The Pennsylvania State University
The Graduate School
College of Health and Human Development
DIFFERENTIAL SUSCEPTIBILITY TO THE EFFECTS OF PEER
PRESSURE AND POSITIVE FRIEND SUPPORT ON ALCOHOL USE
A Thesis in
Human Development and Family Studies
by
Amanda M. Griffin
© 2014 Amanda M. Griffin
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science
December 2014
ii
The thesis of Amanda M. Griffin was reviewed and approved* by the following:
H. H. Cleveland
Associate Professor of Human Development and Family Studies
Thesis Advisor
David J. Vandenbergh
Associate Professor of Biobehavioral Health
Douglas M Teti
Professor of Human Development and Family Studies
Department Head of the Department of Human Development and Family Studies
*Signatures are on file in the Graduate School.
iii
ABSTRACT
Adolescents’ peer relationships have been shown to influence alcohol use, however, not
all peer relationships equally influence adolescents. This study draws from the in-home
subsample of the PROSPER Project to investigate the association between peer relationships
(both positive and negative) and adolescent alcohol use, and the moderating role of DRD4. I
hypothesize that the DRD4 candidate gene (7-vs.7+) moderates the impact of both Peer Deviance
Pressure and Positive Peer Activity on 12th grade alcohol use. Results revealed significant main
effects for both peer measures, but no main effects for the DRD4 variant, on 12th grade alcohol
use. In addition, DRD4 moderated the association between negative peer relationships and
alcohol use. For individuals who carried at least one copy of the DRD4 7+ variant, Peer
Deviance Pressure was associated with increases in both overall alcohol use and recent alcohol
use. However, DRD4 status did not moderate the effect of Positive Peer Activity on alcohol use
for DRD4 7+ individuals.
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TABLE OF CONTENTS
List of Tables ................................................................................................................................................ v
List of Figures .............................................................................................................................................. vi
Acknowledgements ..................................................................................................................................... vii
INTRODUCTION ........................................................................................................................................ 1
Differential Susceptibility Theory ............................................................................................................ 2
Dopamine and Peer Influences ................................................................................................................. 3
Peers and Adolescent Alcohol Use ........................................................................................................... 6
Analytic Challenge to G x E Research...................................................................................................... 7
METHODS ................................................................................................................................................... 8
PROSPER Project ................................................................................................................................. 8
Sample and participants ........................................................................................................................ 9
Measures ................................................................................................................................................... 9
DRD4 Genotyping ................................................................................................................................ 9
PC1 Controls for Population Stratification ......................................................................................... 10
Data Analysis ...................................................................................................................................... 11
Intervention Status .............................................................................................................................. 12
Peer Deviance Pressure ....................................................................................................................... 12
Positive Peer Activity.......................................................................................................................... 12
Alcohol Use Cumulative Index (AUCI) ............................................................................................. 12
Past Month Drunkenness .................................................................................................................... 13
RESULTS ................................................................................................................................................... 14
Preliminary Analysis ............................................................................................................................... 14
Peer Deviance Pressure ........................................................................................................................... 14
Positive Peer Activity.............................................................................................................................. 16
DISCUSSION ............................................................................................................................................. 17
Appendix: Tables and Figures .................................................................................................................... 21
References ................................................................................................................................................... 26
v
LIST OF TABLES
Table 1. Demographic comparisons by DRD4 genotype and for analytic sample ..................................... 21
Table 2. Parameter estimates and standard errors for peer deviance pressure models. .............................. 22
Table 3. Parameter estimates and standard errors for positive peer activity models .................................. 23
vi
LIST OF FIGURES
Figure 1. Differential Effects of Peer Deviance Pressure on Alcohol Use Cumulative Index by DRD4
Genotype. .................................................................................................................................................... 24
Figure 2. Differential Effects of Peer Deviance Pressure on Past Month Use by DRD4 Genotype. .......... 25
vii
ACKNOWLEDGEMENTS
I would like to thank Dr. Deborah Grove and Ms. Ashley Price of the Penn State
Genomics Core Facility for DNA purification and genotyping. For participant recruitment, I
recognize the efforts of Shirley Huck, Cathy Owen, Debra Bahr, and Anthony Connor of the
Iowa State University Survey and Behavioral Research Services; Rob Schofield and Dean
Stankowski of the Penn State University Survey Research Center; and Lee Carpenter of Penn
State. Work on this paper was supported by the National Institute on Drug Abuse (grants
DA030389 and DA013709).
The project described was supported by Award Number T32 DA017629 from the
National Institute on Drug Abuse. The content is solely the responsibility of the authors and does
not necessarily represent the official views of the National Institute on Drug Abuse or the
National Institute of Health.
1
INTRODUCTION
Early alcohol use is linked to reduced educational attainment, increased risky sexual
practices, and amplified mental health risk (US Department of Health and Human Services,
2013). Alcohol use in early adolescence also dramatically increases the odds of alcohol abuse
and alcohol dependency beyond age 21 (DeWit, Adlaf, Offord, & Ogborne, 2000). These
findings demonstrate that alcohol use during early adolescence indicates risks over a broad range
of negative outcomes, both general and substance use related.
Positive and negative peer relationships are both critical influences on adolescent alcohol
use. Their significance is expected given the general increased importance of peer relationships
during early adolescence, as time spent with peers increases and time spent with parents
decreases (Csikszentmihalyi & Larson, 1984; Larson, Richards, Moneta, Holmbeck, & Duckett,
1996). Early adolescent peer relationships have the opportunity to establish and transmit
behavioral norms and attitudes regarding activities, including alcohol use. Thus, it is not
surprising that affiliating with delinquent peers is linked to adolescents’ own alcohol use
(Hawkins, Catalano, & Miller, 1992).
Although exposure to peer delinquency can generally predict adolescent alcohol use,
susceptibility to peer influence on adolescents’ own behaviors can differ across individuals
(Dielman, Campanelli, Shope, & Butchart, 1987; Dielman, Kloska, Leech, Schulenberg, &
Shope, 1992). Varying levels of susceptibility to peer influence have been related to individual
characteristics such as perceived harm of substance, sensation seeking, self-esteem, and
magnitude of non-use social values (Hawkins, Catalano, & Miller, 1992; Petraitis, Flay, &
Miller, 1995).
Susceptibility to peer influences on alcohol use may also be influenced by genetics.
2
Recent research guided by Differential Susceptibility Theory (DST) (Belsky, BakermansKranenburg, & van IJzendoorn, 2007; Belsky & Pluess, 2009; Belsky & Pluess, 2013; Ellis,
Boyce, Belsky, Bakermans-Kranenburg, & van IJzendoorn, 2011) has provided substantial
evidence that genetic variants can confer different amounts of sensitivity to experiences ranging
from interventions (see Brody, Beach, Philibert, Chen & Murry, 2009; Beach, Brody, Lei, &
Philibert, 2010) to parenting (see Bakermans-Kranenburg & Van IJzendoorn, 2006). Most
pertinent to the current inquiry are findings that genetic variants can modify the impact of
experimentally manipulated peer influence on alcohol use (Creswell, Sayette, Manuck, Ferrell,
Hill, & Dimoff, 2012; Larsen et al., 2010).
Based upon longstanding research demonstrating peers’ impact on adolescent risk
behaviors, including but not limited to alcohol use, as well as recent research demonstrating the
effect of specific genetic variants, this study investigates how the DRD4 gene may moderate the
influence of positive and negative peer relationships on adolescent alcohol use. Before providing
specific hypotheses, pertinent literature regarding DST, the biological role DRD4 plays in peer
influence, and how peer influence is related to adolescent alcohol use will be reviewed.
Differential Susceptibility Theory
A growing body of research, guided by Differential Susceptibility Theory (DST), focuses
on the effects of variability in individual environmental experiences (Belsky, BakermansKranenburg, & van IJzendoorn, 2007; Belsky & Pluess, 2007; Belsky & Pluess, 2013; Ellis,
Boyce, Belsky, Bakermans-Kranenburg, & van IJzendoorn, 2011). DST posits that individuals
may be more susceptible to environmental contexts due to genetic differences. At its most basic
level, DST is rooted in evolutionary theory. The core concept behind DST is that environmental
uncertainty results in parents “hedging their bets” by providing offspring with different
biological traits that would help them survive in a multitude of environmental contexts (Belsky,
3
1997; Boyce & Ellis, 2005). As a result, some offspring will be relatively less sensitive to
environmental influences compared to their siblings who will be relatively more sensitive to
them. For example, in adverse environments, offspring with low environmental sensitivity
manifest reduced impacts of the negative experiences encountered. Likewise, in adverse
environments, offspring with high environmental sensitivity manifest increased impacts of the
negative experiences encountered. Conversely, in favorable environments, these highly sensitive
offspring tend to flourish, while less sensitive offspring are relatively unaffected by the favorable
environmental contexts.
One major implication of DST is that increased sensitivity to the environment does not
necessitate genetic main effects. Rather, DST suggests genes will moderate the impact of
environmental contexts. Thus, according to DST, offspring who receive a “sensitive” genetic
makeup from their parents will be more “vulnerable” to both adverse and favorable
environmental contexts, which results in negative consequences in adverse environments and
positive consequences in favorable environments. The net result of such sensitivity to both
negative and positive environments is no direct effects of specific genes on individuals, when
averaged across all environmental contexts.
Dopamine and Peer Influences
Candidate gene research on DST has used various genetic variants, including the DRD2
(Berman, Ozkaragoz, Young, & Noble, 2002) and DAT1 genes (Kahn et al., 2003), but the
genetic variant most commonly studied has been the DRD4 gene (Bakermans-Kranenburg, van
IJzendoorn, Pijlman, Mesman, & Juffer, 2008; Gervai et al., 2007; van IJzendoorn, BakermansKranenburg, & Mesman, 2008). DRD4 is a critical candidate gene because it regulates signaling
in the dopamine system, which significantly affects activation.
DRD4, the dopamine receptor D4 gene, contains a 48-base pair Variable Number of
4
Tandem Repeats (VNTR) in the third exon of the D4 gene (Van Tol et al., 1992). This DRD4
locus has ten alleles that vary from two to eleven repeats, with the 4- and 7-repeats being the
most common. In this study, the DRD4 gene is analyzed by comparing the presence or absence
of the 7-repeat allele (7+ vs. 7-, respectively). DRD4 is often classified by the presence or
absence of the 7-repeat allele because the presence of the sensitive variant (7+) is associated with
less concentration-dependent inhibition of forskolin-stimulated cyclic AMP (cAMP) levels
(Schoots & Van Tol, 2003) and decreased ligand binding (Asghari et al., 1994).
Changes in dopamine activation encompass several midbrain structures (i.e., ventral
tegmental area and substantia nigra) and project to the ventral striatum and prefrontal cortex. In
these brain structures, changes in dopamine production are linked to the processing of reward
salience and magnitude (Apicella, Ljungberg, Scarnati, & Schultz, 1991; Hikosaka & Watanabe,
2000; Wise, 2002). Changes in dopamine activation due to genetic variants, such as DRD4, are
linked with reinforcing the value and salience of stimuli (Montague, Dayan, & Sejnowski, 1996;
Schultz, 1998), enabling neurological change in response to environment (Tobler, Fiorillo, &
Schultz, 2005), and anticipating rewards (Kelley, Schiltz, & Landry, 2005). For example,
dopamine activation is related to the stimulatory and rewarding effects of alcohol (Littleton &
Little, 1994; Samson & Harris, 1992).
DRD4 has been associated with multiple behavior outcomes. The DRD4 7+ variant has
been associated with increased susceptibility to alcohol use (Namkoong et al., 2008), addiction
behavior (McGeary, 2009), novelty-seeking personality traits (Ebstein et al., 1996; but also see
Malhotra, Virkkunen, Rooney, Eggert, Linnoila, & Goldman, 1996), and attention
deficit/hyperactive disorder (Faraone & Mick, 2010). DRD4 7+ individuals have been found to
possess both an increased “urge” to drink after being primed with a dose of alcohol (Hutchinson,
5
McGeary, Smolen, Bryan, & Swift, 2002; Ray et al., 2010) and increased neurological responses
to alcohol taste cues (Filbey et al., 2008), when compared to individuals who possess the less
sensitive DRD4 7- gene.
Of particular importance to the current study is research demonstrating the role of the
DRD4 7+ variant in moderating the impact of social experiences. Experimental research has
shown that the sensitive DRD4 7+ variant increases individuals’ sense of reward from alcohol
consumption when in a group context (Creswell et al., 2012). For individuals who possess this
variant, the increased sense of reward is theorized to be due to an increase in perceived social
bonding. Related research has found that the DRD4 7+ variant is associated with an increased
susceptibility to alcohol related cues, such as being in the presence of a same-age individual who
drinks heavily (Larsen et al., 2010). Thus, individuals with the DRD4 7+ variant are more likely
to consume alcohol in an attempt to conform to their social contexts (Larsen et al., 2010).
Theoretical literature also suggests that adolescents who possess the 7+ variant are particularly
susceptible to the company of peers because of a heightened desire to conform due to the
important role peers play in autonomy development (Warr, 2002).
Contrary to the experimental findings above, one study failed to find a significant
moderating effect of the DRD4 7+ variant on the association between best friend alcohol use and
adolescents’ own alcohol use (van der Zwaluw, Larsen, & Engels, 2012). This inconsistent result
may be due to the use of youth self-report of friends’ alcohol consumption and that peer
influences on alcohol use differ depending on whether youth are adolescent or college-age.
Additionally, this result may be due to lower alcohol consumption rates for adolescent samples
in comparison to college-age samples (i.e., Creswell et al., 2012; Larsen et al., 2010).
6
Peers and Adolescent Alcohol Use
Peer relationships result in social contexts with novel sources of deviant and/or prosocial
influences. Associating with deviant peers is linked to an increase in problem behavior such as
delinquency and substance use (Patterson, Dishion, & Yoerger, 2000; Scaramella, Conger,
Spoth, & Simons, 2002; Rowe, Woulbroun, & Gulley, 1994). Conversely, associating with
prosocial peers is linked to abstinence from alcohol (Spoth, Redmond, Hockaday, & Yao, 1996).
Although ample research focuses on the negative effects of peer influences, prosocial
peer relationships have been shown to be equally as important, but rather in reducing the
likelihood of alcohol use during adolescence (i.e., Hawkins, Catalano, & Miller, 1992; Kandel &
Andres, 1987; Newcomb, Maddahian, & Bentler, 1986). Prosocial peer relationships,
conceptualized as affiliating with prosocial peers, are associated with direct and long-term
effects on youth abstaining from alcohol (Spoth, Redmond, Hockaday, & Yao, 1996).
Additionally, friends' prosocial behavior was negatively associated with adolescent violence and
substance use (Prinstein, Boergers, & Spirito, 2001). Prosocial peer relationships are theorized to
influence adolescents by prompting youth to share in prosocial attitudes and norms (Bandura,
1986). These relationships provide opportunities for youth to practice egalitarian social
exchanges and acquire behavioral and emotional competencies (Bukowski, Buhrmester, &
Underwood, 2011).
The reviewed literature suggests that both positive and negative peer experiences can
have important influences on adolescents’ alcohol use behaviors. DST research has shown that
specific genes can moderate the impact of these individual environmental experiences. Thus, it is
possible that individuals with different genotypes will experience different effects from these
peer experiences. To investigate this possibility, the current study examines how the DRD4 gene
may moderate the effects of peer relationships on adolescent alcohol use. Based on the reviewed
7
peer influence literature, I expect to find main effects on alcohol use for both negative and
positive peer relationships. Specifically, 7th grade Peer Deviance Pressure will be associated with
greater alcohol use, while 7th grade Positive Peer Activity will be associated with lower alcohol
use. I do not expect main effects for DRD4 status. Finally, I expect to find that DRD4 will
moderate the effect of peer relationships on alcohol use, both for Peer Deviance Pressure and
Positive Peer Activity.
Analytic Challenge to G x E Research
An analytic challenge when doing gene by environment research (G x E) is addressing
the potential effects of population stratification. Population stratification refers to differences in
allele distribution across different ancestral population groups. Such differences in allele
distribution can create spurious, rather than causal, associations between alleles and phenotypes
of interest (Freedman et. al., 2004). Therefore, if the population structure within a sample is not
accounted for, research findings can be false positives (Cardon & Palmer, 2003).
To assess and control for population stratification, this study conducted a principal
coordinates analysis (PCoA). This method has been used previously to assess genetic differences
in multiple populations due to geographical ancestry and admixture (Halder, Shriver, Thomas,
Fernandez, & Frudakis, 2008). Allele-sharing distances are used to extract principal coordinates
(PCs) that describe the dimensions of the sample. PCs are used in two ways: 1) To assess the
extent to which population stratification contributes to variation in analysis variables, and 2) To
determine whether the study’s results were robust against population stratification controls (i.e.,
not spurious). These approaches are outlined in the methods section.
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METHODS
PROSPER Project. The data used to investigate the above specified research questions are
drawn from the PROSPER Project. PROSPER (PROmoting School-community-university
Partnerships to Enhance Resilience) is an evidence-based intervention program aimed at
reducing future substance use by targeting middle school-aged youth and their families. The
PROSPER Project consists of 28 communities in Iowa and Pennsylvania randomized into 14
control and 14 intervention units (for more information on intervention design see Spoth,
Redmond, Shin, Greenberg, Clair, & Feinberg, 2007; Spoth, Redmond, Shin, Greenberg,
Feinberg, & Schainker, 2013). PROSPER investigates the effectiveness of interventions on a
variety of substance use outcomes, such as alcohol, tobacco, and marijuana use. In-school
assessments included pre-test assessments during the fall semester of the 6th grade, post-test
assessments during the spring semester of the 6th grade, and annual follow-ups assessments until
the completion of high school.
In addition to the in-school assessments, participants were subsampled to participate in
detailed in-home data collection. In-home assessments were provided to a subsample of
PROSPER youth who were randomly selected and invited to take part in in-home interviews. Of
the initial 2,267 youth who were invited, 977 (43.1%) agreed to participate in in-home data
collection. In-home assessments were conducted twice in the 6th grade (once in the fall and once
in the spring) and annually thereafter until the 9th grade (Waves 3, 4, and 5). The in-home
assessments included written questionnaires completed independently by the participant and one
(typically the mother) or both parents/guardians. The in-home subsample provided extensive,
phenotypically rich data measurements of participants’ environments, and a unique opportunity
9
to examine gene by environment interplay. The in-home sample measured peer relationships in
detail.
Sample and participants. The data analyzed in this study were from youth who
provided DNA and had valid data for in-home and in-school variables. Predictor items were
surveyed during the 7th grade in-home assessment at Wave 3 (Mean age=13.44), while outcome
items were surveyed during the 12th grade in-school assessment at Wave 8 (Mean age=18.16).
During the Wave 5 (9th grade) in-home data collection, DNA was collected from 594 (74.2%) of
the 740 families who took part in the Wave 5 data collection. Of the 594, 340 (57.2%) provided
in-school data during the 7th and 12th grade. The sample was predominantly self-identified
Caucasians (92.6%), with a limited number of self- identified Hispanics/Latinos (4.4%), African
Americans (0.6%), Asians (0.6%), and other (1.6%) individuals. Sources of missing data for
each variable will be explained below.
Measures
DRD4 Genotyping. DNA was collected using buccal swabs and extracted using a
modified phenol-chloroform technique (Freeman et al., 2003). The 3730XL DNA Analyzer and
Genotyper software v4.0 (Applied Biosystems, Foster City, CA) were used to analyze
amplification products of the extracted DNA. The 48-base pair VNTR in the third exon of the D4
gene (Anchordoquy, McGeary, Liu, Krauter, & Smolen, 2003; Sander et al., 1997) was
genotyped by the Penn State Genomics Core using primer sequences developed by Lichter et al.
(1993). Of the total sample, 98.5% were successfully genotyped for the DRD4 polymorphism
(n=584). An error rate of 7.5% was determined by re-genotyping 10% of the entire sample. The
error rate is a by-product of the difficulty in genotyping DRD4 heterozygous individuals. Errors
occur when amplifying the 7-copy allele in the presence of the 4-copy allele. Hardy-Weinberg
Equilibrium was tested [χ2(1) = 0.03, ns], and indicated genotyping was consistent with
10
population level statistics. DRD4 status was coded as those who possessed the 7+ variant (1) (N
= 120, 35.3%), and all other youth (0) (7-; N = 220 64.7%). Before analysis, I tested for
differences in gender, intervention condition, and ethnicity by DRD4 status (see Table 1).
PC1 Controls for Population Stratification. Principal Coordinates Analysis (PCoA)
was conducted to assess and control for population stratification. Allelic distances were used to
identify dimensions of population ancestry stratification (for details see Cleveland et al., in
press). Single Nucleotide Polymorphisms (SNPs) were selected from the Affymetrix Exome
Array using PLINK (v1.07, Purecell et al., 2007). To estimate genetic ancestry, SNPs with a
minor allele frequency greater than 0.05 (~47,000 SNPs) were used to generate allele-sharing
vectors between all pairs of participants. Of the total in-home sample of 594, Affymetrix data
were available for 511 (93.5%) participants. Using the statistical package R, PCoA was
performed on the allelic distance matrix to generate multiple PCs representing the major axes of
genetic variation in the sample. The first PC (PC1) accounted for approximately 10% of total
allele-sharing distance, while the second PC (PC2) accounted for approximately 6%. Biplots of
the PCs were then merged with self-reported ancestry to visualize the axis of genetic variation
represented by each PC.
PC1 provided an index of non-European ancestry that complimented information from
self-reported ethnicity. The highest PC1 scores belonged to individuals who self-reported
African-American ethnicity. Comparing PC1 means across self-reported non-Hispanic Whites
(M = -0.008, SD=0.008, n=493) and those reporting a different ethnicity (i.e., Hispanic and
African-American; M = 0.067, SD=0.035, n=53) revealed a significant difference between selfreported Whites and minorities (t(544)=39.79, p<0.001). The Cohen’s D for this difference was
3.0. PC2 distinguished African-Americans and Hispanics compared to self-report status. The
11
overlap of PC1’s and PC2’s distinguishing ability resulted in PC1 being selected as the primary
linear indicator of European ancestry.
First, correlations were computed between PC1 and the predictor and outcome variables.
As mentioned above, if population stratification significantly covaries with predictors and
outcome variables, there is a possibility that the candidate-gene and phenotype associations are
spurious and due to genetic ancestry. Second, after fitting the full model of all cases with valid
candidate gene and phenotype data, models were re-run in two ways: 1) On the full sample using
PC1 as the control, and 2) On a European descent only subsample constructed by removing PC1identified non-Europeans.
The subsample of genetically identified Europeans was classified based on the criteria of
being one standard deviation below the mean PC1 score of all self-reported non-Europeans
(0.0664). This value, 0.031, classified 507 participants as having European ancestry, and 48
participants as having significant non-European ancestry. The PC1-informed classification
strongly agreed with adolescent self-report. Using the PC1 criteria, 6 of 7 individuals who selfreported as African-American, 26 of 27 who self-reported as Hispanic, and 3 of 4 who selfreported as Asian matched their self-report classification as non-European. The PC1 derived
cutoff classified two self-reported Caucasians as having significant non-European ancestry.
PCoA also identified three sets of siblings; one sibling from each pair was randomly removed
from the sample.
Data Analysis. The assumption of independence was violated because children were
nested within school districts. To address the correlated structure of the data, the analyses applied
multilevel (mixed model) analysis of covariance with the REPEATED statement in SAS
PROCMIXED and restricted maximum likelihood estimation.
12
Intervention Status. Among the 340 participants in the analysis sample, 202 (59.4%)
attended and participated in the school intervention, while the remaining 138 (40.6%) were in the
control condition. Intervention status was dummy coded as either “control” (0) or “intervention”
(1) (See Table 1). Once intervention status differences by genotype were tested, further analysis
controlled for intervention status.
Peer Deviance Pressure. Early adolescent Peer Deviance Pressure was assessed with a
seven-item self-reported measure during 7th grade (Wave 3) in-home interviews (Mean
age=13.44). Items asked about the frequency of friends’ attempts to persuade the interviewed
youth to engage in delinquent behavior. Example items included, “Do your friends try to…get
you to skip school without an excuse” and “…do things at school that can get you into trouble.”
Items were coded on a four-point scale from “Never” (1) to “Often” (4) and showed positive
internal consistency. The scale was non-normally distributed with a skewness of 4.11. Thus, the
scale was Winsorized so that all responses of 4 were recoded as 3 to correct for skewness,
thereby affecting the range of responses (Cronbach’s α = 0.93, M = 1.15, SD = 0.30, range = 1 to
3).
Positive Peer Activity. Early adolescence Positive Peer Activity was assessed with a
two-item self-reported measure during 7th grade (Wave 3) in-home interviews (Mean
age=13.44). The items asked youth to think about the activities their friends participate in.
Example items included “How often in the past month have you spent time with kids who take
school seriously and complete their homework?”. Items were coded on a five-point scale from
“Never” (1) to “Always” (5) (Cronbach’s α = 0.76, M = 3.71, SD = 1.02, range = 1 to 5).
Alcohol Use Cumulative Index (AUCI). Annual in-school assessments included items
regarding personal alcohol use. A cumulative index of six items regarding alcohol use was
13
created from the 12th grade assessment (Wave 8; mean age=18.16). The index was sensitive to
early and later adolescence alcohol use. Example items included, “Have you ever drunk more
than just a few sips of alcohol?” and “During the past year, how many times have you been
drunk from drinking wine, wine coolers, or other liquor?” All six items were dichotomized, with
answers of “Yes” (1) or “No” (0). The index was created by summing the dichotomized
responses from all six items, yielding a measure with values ranging from 0 to 6 (Cronbach’s α =
0.78, M = 2.59, SD = 0.33, range = 0 to 6).
Past Month Drunkenness. In-school assessments included an item regarding 12th grade
recent adolescent drunkenness (Wave 8; mean age=18.16). The item, “During the past month,
how many times have you been drunk from drinking wine, wine coolers, or other liquor?”, was
coded on a scale of “Not at all” (0) to “More than once a week” (4) (M= 0.67, SD = 1.25, range
= 0 to 4).
14
RESULTS
Preliminary Analysis
Three sets of preliminary analyses were conducted. First, I analyzed the distribution of
DRD4 genotypes for Hardy-Weinberg Equilibrium. These analyses indicated that DRD4 allele
distribution was in equilibrium [χ2(1) = 0.03, ns]. Second, I tested for sample differences in
demographic variables (i.e., age, intervention status, ethnicity, and PC1 ethnicity control) across
genotype. These analyses showed that the demographic variables did not differ based on DRD4
7+ vs. 7- status (see Table 1). Finally, I attempted to find associations between PC1 and the
primary study variables. Analyses revealed that primary study variables were not related to
European ancestry, and therefore population stratification was unlikely to be a confound.
Peer Deviance Pressure
A two-step analysis was run on both outcomes of interest: Model 1 estimated the main
effects of Peer Deviance Pressure and DRD4 status, and Model 2 added the two-way interactions
between Peer Deviance Pressure and DRD4. Table 2 provides Peer Deviance Pressure results for
both AUCI and Past Month Drunkenness outcomes. AUCI models are presented on the left of
the table and Past Month Drunkenness models are presented on the right.
The first set of hypotheses tested were the effects of Peer Deviance Pressure on both
alcohol use outcomes, AUCI and Past Month Drunkenness. Model 1 presents the main effect
results for both outcomes; Peer Deviance Pressure significantly predicted both AUCI (b=.55,
p<.01) and Past Month Drunkenness (b=0.24, p<.01). These main effects indicated that higher
levels of peer pressure predict increases in both overall and recent 12th grade alcohol use. As
hypothesized, the main effects of the DRD4 7+ genotype on AUCI (b=0.13, ns) and Past Month
Drunkenness (b=0.06, ns) were not significant. Model 2 added the effects of the two-way
interaction between Peer Deviance Pressure and DRD4 on both alcohol use outcomes. These
15
two-way interactions between Peer Deviance Pressure and DRD4 were significant for both AUCI
(b=1.01, p<.05) and Past Month Drunkenness (b=.41, p<.05), indicating that increases in both
overall and recent 12th grade alcohol use were dependent on early adolescent peer pressure and
DRD4 status.
To investigate the meaning of the two-way interactions between Peer Deviance Pressure
and DRD4 on AUCI, probing techniques were used to analyze the conditional main effects (see
Frazier, Tix, & Barron, 2004). DRD4 status was reverse coded (i.e., 1 = 7- vs. 0 = 7+) and Peer
Deviance Pressure was re-centered (i.e., ±1SD). The associations between Peer Deviance
Pressure and AUCI are shown separately for DRD4 7+ vs. 7- youth in Figure 1. Among 7- youth,
there was no significant difference in AUCI based on the level of Peer Deviance Pressure.
However, for youth who possess the 7+ variant, Peer Deviance Pressure was positively
associated with AUCI, with levels of AUCI increasing from 0.91 to 2.35 to 3.78 across low,
mean, and high levels of Peer Deviance Pressure. At low levels (-1SD) of Peer Deviance
Pressure, there was a significant difference in AUCI between DRD4 7+ and 7- youth (0.92 vs
1.89). At high levels (+1SD) of Peer Deviance Pressure, this difference was near significance
(p=0.051).
Using the same recoding methods, I examined the 2-way interaction between Peer
Deviance Pressure and DRD4 on Past Month Drunkenness (see Figure 2). Among 7- youth, there
was no significant difference in Past Month Drunkenness based on the level of Peer Deviance
Pressure. Among 7+ youth, however, Peer Deviance Pressure was positively associated with Past
Month Drunkenness, with levels of Past Month Drunkenness increasing from 0.21 to 0.81 to 1.40
across low, mean, and high levels of Peer Deviance Pressure. At high levels (+1SD) of Peer
16
Deviance Pressure, there was a significant difference in Past Month Drunkenness between DRD4
7+ and 7- youth (1.4 vs. 0.87).
Positive Peer Activity
The second set of models examined the association between early Positive Peer Activity
and both alcohol use outcomes. Similar to Peer Deviance Pressure, a two-step analysis occurred
for both outcomes of interest: Model 1 contained the main effects for Positive Peer Activity and
DRD4 status, and Model 2 added the two-way interactions between Positive Peer Activity and
DRD4. Table 3 presents the Positive Peer Activity model results for both AUCI and Past Month
Drunkenness outcomes. AUCI models are presented on the left of the table and Past Month
Drunkenness models are presented on the right.
The hypotheses tested in the second set of models were the effects of Positive Peer
Activity on both alcohol use outcomes, AUCI and Past Month Drunkenness. Model 1 presents
the main effects for both outcomes; Positive Peer Activity significantly predicted both AUCI
(b=-0.22, p<.01) and Past Month Drunkenness (b=-0.15, p<.05). The negative direction of these
main effects indicates that higher amounts of positive peer influence are associated with a
decrease in overall and recent 12th grade alcohol use. As expected, the main effects for the DRD4
7+ genotype on AUCI (b=-0.22, ns) and Past Month Drunkenness (b=-0.02, ns) were not
significant. Model 2 added the effects of the two-way interaction between Positive Peer Activity
and DRD4 on both alcohol use outcomes. Contrary to my hypotheses, the two-way interactions
were not significant for either AUCI (b=0.35, ns) or Past Month Drunkenness (b=0.16, ns). The
non-significant interactions indicated that the effect of Positive Peer Activities on overall and
recent alcohol use did not differ by DRD4 genotype.
17
DISCUSSION
This study was the first to focus on whether inter-individual genetic differences affect
adolescent susceptibility to negative and positive peer relationships. I examined whether DRD4
moderated the association between 7th grade negative and positive peer relationships, Peer
Deviance Pressure and Positive Peer Activity, and two 12th grade alcohol use outcomes, AUCI
and Past Month Drunkenness.
As hypothesized, analyses revealed main effects of Peer Deviance Pressure that resulted
in an increase in alcohol use, specifically increases in 12th grade AUCI and Past Month
Drunkenness. Also as hypothesized, analyses revealed no main effects for DRD4 on either
alcohol use outcome; the lack of genetic main effects is congruent with DST. DST postulates that
rather than genetic variation directly causing differences in outcomes, genetic variation only
contributes to individuals’ overall sensitivity to environmental experiences. Genetic sensitivity to
environmental experiences is theorized to lead to differential negative or positive outcomes
depending on the experiences encountered (Belsky, Bakermans-Kranenburg, & van IJzendoorn,
2007; Belsky & Pluess, 2009, 2013; Bakermanss–Kranenberg & van IJzendoorn, 2011).
The specific results of this study include finding a significant difference in AUCI
between DRD4 7- and DRD4 7+ adolescents at low levels of Peer Deviance Pressure. For Past
Month Drunkenness, there was a significant difference between DRD4 7- and DRD4 7+
adolescents at high levels of Peer Deviance Pressure. While the effects of Peer Deviance
Pressure on AUCI and Past Month Drunkenness are similar, the effects vary by the level of Peer
Deviance Pressure. This difference is possibly due to the fact that AUCI measured multiple
drinking behaviors indexed over time, while Past Month Drunkenness only measures recent
alcohol use.
18
Previous literature supports the finding that the DRD4 gene moderates the effect of peer
deviance on alcohol use. Research has found that the dopamine system mediates social rewards
and the reinforcement of alcohol use (Krach, Paulus, Bodden, & Kircher, 2010). Specifically,
DRD4 research has found that 7+ individuals have increased sensitivity to dopamine responses
triggered by priming doses of alcohol (Hutchinson, McGeary, Smolen, Bryan, & Swift, 2002).
Experimental research has found that 7+ individuals do not have increased euphoric effects of
alcohol consumption, but rather have an increased perceived ability to bond with peers while
consuming alcohol (Larsen et al., 2010). Related research has found that the DRD4 7+ variant
increases the sense of reward from alcohol consumption when in a group context (Creswell et al.,
2012).
As hypothesized, analyses revealed main effects of Positive Peer Activity that resulted in
reduced levels of both AUCI and Past Month Drunkenness. Consistent with my hypotheses,
analyses again revealed a lack of main effects of DRD4 on both alcohol use outcomes. However,
associations between Positive Peer Activity and both AUCI and Past Month Drunkenness did not
differ between youth who carried at least one copy of the DRD4 7-repeat allele (7+) and those
who were homozygous for the DRD4 short allele (7-).
A number of reasons may explain why the DRD4 gene did not moderate the effect of
Positive Peer Activity on alcohol use. First, during early adolescence, prosocial peer
relationships are considered a normative environmental context (Hartup, 1993). Prosocial peer
relationships have been found to be stable over time, with the percentage of reciprocal
friendships that last a year or more being roughly 70% (Berndt, Hawkins, & Hoyle, 1986).
Second, positive and negative peer relationships are not mutually exclusive, meaning youth may
experience both positive and negative peer socialization from the same, or different, group of
19
friends. The social contexts created by peers have been shown to stem from situational activities
and characteristics (Power, Stewart, Hughes, & Arbona, 2005). Finally, my measure of positive
peer relationships did not operationalize positive behaviors meaningfully equivalent to my
measure of negative peer relationships.
To reiterate, DST postulates that individuals vary in their susceptibility to environmental
contexts (i.e., peers) both “for better” and “for worse” (Belsky, Bakermans-Kranenburg, & van
IJzendoorn, 2007). Evidence supporting DST has shown that environmental factors have the
ability to influence individuals differently (Belsky, 1997). While DST was the framework that
guided these research questions, diathesis–stress is a corresponding theory. In contrast to DST,
diathesis–stress hypothesizes that environmental stressors (i.e., peer deviance pressure) have an
increased impact on the development of sensitive individuals when “triggered” by poor
experiences (Zuckerman, 1999). The diathesis–stress theory states that in the absence of an
environmental trigger there should be no differences between vulnerable and resilient
individuals. In the context of this study, Peer Deviance Pressure may be the trigger for youth
with the DRD4 7+ variant that results in increased alcohol use.
The results of this study should be interpreted in the context of its limitations. First, the
PROSPER sample relied on a school-based sample, which risks excluding the population that
may be at the greatest risk for alcohol use (i.e., dropouts; Berk, 1983). Within the context of
G x E research, it is particularly important to study a sample with individuals who experience the
full range of environmental contexts in order to avoid the danger of false positives and negatives
(Moffitt, Caspi, & Rutter, 2005). Second, this sample was predominantly of European descent
and located within rural and semi-rural towns. The sample’s demographics limit the
generalizability of the findings to similar populations. Finally, peer relationship measures were
20
based on youth self-reports, which are subject to social desirability bias. Using network data or
parent-reports on peer relationships in the future may increase the quality of peer relationship
measures.
This study was limited to focusing on the effect of peer social influence. Future directions
include further elucidating the mechanism behind how peer relationships are genetically
influenced. By using network data, such as PROSPER Peers, future studies can utilize youth
nominations of friends to determine what aspects of the social networks are moderated by
genetic difference.
In conclusion, this study found that adolescents’ alcohol use in 12th grade increased as
negative peer relationships increased, and this finding was moderated by DRD4 genotype.
Adolescents with the DRD4 7+ allele were more affected by negative peer relationships than
DRD4 7- adolescents. Adolescents’ alcohol use in 12th grade decreased as positive peer
relationships increased, and this relationship was not moderated by DRD4 genotype. Future
research should examine how genetic influences may moderate different aspects of negative peer
relationships.
21
APPENDIX: TABLES AND FIGURES
Table 1. Demographic comparisons by DRD4 genotype and for analytic sample
Variable
Gender
Condition
European
Race
Variable
Age
Negative Peer Pressure
Positive Friend Support
Female
Intervention
White
Hispanic/Latino
Black
Asian
Other
Categories/Unit
Years
Standardized
Standardized M=0
DRD4 766.1 (121)
66.8 (135)
66.9 (194)
65.1 (205)
40
(6)
50
(1)
100 (2)
100 (6)
M (SD)
13.42 (0.35)
0.04 (1.11)
0.11 (0.94)
%(n)
DRD4 7+
33.9 (62)
33.2 (67)
33.1 (96)
34.9 (110)
60
(9)
50
(1)
0
(0)
0
(0)
M (SD)
13.39 (.35)
-0.19 (.59)
0.02 (.99)
Total (n=340)
53.8 (183)
54.9 (202)
85.2 (290)
91.6 (315)
4.4
(15)
0.6
(2)
0.6
(2)
1.8
(6)
M (SD)
13.44 (0.39)
0 (1)
0 (1)
Note: % European is based on PC1, Race is based on participant self-report. PC1 is reported here
in standard deviation units.
22
Table 2. Parameter estimates and standard errors for peer deviance pressure models.
Variables
Main Effects
PDP
DRD4 7R
Two-Way Interaction
PDP*D4
Parameter Estimate (Standard Errors)
AUCI
Past Month Drunkenness
Model 1
Model 2
Model 1
Model 2
.55(.13)*
- .12(.27)
.42(.14)**
.03(.28)
1 .01(.39)**
.24(.06)*
.06(.13)
.19(.07)**
.13(.13)
.41(.19)*
Note: PDP = Peer Deviance Pressure, D4 = DRD4 7R, AUCI= Alcohol Use Cumulative Index.
*
p < .05, *p < .01.
23
Table 3. Parameter estimates and standard errors for positive peer activity models
Variables
Main Effects
PPA
DRD4 7R
Two-Way Interaction
PPA*D4
Parameter Estimate (Standard Errors)
AUCI
Past Month Drunkenness
Model 1
Model 2
Model 1
Model 2
- .22(.13)** - .37(.17)*
- .22(.27)
- .30(.27)
.35(.27)
- .15(.06)* - .22(.08)*
- .02(.13)
.00(.13)
.16(.13)
Note: PPA = Positive Peer Activity, D4 = DRD4 7R, ACUI= Alcohol Use Cumulative Index. *p
< .05, *p < .01.
24
Alcohol Use Cumulative Index
4
3.5
3
2.5
DRD4
Status
2
1.5
7-
1
7+
0.5
0
Low
Medium
Peer Deviance Pressure
High
Figure 1. Differential Effects of Peer Deviance Pressure on Alcohol Use Cumulative Index
by DRD4 Genotype.
25
1.6
Past Month Drunkeness
1.4
1.2
1
0.8
DRD4
Status
0.6
7-
0.4
7+
0.2
0
Low
Medium
Peer Deviance Pressure
High
Figure 2. Differential Effects of Peer Deviance Pressure on Past Drunkenness by DRD4
Genotype.
26
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