Dopamine D4 receptor gene exon III polymorphism associated with binge
drinking attitudinal phenotype
Michael G. Vaughna,*, Kevin M. Beaverb, Matt DeLisic, Matthew O. Howardd, Brian E. Perrone
aSchool of Social Work, Department of Community Health, Division of Epidemiology, School of Public Health, Saint Louis University, 3550 Lindell
Boulevard, St. Louis, MO 63103, USA
bCollege of Criminology and Criminal Justice, Florida State University, Tallahassee, FL, USA
cDepartment of Sociology, Iowa State University, Ames, IA, USA
dSchool of Social Work, University of North Carolina, Chapel Hill, NC, USA
eSchool of Social Work, University of Michigan, Ann Arbor, MI, USA
Received 4 September 2008; received in revised form 21 January 2009; accepted 2 February 2009
Although binge drinking is a serious public health problem, relatively few studies have investigated the relationship between specific
dopaminergic genes such as the dopamine D4 receptor (DRD4) and binge drinking attitudinal phenotypes. This study used the DNA
subsample (N 5233, mean age19.8, standard deviation, 0.89) of the National Longitudinal Study of Adolescent Health to investigate
the association between a 48 base-pair variable number of tandem repeats in the DRD4 gene and a measure of binge drinking. Multivariate
regression models indicated that the 7-repeat (7R) allele of the exon III polymorphism is significantly positively associated (b 5 0.16,
P! .05) with binge drinking while controlling for low self-control and demographic variables. Findings were sturdy across race and
gender. The present study provides unique evidence to the genetic underpinnings of binge drinking. Results suggest that the 7R allele
may be an important contributor to the liability to binge drinking. ? 2009 Elsevier Inc. All rights reserved.
Keywords: Binge drinking; DRD4; Alcohol abuse; Self control; Alcohol; Genes and alcohol
Binge drinking constitutes a serious public health threat.
According to the National Institute on Alcohol Abuse and
a person’s blood alcohol concentration to 0.08 g percent or
above, which typically occurs when males consume five or
more drinks and females consume four or more drinks over
a two-hour span. Like most forms of antisocial behavior,
binge drinking is more prevalent among males than females
and among those in late adolescence and early adulthood. A
national epidemiologic study found that males accounted
the ages of 18 and 25 accounted for nearly one-third of all
binge drinking (Naimi et al., 2003). The national lifetime
college students in the United States (Wechsler et al., 1995).
Binge drinking has been linked to a range of antisocial
behaviors and other forms of psychopathology, including
early-onset alcohol use (Burek and Wright, 2005) and
co-occurring tobacco and marijuana use (Tucker et al.,
2005). Using data from Russian respondents, Pridemore
(2004) found links between binge drinking and violent
criminal offenses, including homicide. The co-occurrence
of binge drinking with other substance abuse and antisocial
behaviors is suggestive of a general propensity that likely
has a partially genetic etiology.
A number of genes have been linked to alcohol depen-
dence (Batel et al., 2008; Covault et al., 2004; Devor and
Cloninger, 1989; Dick et al., 2004; Edenberg et al., 2008;
Fehr et al., 2006; Lappalainen et al., 2005; Reich et al.,
1998; Wetherill et al., 2008; Xuei et al., 2006), but fewer
candidate genes have been associated with binge drinking.
Treutlein et al. (2006) found an association between
two haplotype tagging single nucleotide polymorphisms
(htSNPs) of the corticotropin-releasing hormone receptor
1 gene (CRHR1) and binge drinking, lifetime prevalence
of alcohol use, and lifetime prevalence of alcohol intoxica-
tion. Herman et al. (2003) found a significant association
between the serotonin transporter promoter polymorphism
5-HTTLPR (SLC6A4) and frequency of binge drinking,
frequency of drinking to intoxication, and greater quantity
* Corresponding author. Tel.: þ1-314-977-2718.
E-mail address: email@example.com (M.G. Vaughn).
0741-8329/09/$ e see front matter ? 2009 Elsevier Inc. All rights reserved.
Alcohol 43 (2009) 179e184
of alcoholic drinks consumed during drinking occasions
among a Caucasian college student sample. Severity of
alcohol dependence has also been linked to the 30Taq1
A1 allele of the D2 dopamine receptor gene (DRD2) (Blum
et al., 1993).
The 7-repeat (7R) allele in the third exon of the dopa-
mine D4 receptor gene (DRD4) is hypothesized to be asso-
ciated with the binge drinking phenotype based on its
relationship with novelty seeking. Novelty seeking is
a specific domain of the temperament model developed
by Cloninger (1987) (Cloninger et al., 1993) that represents
a dopaminergically modulated tendency toward exploratory
activity and excitement in response to novelty. The person-
ality traits associated with high novelty seeking are impul-
siveness, fickleness, quick-tempered, excitability, and
propensity to risk-taking. The profile of persons scoring
high on novelty seeking is isomorphic to the profile of Type
2 alcoholics (Cloninger, 1987) and is hypothesized to be
associated with the binge drinking phenotype. Research
has yielded mixed findings vis-a `-vis linkages between the
DRD4 variable number of tandem repeat (VNTR) and
novelty seeking (cf. Ebstein et al., 1996; Sander et al.,
1997); however, Laucht et al. (2007) recently found that
males carrying the 7R allele consumed more alcohol per
occasion and had greater lifetime rates of heavy drinking
consistent with binge drinking. The present study is unique
in that it evaluates the association between the 48 base-pair
(bp) VNTR in exon III of the DRD4 gene and binge
drinking using data from a nationally representative sample
of youth in the United States.
Data for this study come from the DNA subsample of the
National Longitudinal Study of Adolescent Health (Add
Health; Udry, 2003). Detailed information about the data,
including the sampling design, has been published else-
where (Resnick et al., 1997). Briefly, Add Health is a study
of a nationally representative sample of youths assessed
across three waves of follow-up. The first wave of data
was collected in 1994e1995 and included more than
20,745 participants. Approximately one-and-a-half-years
later, the second wave of questionnaires was administered
to 14,738 respondents. Finally, during 2001e2002, the third
wave of data was collected. In total, 15,197 subjects
completed the wave three surveys. Overall the Add Health
data span seven years of adolescent and young adult devel-
opment (Harris et al., 2003).
The Add Health data contain a rich array of items that
measure the respondent’s behavioral patterns, social rela-
tionships, and psychologic functioning. During wave three
interviews, a subsample of respondents was selected to
submit buccal cells for genotyping. To be eligible for this
part of the study, the respondent had to have a sibling
who was also an Add Health participant. More than 2,500
subjects were included in the DNA component to the Add
Health study (Harris et al., 2003).
At wave three, a subsample of all Add Health respon-
dents was asked a series of questions pertaining to their
binge drinking behaviors. Binge drinking was defined as
consuming five or more drinks in a row at one time in
the past 12 months on one or more occasions. This item
used a gated question to identify a subgroup (N5 233) of
add health participants as binge drinkers. Several items
asked respondents about their attitudes pertaining to binge
drinking. These items were used to construct a binge
drinking phenotype. The introduction to these questions
was as follows: ‘‘The next questions are about ‘binge
drinking.’ This is when a person drinks with the idea of
getting drunk.’’ Respondents were then queried about how
favorably they feel about binge drinking, whether binge
drinking allows them to have fun, whether binge drinking
helps them relax, how positive they feel about their binge
drinking, and how excited they get when thinking about
binge drinking, how positive or negative it would be if
you had lost your inhibitions as a result of binge drinking,
if thinking about binge drinking how aroused and pumped
up would that feel, how positive it would feel to lose control
of yourself as a result of binge drinking, it would be
pleasing to go out and binge drink. In total, nine items were
included in the binge drinking scale (15 strongly agree,
2 5 agree, 3 5 neither agree nor disagree, 45 disagree,
and 5 5 strongly disagree). All of the items were standard-
ized before constructing the scale. Higher scores on this
scale reflected greater involvement in binge drinking and
more favorable attitudes toward binge drinking (a 5 0.89).
Table 1 displays descriptive statistics for all of the variables
Risk-taking propensity and low self-control
Risk-taking propensity and associated low self-control is
a robust correlate of a wide range of antisocial behaviors
(Pratt and Cullen, 2000), including binge drinking (Piquero
et al., 2006). As a result, we included a nine-item low self-
control scale that has been used previously (Vaughn et al.,
2009). During wave three interviews, respondents were
asked a series of questions that tapped their risk-taking
propensity. Respondents were asked questions about
whether they try things just for fun or thrills, whether they
do things based on how they feel at the moment, and
whether they often get so excited that they lose control,
among others. Responses to each question were summed
together to form the low self-control scale (a 5 0.88).
Higher scores indicated lower levels of self-control.
180M.G. Vaughn et al. / Alcohol 43 (2009) 179e184
To take into account the effects of socioeconomic status
vis-a `-visthe binge drinkingphenotype, we created a 10-item
socioeconomic status scale. During wave three interviews,
respondents were asked about their financial and economic
well-being. Specifically, they were asked whether they had
a checking account, whether they had a credit card, whether
the past 12 months, whether they had not paid their rent or
evicted from their house in the past 12 months, whether they
had not paid their gas, electric, or oil bill in full in the past
12 months, whether they had their utilities turned off in the
past 12 months, whether theywere unable togo to the doctor
or hospital in the past 12 months because they could not
afford it, and whether they could not go to the dentist in
the past 12 months because they could not afford it. All of
the items were coded dichotomously and responses to the
10 items were summed together to form the socioeconomic
status scale (a 5 0.65). Higher scores on this scale corres-
ponded to lower socioeconomic status.
Three control variables were included in the statistical
models to help prevent model misspecification. First, given
that alcohol consumption varies across the life course, age
was included in all models. Age was a continuous variable
measured in years. Second, a dichotomous measure of
gender (0 5 female, 1 5 male) was included to take into
account gender differences in alcohol consumption. Third,
race was measured as a dichotomous dummy variable
(05 Caucasian, 15 nonwhite).
Respondents who were part of the DNA subsample had
their buccal cells genotyped for a polymorphism found in
the DRD4 gene. This polymorphism is 48 bp VNTR
ing for the DRD4. The two proceeding primer sequences
and reverse, 50-GCGACTACGTGGTCTACTCG-30. This
genotyping process produced polymerase chain reaction
products of 379, 427, 475, 523, 571, 619, 667, 715, 763,
and 811 bps. Consistent with extant research (Hopfer et al.,
2005), two groups were created by pooling together the
379 (2R), 427 (3R), 475 (4R), 523 (5R), and 571 (6R) bp
alleles and by pooling together the 619 (7R), 667 (8R), 715
(9R), and 763 (10R) bp alleles. This polymorphism was then
coded co-dominantly; Table 1 shows the distribution of
alleles using this nomenclature. No deviations from Har-
dyeWeinberg equilibrium were detected (c25 1.33, degree
of freedom 5 1, P O .05).
The analyses for this study were carried out in a series of
linked steps. First, the association between the 7R allele of
nary least-squares (OLS) regression models.OLS regression
is appropriate because the binge drinking scale is normally
distributed. These models also tested the possibility that
tionship between the 7R allele and binge drinking. The
and for Caucasians and nonwhites to determine whether the
effect of the 7R allele on binge drinking varied across
different racial and gender categories. Following prior
researchers analyzing the Add Health data (Beaver et al.,
2007), all of the models used Huber/White standard errors
to correct for nonindependence in some of the observations.
The final analytical sample consisted of N 5233 respon-
dents. This sample size was arrived at because of three
different criteria. First, only a subsample of respondents
was genotyped (N 5 2,574). Second, only a subsample of
respondents was asked the binge drinking questions. Third,
in some cases two monozygotic (MZ) twins from the same
MZ twin pair were included in the sample. To provide
conservative parameter estimates, one MZ twin from each
MZ twin pair was randomly removed from the sample
(Haberstick et al., 2005).
The analysis began by examining the effects of the 7R
allele, age, gender, and race on the binge drinking scale.
tive and statistically significant association with binge
dents who possess alleles of 7R or greater also have greater
we examine whether the association between DRD4 and
binge drinking is mediated by low self-control and/or
Descriptive statistics for add health sample variables and scales (N5233)
Abbreviations: S.D., standard deviation; Min., minimum; Max.,
maximum; DRD4, dopamine D4 receptor; 7R, 7-repeat allele.
181 M.G. Vaughn et al. / Alcohol 43 (2009) 179e184
socioeconomic status. As can be seen, the association
significant even when controlling for the effects of low self-
control (Model 2), socioeconomic status (Model 3), and the
combined effects of both of these measures (Model 4).
The next set of OLS models examined whether the rela-
tionship between the 7R allele and binge drinking was
consistent between males and females and between Cauca-
sians and nonwhites. All of these models (i.e., the models
for males, females, Caucasians, and nonwhites) controlled
for the effects of low self-control, socioeconomic status,
and demographic characteristics. Table 3 displays the
results of these models and shows that the 7R allele had
a positive and statistically significant effect on males,
Caucasians, and nonwhites. The association between binge
drinking and the 7R allele for females was marginally
significant (P 5 .069).
Analysis of the Add health data indicated a robust associ-
ation between the 48 bp VNTR in exonIII of the DRD4 gene
and a measure of binge drinking while controlling for self-
control and relevant demographic variables. Findings were
stable across gender and race with one caveat; socioeco-
when low self-control was left out of the models. When low
self-control was entered into the models socioeconomic
possessed a stronger effect on binge drinking attitudes than
socioeconomic status. Current findings suggest that DRD4
may contribute to liability to binge drinking. Whether this
susceptibility operates through a personality trait such as
novelty seeking is unresolved. Caution regarding this poten-
tial association is in order given the conflicting findings in
OLS regression models predicting binge drinking attitudinal phenotype for the full sample
Model 1 Model 2 Model 3Model 4
DRD41.65.15* 1.79.16* 1.68.15*1.78.16*
Low self-control0.29 .38*0.28.37*
Socioeconomic status0.38.10*0.09 (0.05)0.03
?0.00 0.08 0.01
Gender5.54.42*4.05 .31*5.60 .42* 4.15.31*
(0.75) (0.78)(0.77) (0.80)
Abbreviations: OLS, ordinary least squares; DRD4, dopamine D4 receptor.
*P!.05, two-tailed tests.
Note: Huber/White standard errors in parentheses.
OLS regression models predicting binge drinking attitudinal phenotype by gender and by race
(0.92)(0.77) (0.67) (1.35)
Low self-control0.30 .38*0.26 .41*0.31.42*0.21 .26*
(0.08) (0.07) (0.06)(0.11)
Abbreviations: OLS, ordinary least squares; DRD4, dopamine D4 receptor.
*P!.05, two-tailed tests.
Note: Huber/White standard errors in parentheses.
182M.G. Vaughn et al. / Alcohol 43 (2009) 179e184
previous studies of the 7R allele, novelty seeking, and addic-
tion (e.g., Lusher et al., 2001).
The present study results, however, should be considered
in light of several study limitations. First, the measure of
binge drinking was based on self-reports. Accompanying
response bias of self-reports is important given disagree-
ments about what constitutes binge drinking. In a related
study, Laucht et al. (2007) found in a sample of 303 adoles-
cents that a significant association between the 7R allele of
DRD4 and heavy drinking. However, this relationship was
mediated by a measure of novelty seeking but only in male
participants. Our findings are convergent with Laucht and
colleagues with the limitation that our measure of binge
drinking, although broadly similar, was clearly not iden-
tical. Furthermore, no measure of the personality dimension
of novelty seeking was available to assess its mediating
effect. Another limitation is that only a subsample of
respondents was genotyped, which necessarily raises ques-
tions regarding the generalizability of the findings. Last, we
should note that our subsample of ‘‘nonwhites’’ included
respondents from a range of different racial and ethnic heri-
tages. As a result, it is possible that the association between
the 7R allele and binge drinking is the result of population
stratification. Future research should explore this possibility
in greater detail.
The impact of the 7R allele on binge drinking behavior
remains to be determined. Future research elucidating the
relationship between binge drinking and genetic polymor-
phisms such as the VNTR in the third exon of DRD4 should
use theoretically meaningful personality measures that
capture intermediate traits that increase the susceptibility
to binge drinking. As binge drinking is a major health
concern, identifying the causal mechanisms that underpin
it may improve prevention efforts.
This research uses data from Add Health, a program
project designed by J. Richard Udry, Peter S. Bearman,
and Kathleen Mullan Harris, and funded by a grant
P01-HD31921 from the Eunice Kennedy Shriver National
Institute of Child Health and Human Development, with
cooperative funding from 17 other agencies. Special
acknowledgment is due to Ronald R. Rindfuss and Barbara
Entwisle for assistance in the original design. Persons inter-
ested in obtaining data files from Add Health should
contact Add Health, Carolina Population Center, 123 W.
Franklin Street, Chapel Hill, NC 27516-2524 (addhealth@
unc.edu). No direct support was received from grant P01-
HD31921 for this analysis.
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