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. Author manuscript; available in PMC: 2009 Jul 1.
Published in final edited form as: Prev Med. 2008 Apr 1;47(1):116–122. doi: 10.1016/j.ypmed.2008.03.013

A Community-Based Study of Cigarette Smoking Behavior in Relation to Variation in Three Genes Involved in Dopamine Metabolism: Catechol-O-methyltransferase (COMT), Dopamine Beta-Hydroxylase (DBH) and Monoamine Oxidase-A (MAO-A)

Meredith S Shiels 1, Han Yao Huang 1, Sandra C Hoffman 1,2, Yin Yao Shugart 1, Judy Hoffman Bolton 2, Elizabeth A Platz 1, Kathy J Helzlsouer 1,2,3, Anthony J Alberg 1,2,4,5
PMCID: PMC2577854  NIHMSID: NIHMS59079  PMID: 18486967

Abstract

Objective

Cigarette smoking behavior may be influenced by catechol-O-methlyltransferase (COMT), dopamine beta-hydroxylase (DBH), and monamine oxidase-A (MAO-A), genes that play roles in dopamine metabolism. The association between common polymorphisms of these genes and smoking behavior was assessed among 10,059 Caucasian volunteers in Washington County, MD in 1989.

Methods

Age-adjusted logistic regression was used to measure the association between variants of these single nucleotide polymorphisms and smoking initiation and persistent smoking.

Results

Overall, no association was seen between each genotype and smoking behavior. However, among younger (<54 years) women, the COMT GG genotype was positively associated with smoking initiation (OR=1.3; 95% CI: 1.1, 1.5), and the MAO-A TT genotype was inversely associated with persistent smoking (OR=0.5; 95% CI: 0.3, 0.9). Men who smoked fewer than 10 cigarettes per day were more likely to be persistent smokers if they had the COMT GG (OR=1.7; 95% CI: 1.0, 2.9) or the DBH GG (OR=1.6; 95% CI: 1.0, 2.6) genotypes.

Conclusion

Overall the results of this large community-based study do not provide evidence to support the presence of important associations between variants of COMT, DBH, or MAO-A and smoking initiation or persistent smoking.

Keywords: tobacco, cigarette smoking, COMT, DBH, MAO-A

INTRODUCTION

In the United States, smoking is the major preventable cause of premature mortality (Centers for Disease Control 2004). The addictive nature of nicotine influences the intensity and duration of exposure to tobacco toxins that lead to smoking-caused diseases.

A complex interplay of social influences contributes to the initiation and maintenance of cigarette smoking. For example, among youths, exposure to parents, siblings, and peers who smoke cigarettes, as well as exposure to pro-smoking media messages are strong risk factors for smoking initiation (US Department of Health and Human Services 1994). Given exposure to the social influences to smoke, individual characteristics such as poor school performance can be conceptualized as social susceptibility factors that affect the likelihood of progressing on the pathway to smoking. Once an individual tries smoking cigarettes, variants in genes related to the metabolism of cigarette smoke constituents, such as nicotine, may also affect the likelihood of progressing to regular smoking (smoking initiation) and remaining a smoker (smoking persistence).

In fact, estimates indicate genetic variation may account for 44–56% (Li et al. 2003, Sullivan, Kendler 1999, Vink, Willemsen & Boomsma 2005) of smoking initiation, 46–59% of persistent smoking (Li et al. 2003) and 67–75% of nicotine addiction (Sullivan, Kendler 1999, Vink, Willemsen & Boomsma 2005). Genotypic variation may influence specific stages of smoking behavior. Studies have identified polymorphisms of genes responsible for the production of acetylcholine (Greenbaum et al. 2006), serotonin (Lerer et al. 2006), tryptophan (Sullivan et al. 2001) and dopamine (Sullivan et al. 2001) that may differentially influence smoking initiation and nicotine dependence.

In this study, we investigated two specific aspects of cigarette smoking behavior, smoking initiation and smoking persistence, in relation to single nucleotide polymorphisms (SNPs) in three genes that encode enzymes involved in different aspects of dopamine metabolism: catechol-O-methlyltransferase (COMT), dopamine beta-hydroxylase (DBH), and monamine oxidase-A (MAO-A). The MAO-A and COMT enzymes contribute to dopamine metabolism in the brain, whereas DBH plays a role in converting dopamine to norepinephrine. The drive to continue to smoke is partially due to behavioral reinforcement from the stimulant effects and antidepressant nature of cigarette smoking. These effects occur when nicotinic cholinergic receptors in the central nervous system are stimulated by nicotine, leading to the release of the neurotransmitters serotonin and dopamine (Julien 1998). Variation in dopamine metabolism may influence inter-individual susceptibility to the smoking-induced nicotine response and therefore affect the likelihood that one becomes a smoker and, once a smoker, the likelihood of quitting smoking.

We studied specific SNPs in these genes for several reasons. The COMT 1947 A>G polymorphism is a functional SNP associated with a 2–3 fold variation in enzyme activity (Syvanen et al. 1997), which may result in variation in dopamine activity. COMT has been linked with alcohol and substance abuse (Tiihonen et al. 1999, Kauhanen et al. 2000, Vandenbergh et al. 1997). Cigarette smoke (Rose et al. 2001) decreases MAO activity, thus increasing brain dopamine concentrations, which in turn could contribute to the addictiveness of smoking and enhance the likelihood of smoking initiation and smoking persistence (McKinney et al. 2000). Furthermore, in the MAO-A 1460 C>T SNP, the T allele reduces MAO-A enzyme activity (Hotamisligil, Breakefield 1991). Higher DBH plasma concentrations may influence drug dependence (Cubells et al. 2000, Gabel et al. 1995). However, so far the evidence concerning variants in the COMT, DBH and MAO-A genes in relation to smoking behaviors has been inconsistent (Beuten et al. 2006, Colilla et al. 2005, David et al. 2002, Enoch et al. 2006, Guo et al. 2007, Han et al. 2008, Jin et al. 2005, Johnstone et al. 2002, Johnstone et al. 2004, McKinney et al. 2000, Munafo et al. 2008, Tochigi et al. 2007, Ton et al. 2007).

Currently, the social and cultural contributions to smoking initiation and smoking persistence are better understood than the role of inter-individual genetic susceptibility. The need to enrich the body of evidence concerning the potential genetic role in the etiology of cigarette smoking behavior with a large-scale, community-based study motivated the present investigation. We tested the hypothesis that variants in the COMT, DBH and MAO-A genes are associated with smoking initiation and persistent smoking.

METHODS

Population

This cross-sectional study was embedded in a prospective community-based cohort study established in Washington County, Maryland. In 1989, the CLUE II cohort study was conducted as a follow-up study to the CLUE Cohort (CLUE I) conducted in 1974. The name CLUE refers to the recruiting campaign slogan “Give us a clue to cancer and heart disease.” Blood specimens and baseline data were collected from 25,802 participants in CLUE I and 32,898 participants in CLUE II. About one third of CLUE I participants (N=8,394) also participated in CLUE II and formed the “Odyssey Cohort”. For this study, Caucasian adults in either the Odyssey Cohort or in a sub-cohort comprised of an age-stratified 10% random sample of CLUE II participants (N=3,076) were included, for a total of 10,278 participants (Figure 1). Of these, only those with complete data on smoking status at baseline and complete genotype data on at least one of the SNPs were included (n=10,059). Twenty-four men who were heterozygous at MAO-A 1460 C>T were excluded, because MAO-A is sex-linked

Figure 1.

Figure 1

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Description of study populations in the cross-sectional study and sub-study with participants from the CLUE I and CLUE II studies in Washington County, Maryland.

This study was approved by the Institutional Review Board at the Johns Hopkins University Bloomberg School of Public Health.

Genotype Assessment

The alkaline lysis method (Klintschar, Neuhuber 2000) was utilized to extract DNA from the buffy coat of the blood specimens collected in 1989 and stored at −70° C. Taqman assays were used to genotype COMT (rs4680), DBH (rs77905) (analyzed by Celera Genomics; Rockville, MD in 2002), and MAO-A (rs1801291) (analyzed by Applied Biosystems; Foster City, CA in 2003–4). The percent concordance for COMT was 91.7% among samples that overlapped with a previous study (n=252).

Outcome Assessment

Age, race, sex, education, and smoking history were collected in the 1989 questionnaire. The two outcomes in the present study were smoking initiation (ever smokers compared to never smokers) and persistent smoking (current smokers compared to former smokers).

Among those who were current smokers in 1989 and who provided smoking status in at least one follow-up survey in 1998, 2000 and 2003 (n=949), we also carried out a longitudinal sub-study. Participants were categorized as successful quitters if they self-reported their smoking status as “former smoker” in 1998, 2000 or 2003, and did not self-report their smoking status as “current smoker” on any of the subsequent surveys; otherwise they were considered to be persistent smokers. The association between the gene variants and persistent smoking was assessed by comparing persistent smokers to successful quitters.

Statistical Analysis

COMT, DBH and MAO-A were tested for Hardy-Weinberg equilibrium (HWE) among never smokers with the chi-squared test.

All analyses were stratified by gender. The associations between genotypes and smoking behavior were measured using odds ratios (OR) and 95% confidence intervals (CI). Logistic regression, adjusted for age, was carried out for each gene assuming a recessive model: GG vs. AG/AA for COMT, GG vs. AG/AA for DBH and TT/TO vs. CT/CC/CO for MAO-A. Dominant and additive models were also considered, but were not found to improve the fit of the data over the recessive model so we only report results of the recessive model. To assess the independent effects of each polymorphism, a third model was adjusted for potential confounders and the other two polymorphisms. Gene-by-gene interactions were assessed using the likelihood ratio test from the base model without the interaction term to the model extended to include the cross-product term. To evaluate the presence of effect modification, stratified analyses were carried out by age (median age 54 years) and by smoking intensity (≤10, 11–29, and 30+ cigarettes per day) among persistent smokers. P-values for interactions were estimated with the likelihood ratio test by comparing models with and without a cross-product term between genotype and age category or category of cigarette consumption. All analyses were two-sided with α=0.05, and were performed by Intercooled STATA 8.2 (StataCorp 2001).

RESULTS

Only the MAO-A polymorphism (women only, because the gene is on the X-chromosome) was found to be in HWE among never-smokers. The COMT and DBH polymorphisms deviated significantly from HWE (chi-squared=5.2; p=0.02 and chi-squared=9.9; p=0.001, respectively).

The final study population included 10,059 people, 38.5% male and 61.5% female. Men were significantly more likely than women to have a positive smoking history (61% versus 38%), but the likelihood of current smoking was similar in men and women (18% versus 16%). On average, current smokers were significantly younger than former or never smokers (Table 1).

Table 1.

Distribution of descriptive characteristics and genotypes of participants by smoking status in Washington County, Maryland, 1989.

Never Smokers Former Smokers Current Smokers
MEN (n=3,871)
Participants* 1,507 38.9 1,681 43.4 683 17.6
Age in 1989†‡ 51.4 (50.7, 52.2) 57.8 (57.2, 58.5) 47.5 (46.5, 48.5)
Education*‡ <12 years 264 17.5 450 26.8 168 24.6
≥12 years 1,243 82.5 1,231 73.2 515 75.4
Number of Cigarettes per day† --- 25.6 (24.8, 26.4) 24.5 (23.5, 25.5)
COMT* AA 371 26.2 394 25.6 173 27.4
AG 734 50.3 804 52.2 317 50.2
GG 310 21.9 341 22.2 142 22.5
DBH* AA 352 21.7 413 25.4 154 23.2
AG 696 42.9 770 47.4 331 49.9
GG 412 28.2 441 27.2 178 26.8
MAO-A* CO 1,067 73.0 1,181 72.7 490 73.1
TO 395 27.0 443 27.3 180 26.9
WOMEN (n=6,188)
Participants 3,829 61.9 1,342 21.7 1,017 16.4
Age in 1989†‡ 54.4 (53.9, 54.9) 55.1 (54.4, 55.9) 48.7 (47.9, 49.6)
Education*‡ <12 years 858 22.4 301 22.4 276 27.1
≥12 years 2.971 77.6 1,041 77.6 741 72.9
Number of Cigarettes per day†‡ --- 17.3 (16.5, 18.1) 19.3 (18.6, 19.9)
COMT* AA 948 26.9 365 29.7 248 26.6
AG 1,817 51.6 593 48.2 468 50.2
GG 757 21.5 272 22.1 216 23.2
DBH* AA 973 26.2 341 26.2 235 23.8
AG 1,812 48.8 616 47.3 493 50.0
GG 928 25.0 345 26.5 258 26.2
MA0-A* CC 1,954 52.4 669 51.5 527 53.2
TC 1,475 39.6 515 39.7 390 39.4
TT 298 8.0 114 8.8 73 7.4
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*

Number, percentage

†

Mean (95% CI)

‡

Indicates statistically significant (p ≤ 0.05) heterogeneity.

Overall, no significant associations were observed between polymorphisms of COMT, DBH or MAO-A and smoking initiation (Table 2) or persistent smoking (Table 3).

Table 2.

The association between genotypes of the COMT, DBH and MAO-A genes and smoking initiation, stratified by sex and median age in Washington County, Maryland, 1989.

MEN WOMEN
Ever Smokers Never Smokers Odds Ratio* 95% Confidence Interval Ever Smokers Never Smokers Odds Ratio* 95% Confidence Interval
COMT Total AA/AG 1,688 1,105 1.0 1,674 2,765 1.0
GG 483 310 1.00 0.85, 1.17 488 757 1.06 0.93, 1.21
<54 years old AA/AG 798 616 1.0 841 1,300 1.0
GG 186 163 0.88 0.69, 1.11 270 334 1.25† 1.04, 1.50
54≤ years old AA/AG 890 489 1.0 833 1,465 1.0
GG 297 147 1.11 0.88, 1.39 218 423 0.91 0.76, 1.10
DBH Total AA/AG 1,668 1,048 1.0 1,685 2,785 1.0
GG 619 412 0.94 0.81, 1.09 603 928 1.08 0.96, 1.21
<54 years old AA/AG 752 561 1.0 853 1,294
GG 275 230 0.88 0.71, 1.08 311 426 1.11 0.93, 1.31
54≤ years old AA/AG 916 487 1.0 832 1,491 1.0
GG 344 182 1.00 0.81, 1.24 292 502 1.06 0.90, 1.26
MAO-A Total CO/CC/CT 1,671 1,067 1.0 2,101 3,429 1.0
TO/TT 623 395 1.02 0.88, 1.18 187 298 1.02 0.84, 1.24
<54 years old CO/CC/CT 746 574 1.0 1,084 1,598 1.0
TO/TT 282 225 0.97 0.79, 1.20 89 141 0.93 0.71, 1.23
54≤ years old CO/CC/CT 925 493 1.0 1,017 1,831 1.0
TO/TT 341 170 1.07 0.86, 1.32 98 157 1.11 0.85, 1.45
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*

Analysis adjusted for age

†

Indicates p ≤ 0.05.

Table 3.

The association between genotypes of the COMT, DBH and MAO-A genes and persistent smoking, stratified by sex and median age in Washington County, Maryland, 1989.

MEN WOMEN
Number Current Number Former Odds Ratio* 95% Confidence Interval Number Current Number Former Odds Ratio* 95% Confidence Interval
COMT Total AA/AG 490 1,198 1.0 716 958 1.0
GG 142 341 1.15 0.91, 1.46 216 272 1.01 0.82, 1.25
<54 years old AA/AG 336 462 1.0 440 401 1.0
GG 89 97 1.29 0.93, 1.78 139 131 0.95 0.72, 1.25
54≤ years old AA/AG 154 736 1.0 276 557 1.0
GG 53 244 1.02 0.72, 1.44 77 141 1.13 0.82, 1.55
DBH Total AA/AG 485 1,183 1.0 728 957 1.0
GG 178 441 1.01 0.82, 1.26 258 345 0.99 0.82, 1.20
<54 years old AA/AG 323 429 1.0 444 409 1.0
GG 121 154 1.09 0.82, 1.45 166 145 1.06 0.82, 1.38
54≤ years old AA/AG 162 754 1.0 284 548 1.0
GG 57 287 0.92 0.66, 1.29 92 200 0.92 0.69, 1.23
MAO-A Total CO/CC/CT 490 1,181 1.0 917 1,184 1.0
TO/TT 180 443 0.96 0.77, 1.19 73 114 0.82 0.60, 1.12
<54 years old CO/CC/CT 327 419 1.0 572 512 1.0
TO/TT 119 163 0.92 0.69, 1.22 38 51 0.65 0.42, 1.01
54≤ years old CO/CC/CT 163 762 1.0 345 672 1.0
TO/TT 61 280 1.02 0.73, 1.41 35 63 1.02 0.66, 1.58
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*

Analysis adjusted for age

†

Indicates p ≤ 0.05.

A statistically significant age-by-genotype interaction (p-interaction 0.03) was observed among women for the COMT GG genotype, which was positively and statistically significantly associated with smoking initiation among younger (OR=1.25; 95% CI: 1.04, 1.50) but not among older (OR=0.91; 95% CI 0.76–1.10) women. Among men, a statistically significant interaction (p-interaction 0.04) was observed between the COMT AG/AA genotype and smoking intensity in persistent smokers, with a statistically significant association among those who smoked ≤10 cigarettes per day (OR=1.73; 95% CI: 1.04, 2.87) but not among those who smoked 11–29 (OR=0.88; 95% CI: 0.61, 1.26) or >=30 (OR=1.30; 95% CI 0.86, 1.96) cigarettes per day, respectively) (Table 4). Among men the association between the COMT AG/AA genotype and persistent smoking differed between younger (OR=1.29; 95% CI: 0.93, 1.78), and older (OR=1.02; 95% CI 0.72, 1.44) men, but neither the age-specific odds ratios nor the test for interaction (p-interaction 0.63) was statistically significant.

Table 4.

The association between genotypes of the COMT, DBH and MAO, A genes and persistent smoking, stratified by sex and smoking intensity in Washington County, Maryland, 1989.

MEN WOMEN
Smoking intensity Number Current Number Former Odds Ratio* 95% Confidence Interval Number Current Number Former Odds Ratio* 95% Confidence Interval
COMT ≤10 per day AA/AG 80 257 1.0 191 421 1.0
GG 31 63 1.73† 1.04, 2.87 62 127 1.08 0.76, 1.53
11–29 per day AA/AG 229 509 1.0 371 336 1.0
GG 57 156 0.88 0.61, 1.26 118 83 1.15 0.82, 1.60
30≤ per day AA/AG 181 432 1.0 154 201 1.0
GG 54 122 1.30 0.86, 1.96 36 62 0.68 0.42, 1.12
DBH ≤10 per day AA/AG 78 258 1.0 199 436 1.0
GG 39 82 1.59 1.00, 2.54 66 149 0.98 0.70, 1.38
11–29 per day AA/AG 230 492 1.0 382 319 1.0
GG 68 206 0.72 0.52, 1.00 139 124 0.92 0.69, 1.24
30≤ per day AA/AG 177 433 1.0 147 202 1.0
GG 71 153 1.22 0.84, 1.76 53 72 1.01 0.65, 1.57
MAO-A ≤10 per day CO/CC/CT 87 245 1.0 252 530 1.0
TO/TT 34 93 1.00 0.63, 1.60 18 49 0.78 0.44, 1.36
11–29 per day CO/CC/CT 215 507 1.0 482 399 1.0
TO/TT 79 191 0.95 0.68, 1.31 38 42 0.70 0.44, 1.13
30≤ per day CO/CC/CT 188 429 1.0 183 255 1.0
TO/TT 67 159 1.00 0.68, 1.45 17 23 1.12 0.56, 2.21
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*

Analysis adjusted for age

†

Indicates p ≤ 0.05.

Among men only, the association between DBH and persistent smoking was stronger among light smokers than heavier smokers (p-interaction 0.01). Men with the DBH GG genotype were more likely to be persistent smokers if they smoked <=10 cigarettes per day (OR=1.59; 95% CI: 1.00, 2.54), but not if they smoked 11–29 (OR=0.72; 95% CI 0.52–1.00) or ≥30 (OR=1.22; 95% CI 0.84, 1.76) cigarettes per day (Table 4).

Among women, the age-stratified analyses revealed the MAO-A TT genotype was inversely associated with persistent smoking in younger (OR=0.65; 95% CI: 0.42, 1.01) but not among older (OR=1.02; 95% CI 0.66, 1.58) women, though this was not a statistically significant interaction (p-interaction=0.3). For each gene, allelic frequencies did not vary across ten-year age categories, suggesting that the observed age-by-genotype differences do not appear to be due to survival biases by genotype.

The results of the longitudinal sub-study of smoking persistence were consistent with the cross-sectional analyses (Table 5). Specifically, no overall associations were seen between DBH or MAO-A and persistent smoking in the longitudinal analyses. The only potentially notable difference was that the COMT GG genotype (vs. AG/AA) was slightly inversely associated with persistent smoking among women (OR=0.71; 95% CI: 0.47, 1.08), but this association was not statistically significant and was not seen among men (OR=0.97; 95% CI 0.55, 1.71).

Table 5.

The association between genotypes of the COMT, DBH and MAO, A genes and persistent smoking, among those followed longitudinally from 1989 to 2003 in Washington County, Maryland.

MEN WOMEN
Number Quit Number Persisted Odds Ratio* 95% Confidence Interval Number Quit Number Persisted Odds Ratio* 95% Confidence Interval
COMT AA/AG 126 109 1.0 202 209 1.0
GG 37 29 0.97 0.55, 1.71 69 51 0.71 0.47, 1.08
DBH AA/AG 127 112 1.0 217 206 1.0
GG 43 36 0.90 0.53, 1.53 69 72 1.10 0.74, 1.62
MAO-A CO/CC/CT 127 111 1.0 274 255 1.0
TO/TT 49 40 1.14 0.68, 1.91 21 22 1.15 0.61, 2.17
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*

Analysis adjusted for age

†

Indicates p ≤ 0.05.

Analyses that included all three genotypes did not yield associations that differed from the analyses of each SNP individually, suggesting the associations between each gene and smoking outcome was not confounded by the other genes studied. No significant gene-gene interactions in relation to smoking initiation or persistent smoking were observed (all p-interaction>0.3).

DISCUSSION

We carried out a community-based study to determine if common variants in genes related to dopamine metabolism were associated with cigarette smoking initiation and persistence. This investigation was hypothesis-driven, using a candidate gene approach with genes selected based on the fact that they encode for enzymes involved in dopamine metabolism and on prior research that indicated potential linkages between these genes and addictive behaviors.

In the total study population, there were no notable associations between COMT, DBH or MAO-A and smoking initiation and persistent smoking. Our results are consistent with previous reports of no association between COMT and smoking initiation (David et al. 2002, Guo et al. 2007) and persistent smoking (David et al. 2002, Guo et al. 2007). However, one study found the G allele to be associated with persistent smoking among women (Colilla et al. 2005), and another found it to be associated with current smoking status (Enoch et al. 2006). The associations between COMT and other smoking outcomes have been examined with mixed results. One study found no association between COMT genotype and smoking intensity (David et al. 2002, McKinney et al. 2000), whereas another found the GG genotype to be associated with smoking at least one pack per day (Tochigi et al. 2007). In studies that examined COMT in relation to smoking abstinence in randomized treatment trials of smoking cessation, observations have varied from no association (Ton et al. 2007), to the AG/GG genotypes being associated with quicker relapse (Munafo et al. 2008), to the GG genotype being associated with smoking abstinence (Han et al. 2008). In another study, the G allele was observed to be protective against nicotine dependence (Beuten et al. 2006).

DBH and MAO-A have previously been investigated in relation to cigarette smoking behavior in only a few studies. DBH has not been previously studied in relation to our study outcomes of smoking initiation or smoking persistence, though in one study no association was observed between variants of the DBH 1368 A>G and cigarette smoking intensity (Johnstone et al. 2002).

The null association we observed between MAO-A 1460 SNPs and smoking initiation and persistent smoking conflicts with a previous observation of a positive association between the T allele of MAO-A 1460 and current smoking status among Chinese men (Jin et al. 2005). With respect to smoking intensity, two studies of MAO-A 1460 variants reported no association (Johnstone et al. 2002, Tochigi et al. 2007).

In the present study, the overall results were null. However, associations were observed among population subgroups, specifically, those who smoked fewer cigarettes per day (COMT, DBH) or who were younger (COMT, MAO-A). Even in these subgroups, the statistically significant associations were not strong and were not consistently present in both men and women. Weighed against the overall null results in the total study population and the inconsistencies in the subgroup analyses, the results of the subgroup analyses should only be viewed as hypothesis generating.

The associations with persistent smoking among light smokers, but not among heavier smokers, may be due to a differential effect according to nicotine dose, i.e., the large amounts of nicotine consumed by heavy smokers may overshadow the relatively minor influence that a single SNP may exert on dependence. If so, an association may only become apparent in the setting of lower nicotine doses consumed by lighter smokers.

Associations may be observed among younger—but not older—smokers due to variation of the effect of specific alleles during different development stages. In our study, among those <54 years old, positive associations were observed between the GG genotype of COMT 1947 A>G and persistent smoking in men and smoking initiation in women. Perhaps the association seen solely among younger participants could be influenced by the doubling of COMT activity in the prefrontal cortex from birth to late adulthood (Tunbridge et al. 2006). We also observed an association among women <54 years old with the TT genotype of MAO-A 1460 C>T being less likely to be persistent smokers. Among women, age may be acting as a surrogate for menopausal status if the hormonal milieu influences enzyme production. In animal studies (Gundlah, Lu & Bethea 2002, Holschneider et al. 1998), ovarian steroids decreased brain levels of MAO-A. Conversely, skepticism is warranted because the observed pattern of associations between COMT and MAO-A with smoking behavior among younger participants and between COMT and DBH and persistent smoking among light smoking men may simply be due to chance.

Study Limitations and Strengths

Certain features of the present study warrant caution in drawing inferences, including the cross-sectional study design and deviations from HWE. The COMT and DBH allelic frequencies deviated significantly from HWE among never smokers. The departures from HWE may indicate genotyping error (Hosking et al. 2004), or may result from violations in the Hardy-Weinberg assumptions such as random mating or selection. Of note, DBH also deviated from HWE among Caucasians in the SNP500 database (National Cancer Institute 2006). Additionally, the percent concordance for COMT was 91.7% among samples that overlapped with a previous study. Therefore, it is less likely that deviations from HWE for DBH and COMT indicate that genotyping quality was poor in our study. Another potential limitation is population stratification. We reduced the potential for population stratification to have a major impact on our results by restricting the analysis to Caucasians, though allelic variation may remain across the population substructure of those included in the study.

Multiple testing may also be raised as an issue, due to the large number of analyses and sub-analyses carried out. However, we focused on the patterns of associations rather than concentrating on p-values to draw inferences. Replication of our findings by others will help to sort out the true associations from the potential false-positive findings.

The main strengths of our study are the large sample size and community-based study design. The present study is the largest study to date to investigate the relationships between cigarette smoking and COMT, DBH and MAO-A. The sample size provided enough statistical precision to detect meaningful gender-specific differences between genotypes and cigarette smoking, as well as analyses stratified by median age and smoking intensity.

The community-based design enhanced the generalizability of our results. For example, the allelic frequencies observed among Caucasians in our study did not vary significantly from those reported in the SNP500 database. However, by limiting our study to Caucasians, our findings may not be generalizable to other racial/ethnic groups.

By using information from follow-up surveys, the longitudinal sub-study better captured changes in smoking. However, the much smaller sample size of the longitudinal study precluded further in-depth analyses. Nevertheless, the inferences were strengthened by the corroboration of the overall null results observed in the main cross-sectional study by the generally null results of the longitudinal sub-study.

Conclusions

The overall results of our study do not support the presence of any association between the COMT, DBH and MAO-A SNPs we studied and smoking initiation or persistence.

Acknowledgements

This research was made possible through funding from the National Institute of Aging (5U01AG018033) and National Cancer Institute (CA105069). Ms. Shiels was supported by the National Institutes of Health National Research Service Award T32 CA009314.

Footnotes

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