0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Original Article |

Polygenic Risk and the Developmental Progression to Heavy, Persistent Smoking and Nicotine Dependence:  Evidence From a 4-Decade Longitudinal Study FREE

Daniel W. Belsky, PhD; Terrie E. Moffitt, PhD; Timothy B. Baker, PhD; Andrea K. Biddle, PhD; James P. Evans, MD, PhD; HonaLee Harrington, BA; Renate Houts, PhD; Madeline Meier, PhD; Karen Sugden, PhD; Benjamin Williams, BS; Richie Poulton, PhD; Avshalom Caspi, PhD
[+] Author Affiliations

Author Affiliations: Department of Health Policy and Management, Gillings School of Public Health (Drs Belsky and Biddle), and Department of Genetics, School of Medicine (Dr Evans), University of North Carolina–Chapel Hill; Center for the Study of Aging and Human Development (Dr Belsky) and Department of Psychiatry and Behavioral Sciences (Drs Belsky, Moffitt, Houts, Meier, Sugden, and Caspi; Ms Harrington; and Mr Williams), Duke University Medical Center, and Institute for Genome Sciences and Policy (Drs Belsky, Moffitt, Houts, Meier, Sugden, and Caspi; Ms Harrington; and Mr Williams) and Department of Psychology and Neuroscience (Drs Belsky, Moffitt, Houts, Meier, Sugden, and Caspi; Ms Harrington; and Mr Williams), Duke University, Durham, North Carolina; Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, London, United Kingdom (Drs Moffitt, Sugden, and Caspi and Mr Williams); Center for Tobacco Research and Intervention, University of Wisconsin, and Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison (Dr Baker); and Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, Otago, New Zealand (Dr Poulton).


JAMA Psychiatry. 2013;70(5):534-542. doi:10.1001/jamapsychiatry.2013.736.
Text Size: A A A
Published online

Importance Genome-wide hypothesis-free discovery methods have identified loci that are associated with heavy smoking in adulthood. Research is needed to understand developmental processes that link newly discovered genetic risks with adult heavy smoking.

Objective To test how genetic risks discovered in genome-wide association studies of adult smoking influence the developmental progression of smoking behavior from initiation through conversion to daily smoking, progression to heavy smoking, nicotine dependence, and struggles with cessation.

Design A 38-year, prospective, longitudinal study of a representative birth cohort.

Setting The Dunedin Multidisciplinary Health and Development Study of New Zealand.

Participants The study included 1037 male and female participants.

Exposure We assessed genetic risk with a multilocus genetic risk score. The genetic risk score was composed of single-nucleotide polymorphisms identified in 3 meta-analyses of genome-wide association studies of smoking quantity phenotypes.

Main Outcomes and Measures Smoking initiation, conversion to daily smoking, progression to heavy smoking, nicotine dependence (Fagerström Test of Nicotine Dependence), and cessation difficulties were evaluated at 8 assessments spanning the ages of 11 to 38 years.

Results Genetic risk score was unrelated to smoking initiation. However, individuals at higher genetic risk were more likely to convert to daily smoking as teenagers, progressed more rapidly from smoking initiation to heavy smoking, persisted longer in smoking heavily, developed nicotine dependence more frequently, were more reliant on smoking to cope with stress, and were more likely to fail in their cessation attempts. Further analysis revealed that 2 adolescent developmental phenotypes—early conversion to daily smoking and rapid progression to heavy smoking—mediated associations between the genetic risk score and mature phenotypes of persistent heavy smoking, nicotine dependence, and cessation failure. The genetic risk score predicted smoking risk over and above family history.

Conclusions and Relevance Initiatives that disrupt the developmental progression of smoking behavior among adolescents may mitigate genetic risks for developing adult smoking problems. Future genetic research may maximize discovery potential by focusing on smoking behavior soon after smoking initiation and by studying young smokers.

Figures in this Article

Cigarette smoking is a costly, prevalent public health problem. The US Centers for Disease Control and Prevention attribute more than 400 000 deaths and $95 million in lost productivity to smoking during 2000-2004.1 Approximately 20% of adults still smoke daily despite widespread knowledge of smoking's health effects and increasing economic costs to smokers due to increasing taxes.2 Thus, more effective interventions to prevent smoking, motivate smoking cessation, and prevent relapse are needed.35

Studies of twins6 suggest that genetic differences among individuals have an important role in smoking behavior, cessation, and response to antismoking interventions. Recent genome-wide association studies (GWASs)79 in adult smokers and former smokers revealed genes that relate with genome-wide significance to smoking quantity (number of cigarettes smoked per day). These genes are already being used in clinical applications (eg, to predict smoking cessation likelihood and in pharmacogenetic analyses).1014 An important additional step in the translation of these GWAS findings is to test whether genetic markers that predicted smoking quantity in GWASs also predict the development of smoking behavior in adolescence.15,16 This question is of critical importance for public health practice because intervention to disrupt genetic risk is likely to be most effective early in the development of dependence. Important developmental phenotypes in the pathogenesis of adult dependence include smoking initiation, conversion to daily smoking during adolescence, and rapid progression to heavy smoking.17 Early, rapid progression from smoking initiation to heavy use is a signal risk for adult nicotine dependence.1821 Therefore, the present study tested relations of GWAS-identified genetic risk with adolescent and adult smoking phenotypes and then determined the extent to which genetic effects on the former affected the adult phenotype outcomes.

In this study, we tested prospective associations between genetic risks and adolescent developmental and mature adult phenotypes of smoking behavior (Figure 1). We examined genetic risks in the Dunedin Study, a birth cohort (n = 1037) followed up to the age of 38 years with 95% retention. We collected smoking behavior data at 8 assessments spanning the ages of 11 to 38 years. This approach allowed us to study the effects of genetic risk in the cohort as members initiated smoking during adolescence, converted to daily smoking, and progressed to heavy smoking during the teenage and young adult years and as they developed nicotine dependence and struggled with cessation in their 20s and 30s. We tested whether individuals at higher genetic risk progressed more rapidly from smoking initiation to heavy smoking, if they smoked more heavily as adults, if they were more nicotine dependent, and if they were more likely to fail in their cessation attempts. Finally, we tested the hypothesis that genetic risk accelerates the developmental progression from smoking initiation to heavy smoking, and this, in turn, increases the severity of adult smoking problems, such as heavy, intractable smoking and nicotine dependence. This model has relevance to public health interventions that might delay the developmental progression to heavy smoking. To put the magnitudes of genetic risk effects in context and to determine whether molecular genetic measurements provided novel information about risk, we conducted an additional analysis comparing molecular genetic information to family history information. These analyses asked how large molecular genetic effects were relative to family history effects and whether molecular genetic effects were independent of family history effects in predicting risk.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. Genetic risk and the developmental progression of smoking behavior. In the hypothesized model, genetic risk influences the mature phenotypes of heavy smoking persistence, nicotine dependence, and cessation failure through a pathway mediated by 3 developmental phenotypes: smoking initiation, conversion to daily smoking, and progression to heavy smoking.

SAMPLE

Participants are members of the Dunedin Multidisciplinary Health and Development Study, a longitudinal investigation of health and behavior in a complete birth cohort. Study members (N = 1037, 91% of eligible births, 52% male) were all individuals born between April 1972 and March 1973 in Dunedin, New Zealand, who were eligible for the longitudinal study based on residence in the province at age 3 years and who participated in the first follow-up assessment at age 3 years. The cohort represents the full range of socioeconomic status in the general population of New Zealand's South Island and is primarily white.22 Assessments were performed at birth and at the ages of 3, 5, 7, 9, 11, 13, 15, 18, 21, 26, 32, and, most recently, 38 years, when 1007 study members were still alive, with 95% retention. At each assessment wave, study members are brought to the Dunedin research unit for a full day of interviews and examinations. The Otago Ethics Committee approved each phase of the study, and informed consent was obtained from all study members.

MEASURES
Genetic Risk Score

A challenge for developmental research following up GWAS discoveries is that effect sizes for individual single-nucleotide polymorphisms (SNPs) are small; the largest effects for smoking quantity approach a change of 1 cigarette per day per risk allele. Moreover, many of the longitudinal studies23 with data necessary to investigate developmental phenotypes are underpowered to test individual SNP effects. However, evidence shows that smoking-associated loci make additive contributions to risk, recommending aggregating risk alleles.2427 Summing risk alleles across GWAS-identified SNPs to compute a genetic risk score (GRS) yields a quantitative index of genetic risk with a normal distribution28 and a potentially larger effect size.

We derived the GRS from 3 recent meta-analyses of GWAS that used as their phenotype cigarettes smoked per day.79 To construct the GRS, we considered SNPs from regions with genome-wide significant associations in at least 2 meta-analyses. All 3 meta-analyses identified SNPs in the q25.1 region of chromosome 15 containing the CHRNA5-CHRNA3-CHRNB4 gene cluster. Two meta-analyses identified SNPs in the q13.2 region of chromosome 19 containing the gene CYP2A6. These genes influence nicotine response and nicotine metabolism, have been linked with nicotine dependence, and are candidate genes in research into the development of smoking behavior.26,2935 Therefore, we focused our inquiry on the top GWAS SNPs in these 2 regions (eMethods). In 15q25.1, we selected the SNPs rs16969968, rs6495308, rs8032771, and rs12595538. The SNPs rs16969968 and rs6495308, which fall within the CHRNA5-CHRNA3-CHRNB4 gene cluster, were reported previously to have independent associations with smoking quantity.8,36 The SNPs rs8032771 and rs12595538, which are located downstream of the CHRNA5-CHRNA3-CHRNB4 gene cluster, were in weak linkage disequilibrium with rs16969968 and rs6495308 (R2≤0.10) and were genome-wide significant in the largest meta-analysis7 (P < 1 × 10−16 for both; P values for these SNPs were not published in the other 2 meta-analyses). In 19q13.2, we selected the SNPs rs7937 and rs4105144. Following 2 previous studies25,27 using multilocus measures of genetic risk for smoking, we assumed an additive model and summed alleles associated with higher smoking quantity to calculate the GRS. Because no reference data exist to determine the exact contributions of individual SNPs in our GRS to developmental phenotypes of smoking behavior, we used unweighted counts of risk alleles to construct the score.

To validate this GRS, we used independent data from the Atherosclerosis Risk in the Communities database and the Study of Addiction: Genetics and Environment database, accessed through the National Institutes of Health Database of Genotypes and Phenotypes.37,38 When a GRS SNP was unavailable in one of these databases, we selected the closest linkage disequilibrium proxy for that SNP to include in the GRS. Among European-descent Atherosclerosis Risk in the Communities participants (n = 8293), each SD increase in the GRS predicted a 1.45-pack-year increase in lifetime cigarette consumption among individuals who had ever smoked (P < .001) and a 1.02-cigarette increase in daily consumption among these ever smokers (P < .001). Replication of the GRS–smoking quantity association in the Study of Addiction: Genetics and Environment database and additional validation analyses testing versions of the GRS that exclude the SNPs rs16969968 and rs6495308 are presented in eTable 1.

Dunedin cohort genotyping was conducted with a commercially available array (BeadPlex Array; Illumina, Inc) using DNA extracted from whole blood (93% of the sample) or buccal swabs (7% of the sample). The GRS SNPs or proxies (linkage R2≥0.85) were called successfully in 95% of European-descent study members (eTable 2). These 880 individuals formed the analysis sample. Cohort members carried a mean (SD) of 7.06 (2.27) of 12 possible risk alleles. Cohort members' sex and socioeconomic status39 were unrelated to their genetic risk (Pearson r≤0.01). The GRS was standardized to have a mean (SD) of 0 (1) for analyses (GRS).

Family History of Smoking

Family histories of smoking were available for 99% of the cohort. The family history consisted of reports of smoking history provided by study members and both parents for study members' siblings, parents, and grandparents. The family history was summarized as the proportion of family members in the pedigree who were ever regular smokers, adjusted to account for differences in genetic relatedness to the proband of first- and second-degree relatives.40

Smoking Behavior

The developmental progression of smoking behavior in the Dunedin cohort is shown in Figure 2A. Measurement of adolescent developmental phenotypes and mature phenotypes of smoking behavior is shown in Figure 2B.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. Smoking behavior in the Dunedin cohort. A, Developmental progression of smoking behavior in the Dunedin cohort. Study members reported their smoking status during in-person assessments at the ages of 11 (percentage of ever-smokers =  7%), 13 (13%), 15 (62%), 18 (66%), 21 (70%), 26 (70%), 32 (71%), and 38 (71%) years and their daily cigarette consumption at the ages of 13 (percentage of daily smokers = 1%), 15 (14%), 18 (31%), 21 (34%), 26 (35%), 32 (30%), and 38 (20%) years. We assessed nicotine dependence using the Fagerström Test of Nicotine Dependence (FTND),41 completed by study members at the ages of 21, 26, and 38 years. We assessed cessation failure using study members' reports of quit attempts and outcomes at the ages of 18, 21, 26, 32, and 38 years. B, Measurements of developmental and mature smoking phenotypes. Data are number (percentage) of study members unless otherwise indicated.

STATISTICAL ANALYSIS

Data analysis was divided into 3 parts. First, we analyzed associations between the GRS and developmental phenotypes of smoking behavior. Second, we analyzed associations between the GRS and mature phenotypes. Third, we tested whether developmental phenotypes mediated associations between the GRS and mature phenotypes. We used different statistical models to analyze outcome data as required by the outcome's distribution. We analyzed continuously distributed outcome data (eg, lifetime cigarette consumption in pack-years) using ordinary least squares. We analyzed dichotomous outcome data (eg, daily smoker by age 15 years) using Poisson regression models because this is a standard method to derive relative risks.47 We analyzed count outcome data (eg, the number of assessments at which the study member met criteria for nicotine dependence) using negative binomial regression models to account for the overdispersion of many of the count measures.48 We analyzed hazards of smoking initiation, progression to heavy smoking, becoming nicotine dependent, and relapsing from a quit attempt using Cox proportional hazards regression models. To account for differences in the frequency with which study members attempted cessation, we constructed panel data sets that included one observation per study member per assessment (for the data for ages 18-32 years) and one observation per study member per quit attempt (for the data for life-history calendars). We used these panel data sets to analyze the genetic effect on smokers' risks of cessation failure during ages 18 to 32 years and on their hazards of relapse during ages 32 to 38 years. We accounted for nonindependence of repeated observations of individuals using generalized estimating equation models of risks and conditional risk-set models of hazards.49,50 We tested whether genetic effects on the mature phenotypes of persistent heavy smoking, nicotine dependence, and relapse were mediated by adolescent developmental phenotypes using the structural equation described by McKinnon and Dwyer51 and the methods described by Preacher et al.52,53 To allow for a single test of mediation, we conducted a principal components analysis54 of the mature phenotypes of persistent heavy smoking (pack-years smoked at age 38 years), nicotine dependence (total number of symptoms across all assessments), and cessation failure (number of assessments with relapse). This analysis indicated that the mature phenotypes were positively and significantly correlated (eTable 3) and could be summarized in a single component that explained 78% of the variance in the 3 measures (factor loading = 0.61 for persistent heavy smoking, 0.60 for nicotine dependence, and 0.52 for cessation failure). We used this component as the dependent variable in our mediation analysis. Analyses were adjusted for sex and conducted using STATA statistical software, version 11.0 (StataCorp LP).55 Panel-data models were fitted to longitudinal repeated-measures data using the XT and ST commands in STATA statistical software, version 11.0. Unless otherwise noted, effect sizes are presented for 1-SD increase in genetic risk.

GENETIC RISK AND SMOKING INITIATION

The GRS was not associated with whether individuals initiated smoking or with the timing of initiation (relative risk [RR] for smoking initiation = 0.98; 95% CI, 0.95-1.02; cumulative hazard ratio [HR] for initiation = 1.01; 95% CI, 0.94-1.09; based on a 1-SD increase in genetic risk; Table). Subsequent analyses focused on the 627 Dunedin cohort members who initiated smoking at some point during follow-up (Figure 2).

Table Graphic Jump LocationTable. Effect Sizes for Genetic and Family History Associations With Developmental and Clinical Phenotypes of Smoking Behaviora
GENETIC RISK AND THE PROGRESSION OF SMOKING BEHAVIOR

Individuals at higher genetic risk were more likely to progress to smoking 20 or more cigarettes per day and did so more rapidly (HR = 1.35; 95% CI, 1.14-1.58). Figure 3A shows the cumulative hazards for smoking 20 cigarettes or more per day for individuals at low, average, and high genetic risk. An unexpected finding was that individuals who initiated smoking but who did not progress to daily smoking or to heavy smoking, so-called chippers, were at the lowest genetic risk of any group in the cohort (Figure 3B).

Place holder to copy figure label and caption
Graphic Jump Location

Figure 3. Genetic risk score (GRS) derived from genome-wide association study of smoking quantity is associated with the developmental progression of smoking behavior in a birth cohort of European-descent individuals. A, Individuals at higher genetic risk progressed more rapidly from smoking initiation to heavy smoking. This panel graphs hazard functions for onset of heavy smoking among individuals at low genetic risk (GRS = −1), average genetic risk (GRS = 0), and high genetic risk (GRS = 1). The dashed gray line marks the cumulative hazard for individuals at average genetic risk. The hazard function was estimated from a Cox proportional hazard model with time since onset of ever-smoking as the exposure time and the first assessment a study member reported smoking 20 or more cigarettes per day (CPD) as the failure event. The hazard model included all individuals who ever initiated smoking (n = 627). Individuals at higher genetic risk progressed more rapidly from smoking initiation to smoking 20 or more CPD (hazard ratio = 1.35; 95% CI, 1.14-1.58). B, Genetic risk was highest among individuals who progressed to heavy smoking and lowest among individuals who initiated smoking but who did not progress to heavy smoking. This panel shows the GRSs (±1 SE) for each group. A GRS of 0 corresponds to the average genetic risk in the cohort. Error bars reflect SEs of the subgroup means.

Among ever-smokers, 19% converted to daily smoking by age 15 years (early conversion) and 10% progressed to smoking 20 cigarettes or more per day by age 18 years (rapid progression to heavy smoking). Adolescents at higher genetic risk were more likely to convert to daily smoking early (RR = 1.24; 95% CI, 1.06-1.45) and to progress rapidly from smoking initiation to heavy smoking (RR = 1.43; 95% CI, 1.10-1.86).

Individuals at higher genetic risk accumulated more pack-years across 38 years of follow-up. Results from an ordinary least squares model indicated that each 1-unit increase in the GRS predicted an additional pack-year in lifetime cigarette consumption among ever-smokers (B = 1.05; 95% CI, 0.36-1.73) (Figure 4A). We also analyzed the persistence of heavy smoking as the number of assessments at which individuals smoked 20 cigarettes or more per day. Individuals at higher genetic risk smoked heavily at more assessments (incidence rate ratio [IRR] for number of assessments as a heavy smoker = 1.26; 95% CI, 1.07-1.49).

Place holder to copy figure label and caption
Graphic Jump Location

Figure 4. Genetic risk predicts mature phenotypes of smoking behavior. A, Among individuals who initiated smoking, those at higher genetic risk smoked more cigarettes by 38 years of age. Ever-smokers were all individuals who initiated smoking by 38 years of age (n = 627). The bars of the histogram graph the percentages of the sample carrying 1 to 12 risk alleles. The dots and SE bars reflect mean lifetime cigarette consumption (in pack-years) for ever-smokers carrying 1 to 3, 4, 5, 6, 7, 8, 9, 10, and 11 to 12 risk alleles. The regression line shows the association between the genetic risk score (GRS) and pack-years smoked by 38 years of age (Pearson correlation r = 0.12, P = .003). B, Ever-smokers at higher genetic risk were more likely to be nicotine dependent. The bars of the chart graph the proportion of ever-smokers at low (n = 157), average (n = 292), and high (n = 178) genetic risk who became nicotine dependent (≥4 Fagerström symptoms) by 38 years of age and who were nicotine dependent at 2 or more assessments. C, Smokers at higher genetic risk were more likely to experience cessation failure during their 30s. The bars of the chart graph the proportions of daily smokers at low, average, and high genetic risk who experienced relapse after a quit attempt lasting 1 month or longer and who achieved successful cessation (abstinence ≥1 year) through 38 years of age. Percentage with relapse was calculated from cohort members who quit smoking for 1 month or longer during 32 to 38 years of age (n = 36 for the low genetic risk group, n = 61 for the average genetic risk group, and n = 34 for the high genetic risk group). Percentage with successful cessation was calculated for cohort members who smoked daily during their 30s (n = 65 for the low genetic risk group, n = 120 for the average genetic risk group, and n = 77 for the high genetic risk group). B and C, Low genetic risk individuals had GRSs more than 0.5 SD below the cohort mean, average genetic risk individuals had GRSs within 0.5 SD of the cohort mean, and high genetic risk individuals had GRSs more than 0.5 SD above the cohort mean. Error bars reflect SEs.

GENETIC RISK AND NICOTINE DEPENDENCE

Through age 38 years, 27% of ever-smokers developed nicotine dependence. Individuals at higher genetic risk were more likely to become nicotine dependent compared with individuals at lower genetic risk and were nicotine dependent at more assessments (HR for nicotine dependence = 1.27; 95% CI, 1.09-1.47; IRR for assessments with nicotine dependence = 1.22; 95% CI, 1.06-1.41) (Figure 4B).

In addition to testing genetic associations with nicotine dependence, we also asked whether cohort members at higher genetic risk were more reliant on smoking to cope with stress. Among the 277 study members who smoked daily during ages 32 to 38 years, those at higher genetic risk relied more heavily on smoking as a coping strategy (B = 0.22; 95% CI, 0.11-0.32).

GENETIC RISK AND SMOKING CESSATION

Assessment of cessation failure is challenging.56 Therefore, we looked for convergent evidence across 2 approaches to testing genetic associations with cessation failure. We first analyzed study members' reports of cessation failure between the ages of 18 and 32 years. Across 14 years of follow-up, 405 cohort members smoked daily. A total of 90% of this group made at least one quit attempt, and 51% reported a cessation failure at 1 or more assessments. Cohort members at higher genetic risk were more likely to experience cessation failure in their quit attempts (RR = 1.11; 95% CI, 1.01-1.22).

We next used the month-to-month life-history calendars to look closely at cohort members' smoking behavior during their 30s, when cessation was most common. Across 72 months of follow-up, 277 cohort members smoked daily, and 53% of these smokers made a quit attempt lasting 1 month or more. Relapse was common (occurring in 62% of quitters). Quitters at higher genetic risk were more likely to relapse and did so sooner after quitting (HR = 1.22; 95% CI, 1.02-1.45). Only 20% of daily smokers achieved successful cessation (abstinent for ≥1 year through age 38 years). Smokers at higher genetic risk were less likely to have achieved successful cessation at the end of follow-up (RR = 0.73; 95% CI, 0.57-0.93) (Figure 4C).

GENETIC RISK, DEVELOPMENTAL PHENOTYPES OF SMOKING BEHAVIOR, AND ADULT SMOKING PROBLEMS

We derived an index of adult smoking problems from a principal components analysis of 3 indicators: (1) pack-years smoked by age 38 years, (2) total number of Fagerström Test of Nicotine Dependence symptoms across assessments, and (3) the number of assessments at which study members reported cessation failure. The adult smoking problems factor explained 78% of the variance in the 3 indicators. Individuals at higher genetic risk developed more smoking problems in adulthood (r = 0.10, P = .01). We next tested whether this association was accounted for by the more rapid developmental progression of smoking behavior among individuals at higher genetic risk. A total of 81% of this association was accounted for by the 2 adolescent developmental phenotypes of early conversion to daily smoking and rapid progression to smoking 20 or more cigarettes per day (eTable 4). As a further attempt to address the question of whether preventing rapid progression from smoking initiation to heavy smoking could mitigate genetic risks, we conducted a utopian control analysis.57 We asked whether genetic risks continued to predict adult smoking problems in the subset of individuals who initiated smoking but who did not exhibit either of the rapid progression phenotypes (n = 454). In this subgroup, genetic risk was uncoupled from the development of smoking problems in adulthood (r = 0.05, P = .18).

OVERLAP OF MOLECULAR GENETIC RISK AND FAMILY HISTORY OF SMOKING BEHAVIOR

The family history score and the GRS were uncorrelated (r = 0.011). Both family history and the GRS predicted study members' smoking phenotypes (Table). When family history and the GRS were standardized and included in regression models simultaneously, the GRS and family history coefficients were unchanged and remained statistically significant (ie, genetic risk and family history were independent and additive predictors of smoking phenotypes). In the mediation analyses, adjustment for family history did not change results. Thus, the GRS contained different information about risk for developmental and mature phenotypes of smoking behavior compared with family history.

Etiologic research on substance abuse highlights the importance of progression from initiation to heavy use during adolescence in the development of dependence in adulthood.58,59 In this study, we linked the developmental progression of smoking behavior to genetic risk. We derived a GRS from GWASs of smoking quantity. This GRS was not related to smoking initiation. In fact, daily smokers who did not progress to heavy use were at lower genetic risk than individuals who never smoked. Among individuals who initiated smoking, those at higher genetic risk progressed more rapidly to heavy smoking and nicotine dependence, were more likely to become persistent heavy smokers and persistently nicotine dependent, and had more difficulty quitting. Critically, high genetic risk led individuals to become persistent heavy smokers, nicotine dependent, and unable to quit only to the extent that they progressed rapidly from smoking initiation to heavy smoking during adolescence.

The GWASs from which we derived our measure of genetic risk were designed to discover genetic correlates of smoking quantity. Therefore, the fact that genetic risks discovered by these GWASs do not predict smoking initiation is not entirely unexpected. Nevertheless, that so-called chippers (light but persistent smokers)60 in our cohort had below average genetic risk is consistent with the theory that the genetic risks captured in our score influence response to nicotine, not the propensity to initiate smoking.17,61 Thus, our result affirms the value of using former and light smokers as a comparison group to heavy and nicotine dependent smokers in discovery analyses targeting these risks.

Previous research has related polymorphisms in the genes included in our genetic risk score to developmental phenotypes of smoking behavior24,26,3235 and to mature phenotypes of adult smoking problems.2931,6264 To our knowledge, ours is the first study to track the relations of particular genetic risk variants with the development of smoking behavior from initiation through conversion to daily smoking and progression to heavy smoking and on to the mature phenotypes of persistent of heavy smoking, nicotine dependence, and struggles with cessation through midlife. Moreover, this extended follow-up allowed us to find, for the first time, that GWAS-identified variation in 15q25.1 and 19q13.2 influences adult smoking problems through a pathway mediated by adolescent progression from smoking initiation to heavy smoking. Our study is also the first, to our knowledge, to find that GWAS-identified SNPs provide information about smoking risks that cannot be ascertained from a family history, including information about risk for cessation failure.

These findings should be considered in light of 3 limitations. First, although the Dunedin Study sample consisted of European-descent individuals, as did the samples analyzed in the GWASs used to develop the GRS, we cannot rule out the possibility of population stratification. Further, replication in other populations is needed.65 Second, our analyses of cessation were subject to censored data. The life-history calendars ended at the age of 38 years, and thus these data do not reflect relations with phenotypic events occurring after this age. In addition, self-reports of temporally remote events could be inaccurate because of forgetting or other biases. Third, the 4 decades of follow-up in the Dunedin Study coincided with major secular events, such as bans against smoking in the workplace. Comparisons of cohorts born at different times might elucidate gene-policy interactions in smoking behavior and speak to the generalizability of the current findings.66,67

Despite these limitations, this study has implications for etiologic research and public health. With respect to etiology, our study makes 3 contributions. First, next-generation sequencing studies and other efforts to ascertain causal variants responsible for GWAS signals may maximize their discovery potential by focusing on samples of young people strategically selected to reflect important developmental transitions. Such work could use experimental designs to test hypotheses about mechanisms of genetic risk on postinitiation phenotypes. Second, we demonstrated that a GRS based on the assumption of additive risks can be used to follow up GWAS results in a birth cohort far smaller than the original discovery samples. Future etiologic research can use GRSs to apply GWAS results to longitudinal studies. Third, results are consistent with the hypothesis in pediatric medicine that some adolescents, after only experimental use, are prone to quickly become heavy users and dependent.68 This finding suggests that gene-environment interaction analyses of smoking and nicotine dependence may profit from a focus on environments that coincide with or immediately precede the adolescent period and influence the propensity of children at high genetic risk to initiate smoking. Smoking by peers is one such environment.69 Tobacco control policies targeting youth may be another.70,71

Turning to public health, our research adds a genetic dimension to long-standing arguments that early prevention could be a critical strategy in reducing cigarette consumption.72 Specifically, our findings and others’32 suggest that initiatives that disrupt the developmental progression of smoking behavior, such as surtaxes and age restrictions on tobacco purchases, may ameliorate some genetic risks.73 Moving beyond population-level prevention, we found that information about smoking risk captured in a score composed of GWAS-identified variants was independent of information that could be derived from a family history of smoking behavior. This novel finding suggests that genetic information could be used to identify high-risk youngsters for targeted prevention.68,74 However, the associations we detected between the GRS and smoking phenotypes were small in magnitude. Small effect sizes do not preclude public health relevance,75 but they caution against the use of genetic information to evaluate risk in individuals76; children who our study would classify at high genetic risk are not guaranteed to become addicted if they try smoking, and, even more importantly, children we would classify at low genetic risk are not immune to addiction. The public health use of the current findings must be tempered with recognition that most risk-associated genetic variation does not determine poor health outcomes, and, correspondingly, its absence does not guarantee protection.77,78

Correspondence: Avshalom Caspi, PhD, Duke University/Duke University Medical Center, Center for Aging and the Study of Human Development, 2020 W Main St, Ste 201, Durham, NC 27708 (ac115@duke.edu).

Submitted for Publication: March 9, 2012; final revision received July 12, 2012; accepted September 17, 2012.

Published Online: March 27, 2013. doi:10.1001/jamapsychiatry.2013.736

Conflict of Interest Disclosures: None reported.

Funding/Support: This research received support from grant AG032282 from the US National Institute on Aging, grant MH077874 from the US National Institute on Mental Health, and grant G0101483 from the UK Medical Research Council. The Dunedin Multidisciplinary Health and Development Research Unit is supported by The New Zealand Health Research Council. Dr Belsky was supported in part by grant 1R36HS020524-01 from the US Agency for Healthcare Research and Quality and grant T32-AG000029 from the National Institute on Aging. Dr Baker was supported in part by grant 1K05CA139871 from the US National Cancer Institute. Dr Meier was supported in part by grant P30DA023026 from the US National Institute on Drug Abuse. Additional support was provided by the Jacobs Foundation.

Additional Contributions: We thank the Dunedin Study members, their families, unit research staff, and study founder Phil Silva.

Centers for Disease Control and Prevention (CDC).  Smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 2000-2004.  MMWR Morb Mortal Wkly Rep. 2008;57(45):1226-1228
PubMed
Centers for Disease Control and Prevention (CDC).  Current cigarette smoking prevalence among working adults—United States, 2004-2010.  MMWR Morb Mortal Wkly Rep. 2011;60(38):1305-1309
PubMed
Joseph AM, Fu SS, Lindgren B,  et al.  Chronic disease management for tobacco dependence: a randomized, controlled trial.  Arch Intern Med. 2011;171(21):1894-1900
PubMed   |  Link to Article
Carpenter MJ, Hughes JR, Gray KM, Wahlquist AE, Saladin ME, Alberg AJ. Nicotine therapy sampling to induce quit attempts among smokers unmotivated to quit: a randomized clinical trial.  Arch Intern Med. 2011;171(21):1901-1907
PubMed   |  Link to Article
Carson KV, Brinn MP, Labiszewski NA, Esterman AJ, Chang AB, Smith BJ. Community interventions for preventing smoking in young people  Cochrane Database Syst Rev. 2011;(7):CD001291
PubMed  |  Link to Article
Xian H, Scherrer JF, Madden PA,  et al.  The heritability of failed smoking cessation and nicotine withdrawal in twins who smoked and attempted to quit.  Nicotine Tob Res. 2003;5(2):245-254
PubMed   |  Link to Article
Thorgeirsson TE, Gudbjartsson DF, Surakka I,  et al; ENGAGE Consortium.  Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior.  Nat Genet. 2010;42(5):448-453
PubMed   |  Link to Article
Liu JZ, Tozzi F, Waterworth DM,  et al; Wellcome Trust Case Control Consortium.  Meta-analysis and imputation refines the association of 15q25 with smoking quantity.  Nat Genet. 2010;42(5):436-440
PubMed   |  Link to Article
Tobacco and Genetics Consortium.  Genome-wide meta-analyses identify multiple loci associated with smoking behavior.  Nat Genet. 2010;42(5):441-447
PubMed   |  Link to Article
Sullivan PF, Kendler KS. The genetic epidemiology of smoking.  Nicotine Tob Res. 1999;1:(suppl 2)  S51-S57
PubMed   |  Link to Article
Baker TB, Weiss RB, Bolt D,  et al.  Human neuronal acetylcholine receptor A5-A3-B4 haplotypes are associated with multiple nicotine dependence phenotypes.  Nicotine Tob Res. 2009;11(7):785-796
PubMed   |  Link to Article
Munafò MR, Johnstone EC, Walther D, Uhl GR, Murphy MF, Aveyard P. CHRNA3 rs1051730 genotype and short-term smoking cessation.  Nicotine Tob Res. 2011;13(10):982-988
PubMed   |  Link to Article
Lerman C, Jepson C, Wileyto EP,  et al.  Genetic variation in nicotine metabolism predicts the efficacy of extended-duration transdermal nicotine therapy.  Clin Pharmacol Ther. 2010;87(5):553-557
PubMed   |  Link to Article
King DP, Paciga S, Pickering E,  et al.  Smoking cessation pharmacogenetics: analysis of varenicline and bupropion in placebo-controlled clinical trials.  Neuropsychopharmacology. 2012;37(3):641-650
PubMed   |  Link to Article
Baker TB, Conti DV, Moffitt TE, Caspi A. The Nicotine Dependence Phenotype: Translating Theoretical Perspectives and Extant Data Into Recommendations for Genetic Mapping. Bethesda, MD: National Cancer Insititute; 2009
Gelernter J. Developmental perspective on the role of genes in smoking risk.  Biol Psychiatry. 2011;69(7):616-617
PubMed   |  Link to Article
Bierut LJ. Nicotine dependence and genetic variation in the nicotinic receptors.  Drug Alcohol Depend. 2009;104:(suppl 1)  S64-S69
PubMed   |  Link to Article
Breslau N, Peterson EL. Smoking cessation in young adults: age at initiation of cigarette smoking and other suspected influences.  Am J Public Health. 1996;86(2):214-220
PubMed   |  Link to Article
Buchmann AF, Blomeyer D, Jennen-Steinmetz C,  et al.  Early smoking onset may promise initial pleasurable sensations and later addiction [published online ahead of print October 4, 2011].  Addict Biol
PubMed  |  Link to Article
Kandel DB, Hu MC, Griesler PC, Schaffran C. On the development of nicotine dependence in adolescence.  Drug Alcohol Depend. 2007;91(1):26-39
PubMed   |  Link to Article
Dierker L, Swendsen J, Rose J, He J, Merikangas K. Transitions to regular smoking and nicotine dependence in the Adolescent National Comorbidity Survey (NCS-A). Ann Behav Med. 2012;43(3):394-401
Moffitt TE, Caspi A, Rutter M, Silva PA. Sex Differences in Antisocial Behavior: Conduct Disorder, Delinquency, and Violence in the Dunedin Longitudinal Study. Cambridge, England: Cambridge University Press; 2001
Sullivan PF. Spurious genetic associations.  Biol Psychiatry. 2007;61(10):1121-1126
PubMed   |  Link to Article
Ducci F, Kaakinen M, Pouta A,  et al.  TTC12-ANKK1-DRD2 and CHRNA5-CHRNA3-CHRNB4 influence different pathways leading to smoking behavior from adolescence to mid-adulthood.  Biol Psychiatry. 2011;69(7):650-660
PubMed   |  Link to Article
Wassenaar CA, Dong Q, Wei Q, Amos CI, Spitz MR, Tyndale RF. Relationship between CYP2A6 and CHRNA5-CHRNA3-CHRNB4 variation and smoking behaviors and lung cancer risk.  J Natl Cancer Inst. 2011;103(17):1342-1346
PubMed   |  Link to Article
Rodriguez S, Cook DG, Gaunt TR, Nightingale CM, Whincup PH, Day IN. Combined analysis of CHRNA5, CHRNA3 and CYP2A6 in relation to adolescent smoking behaviour.  J Psychopharmacol. 2011;25(7):915-923
PubMed   |  Link to Article
Vrieze SI, McGue M, Iacono WG. The interplay of genes and adolescent development in substance use disorders: Leveraging findings from GWAS meta-analyses to test developmental hypotheses about nicotine consumption.  Hum Genet. 2012;131(5):791-801
PubMed   |  Link to Article
Plomin R, Haworth CMA, Davis OSP. Common disorders are quantitative traits.  Nat Rev Genet. 2009;10(12):872-878
PubMed   |  Link to Article
Berrettini WH, Doyle GA. The CHRNA5-A3-B4 gene cluster in nicotine addiction.  Mol Psychiatry. 2012;17(9):856-866
PubMed   |  Link to Article
Malaiyandi V, Sellers EM, Tyndale RF. Implications of CYP2A6 genetic variation for smoking behaviors and nicotine dependence.  Clin Pharmacol Ther. 2005;77(3):145-158
PubMed   |  Link to Article
Kubota T, Nakajima-Taniguchi C, Fukuda T,  et al.  CYP2A6 polymorphisms are associated with nicotine dependence and influence withdrawal symptoms in smoking cessation.  Pharmacogenomics J. 2006;6(2):115-119
PubMed   |  Link to Article
Weiss RB, Baker TB, Cannon DS,  et al.  A candidate gene approach identifies the CHRNA5-A3-B4 region as a risk factor for age-dependent nicotine addiction.  PLoS Genet. 2008;4(7):e1000125
PubMed   |  Link to Article
Audrain-McGovern J, Al Koudsi N, Rodriguez D, Wileyto EP, Shields PG, Tyndale RF. The role of CYP2A6 in the emergence of nicotine dependence in adolescents.  Pediatrics. 2007;119(1):e264-e274
PubMed   |  Link to Article
O’Loughlin J, Paradis G, Kim W,  et al.  Genetically decreased CYP2A6 and the risk of tobacco dependence: a prospective study of novice smokers.  Tob Control. 2004;13(4):422-428
PubMed   |  Link to Article
Huang S, Cook DG, Hinks LJ,  et al.  CYP2A6, MAOA, DBH, DRD4, and 5HT2A genotypes, smoking behaviour and cotinine levels in 1518 UK adolescents.  Pharmacogenet Genomics. 2005;15(12):839-850
PubMed   |  Link to Article
Saccone NL, Culverhouse RC, Schwantes-An TH,  et al.  Multiple independent loci at chromosome 15q25.1 affect smoking quantity: a meta-analysis and comparison with lung cancer and COPD.  PLoS Genet. 2010;6(8):e1001053
PubMed  |  Link to Article   |  Link to Article
GENEVA ARIC Project.  Quality Control Report for the ARIC GWAS Database. Bethesda, MD: The National Institutes of Health Database of Genotypes and Phenotypes (dbGaP); 2009. dbGaP accession No. phs000090.v1.p1 ed
GENEVA Alcohol Dependence Project.  Quality Control Report for the SAGE GWAS. Bethesda, MD: National Institutes of Health Database of Genotypes and Phenotypes (dbGaP); 2008. dbGaP accession No. phs000092.v1.p1 ed
Poulton R, Caspi A, Milne BJ,  et al.  Association between children's experience of socioeconomic disadvantage and adult health: a life-course study.  Lancet. 2002;360(9346):1640-1645
PubMed   |  Link to Article
Milne BJ, Moffitt TE, Crump R,  et al.  How should we construct psychiatric family history scores? a comparison of alternative approaches from the Dunedin Family Health History Study.  Psychol Med. 2008;38(12):1793-1802
PubMed   |  Link to Article
Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO. The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire.  Br J Addict. 1991;86(9):1119-1127
PubMed   |  Link to Article
Fagerström KO. Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment.  Addict Behav. 1978;3(3-4):235-241
PubMed   |  Link to Article
Bierut LJ, Madden PA, Breslau N,  et al.  Novel genes identified in a high-density genome wide association study for nicotine dependence.  Hum Mol Genet. 2007;16(1):24-35
PubMed   |  Link to Article
Saccone SF, Hinrichs AL, Saccone NL,  et al.  Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs.  Hum Mol Genet. 2007;16(1):36-49
PubMed   |  Link to Article
Caspi A, Moffitt TE, Thornton A,  et al.  The life history calendar: a research and clinical assessment method for collecting retrospective event-history data.  Int J Method Psych. 1996;6(2):101-114
Link to Article
Belli RF, Smith LM, Andreski PM, Agrawal S. Methodological comparisons between CATI event history calendar and standardized conventional questionnaire instruments.  Public Opin Q. 2007;71(4):603-622
Link to Article
McNutt LA, Wu C, Xue X, Hafner JP. Estimating the relative risk in cohort studies and clinical trials of common outcomes.  Am J Epidemiol. 2003;157(10):940-943
PubMed   |  Link to Article
Gardner W, Mulvey EP, Shaw EC. Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models.  Psychol Bull. 1995;118(3):392-404
PubMed   |  Link to Article
Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach.  Biometrics. 1988;44(4):1049-1060
PubMed   |  Link to Article
Prentice RL, Williams BJ, Peterson AV. On the regression-analysis of multivariate failure time data.  Biometrika. 1981;68(2):373-379
Link to Article
Mackinnon DP, Dwyer JH. Estimating mediated effects in prevention studies.  Eval Rev. 1993;17(2):144-158
Link to Article
Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models.  Behav Res Methods. 2008;40(3):879-891
PubMed   |  Link to Article
Preacher KJ, Kelley K. Effect size measures for mediation models: quantitative strategies for communicating indirect effects.  Psychol Methods. 2011;16(2):93-115
PubMed   |  Link to Article
Preacher KJ, MacCallum RC. Repairing Tom Swift's electric factor analysis machine.  Underst Stat. 2003;2(1):13-43
Link to Article
 Stata/MP for Windows [computer program]. Version 11.1. College Station, TX: Stata Corp LP; 2010 
Hughes JR, Callas PW. Definition of a quit attempt: a replication test.  Nicotine Tob Res. 2010;12(11):1176-1179
PubMed   |  Link to Article
Lubinski D. Cognitive epidemiology: with emphasis on untangling cognitive ability and socioeconomic status.  Intelligence. 2009;37(6):625-633
Link to Article
Jackson KM, Sher KJ, Rose RJ, Kapiro J. Trajectories of Tobacco Use From Adolescence to Adulthood: Are the Most Informative Phenotypes Tobacco Specific? Bethesda, MD: National Cancer Institute; 2009
Bierut LJ. Genetic vulnerability and susceptibility to substance dependence.  Neuron. 2011;69(4):618-627
PubMed   |  Link to Article
Shiffman S. Tobacco “chippers”: individual differences in tobacco dependence.  Psychopharmacology (Berl). 1989;97(4):539-547
PubMed   |  Link to Article
Greenbaum L, Lerer B. Differential contribution of genetic variation in multiple brain nicotinic cholinergic receptors to nicotine dependence: recent progress and emerging open questions.  Mol Psychiatry. 2009;14(10):912-945
PubMed   |  Link to Article
Broms U, Wedenoja J, Largeau MR,  et al.  Analysis of detailed phenotype profiles reveals CHRNA5-CHRNA3-CHRNB4 gene cluster association with several nicotine dependence traits.  Nicotine Tob Res. 2012;14(6):720-733
PubMed   |  Link to Article
Benowitz NL. Nicotine addiction.  N Engl J Med. 2010;362(24):2295-2303
PubMed   |  Link to Article
Chen LS, Baker TB, Piper ME, Breslau N, Cannon DS, Doheny KF, Gogarten SM, Johnson EO, Saccone NL, Wang JC, Weiss RB, Goate AM, Bierut LJ. Interplay of genetic risk factors (CHRNA5-CHRNA3-CHRNB4) and cessation treatments in smoking cessation success  Am J Psychiatry. 2012;169(7):735-742
PubMed
Saccone NL, Schwantes-An TH, Wang JC,  et al.  Multiple cholinergic nicotinic receptor genes affect nicotine dependence risk in African and European Americans.  Genes Brain Behav. 2010;9(7):741-750
PubMed   |  Link to Article
Boardman JD, Blalock CL, Pampel FC, Hatemi PK, Heath AC, Eaves LJ. Population composition, public policy, and the genetics of smoking.  Demography. 2011;48(4):1517-1533
PubMed   |  Link to Article
Boardman JD, Blalock CL, Pampel FC. Trends in the genetic influences on smoking.  J Health Soc Behav. 2010;51(1):108-123
PubMed   |  Link to Article
DiFranza JR, Richmond JB. Let the children be heard: lessons from studies of the early onset of tobacco addiction.  Pediatrics. 2008;121(3):623-624
PubMed   |  Link to Article
Johnson EO, Chen LS, Breslau N,  et al.  Peer smoking and the nicotinic receptor genes: an examination of genetic and environmental risks for nicotine dependence.  Addiction. 2010;105(11):2014-2022
PubMed   |  Link to Article
Boardman JD. State-level moderation of genetic tendencies to smoke.  Am J Public Health. 2009;99(3):480-486
PubMed   |  Link to Article
Boardman JD, Saint Onge JM, Haberstick BC, Timberlake DS, Hewitt JK. Do schools moderate the genetic determinants of smoking?  Behav Genet. 2008;38(3):234-246
PubMed   |  Link to Article
Galea S, Nandi A, Vlahov D. The social epidemiology of substance use.  Epidemiol Rev. 2004;26:36-52
PubMed   |  Link to Article
Fiore MC, Baker TB. Stealing a march in the 21st century: accelerating progress in the 100-year war against tobacco addiction in the United States.  Am J Public Health. 2009;99(7):1170-1175
PubMed   |  Link to Article
Rose G. Sick individuals and sick populations.  Int J Epidemiol. 1985;14(1):32-38
PubMed   |  Link to Article
Smith GD. Epidemiology, epigenetics and the ‘Gloomy Prospect’: embracing randomness in population health research and practice.  Int J Epidemiol. 2011;40(3):537-562
PubMed   |  Link to Article
Ioannidis JPA. Personalized genetic prediction: too limited, too expensive, or too soon?  Ann Intern Med. 2009;150(2):139-141
PubMed   |  Link to Article
Evans JP, Meslin EM, Marteau TM, Caulfield T. Genomics. Deflating the genomic bubble.  Science. 2011;331(6019):861-862
PubMed   |  Link to Article
Guttmacher AE, Porteous ME, McInerney JD. Educating health-care professionals about genetics and genomics.  Nat Rev Genet. 2007;8(2):151-157
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. Genetic risk and the developmental progression of smoking behavior. In the hypothesized model, genetic risk influences the mature phenotypes of heavy smoking persistence, nicotine dependence, and cessation failure through a pathway mediated by 3 developmental phenotypes: smoking initiation, conversion to daily smoking, and progression to heavy smoking.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. Smoking behavior in the Dunedin cohort. A, Developmental progression of smoking behavior in the Dunedin cohort. Study members reported their smoking status during in-person assessments at the ages of 11 (percentage of ever-smokers =  7%), 13 (13%), 15 (62%), 18 (66%), 21 (70%), 26 (70%), 32 (71%), and 38 (71%) years and their daily cigarette consumption at the ages of 13 (percentage of daily smokers = 1%), 15 (14%), 18 (31%), 21 (34%), 26 (35%), 32 (30%), and 38 (20%) years. We assessed nicotine dependence using the Fagerström Test of Nicotine Dependence (FTND),41 completed by study members at the ages of 21, 26, and 38 years. We assessed cessation failure using study members' reports of quit attempts and outcomes at the ages of 18, 21, 26, 32, and 38 years. B, Measurements of developmental and mature smoking phenotypes. Data are number (percentage) of study members unless otherwise indicated.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 3. Genetic risk score (GRS) derived from genome-wide association study of smoking quantity is associated with the developmental progression of smoking behavior in a birth cohort of European-descent individuals. A, Individuals at higher genetic risk progressed more rapidly from smoking initiation to heavy smoking. This panel graphs hazard functions for onset of heavy smoking among individuals at low genetic risk (GRS = −1), average genetic risk (GRS = 0), and high genetic risk (GRS = 1). The dashed gray line marks the cumulative hazard for individuals at average genetic risk. The hazard function was estimated from a Cox proportional hazard model with time since onset of ever-smoking as the exposure time and the first assessment a study member reported smoking 20 or more cigarettes per day (CPD) as the failure event. The hazard model included all individuals who ever initiated smoking (n = 627). Individuals at higher genetic risk progressed more rapidly from smoking initiation to smoking 20 or more CPD (hazard ratio = 1.35; 95% CI, 1.14-1.58). B, Genetic risk was highest among individuals who progressed to heavy smoking and lowest among individuals who initiated smoking but who did not progress to heavy smoking. This panel shows the GRSs (±1 SE) for each group. A GRS of 0 corresponds to the average genetic risk in the cohort. Error bars reflect SEs of the subgroup means.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 4. Genetic risk predicts mature phenotypes of smoking behavior. A, Among individuals who initiated smoking, those at higher genetic risk smoked more cigarettes by 38 years of age. Ever-smokers were all individuals who initiated smoking by 38 years of age (n = 627). The bars of the histogram graph the percentages of the sample carrying 1 to 12 risk alleles. The dots and SE bars reflect mean lifetime cigarette consumption (in pack-years) for ever-smokers carrying 1 to 3, 4, 5, 6, 7, 8, 9, 10, and 11 to 12 risk alleles. The regression line shows the association between the genetic risk score (GRS) and pack-years smoked by 38 years of age (Pearson correlation r = 0.12, P = .003). B, Ever-smokers at higher genetic risk were more likely to be nicotine dependent. The bars of the chart graph the proportion of ever-smokers at low (n = 157), average (n = 292), and high (n = 178) genetic risk who became nicotine dependent (≥4 Fagerström symptoms) by 38 years of age and who were nicotine dependent at 2 or more assessments. C, Smokers at higher genetic risk were more likely to experience cessation failure during their 30s. The bars of the chart graph the proportions of daily smokers at low, average, and high genetic risk who experienced relapse after a quit attempt lasting 1 month or longer and who achieved successful cessation (abstinence ≥1 year) through 38 years of age. Percentage with relapse was calculated from cohort members who quit smoking for 1 month or longer during 32 to 38 years of age (n = 36 for the low genetic risk group, n = 61 for the average genetic risk group, and n = 34 for the high genetic risk group). Percentage with successful cessation was calculated for cohort members who smoked daily during their 30s (n = 65 for the low genetic risk group, n = 120 for the average genetic risk group, and n = 77 for the high genetic risk group). B and C, Low genetic risk individuals had GRSs more than 0.5 SD below the cohort mean, average genetic risk individuals had GRSs within 0.5 SD of the cohort mean, and high genetic risk individuals had GRSs more than 0.5 SD above the cohort mean. Error bars reflect SEs.

Tables

Table Graphic Jump LocationTable. Effect Sizes for Genetic and Family History Associations With Developmental and Clinical Phenotypes of Smoking Behaviora

References

Centers for Disease Control and Prevention (CDC).  Smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 2000-2004.  MMWR Morb Mortal Wkly Rep. 2008;57(45):1226-1228
PubMed
Centers for Disease Control and Prevention (CDC).  Current cigarette smoking prevalence among working adults—United States, 2004-2010.  MMWR Morb Mortal Wkly Rep. 2011;60(38):1305-1309
PubMed
Joseph AM, Fu SS, Lindgren B,  et al.  Chronic disease management for tobacco dependence: a randomized, controlled trial.  Arch Intern Med. 2011;171(21):1894-1900
PubMed   |  Link to Article
Carpenter MJ, Hughes JR, Gray KM, Wahlquist AE, Saladin ME, Alberg AJ. Nicotine therapy sampling to induce quit attempts among smokers unmotivated to quit: a randomized clinical trial.  Arch Intern Med. 2011;171(21):1901-1907
PubMed   |  Link to Article
Carson KV, Brinn MP, Labiszewski NA, Esterman AJ, Chang AB, Smith BJ. Community interventions for preventing smoking in young people  Cochrane Database Syst Rev. 2011;(7):CD001291
PubMed  |  Link to Article
Xian H, Scherrer JF, Madden PA,  et al.  The heritability of failed smoking cessation and nicotine withdrawal in twins who smoked and attempted to quit.  Nicotine Tob Res. 2003;5(2):245-254
PubMed   |  Link to Article
Thorgeirsson TE, Gudbjartsson DF, Surakka I,  et al; ENGAGE Consortium.  Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior.  Nat Genet. 2010;42(5):448-453
PubMed   |  Link to Article
Liu JZ, Tozzi F, Waterworth DM,  et al; Wellcome Trust Case Control Consortium.  Meta-analysis and imputation refines the association of 15q25 with smoking quantity.  Nat Genet. 2010;42(5):436-440
PubMed   |  Link to Article
Tobacco and Genetics Consortium.  Genome-wide meta-analyses identify multiple loci associated with smoking behavior.  Nat Genet. 2010;42(5):441-447
PubMed   |  Link to Article
Sullivan PF, Kendler KS. The genetic epidemiology of smoking.  Nicotine Tob Res. 1999;1:(suppl 2)  S51-S57
PubMed   |  Link to Article
Baker TB, Weiss RB, Bolt D,  et al.  Human neuronal acetylcholine receptor A5-A3-B4 haplotypes are associated with multiple nicotine dependence phenotypes.  Nicotine Tob Res. 2009;11(7):785-796
PubMed   |  Link to Article
Munafò MR, Johnstone EC, Walther D, Uhl GR, Murphy MF, Aveyard P. CHRNA3 rs1051730 genotype and short-term smoking cessation.  Nicotine Tob Res. 2011;13(10):982-988
PubMed   |  Link to Article
Lerman C, Jepson C, Wileyto EP,  et al.  Genetic variation in nicotine metabolism predicts the efficacy of extended-duration transdermal nicotine therapy.  Clin Pharmacol Ther. 2010;87(5):553-557
PubMed   |  Link to Article
King DP, Paciga S, Pickering E,  et al.  Smoking cessation pharmacogenetics: analysis of varenicline and bupropion in placebo-controlled clinical trials.  Neuropsychopharmacology. 2012;37(3):641-650
PubMed   |  Link to Article
Baker TB, Conti DV, Moffitt TE, Caspi A. The Nicotine Dependence Phenotype: Translating Theoretical Perspectives and Extant Data Into Recommendations for Genetic Mapping. Bethesda, MD: National Cancer Insititute; 2009
Gelernter J. Developmental perspective on the role of genes in smoking risk.  Biol Psychiatry. 2011;69(7):616-617
PubMed   |  Link to Article
Bierut LJ. Nicotine dependence and genetic variation in the nicotinic receptors.  Drug Alcohol Depend. 2009;104:(suppl 1)  S64-S69
PubMed   |  Link to Article
Breslau N, Peterson EL. Smoking cessation in young adults: age at initiation of cigarette smoking and other suspected influences.  Am J Public Health. 1996;86(2):214-220
PubMed   |  Link to Article
Buchmann AF, Blomeyer D, Jennen-Steinmetz C,  et al.  Early smoking onset may promise initial pleasurable sensations and later addiction [published online ahead of print October 4, 2011].  Addict Biol
PubMed  |  Link to Article
Kandel DB, Hu MC, Griesler PC, Schaffran C. On the development of nicotine dependence in adolescence.  Drug Alcohol Depend. 2007;91(1):26-39
PubMed   |  Link to Article
Dierker L, Swendsen J, Rose J, He J, Merikangas K. Transitions to regular smoking and nicotine dependence in the Adolescent National Comorbidity Survey (NCS-A). Ann Behav Med. 2012;43(3):394-401
Moffitt TE, Caspi A, Rutter M, Silva PA. Sex Differences in Antisocial Behavior: Conduct Disorder, Delinquency, and Violence in the Dunedin Longitudinal Study. Cambridge, England: Cambridge University Press; 2001
Sullivan PF. Spurious genetic associations.  Biol Psychiatry. 2007;61(10):1121-1126
PubMed   |  Link to Article
Ducci F, Kaakinen M, Pouta A,  et al.  TTC12-ANKK1-DRD2 and CHRNA5-CHRNA3-CHRNB4 influence different pathways leading to smoking behavior from adolescence to mid-adulthood.  Biol Psychiatry. 2011;69(7):650-660
PubMed   |  Link to Article
Wassenaar CA, Dong Q, Wei Q, Amos CI, Spitz MR, Tyndale RF. Relationship between CYP2A6 and CHRNA5-CHRNA3-CHRNB4 variation and smoking behaviors and lung cancer risk.  J Natl Cancer Inst. 2011;103(17):1342-1346
PubMed   |  Link to Article
Rodriguez S, Cook DG, Gaunt TR, Nightingale CM, Whincup PH, Day IN. Combined analysis of CHRNA5, CHRNA3 and CYP2A6 in relation to adolescent smoking behaviour.  J Psychopharmacol. 2011;25(7):915-923
PubMed   |  Link to Article
Vrieze SI, McGue M, Iacono WG. The interplay of genes and adolescent development in substance use disorders: Leveraging findings from GWAS meta-analyses to test developmental hypotheses about nicotine consumption.  Hum Genet. 2012;131(5):791-801
PubMed   |  Link to Article
Plomin R, Haworth CMA, Davis OSP. Common disorders are quantitative traits.  Nat Rev Genet. 2009;10(12):872-878
PubMed   |  Link to Article
Berrettini WH, Doyle GA. The CHRNA5-A3-B4 gene cluster in nicotine addiction.  Mol Psychiatry. 2012;17(9):856-866
PubMed   |  Link to Article
Malaiyandi V, Sellers EM, Tyndale RF. Implications of CYP2A6 genetic variation for smoking behaviors and nicotine dependence.  Clin Pharmacol Ther. 2005;77(3):145-158
PubMed   |  Link to Article
Kubota T, Nakajima-Taniguchi C, Fukuda T,  et al.  CYP2A6 polymorphisms are associated with nicotine dependence and influence withdrawal symptoms in smoking cessation.  Pharmacogenomics J. 2006;6(2):115-119
PubMed   |  Link to Article
Weiss RB, Baker TB, Cannon DS,  et al.  A candidate gene approach identifies the CHRNA5-A3-B4 region as a risk factor for age-dependent nicotine addiction.  PLoS Genet. 2008;4(7):e1000125
PubMed   |  Link to Article
Audrain-McGovern J, Al Koudsi N, Rodriguez D, Wileyto EP, Shields PG, Tyndale RF. The role of CYP2A6 in the emergence of nicotine dependence in adolescents.  Pediatrics. 2007;119(1):e264-e274
PubMed   |  Link to Article
O’Loughlin J, Paradis G, Kim W,  et al.  Genetically decreased CYP2A6 and the risk of tobacco dependence: a prospective study of novice smokers.  Tob Control. 2004;13(4):422-428
PubMed   |  Link to Article
Huang S, Cook DG, Hinks LJ,  et al.  CYP2A6, MAOA, DBH, DRD4, and 5HT2A genotypes, smoking behaviour and cotinine levels in 1518 UK adolescents.  Pharmacogenet Genomics. 2005;15(12):839-850
PubMed   |  Link to Article
Saccone NL, Culverhouse RC, Schwantes-An TH,  et al.  Multiple independent loci at chromosome 15q25.1 affect smoking quantity: a meta-analysis and comparison with lung cancer and COPD.  PLoS Genet. 2010;6(8):e1001053
PubMed  |  Link to Article   |  Link to Article
GENEVA ARIC Project.  Quality Control Report for the ARIC GWAS Database. Bethesda, MD: The National Institutes of Health Database of Genotypes and Phenotypes (dbGaP); 2009. dbGaP accession No. phs000090.v1.p1 ed
GENEVA Alcohol Dependence Project.  Quality Control Report for the SAGE GWAS. Bethesda, MD: National Institutes of Health Database of Genotypes and Phenotypes (dbGaP); 2008. dbGaP accession No. phs000092.v1.p1 ed
Poulton R, Caspi A, Milne BJ,  et al.  Association between children's experience of socioeconomic disadvantage and adult health: a life-course study.  Lancet. 2002;360(9346):1640-1645
PubMed   |  Link to Article
Milne BJ, Moffitt TE, Crump R,  et al.  How should we construct psychiatric family history scores? a comparison of alternative approaches from the Dunedin Family Health History Study.  Psychol Med. 2008;38(12):1793-1802
PubMed   |  Link to Article
Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO. The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire.  Br J Addict. 1991;86(9):1119-1127
PubMed   |  Link to Article
Fagerström KO. Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment.  Addict Behav. 1978;3(3-4):235-241
PubMed   |  Link to Article
Bierut LJ, Madden PA, Breslau N,  et al.  Novel genes identified in a high-density genome wide association study for nicotine dependence.  Hum Mol Genet. 2007;16(1):24-35
PubMed   |  Link to Article
Saccone SF, Hinrichs AL, Saccone NL,  et al.  Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs.  Hum Mol Genet. 2007;16(1):36-49
PubMed   |  Link to Article
Caspi A, Moffitt TE, Thornton A,  et al.  The life history calendar: a research and clinical assessment method for collecting retrospective event-history data.  Int J Method Psych. 1996;6(2):101-114
Link to Article
Belli RF, Smith LM, Andreski PM, Agrawal S. Methodological comparisons between CATI event history calendar and standardized conventional questionnaire instruments.  Public Opin Q. 2007;71(4):603-622
Link to Article
McNutt LA, Wu C, Xue X, Hafner JP. Estimating the relative risk in cohort studies and clinical trials of common outcomes.  Am J Epidemiol. 2003;157(10):940-943
PubMed   |  Link to Article
Gardner W, Mulvey EP, Shaw EC. Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models.  Psychol Bull. 1995;118(3):392-404
PubMed   |  Link to Article
Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach.  Biometrics. 1988;44(4):1049-1060
PubMed   |  Link to Article
Prentice RL, Williams BJ, Peterson AV. On the regression-analysis of multivariate failure time data.  Biometrika. 1981;68(2):373-379
Link to Article
Mackinnon DP, Dwyer JH. Estimating mediated effects in prevention studies.  Eval Rev. 1993;17(2):144-158
Link to Article
Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models.  Behav Res Methods. 2008;40(3):879-891
PubMed   |  Link to Article
Preacher KJ, Kelley K. Effect size measures for mediation models: quantitative strategies for communicating indirect effects.  Psychol Methods. 2011;16(2):93-115
PubMed   |  Link to Article
Preacher KJ, MacCallum RC. Repairing Tom Swift's electric factor analysis machine.  Underst Stat. 2003;2(1):13-43
Link to Article
 Stata/MP for Windows [computer program]. Version 11.1. College Station, TX: Stata Corp LP; 2010 
Hughes JR, Callas PW. Definition of a quit attempt: a replication test.  Nicotine Tob Res. 2010;12(11):1176-1179
PubMed   |  Link to Article
Lubinski D. Cognitive epidemiology: with emphasis on untangling cognitive ability and socioeconomic status.  Intelligence. 2009;37(6):625-633
Link to Article
Jackson KM, Sher KJ, Rose RJ, Kapiro J. Trajectories of Tobacco Use From Adolescence to Adulthood: Are the Most Informative Phenotypes Tobacco Specific? Bethesda, MD: National Cancer Institute; 2009
Bierut LJ. Genetic vulnerability and susceptibility to substance dependence.  Neuron. 2011;69(4):618-627
PubMed   |  Link to Article
Shiffman S. Tobacco “chippers”: individual differences in tobacco dependence.  Psychopharmacology (Berl). 1989;97(4):539-547
PubMed   |  Link to Article
Greenbaum L, Lerer B. Differential contribution of genetic variation in multiple brain nicotinic cholinergic receptors to nicotine dependence: recent progress and emerging open questions.  Mol Psychiatry. 2009;14(10):912-945
PubMed   |  Link to Article
Broms U, Wedenoja J, Largeau MR,  et al.  Analysis of detailed phenotype profiles reveals CHRNA5-CHRNA3-CHRNB4 gene cluster association with several nicotine dependence traits.  Nicotine Tob Res. 2012;14(6):720-733
PubMed   |  Link to Article
Benowitz NL. Nicotine addiction.  N Engl J Med. 2010;362(24):2295-2303
PubMed   |  Link to Article
Chen LS, Baker TB, Piper ME, Breslau N, Cannon DS, Doheny KF, Gogarten SM, Johnson EO, Saccone NL, Wang JC, Weiss RB, Goate AM, Bierut LJ. Interplay of genetic risk factors (CHRNA5-CHRNA3-CHRNB4) and cessation treatments in smoking cessation success  Am J Psychiatry. 2012;169(7):735-742
PubMed
Saccone NL, Schwantes-An TH, Wang JC,  et al.  Multiple cholinergic nicotinic receptor genes affect nicotine dependence risk in African and European Americans.  Genes Brain Behav. 2010;9(7):741-750
PubMed   |  Link to Article
Boardman JD, Blalock CL, Pampel FC, Hatemi PK, Heath AC, Eaves LJ. Population composition, public policy, and the genetics of smoking.  Demography. 2011;48(4):1517-1533
PubMed   |  Link to Article
Boardman JD, Blalock CL, Pampel FC. Trends in the genetic influences on smoking.  J Health Soc Behav. 2010;51(1):108-123
PubMed   |  Link to Article
DiFranza JR, Richmond JB. Let the children be heard: lessons from studies of the early onset of tobacco addiction.  Pediatrics. 2008;121(3):623-624
PubMed   |  Link to Article
Johnson EO, Chen LS, Breslau N,  et al.  Peer smoking and the nicotinic receptor genes: an examination of genetic and environmental risks for nicotine dependence.  Addiction. 2010;105(11):2014-2022
PubMed   |  Link to Article
Boardman JD. State-level moderation of genetic tendencies to smoke.  Am J Public Health. 2009;99(3):480-486
PubMed   |  Link to Article
Boardman JD, Saint Onge JM, Haberstick BC, Timberlake DS, Hewitt JK. Do schools moderate the genetic determinants of smoking?  Behav Genet. 2008;38(3):234-246
PubMed   |  Link to Article
Galea S, Nandi A, Vlahov D. The social epidemiology of substance use.  Epidemiol Rev. 2004;26:36-52
PubMed   |  Link to Article
Fiore MC, Baker TB. Stealing a march in the 21st century: accelerating progress in the 100-year war against tobacco addiction in the United States.  Am J Public Health. 2009;99(7):1170-1175
PubMed   |  Link to Article
Rose G. Sick individuals and sick populations.  Int J Epidemiol. 1985;14(1):32-38
PubMed   |  Link to Article
Smith GD. Epidemiology, epigenetics and the ‘Gloomy Prospect’: embracing randomness in population health research and practice.  Int J Epidemiol. 2011;40(3):537-562
PubMed   |  Link to Article
Ioannidis JPA. Personalized genetic prediction: too limited, too expensive, or too soon?  Ann Intern Med. 2009;150(2):139-141
PubMed   |  Link to Article
Evans JP, Meslin EM, Marteau TM, Caulfield T. Genomics. Deflating the genomic bubble.  Science. 2011;331(6019):861-862
PubMed   |  Link to Article
Guttmacher AE, Porteous ME, McInerney JD. Educating health-care professionals about genetics and genomics.  Nat Rev Genet. 2007;8(2):151-157
PubMed   |  Link to Article

Correspondence

CME
Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
Submit a Comment

Multimedia

Supplemental Content

Belsky DW, Moffitt TE, Baker TB, et al. Polygenic risk and the developmental progression to heavy, persistent smoking and nicotine dependence [published online March 27, 2013]. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2013.736.

eAppendix 1. Search strategy for MEDLINE (using PubMed).

eAppendix 2. Search strategy for EMBASE (using Embase.com).

eFigure. Flow of information through the different phases of the review

eTable 1. Diagnostic performance of serologic tests: test combinations

eTable 2. Results of quality assessment per study. eReferences

Supplemental Content

Some tools below are only available to our subscribers or users with an online account.

Web of Science® Times Cited: 13

Related Content

Customize your page view by dragging & repositioning the boxes below.

Articles Related By Topic
Related Collections
PubMed Articles