much or even most of theenetic influences on addiction vulnerability are common to depen-ence on multiple different substances, though others do appearo be substance-specific (Tsuang et al., 1998; Agrawal et al., 2004;endler et al., 2006, 2008).

Data from classical genetic studies of smoking reveal severalspecially interesting features. Overall heritability for vulnerabil-ty to become dependent on nicotine has been well documented in

ales and females sampled in a number of environments. But notll. Studies of twins raised in late 19th–early 20th century Swedishnvironments document the progressively greater emergence ofpparent heritable influences on smoking in women over this timeKendler et al., 2000). During this time, the initially strong socialonstraints against smoking in women were relaxed. Interestingly,eritability estimates in men did not change over this same timeeriod. The allelic variants that predispose modern Swedish womeno smoke are likely to be virtually identical to those present inheir grandmothers who were environmentally constrained againstmoking (Kendler et al., 2000). This work thus provides one of theost striking examples of influences that a strongly nonpermissive

nvironment can have on the expression of an underlying geneticulnerability in women. Conceivably, the disproportionate fractionf the cigarettes consumed in the United State by disadvantagedomen (see other chapters in this volume) could be viewed as based,

n part, on interactions between environmental features that might

e more permissive for these individuals, allowing them to expressnderlying genetic predispositions.

Metaanalyses do identify modest differences in heritability inomparing male vs female twin pairs, though recent studies iden-ify nearly the same heritability in men and women (Li et al., 2003;

endence 104S (2009) S58–S63 S59

Broms et al., 2006) Other features that can contribute to socioeco-nomic status can also display heritability, as noted in other chaptersin this volume.

Heritability can also be demonstrated for a number of distinctsmoking-related phenotypes. Diagnostic and Statistical Manual(DSM) criteria measure nicotine dependence in ways that are moreanalogous to those in which dependence on other addictive sub-stances in measured. The Fagerstrom Test for Nicotine Dependence(FTND) assesses a battery of items that reflect more physiologicalnicotine dependence (Fagerstrom, 1978; Fagerstrom and Schneider,1989; Pomerleau et al., 1989, 1994; Heatherton et al., 1991; Lessovet al., 2004). Bierut and colleagues have documented evidence forapparent heritability for comparisons between smokers with FTNDdependence and smokers who do not display FTND dependence(although a significant number display DSM dependence) (Bierutet al., 2007). Finally, and importantly, the ability to quit smokingcan display a remarkably robust heritable component, even thoughthe exact questions that provide evidence about this phenotype dif-fer between the studies that have studied heritability of success inquitting (Broms et al., 2006).

Not all of the genetics of these heritable, smoking-related phe-notypes are identical. There are likely to be substantial differencesbetween the genetics of becoming dependent on nicotine and thegenetics of ability to quit (Broms et al., 2006).

This review aims to provide an introduction to the rapidly mov-ing area of the molecular genetics that underlie some of theseclassical genetic observations. Additional perspectives can be foundin 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 (andother phenotypes related to) substances including nicotine

One of the largest single smoking-related molecular geneticeffects is found in data that compares heavy smokers with highFTND scores to smokers without evidence for dependence byFTND criteria. Markers in the chromosome 15 gene cluster thatencodes the �3, �5 and �4 nicotinic acetylcholine receptors dis-play different allelic frequencies between these heavy vs lightsmokers in each of several studies (Bierut et al., 2007; Sacconeet al., 2007; Berrettini et al., 2008). Elsewhere, we have defined“primary” pharmacogenomics based on individual differences in“ADME” adsorption/distribution/metabolism/excretion features ofsubstances, “secondary” pharmacogenomics based on individualdifferences in the sites that initially recognize drugs and “higherorder” pharmacogenomics based on individual differences in “postreceptor” sites that are also responsible for individual differ-ences in drug actions (Uhl et al., 2008a,b,c). This chromosome15 locus is thus likely to provide a good example of “secondary”pharmacogenomics, since (1) it is identified in relation to thisquantity–frequency related phenotype, (2) it has not been iden-tified in comparisons between FTND dependent and control,nonsmokers, and (3) it has not been associated as reproduciblywith dependence on other substances (but see Grucza et al., 2008).Markers in this chromosomal location have now been associatedwith differences between light and heavy smokers (and/or withlung cancers whose cell types are intimately associated with smok-ing histories) in samples from Iceland, Spain, Australia and severalUS 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 DSMor FTND nicotine dependence, per se. Comparisons of dependentsmokers to controls with modest or no lifetime smoking identifypolygenic 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 Dep

Table 1Summary of gene classes with addiction vulnerability gene variantsidentified in Uhl et al. (2008a,b,c) and Drgon et al. (2008, manuscriptin preparation).

Functional gene class Genes identified

Cell adhesion related 13DNA/RNA handling 7Enzyme 15Ligand 1Protein handling/modification 10Receptor 10Signaling 4Structure 15

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chippers” to dependent smokers, identify the same chromoso-al loci that have been identified in genome wide association

tudies of dependence on other addictive substances (Uhl et al.,008a,b,c). Findings in the neurexin NRXN3 cell adhesion moleculeene (although in a slightly different region of this gene) (Bierut etl., 2007) are likely to support prior identification of variants in thisene in vulnerability to dependence on illegal substances (Liu et al.,005). Further work will allow us to identify the extent to whichndings in different portions of the same gene identify the same

unctional alleles, identify different functional alleles that displayimilar functional consequences, or even provide differing effectsHishimoto et al., 2007).

Many of these genes have also been identified in other studiesf smokers. We have recently compared GWA data from almost 500uropean American NIH research volunteers sampled in Bethesda,aryland who never smoked to those who smoked substantial

uantities (Drgon et al., 2009). Clustered nominally positive SNPsdentified by comparisons between dependent and nondepen-ent subjects provide highly significant overlap with the subset of8,000 SNPs that were identified as nominally significant by Bierutnd colleagues in comparisons of dependent smokers to nondepen-ent smokers (Monte Carlo p < 0.0001). The degree to which theseenes were also identified in 600,000–1 M GWA for dependence ont least one illegal substance vs ethnically matched control individ-als provided p values reached statistical significance, but at a moreodest level (p = 0.047).Work on candidate genes for substance dependence has also

dentified a list of the genes that were largely identified in rela-ion to activities in the dopaminergic, opioid, cannabinoid andther circuits that provide targets for abused drugs and/or arectivated by acute administration of many addictive substances,ncluding nicotine, in animal studies (Table 2). While a few of

able 2enes and polymorphisms identified in a recent meta-analysis of 212 papers concernireparation) based on their display of nominally significant odds ratios in meta-analysesaird random-effects model and fixed-effects model. Genes that have also been associatighlighted (references include David et al., 2007; Ray et al., 2007; Munafo et al., 2008).

ene(s) Polymorphism Model N (s

RD2/ANKK1 Taq1A A2 > A1 20DNF rs6265 G > A 9PRM1 rs1799971 A > G 9NR1 (AAT)n >14/other 8CK −45 C/T C > T 6RD4 48-bp repeat 7/8 < other 6OMT rs4680 Val > Met 3AAH rs324420 P > T 3NMT rs35953316 Thr > Ile 3PRK1 rs702764 A > G 3PRM1 C691G C > G 3LC4A7 rs3278 G > A 3

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these samples have identified modest differences in associationsin male and female samples, more study will be required to estab-lish reproducible gender-specific effects of these and other allelicvariants.

3. Molecular genetic observations for smoking cessation

We have recently reported data from genome wide associa-tion in three samples of smokers who were successful, comparedto those who were unsuccessful, in clinical trials conducted inPhiladelphia, Washington, DC, Buffalo, Providence and Durham(Uhl et al., 2007, 2008a,b,c). These subjects for clinical trials weretreated with nicotine replacement or with buproion, accompaniedby standardized behavioral counseling.

There is remarkably convergent data from comparisons ofthese three “smoking cessation success” GWA datasets. Nominallypositive clustered SNPs from successful vs unsuccessful quittercomparisons from these samples cluster together on small chro-mosomal regions to extents much greater than chance (Uhl et al.,2008a,b,c). The Monte Carlo p values for the replication for thesesamples, taken two at a time, were 0.00054, 0.0016 and 0.00063,respectively.

Among the smokers identified in the NIH samples describedabove, we were also able to compare data from individuals whoreported lifetime nicotine dependence and current smoking wheninterviewed vs individuals who reported having been nicotinedependent at sometime in their lives but who achieved absti-nence (Drgon et al., 2009). The “current smokers” started to smokeat age 17 (±4), smoked for 18 (±13) years, consumed 20 (±13)cigarettes/day and continued to smoke when interviewed, whilethe “quitters” starting smoking at 17 (±3) years of age, smoked anaverage of 20 (±13) cigarettes/day for 13 (±11) years but subse-quently maintained abstinence for 16 (±12) years by the time ofinterviews (Lueders et al., 2002).

Remarkably, the data from these “community quitter” compar-isons identified chromosomal regions that were also identified bydata from quit success in two of the three clinical trial samplesin Uhl et al. (2008a,b,c) (p ≤ 0.0001). Genes that we have identi-fied by clusters of nominally positive SNPs in both clinical trial andcommunity based samples for ability to successfully quit smokinginclude ataxin 2-binding protein 1; CUB and Sushi multiple domain1, Down syndrome cell adhesion molecule, protocadherin 15 and

the retinoic acid receptor �. (See supplementary material availablewith the online version of this paper listing SNPs associated withsuccessful vs unsuccessful quitters.) As for a number of the othercomparisons noted here, a disproportionate number of these genesthus represent cell adhesion molecules.

ng candidate gene association studies for substance dependence (C.Y. Li et al., in. Summary ORs and 95% CI values were calculated using both the DerSimonian anded with individual differences in ability to quit smoking in at least one study are

tudies) N (cases/controls) Random effects OR (95% CI)

6312/7424 1.38 (1.096–1.733)2530/4126 1.38 (1.056–1.790)2846/4072 1.31 (0.958–1.790)2304/2144 0.75 (0.619–0.906)860/2002 1.34 (1.083–1.646)2324/1932 1.48 (1.000–2.197)862/1594 0.76 (0.634–0.922)498/1570 1.32 (0.807–2.171)1540/1306 0.72 (0.444–1.179)292/246 0.62 (0.412–0.944)796/786 0.61 (0.330–1.095)1410/906 2.28 (1.555–3.333)

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A number of candidate gene markers, generally those identifiedn studies of substance dependence, have also been examined inelation to ability to quit smoking. Several of the genes that aredentified in Table 2 based on metaanalysis of candidate gene studyesults for addiction have also displayed initial or even reproduciblessociations with ability to quit smoking in at least some of theubgroups of studied individuals (boldfaced in Table 2).

. Molecular genetics for other possibly relevanthenotypes

Initial genome wide association analyses of traits and disor-ers that co-occur with addictions at frequencies that are higherhan expected by chance provide data that support the ideas thatnfluences of variants in some of the same genes impact vulner-bility to substance dependence and these other phenotypes. Weave recently reviewed data that provide molecular genetic sup-ort for shared genetic underpinnings with heritable, complexhenotypes that include cognitive functions, frontal lobe volumes,ipolar disorder and the personality trait, neuroticism (Uhl et al.,008a,b,c).

Shared genetic influences with individual differences in cogni-ive abilities and individual differences in frontal lobe brain volumes

ay be of especial interest. We have recently completed 500k–1 Menome wide association studies of these traits, and have analyzedur data in relation to 100k genome wide data for frontal lobeolume reported for Framingham study participants (Seshadri etl., 2007) and in relation to 500k genome wide data for a mea-ure of cognitive function reported by Butcher et al. (2008), Uhl etl. (2008a,b,c) and Uhl et al. (submitted for publication). We haverst noted that data for different cognitive function samples andifferent frontal lobe brain volume samples identify many of theame chromosomal regions, as we should anticipate, since eachf these phenotypes displays substantial heritability in classicalenetic datasets. Secondly, we noted that the data for cognitiveunction and that for frontal lobe brain volume identify the samehromosomal regions more than expected by chance, as we shouldgain anticipate based on classical genetic studies (Butcher et al.,008; Uhl et al., 2008a,b,c).

Importantly, genome wide data from both of these phenotypesdentifies the same chromosomal regions that are identified byenome wide data for addiction vulnerability and for ability touit smoking to extents greater than expected by chance (Uhl etl., 2008a,b,c). Put another way, some of the genetic influences oneveloping dependence on nicotine and some of the genetic influ-nces on ability to quit appear to be shared with genetic influencesn cognitive ability and with genetic influences on brain volume.he shared genomic regions identified by these GWA datasets, as weote below, direct our attention to individual differences in brainsnd in core brain functions that provide individual differences inognitive abilities in informing individual differences in vulnerabil-ty to addiction and abilities to quit. Such overlaps, of course, shouldot obscure the large roles that other genetic and environmentallements play in these phenotypes.

. Smoking in women in light of this evidence for geneticnd environmental influences on vulnerability to smokingnd ability to quit

The results and classical and molecular genetic studies reviewed

bove provide a number of potential interpretations that we pursueere.

1) The results of Swedish twin studies (Kendler et al., 2000) pro-vide a relatively clear example of environmental determinants

endence 104S (2009) S58–S63 S61

that can overwhelm any genetic predispositions to smoke inwomen. 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 envi-ronments that sustain high overall rates of smoking (Johnsonet al., 2002). It is thus likely that better understanding of envi-ronmental variables, including those that relate to educationalattainment, other features of socioeconomic status and gender,will help us to improve understanding of the role of genetics indisadvantaged women. In addition, variants in specific genes,including variants in a nicotinic receptor gene cluster, appear tocontribute to a set of genetic and environmental influences thatallow “chippers” to smoke even relatively large total numbersof cigarettes for extended periods of time without experiencingmarked symptoms of physiological dependence as assessed byFTND scores.

(2) Nevertheless, there are predispositions that derive from geneticand from nonfamily environment that result in greater vulner-ability to becoming dependent on tobacco, as well as otherpredispositions that derive from genetic and from nonfamilyenvironment that yield greater likelihood of success in achiev-ing sustained abstinence when smokers try to quit.

(3) There are as yet no convincing evidence that the genetics ofthese predispositions in (1) and (2) differs strikingly betweenmen and women. No large molecular genetic result has yet beenidentified on sex chromosomes. In many environments, menand women display similar heritabilities for developing nico-tine dependence and for ability to quit smoking. It seems likelythat at least some gender-selective genetic influences will ulti-mately be identified, even in current environments. However,most current data fit with the idea that the majority of geneticinfluences on nicotine dependence and ability to quit will beshared by men and women.

(4) The nature of many of the genes that are identified in molec-ular genetic studies point to the likelihood that many of thegenetic influences on smoking-related phenotypes are likelyto be mediated through their impact on individual differencesin brains. The shared genomic regions in which allelic variantsappear to provide of many of the genetic influences on depen-dence to a variety of substances also points in this direction,since drugs differ from each other in susceptibility to “pri-mary pharmacogenomic” individual differences in absorption,distribution, metabolism and excretion as well as in “sec-ondary pharmacogenomic” influences on receptor sites (Uhl etal., 2008a,b,c). The repeated, disproportionate identification ofgenes that encode cell adhesion molecules in molecular geneticstudies, for example, appears to directly support the hypothesisthat brain differences mediate much of the differential vulner-ability to developing dependence on nicotine and differentialability to quit smoking.

(5) The overlap between molecular genetic results for vulnerabil-ity to substance dependence, cognitive ability and frontal brainvolume (Uhl et al., 2008a,b,c) suggests that we cannot ignorethe roles that common brain mechanisms that might be sharedby these phenotypes might play in selected human popula-tions. It is important to note that none of the datasets for thesephenotypes provide an accurate quantitative assessment of theexact magnitude of these likely shared (vs nonshared) geneticinfluences. However, if we proceed without considering rolesthat individual differences in cognitive abilities and frontallylinked 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 needfor the most appropriately targeted and tailored efforts. There isa parallel responsibility for careful framing of the discussion andcareful education to minimize that chances that genetic infor-

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mation is not misinterpreted or misused. Clearly, disadvantagedwomen who provide the focus for this special volume dis-play a wide range of cognitive abilities, for example. Currentlyavailable data thus appears to support the idea that tailoringtreatments and prevention strategies in ways that would max-imize their benefit to disadvantaged women should be aidedby recognition of important individual differences in geneticdeterminants for vulnerability to develop nicotine dependence,genetic determinants for ability to quit, genetic determinantsfor cognitive and executive function and environmental differ-ences between these individuals.

6) This special issue focuses on the complex interactions betweensocioeconomic status, smoking and gender. Given the increas-ing concentration of higher smoking prevalence and lowerlikelihood of successful cessation in some members of lowsocioeconomic status groups (see other papers in this volume),there remain important questions about how genetic influ-ences may (or may not) manifest themselves during periodsof dramatic change in both the prevalence of smoking and inthe apparent “permissiveness” of the general environment forsmoking in many developed countries.

It is an exciting time to be able to summarize and review theapidly emerging data on the complex genetics of human addictionulnerability, ability to quit and of related phenotypes. Genomeide association results for dependence on nicotine, as well as

or several other classes of addictive substances, converge withach other in striking fashion that is highly unlikely to be due tohance. These data fit a genetic architecture for addiction and abil-ty to quit smoking that is based on polygenic contributions fromommon allelic variants that also influence other brain-based phe-otypes. Such a genetic architecture is quite consistent with data

rom family, adoption and twin classical genetic studies. We believehat increasing understanding of genetic contributions to nicotine-elated phenotypes will provide a new tool for studies that seeko elucidate environmental influences and gene × environmentnteractions. Together, this improved understanding will add sig-ificantly to our armamentarium for reducing nicotine dependence

n all individuals.

ole of funding source

Funding for this study was provided by NIH, NIDA Intramu-al Research Program and Peking University (CYL). NIDA-IRP andeking University had no further role in study design; in the col-ection, analysis and interpretation of data; in the writing of theeport; or in the decision to submit the paper for publication.

ontributors

George R. Uhl conceived the study, wrote and edited the draftf the manuscript; Tomas Drgon participated in data collection, lit-rature searches, statistical analysis and editing of the manuscript;huan-Yun Li participated in the data analysis; Catherine Johnsonaintained and queried the genotype databases, performed sta-

istical and Monte Carlo analyses, and edited the manuscript; anding-Rong Liu participated in data acquisition. All authors con-

ributed to and have approved the final manuscript.

onflict of interest

The authors, George R. Uhl, Tomas Drgon, Chuan-Yun Li, Cather-ne Johnson and Qing-Rong Liu report no biomedical financialnterests or perceived or potential conflicts of interest.

endence 104S (2009) S58–S63

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.drugalcdep.2009.03.012.

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