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Different genetic and environmental structures for the overlap of three antisocial behavior factors with alcohol initiation
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Different genetic and environmental structures for the overlap of three antisocial behavior factors with alcohol initiation
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Content
Running head: ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION 1
Different Genetic and Environmental Structures for the Overlap of Three Antisocial Behavior
Factors with Alcohol Initiation
Rubin Khoddam
University of Southern California
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
2
Table of Contents
Abstract 3
Introduction 4
Method 8
Participants 8
Measures 9
Procedures 11
Analysis Plan 11
Results 14
Descriptive Statistics 15
Antisocial Behavior Factor Structure 15
Individual-level Analyses 16
Twin Analyses 18
Multivariate Twin Analyses 19
Discussion 20
References 27
Tables 36
Figures 44
Appendix 53
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
3
Abstract
Behavioral subtypes of antisocial behavior, such as Rule-Breaking and Aggression, are
consistent predictors of early alcohol use. The goal of the current research is to better understand
the etiology of early adolescent alcohol use by using data from twins to examine the genetic and
environmental overlap between antisocial behavior factors and alcohol initiation. Data were
collected by structured interview from 6,812 participants in the Virginia Adult Twin Study of
Psychiatric and Substance Use Disorders. Assessment included retrospective reports of age at
first drink and how many of the 11 antisocial behaviors corresponding to DSM-III-R symptoms
for Conduct Disorder were engaged in prior to age 18. A 3-factor model best fit the structure of
the antisocial behaviors. These factors were termed Rule-Breaking, Object Aggression, and
Personal Aggression. Higher scores on all three factors were associated with early alcohol
initiation. In multivariate twin analyses, 27% of the variance in risk for early alcohol initiation
(drinking by age 15) was shared with the antisocial behavior factors, but the basis for this
overlap differed across the factors. Most of the genetic overlap between early alcohol initiation
and the antisocial behavior factors was accounted for by Rule-Breaking, whereas, the overlap
with Object Aggression was attributed to environments shared by siblings. Personal Aggression
had significant genetic and individual environmental overlap with early alcohol initiation. These
differences in the nature of the associations between specific antisocial behavior factors and
early drinking contribute to a more nuanced understanding of their shared association.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
4
Antisocial behaviors (ASBs) have consistently been associated with adolescent alcohol
and drug use (Brown et al., 1996; Connor, Steingard, Cunningham, Anderson, & Melloni, 2004;
Couwenbergh, van den Brink, Zwart, Vreugdenhil, van Wijngaarden-Cremers, & van der Gaag,
2006). ASBs, which include stealing, fighting, and lying, are characteristic of an adolescent
Conduct Disorder (CD) diagnosis. These behaviors are particularly problematic when combined
with a comorbid Substance Use Disorder diagnoses, as individuals with this dual-diagnosis have
been shown to be the most referred group to the criminal justice system and social services
(Crowley, Mikulich, MacDonald, Young, & Zebre, 1998). Furthermore, adolescents who report
early exposure to alcohol have rates of alcohol consumption and alcohol-related problems 1.9-
2.4 times higher than those who report later introduction (Fergusson, Lynskey, & Horwood,
1994). Risk factors for adolescent alcohol use can include genetic predisposition (i.e. level of
response to alcohol, molecular genetics, family history), environmental circumstances (i.e. peer
and sibling influences, parental monitoring), and ASBs. However, substantial gaps remain in the
literature regarding how these risk factors combine and interact. This study addresses these gaps
using a genetically informative sample to further our understanding about these complex
processes.
A temporal relationship exists between ASBs and alcohol use where young adult alcohol
problems can be predicted from a number of adolescent ASBs (Clapper, Buka, Goldfield, Lipsitt,
& Tsuang, 1995). Recent literature suggests that behavioral subtypes of ASBs may be a stronger
predictor of alcohol-related outcomes than other related risk factors, such as an early age at first
drink (AFD; Burt & Hopwood, 2010). Factor analytic studies have found evidence of at least two
subtypes of ASBs in adolescents, including an overt or aggressive factor (AGG) and a non-
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
5
aggressive/rule-breaking (RB) factor (Burt, 2009; Tackett, Krueger, Sawyer, & Graetz, 2003;
Tackett, Krueger, Iacono, & McGue, 2005).
Some researchers suggest AGG and RB are distinct subtypes of ASBs with unique
correlates and developmental course (Bartels, Hudziak, van den Oord, van Beijsterveldt,
Rietveld, & Boomsma, 2003; Manuck, Flory, McCaffery, Matthews, Mann, & Muldoon, 1998;
Verona, Patrick, & Lang, 2002). Trajectories of the behavioral subtypes of AGG and RB vary
across development (Burt & Hopwood, 2010; Stanger, Achenbach, & Verhulst, 1997). Physical
aggression is more prevalent during the toddler years and declines into adolescence, whereas, RB
is more infrequent throughout early childhood, but increases dramatically over the course of
adolescence (Stanger et al., 1997). Additionally, affective regulation has been shown to be
particularly characteristic of AGG, as compared to impulsivity, which is more strongly
associated with RB (Burt & Larson, 2007; Burt & Donnellan, 2008).
RB has consistently shown to be a stronger predictor of alcohol and substance use
problems than AGG (Buu, Wang, Schroder, Kalaida, Puttler, & Zucker, 2012; Mayzer,
Fitzgerald, & Zucker, 2009). RB, as opposed to AGG, is more strongly associated with an earlier
AFD (Mayzer et al., 2009) and progression to alcohol dependence (Buu et al., 2012). It has been
found that RB, measured at three time points during childhood, predicted substance use initiation
by age 15, whereas, only concurrent AGG was associated with initiation by age 15 (Mayzer et
al., 2009). Similarly, RB and AGG have both been found to predict a number of substance use
disorders in early adulthood, but RB was the strongest predictor (Hayatbakhsh, Najman,
Jamrozik, Al Mamun, Bor, & Alati, 2008). Despite these associations, the exact nature of the
relationship between ASB factors and alcohol use has not been delineated.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
6
Twin studies provide valuable information regarding the sources of individual
differences, including the relative contributions of shared genetic and environmental factors as
well as unshared environmental factors. Twin studies of CD and ASBs have used a number of
methodologies with overall findings supporting the existence of both genetic and shared
environmental contributions on the liability to develop CD (Jacobson, Prescott, & Kendler, 2000;
Maes, Silberg, Neale, & Eaves, 1997; Thapar & Scourfield, 2002). A meta-analysis of ASBs
using twin and adoption studies found that genetic factors accounted for 38-40% of the variance
in ASBs; whereas, unique environment accounted for 39-42%, and shared environment
contributed 18-23% (Rhee & Waldman, 2002).
There is also evidence of an etiological distinction between AGG and RB in twin
research (Eley, Lichtenstein, & Stevenson, 1999; Eley, Lichtenstein, & Moffitt, 2003; Burt,
2009; Tackett et al., 2005). Both AGG and RB are moderately heritable with genetic factors
accounting for 35-76% of the variance in AGG compared to 28-45% of the variance in RB (Burt,
2009; Eley et al., 1999; Tackett et al., 2005). However, multiple studies have found that RB
carries a significant shared environmental component unlike the AGG factor (Burt, 2009;
Tackett et al., 2005). The temporal relation of these constructs can also differ, with AGG having
stable genetic and environmental contributions over time compared to RB, which has been found
to have increasing genetic and decreasing shared environmental components across adolescence
(Burt & Neiderhiser, 2009).
Although there are both genetic and environmental risk factors that can contribute to
alcohol use, this association is not consistent across time (Rhee et al., 2003). In a study using
Finnish twins, shared environmental factors accounted for most of the variance in abstaining or
drinking by age 14 (Rose, Dick, Viken, Pulkkinen, & Kaprio, 2001). This result has been
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
7
replicated in other studies that have found shared environmental factors were more influential for
initiation in adolescence compared to problem use where there was a greater genetic component
(Dick, 2011; Rhee et al., 2003; Rose, Dick, Viken, & Kaprio, 2001; Rose et al., 2003). Both twin
and adoption studies have found moderate to strong heritability estimates of alcohol use
disorders among adults with approximately 40-60% of the variance in risk to develop alcohol use
disorders explained by genetic factors (Dick, Prescott, & McGue, 2009; Heath, Meyer, Jardine,
& Martin, 1991).
Genetic factors have accounted for most of the association between CD and alcohol
dependence liability (Krueger, Hicks, Patrick, Iacono, & McGue, 2002; Slutske et al., 1998;
Young, Stallings, Corley, Krauter, & Hewitt, 2000). In a study of Australian adult twins, genetic
factros common to CD and alcohol dependence accounted for 17% and 35% of the genetic
variation in alcohol dependence in men and women, respectively (Slutske et al., 1998). Similar
results have been found among adolescents in the Colorado Twin Registry and the Colorado
Longitudinal Twin Study where the covariation between CD and alcohol and substance use was
largely due to additive genetic factors (Young et al., 2000; Young et al., 2009). This is especially
true in early adolescence as compared to late adolescence where there was also a significant
shared environmental component.
Many studies have already found genetic and environmental associations with ASBs,
alcohol use, as well as their shared association. However, no research to our knowledge has
looked at genetic and environmental contributions to the specific ASB factors as they relate to
alcohol initiation.
The goal of the proposed research is to better understand the etiology of alcohol use by
examining the association between ASB factors and AFD. Previous research has established that
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
8
adolescent alcohol and substance use problems have been linked to both early alcohol initiation
and ASBs. Furthermore, specific behavioral subtypes of ASBs have been found to have distinct
etiologies. However, there are gaps in the literature regarding how these factors are differentially
associated with the AFD and the underlying biological and environmental mechanisms
associated with ASBs risk for early AFD. The proposed study will address the questions: Are the
ASB factors differentially associated with AFD, and Are there distinct genetic and
environmental factors that can account for differences? We hypothesize there to be an etiological
distinction with RB as a stronger predictor of AFD than AGG. Furthermore, we hypothesize that
genetic factors will account for more of the variance associated with AGG than environmental
factors and that RB will have a significant shared environmental component that AGG does not.
Methods
The current study used data collected as part of Virginia Adult Twin Study of Psychiatric
and Substance Use Disorders (VATSPSUD; Kendler & Prescott, 2006), a longitudinal study of
psychiatric and substance-related disorders in adult twins originally ascertained from the
Virginia Twin Registry (VTR; Corey, Pellock, Boggs, Miller, & DeLorenzo, 1998). The VTR
was formed by a systematic search of all Virginia birth certificates since 1918 to identify
multiple births. Multiples were then matched to state records by name and birthdate to try to
obtain current contact information. The local Institutional Review Board approved VATSPSUD.
All participants were informed about the goals of the study and provided verbal consent before
telephone interviews and written informed consent before in-person interviews as well as DNA
sample collection.
Participants
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
9
The sample used for the present study is from the first wave of interviews with the Male-
Male/Male-Female Twin sample, conducted from 1993 to 1998. Twins in female pairs were
studied separately. Twins were eligible to participate if one or both twins were successfully
matched to state records by name and birthdate to obtain current address and telephone number,
were Caucasian, and were born between 1940 and 1974. The study included 6,812 (5,092 males,
1,720 females) individual twins who were interviewed during the first wave of data collection.
Participants at the time of this interview ranged from 18 to 60 years old (M = 35.06, SD = 9.12)
and reported a mean education of 13.4 years (SD =2.6). To estimate genetic and environmental
components, only individuals in male-male twin pairs were used. This sub-sample included
3,495 individuals from male-male twin pairs (MM), including 854 monozygotic and 643
dizygotic complete pairs, and 501 singletons. Males who were part of opposite-sex twin pairs or
triplet pairs with females were removed from these analyses. This was done to get more accurate
male-male twin estimates, as females from female-female twin pairs were not included in this
study. Based on 1990 Census data, the educational and economic characteristics of the sample
are similar to that of whites from this age group in Virginia and the United States, suggesting
results could be generalized to that of other regions.
Measures
Antisocial Behaviors
Participants were assessed during the first wave of the study that included 11 specific
ASBs corresponding to the criteria for CD in DSM-III-R (APA, 1987; e.g. ‘played hooky a lot
from school’, ‘physically hurt other people a number of times.’). Exact wording and format of
these items can be found in the Appendix. Participants were asked if they had ever engaged in
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
10
each behavior prior to the age of 18. If they endorsed any item, they were subsequently asked
how old they were the first time they engaged in the behavior.
Age at First Drink
To assess AFD, participants were asked “Have you ever in your life had an alcoholic
drink?...When I say a ‘drink,’ I mean one bottle of beer, one glass of wine, or one shot of liquor.”
Those who responded ‘no’ were coded as abstainers. For those that endorsed having had a drink,
onset age was ascertained by the question: “How old were you when you first had a drink, other
than as part of a religious ceremony?” The questions related to ASBs were asked at a different
part of the interview than assessment of AFD to avoid the age of onset for one variable
impacting the report of the other.
Zygosity
Twin pairs were classified as identical (monozygotic) or fraternal (dizygotic) based on a
computer algorithm of questionnaire responses originally developed in the female-female twin
sample of the VATSPSUD and validated in the male-male pair sample using 15 highly
informative DNA polymorphisms. This algorithm correctly classified 177 of the 184 (96.2%)
randomly selected twin pairs (Prescott & Kendler, 1999).
Covariates
Ages of participants at time of interview, father’s education, and father’s occupation were
ascertained and used as covariates. Father’s education was coded in total years with the
maximum years truncated at age 20. Father’s occupation was based on the Hollingshead Index
(Hollingshead, 1965). Data on father’s education and occupation was not collected until Wave 2
where some participants were not interviewed again. This second wave of data collection
occurred at least 12 months after a participant’s initial interview.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
11
Procedures
Subjects were interviewed by interviewers who had (1) a master’s degree in social work,
psychology, or another mental health-related field; or (2) a bachelor’s degree in one of these
areas plus two years of relevant clinical experience. Each interviewer received classroom
training plus regular review sessions. Separate interviewers, who were blind to clinical
information regarding the co-twin, interviewed twins within a pair. Most interviews were
conducted via telephone, but ~5% were done in person because of subject preference, residing in
an institutional setting, or not having telephone service. All interviews were reviewed for
accuracy and consistency by the project coordinator or project investigators.
This interview was based on the Structured Clinical Interview for the DSM-III-R (SCID;
Spitzer & Williams, 1985) and included assessments of multiple disorders, including major
depression and generalized anxiety. It lasted approximately one hour.
Analysis Plan
The first step was to evaluate evidence for multiple ASB factors. Results from an
exploratory factor analysis were compared to those from a confirmatory factor analysis testing
the RB-AGG 2-factor structure. Model fits for each factor analysis were compared using χ
values.
Second, the phenotypic association between the ASB factors and drinking onset age was
studied using logistic regression and survival analysis. Logistic analyses were used to examine
the association between ASB factors and AFD by a specific age (15 or 18). Two dependent
variables were used in the logistic regression analyses: first drink prior to age 15 (AFD15) and
prior to age 18 (AFD18). For the association with AFD15 and AFD18, scores on ASB factors
prior to ages 15 and 18 were used as independent variables, respectively. These ages were
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
12
selected to capture milestones representing the approximate age adolescents enter and leave high
school. Covariates originally included in these analyses were sex, age at interview, father’s
education level, and father’s socioeconomic status.
Third, survival analyses were used to estimate the specific temporal relation of the risk
for alcohol use onset based on ASBs endorsed prior to age of onset. Factor scores for ASBs were
calculated as the total number of items endorsed per factor prior to age of onset. Thus, if a
participant endorsed one RB item at two different ages prior to onset, their RB score would be
two. Participants who reported an AFD at age 18 or later or who reported no never having a
drink were treated as censored in these analyses.
Interaction models were subsequently tested between sex and ASB factors endorsed for
AFD15, AFD18, and onset in survival analyses. These models were compared to models with
just ASB factor main effects. All analyses used SAS v9.2 (SAS Institute, 2008).
Twin Models
A twin model used to estimate genetic and environmental contributions on the association
between ASB factors and AFD was fit to the data. This provided insight into the underlying
mechanisms of the relationship between ASBs and AFD. Individual differences were assumed to
arise from three sources: additive genetic (abbreviated “A”), common environment (“C”), and
specific non-shared environment (“E”). Additive genetic refers to genes whose allelic effects
combine additively. Common environment refers to all sources shared by members of a twin pair
that contribute to making them similar. This may include family environment, social class, and
schools. Lastly, specific environment includes remaining environmental factors that are not
shared within a twin pair. MZ pairs that were reared together share all their genes and common
environmental factors, which include aspects of the environment shared by siblings within a
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
13
family. DZ pairs that were reared together share all common environmental factors and, on
average, half their genes. Both types of twins are assumed to be influenced by the familial
environment to the same degree.
The genetic correlation between twin 1 and twin 2 was assumed to equal 1 for MZ pairs
and .5 for DZ pairs. If twins in MZ pairs resemble each other to the same degree as the DZ pairs,
only environmental factors can account for the association. Conversely, when MZ pairs resemble
each other more than the DZ pairs, genetic factors account for the association, as the only
difference between the two groups is the degree of genetic relatedness.
Twin models assume: (i) independence and additivity of the latent genetic and
environmental components, (ii) random mating in the population with regard to the characteristic
being studied, and (iii) equality of shared environmental effects for MZ and DZ twin pairs. Such
assumptions carry biases in separating familial resemblance into genetic and common
environmental contributions. We assume random mating (or the absence of assortative mating).
However, if this assumption is incorrect genetic proportions may be underestimated and shared
environmental proportions may be overestimated. Furthermore, twin models assume that both
MZ and DZ pairs are equally similar for their environment. This is a necessary statistical tool
because if the shared environment of MZ pairs is more similar than DZ pairs it will inflate the
correlations and overestimate genetic contributions.
Multivariate models for ASB factors and AFD
Univariate twin models for each ASB factor were initially tested to examine genetic and
environmental contributions of each factor individually. Results from these tests allowed us to
examine each factor’s unique etiology. These results were used as the foundation for testing
multivariate models and the association between each of the ASB factors and AFD.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
14
Figures 1 and 2 provide visual depictions of the multivariate models initially tested. Two
Common Phenotype models were tested (Figure 1). These two models tested the hypothesis that
each ASB factor have a relatively equal association with AFD. The Common Phenotype
(Equated Loadings) model calculated the shared variance between AFD and a latent ASB factor.
Specific genetic and environmental contributions on the latent ASB factor as well as each ASB
subfactor were individually estimated. This model equated the variance between each of the ASB
subfactors (i.e. RB and AGG) and the latent ASB factor.
The Common Phenotype (Estimated Loadings) model provided the same estimates as the
above model. However, this model estimated the variance between each of the ASB subfactors
and the latent ASB factor. A better fit of this model as opposed to the Equated Loadings model
would suggest that each of the ASB factors have a unique association with the latent ASB factor.
The Independent Pathway model (Figure 2) represented the hypothesis that each ASB
factor had a unique association with AFD. Each ASB factor was tested independently with no
latent ASB factor. This model tested genetic and environmental variances shared across each
ASB factor and AFD as well as specific variances associated with each ASB factor and AFD.
Models for twin pair resemblance were fit directly to the raw categorical data using
weighted least-squares estimation with M-plus
TM
software version 6.0 (Muthén & Muthén,
2010). Three models were fit to the data. Fits of alternative models were compared using the
difference in χ
values relative to the difference in degrees of freedom (df). According to the
principle of parsimony, models with fewer parameters are considered preferable so long as they
do not provide a significantly worse fit. We operationalized parsimony by using the AIC statistic
(Akaike, 1987), calculated as the model χ
2
– 2 * df.
Results
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
15
Descriptive Statistics
Table 1 presents descriptive statistics on demographic characteristics as well as the
percent of participants who reported drinking by ages 15 and 18 and who remained abstinent.
Approximately 28% of males and 13% of females reported having their first drink by age 15.
Table 2 presents item level prevalence of ASB items by sex prior to ages 15 and 18.
Figures 3 and 4 display descriptive information on the combined sample separated by
males and females, including the frequency of ASBs endorsed as occurring by age 15 and by age
18. Approximately 12%, 18%, and 5% of males endorsed at least one RB, O-AGG, and P-AGG
item by age 15, respectively. Comparatively, 2.5%, 1.9%, and 0.5% of females endorsed at least
one RB, O-AGG, and P-AGG item by age 15, respectively. Figures 5 and 6 display the number
of ASBs endorsed by individuals in MM pairs who onset prior to ages 15 and 18. Similar to the
combined male and female sample, individuals in MM pairs endorsed O-AGG items more than
other factors.
The cumulative distribution of drinking onset by the number of ASBs and sex is
presented in Figure 7. A greater proportion of individuals with ASBs reported earlier ages of
onset compared to individuals without any ASBs. Males with ASBs had the highest rates of
onset at any given time point compared to females without ASBs.
Antisocial Behavior Factor Structure
Exploratory factor analysis (EFA) was conducted in the combined sample to examine the
structure of the 11 DSM-III-R CD symptoms. The EFA represents the best possible fit of these
items on multiple factors. However, in practice, we want to have a simple solution of an item
loading on a single factor. Confirmatory Factor Analyses (CFA) tested the fits of models with
this simple structure.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
16
The first four Eigenvalues obtained in the EFA were 4.599, 1.254, 1.085, and 0.787,
suggesting that a 3-factor model best accounted for the covariation among the symptoms. This
was confirmed by comparing the model fits: the fit of a 3-factor EFA model was χ
2
= 36.0 on 25
df, p < .27. The fit of the two-factor EFA was 210.6 on 34 df, p < .001.
A CFA of the two-factor structure of RB and AGG (χ
2
= 377.0) derived from the CBCL
(Achenbach, 1991) compared the fit of the EFA model with the hypothesized two-factor model.
A CFA of the three-factor model was also done to compare the fit to the EFA model (χ
2
=
146.6).
Based on the better fits with the three-factor model and the item loadings from the EFA,
we opted for a three-factor solution (Table 3). We termed these factors as Rule-Breaking (RB;
i.e., playing hooky, running away, telling lies), Object Aggression (O-AGG; i.e., stealing,
starting a fire, destroying property, animal cruelty, mugging), and Personal Aggression (P-AGG;
i.e., fighting, using a weapon, physically hurting others). Table 3 also presents correlations
between these factors from the EFA analyses. Based on the CFA, the correlation between RB
and O-AGG was .674; the correlation between RB and P-AGG was .688; the correlation between
O-AGG and P-AGG was .642.
Individual-level Analyses
Results of logistic regression analyses predicting AFD15 and AFD18 from RB, O-AGG,
and P-AGG scores are summarized in Table 4. Covariates originally included participant’s age at
interview, father’s education, and father’s occupation. However, father’s education and
occupation were removed after preliminary analyses because of missing data on 21.3% of the
participants. Removing father’s education and occupation did not significantly affect results.
When including them as covariates in the combined sample, the odds ratios (ORs) of RB, O-
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
17
AGG, and P-AGG for predicting AFD15 were 2.04, 1.71, and 1.41, respectively. These numbers
were similar to the ORs of the factors (1.97, 1.74, and 1.51) when removing father’s education
and occupation as covariates in the combined sample. As a result, father’s education and
occupation were subsequently dropped from analyses so that analyses could be based on the full
sample.
Although all three factors were positively associated with AFD15 in a multiple logistic
regression analysis, RB was the strongest predictor. The OR for prediction of AFD15 by RB was
1.97, indicating that for each additional RB item endorsed prior to age 15, the risk for reporting a
first drink prior to age 15 was 97% greater. The association between RB and AFD15 was
strongest for females (OR=2.79). In fact, this was the strongest association found across
analyses. O-AGG had a similar association to AFD15 for both males (OR=1.74) and females
(OR=1.75). P-AGG items were associated with AFD15 only among males (OR=1.53).
Similar results were found among the relationship between AFD18 and ASB factors. RB
behaviors in females had the strongest association with AFD18 (OR=2.09). Comparatively, O-
AGG had the strongest association with AFD18 (OR=1.83) for males. P-AGG was associated
with AFD18 only for males (OR=1.27).
Survival analyses indicated that O-AGG was the most consistent factor that was
positively associated with drinking onset. Hazard ratios (HRs) are reported in Table 4. The risk
of an individual beginning to drink in the subsequent year was 18% greater among those who
endorsed an O-AGG symptom compared to those without any reported. P-AGG was
significantly associated with onset only for females (HR = 1.42). This was the only factor in the
survival model that significantly predicted onset for females. Across both males and females, RB
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
18
had a HR of 0.88, indicating that in the context of scores on the other factors for each additional
RB symptom reported, the probability of reporting an onset decreased by 12%.
Multiple logistic regression models with and without the three interactions of sex by ASB
factor were subsequently compared. With a three degree-of-freedom test a difference of 7.82 in
χ
2
values would indicate a significantly better fit at p < .05. The interaction model for AFD15
did not significantly differ from the main effect model (χ
difference = 6.49, df=3). Similarly, no
difference was found between survival models for that with and without interaction terms
(χ
difference = 6.26, df=3). There was, however, a difference for AFD18 between the
interaction (χ
= 897.34, df=8) and non-interaction model (χ
= 10.66, df=3). The difference
was primarily attributable to females who reported RB items were at greater risk for drinking by
age 18 than males (p < .01).
Table 4 also shows logistic and survival analysis results based on individuals in MM
pairs. The overall pattern of findings was similar to that found in the combined male and female
sample. AFD15 had the strongest association with RB items endorsed prior to age 15. However,
AFD18 had the strongest association with O-AGG. P-AGG was only significantly associated
with AFD15 (OR = 1.41). Lastly, survival analyses indicated that drinking onset was predicted
by the total number of O-AGG items endorsed prior to that age (HR = 1.22).
Twin Analyses
Table 5 provides within-individual and cross-twin correlations of RB, O-AGG, P-AGG,
AFD15, and AFD18 among MZ and DZ pairs. The correlation for RB between Twin 1 and Twin
2 was greater between MZ than DZ twins, indicating that genetic factors are contributing to its
variance. Conversely, the correlation for O-AGG between Twin 1 and Twin 2 did not differ
much between MZ and DZ pairs, suggesting environmental factors are contributing to their
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
19
covariance. Genetic factors also appear to be contributing to P-AGG’s variance, but to a lesser
extent than RB.
Table 6 provides univariate analysis results for each ASB factor prior to ages 15 and 18.
Variation in RB was estimated to be 33% genetic, 21% shared environment, and 48% individual
specific environment, including measurement error. In contrast, the estimates for O-AGG were,
1%, 50%, and 49%, respectively, with the genetic estimate not significantly different from zero.
The estimates for P-AGG were 18%, 28%, and 55%, respectively.
Multivariate Twin Analyses
Correlations across AFD15, AFD18 and ASB factors in MZ and DZ twins can also be
seen in Table 5. The correlation of RB and AFD15 was greater in MZ pairs than DZ pairs, which
indicates genetic factors account for its associated variance. O-AGG and AFD15 correlations
were similar across twin pairs, suggesting environmental factors account for the overlap. Lastly,
the association between P-AGG and AFD15 also appears to genetic.
A series of models was fit to evaluate hypotheses regarding the genetic and
environmental structures underlying the association between ASB factors and AFD. The fit
statistics for models examining the covariation between ASB factors and AFD15 and AFD18 can
be seen in Table 7. The fit of the Common Phenotype (Equated Loadings) was χ
= 102.5
(df=66). The fit of Common Phenotype (Estimated Loadings) was better, χ
= 83.5 (df=61),
indicating that each ASB subfactor’s (i.e. RB, O-AGG, P-AGG) association with the latent factor
is not equal. The most parsimonious model was the Independent Pathway model
(χ
= 72.0, df=57) which tested the hypothesis that each ASB factor has a unique association
with AFD and the other ASB factors that is not better accounted for by a latent ASB factor.
Based on the fit statistics, the Independent Pathway Model was the best fitting model.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
20
Table 8 shows parameter estimates for the ASB factors and AFD15 covariance estimated
from the Independent Pathway model. Approximately 27% of the variation in AFD15 overlaps
with the genetic and environmental contributions of ASB factors. This is largely attributable to
O-AGG with 71% of the variation in O-AGG being shared with AFD15 as well as the other
factors. Figure 8 provides a visual depiction of the specific and shared genetic and environmental
variance proportions associated with each ASB factor and AFD15.
A similar pattern of results can be seen in the lower part of Table 8 for the ASB factors
and AFD18 using the Independent Pathway model. Approximately 17% of the variation in
AFD18 overlaps with the genetic and environmental contributions of ASB factors. This
association can be attributed to O-AGG with 80% of the variation in O-AGG being shared with
AFD18 as well as the other factors. These results are consistent with logistic regression analyses
that found the association between O-AGG and AFD18 to be the strongest. Figure 9 provides a
visual depiction of the specific and shared genetic and environmental variance proportions
associated with each ASB factor and AFD18.
Discussion
The present study aimed at understanding whether certain ASB factors are more closely
associated with the etiology of alcohol use than other factors. No prior research has used
behavioral genetic data to examine the association between specific ASB factors and alcohol use.
The results from the current study extend previous research in several important ways. The first
is the emergence of a three-factor ASB model where the aggression factor hypothesized by
Achenbach (1991) is parsed into personal aggression and object aggression. Second, the present
study further corroborates previous research that has found an etiologic distinction between the
ASB factors, but extends these findings by examining their relation with AFD.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
21
The first step of these analyses was to identify the ASB structure. RB and a general AGG
factor are the two most identified ASB factors in the literature (Achenbach, 1991; Burt, 2009;
Tackett et al., 2003; Tackett et al., 2005). Our results found conceptually similar factors, but in a
3-factor structure with these factors being termed Rule-Breaking (RB), Object-Aggression (O-
AGG) and Personal-Aggression (P-AGG). RB items refer to non-aggressive, delinquent
behaviors, such as skipping school, running away, and telling lies. Aggression was divided into
two separate factors based on the types of aggression involved. O-AGG items are related to
aggression towards a particular object or with the intention of obtaining something tangible (i.e.
stealing or mugging). P-AGG involves physical acts against another person, such as fighting or
using a weapon. Often times these behaviors will be used in the context of an O-AGG related
behavior, such as stealing; however, P-AGG items are considered more severe and were not
endorsed as often as other ASBs. Additionally, univariate models of each ASB factor also found
unique genetic and environmental contributions associated with each factor. This suggests each
factor is a distinct aspect of ASBs.
The first aim of this paper was to test whether these ASB factors are differentially
associated with AFD. We hypothesized that RB would have a stronger association than the AGG
factors. This hypothesis was supported for AFD15. RB had the strongest association with
AFD15, and particularly for females (OR=2.79). This is consistent with multiple studies showing
RB to be a stronger predictor of future alcohol use compared to other ASBs (Buu et al., 2012;
Hayatbakhsh et al., 2008; Mayzer et al., 2009).
The association between the ASB factors and AFD18 was not as consistent as AFD15.
Although RB had the strongest association with AFD15 across sex, this was only true among
females for AFD18 (OR=2.09). Among males, O-AGG had the strongest association for AFD18
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
22
(OR=1.83). These differences between AFD15 and AFD18 could indicate that RB is a greater
risk factor for early onset. However, it could be that as an adolescent progresses through
development and engages in more gender-specific behaviors, different risk factors for males and
females emerge.
These results highlight and extend previous research suggesting a unique developmental
course of RB and AGG types of ASBs (Bartels et al., 2003; Burt & Hopwood, 2010; Stanger et
al., 1997). Prior studies have found a general increase in RB behaviors and decrease in AGG
from childhood into adolescence. Our study found a similar pattern with greater increases in the
report of RB items than AGG between ages 15 and 18 (Figures 3 and 4). Results further suggest
a unique relationship in which RB is more strongly associated with AFD18 for females than
males.
P-AGG was associated with AFD15 and AFD18 only for males. Although no such
association was found among females, there was not enough power to detect true differences due
to a low base rate of females endorsing any ASBs. Fewer than 1% of females endorsed any P-
AGG item (Figures 3 and 4). These differences may exist because the symptoms of CD reflect
disruptive behaviors that are more often shown by males. Some research has suggested females
have higher levels of internalizing disorders and engage in significantly more relationally
aggressive as opposed to overt behaviors than boys (Crick & Grotpeter, 1995; Fergusson et al.,
1993). Females also tend to exhibit CD problems that are low-risk and that tend to be limited to
adolescence (Fergusson & Horwood, 2002).
Survival analyses indicated that O-AGG was the only positive predictor of drinking onset
for males. Among individuals in MM pairs, an adolescent who reported an O-AGG symptom
was 22% more likely than those who did not report any O-AGG symptoms to report drinking
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
23
onset for the subsequent year. This indicates that O-AGG is a more proximal risk factor for onset
compared to RB, which is a stronger predictor of early onset (AFD15). These results underscore
the importance of assessing a range of behavioral problems within adolescence, especially those
related to aggressive acts towards property. Engaging in behaviors such as stealing, fire setting,
destroying property, animal cruelty and mugging are more closely associated with drinking onset
than other types of externalizing behaviors.
When analyzing females separately in the survival analyses, P-AGG was the only ASB
factor associated with onset (HR = 1.42). These results highlight the importance of assessing
aggressive items for both sexes. Although females may engage in aggressive acts less often,
those who do, may be at a particularly greater risk for drinking.
The second aim of this paper was to use a twin design to understand the basis of the
association between each of the ASB factors and AFD. Our hypothesis that each factor would
have a unique etiological association with AFD was confirmed. The ASB structure was not the
same across the three factors. Approximately 27% of the variation in AFD15 was shared with the
ASB factors. Specifically, most of the genetic overlap between early initiation and ASB factor
scores was accounted for by RB, whereas, most of the shared environmental association was
with O-AGG. These results support the hypothesis that these factors are correlated underlying
dimensions of ASBs with unique contributions on their association with AFD. Such findings
indicate that ASB factors have different sources of variance that is associated with whether an
adolescent begins drinking.
Although 27% of the variation in AFD15 was shared with ASBs phenotypically, a
significant portion (73%) of the association was accounted for by other factors not examined in
the present study. Research has suggested that internalizing problems (King, Iacono, & McGue,
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
24
2004; Steele, Forehand, Armistead, & Brody, 1995), parenting practices (Griffin, Gilbert,
Lawrence, Diaz, & Miller, 2000), and comorbid diagnoses (Couwenbergh et al., 2006) can also
account for part of the association with early adolescent alcohol and drug use.
Despite the plethora of risk factors towards early onset, over a quarter of the variance is
associated with ASBs. Shared environmental factors underlying O-AGG were more greatly
shared with AFD compared to the other factors. Most of this association was due to
environmental factors shared by twins that increase their similarity. These might include factors,
such as, parental rearing styles, family conflict, or alcohol use in the home that could impact an
adolescent’s behavior. Although causality can only be inferred using a prospective design, results
emphasize the importance of assessing for ASBs, especially O-AGG related symptoms, along
with alcohol use due to their significant overlap. One reason for this association between O-AGG
and the other indicators may be that there were a greater proportion of participants who endorsed
items related to O-AGG than other ASB symptoms.
Contrary to our hypothesis that RB would have a significant shared environmental
component shared with AFD, this association was mainly due to genetic overlap with minimal
shared environment. Although this finding diverges from previous research, much of the
evidence regarding this association has come from studies (Bartels et al., 2003; Burt, 2009; Eley
et al., 1999; Eley et al., 2003) using broader measures of externalizing behaviors, such as the
CBCL (Achenbach, 1978). Another potential reason for this discrepancy could be the
retrospective nature of the present study’s design. Future studies using a prospective,
longitudinal design would help clarify the exact nature of the etiological differences in the
association between the ASB factors and AFD.
Limitations
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
25
Some limitations should be considered when interpreting these results. First, these
findings are limited by the reliability and validity of retrospective reports of AFD and ASBs.
However, one important benefit of using retrospective data is that it ensures that participants are
past the age of risk for both onset and ASBs, so developmental differences in rates of ASB
cannot bias results. A longitudinal study evaluating stability of reporting of onset age found that
heavier drinkers were no more or less consistent than lighter drinkers (Labouvie, Bates, &
Pandina, 1997), suggesting that there should not be a systematic effect of measurement
unreliability with onset ages. Second, only males were used for the twin analyses so genetic and
environmental estimates may not apply to females. Lastly, subjects were Caucasian twins born in
Virginia and results may not generalize to individuals of other ethnic and geographical
backgrounds. Prospective twin studies will be able to address these measurement limitations.
Future studies should also test whether the association between ASB factors and alcohol use
extends to those with severe Alcohol Use Disorder or Substance Use Disorders.
Our twin model assumes independence and additivity of the latent variables, random
mating, and equality of shared environmental effects for both MZ and DZ pairs. All of these
assumptions concern biases in separating familial resemblance into common environmental and
genetic contributions.
Despite these limitations, this is the first study to use genetically informative data to
study the genetic and environmental basis for the association between specific ASB factors and
AFD. The present study provides a more nuanced understanding of the associations between
externalizing behaviors and alcohol use compared to prior research. Future studies examining
ASBs should do so by looking at the specific factors, as results indicated that the factors have
different genetic and environmental structures in their relationship to alcohol initiation. Results
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
26
would also aid clinicians in better understanding salient risk factors for adolescents who have yet
to begin drinking.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
27
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ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
36
Table 1.
Descriptive Statistics on Male and Female Adult Twins (N = 6812).
Males
N= 5092
Females
N= 1720
Age M (SD) 35.1 (9.2) 35.0 (9.0)
Father’s Education M (SD) 11.3 (4.1) 11.3 (4.2)
AFD15 (%) 27.7 12.9
AFD18 (%) 71.1 51.7
Lifetime Abstinent (%) 4.7 8.8
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
37
Table 2.
Item-Level Prevalence of ASB Items by Sex Prior to Ages 15 and 18.
ASB item prior to age 15 ASB item prior to age 18
Males (%) Females (%) Males (%) Females (%)
Hooky 8.50 4.65 19.66 13.49
Running Away 1.87 1.16 4.03 3.37
Telling Lies 9.05 5.64 11.86 8.72
Stealing 15.36 5.76 19.17 6.92
Starting a Fire 7.44 1.16 8.11 1.22
Destroying Property 7.31 1.63 11.53 2.21
Hurt Animals 2.47 .11 2.91 .17
Mugging .12 0 .27 0
Physical Fights 4.40 1.74 5.79 2.09
Using Weapon .75 .17 1.5 .29
Hurt Others 4.12 .58 5.97 .70
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
38
Table 3.
Results from Factor Analyses of DSM-III-R Antisocial Behavior (ASB) Symptoms
Reported by Male and Female Adult Twins
Item Loadings
Item RB O-AGG P-AGG
Hooky 1.01 -.30 .00
Running Away .58 .01 .15
Telling Lies .43 .20 .11
Stealing .14 .77 .02
Starting a Fire -.04 .45 .26
Destroying Property .02 .45 .34
Hurt Animals -.00 .33 .34
Mugging .38 .49 -.125
Physical Fights .08 -.06 .79
Using Weapon .16 .04 .64
Hurt Others -.02 .00 .94
Correlations
RB 1.00
O-AGG .49 1.00
P-AGG .55 .37 1.00
Note. Data analyzed as categorical items. Geomin rotated loadings used to estimate
items loadings.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
39
Table 4.
Results from Individual-Level Logistic Regression and Survival Analyses Predicting Age at First Drink (AFD) by ASB
factors
Sample Sample Size ASB Factors
Logistic Regression
of AFD15
Logistic Regression
of AFD18
Survival Analysis
of Onset
OR (95% CI) OR (95% CI) HR (p-value)
Sexes
Combined
N = 6,812 RB 1.97 (1.74, 2.23) 1.62 (1.45, 1.80) 0.88 (p < .001)
O-AGG 1.74 (1.58, 1.92) 1.78 (1.59, 1.99) 1.18 (p < .001)
P-AGG 1.51 (1.27, 1.78) 1.29 (1.06, 1.56) 1.06 (p = .23)
Females
N = 1,720 RB 2.79 (2.05, 3.81) 2.09 (1.66, 2.63) 0.98 (p = .82)
O-AGG 1.75 (1.20, 2.55) 1.54 (1.10, 2.16) 1.19 (p = .08)
P-AGG 1.44 (0.76, 2.74) 1.80 (0.88, 3.70) 1.42 (p = .04)
All Males
N = 5,092 RB 1.83 (1.60, 2.09) 1.47 (1.30, 1.66) 0.86 (p < .001)
O-AGG 1.75 (1.58, 1.93) 1.83 (1.62, 2.06) 1.19 (p < .001)
P-AGG 1.53 (1.28, 1.82) 1.27 (1.04, 1.54) 1.05 (p = .35)
Males from
pairs (MM)
N = 3,495 RB 2.09 (1.68, 2.60) 1.42 (1.18, 1.72) 0.84 (p < .01)
O-AGG 1.77 (1.50, 2.09) 1.95 (1.60, 2.37) 1.22 (p < .001)
P-AGG 1.41 (1.08, 1.84) 1.23 (0.92, 1.64) 1.00 (p = .96)
Note. Covariates are age of interview and sex in combined (male and female) models and just age at interview for the Females, All
Males, and MM pairs. CI = confidence interval.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
40
Table 5.
Correlations Among Key Study Variables in Individuals in MM Pairs for AFD15 and AFD18.
Items Endorsed Before Age 15 for Twin 1 Items Endorsed Before Age 18 for Twin 1
Group ASB Factor RB O-AGG P-AGG AFD15 RB O-AGG P-AGG AFD18
RB – Twin 1 1.00 1.00
Within-person O-AGG – Twin 1 .43 1.00 .39 1.00
(N = 3495) P-AGG – Twin 1 .49 .48 1.00 .49 .43 1.00
AFD15 – Twin 1 .37 .37 .36 1.00 .22 .35 .25 1.00
MZ pairs RB – Twin 2 .55 .61
(N = 854) O-AGG – Twin 2 .34 .53 .28 .55
P-AGG – Twin 2 .39 .29 .46 .44 .30 .56
AFD15 – Twin 2 .31 .25 .24 .56 .17 .23 .19 .65
DZ pairs RB – Twin 2 .34 .39
(N = 643) O-AGG – Twin 2 .27 .51 .29 .54
P-AGG – Twin 2 .24 .24 .35 .31 .31 .43
AFD15 – Twin 2 .19 .21 .16 .31 .15 .23 .16 .51
Note. Correlations were model-based estimates (i.e. equating Twin 1 and Twin 2 within MZ and DZ pairs)
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
41
Table 6.
Results from Univariate Twin Analyses for Each ASB Factor and AFD15 and AFD18 Based on MM Pairs.
ASB Factor a (SE) c (SE) e (SE) a
2
c
2
e
2
RB .57 (.16) .46 (.16) .69 (.04) .33 .21 .48
O-AGG .11 (.60) .71 (.08) .70 (.03) .01 .50 .49
P-AGG .42 (.32) .53 (.21) .74 (.06) .18 .28 .55
RB .71 (.10) .34 (.18) .62 (.03) .50 .12 .38
O-AGG .27 (.23) .69 (.08) .67 (.03) .07 .48 .45
P-AGG .46 (.22) .58 (.15) .67 (.05) .21 .34 .45
Note. Analyses based on 854 MZ twins, 643 DZ twins, and 501 singletons.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
42
Table 7.
Goodness of Fit of Alternative Twin Models for the Genetic and Environmental Covariation between ASB Factors and AFD.
Models for AFD Models for AFD18
Model df χ
2
AIC RMS χ
2
AIC RMS
Common Phenotype
(Equated Loadings)
67 102.8 -31.2 .023 160.7 26.7 .037
Common Phenotype
(Estimated Loadings)
62 83.8 -40.2 .019 89.7 -34.3 .021
Independent Pathway 57 72.0 -42.0 .016 72.4 -41.6 .016
Note: df = degrees of freedom; χ
2
= chi-square value; AIC = calculated as the model χ
2
– 2*df; RMS = Root Mean Square Error of
Approximation.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
43
Table 8.
Estimated Shared and Specific Loadings for Each ASB Factor with AFD15 and AFD18 Based on the Independent Pathway Model.
Shared Components Specific Components
Variables Total Shared
Variance
a (SE) c (SE) e (SE) a (SE) c (SE) e
Estimates from Model for AFD15
AFD15 .27 .35 (.15) .26 (.17) .29 (.08) .61 (.14) .02 (3.47) .60
RB – 15 .57 .66 (.13) .29 (.19) .23 (.08) -.01 (16.38) .19 (.38) .63
O-AGG – 15 .71 .20 (.17) .70 (.36) .42 (.10) .00 (--) .00 (--) .54
P-AGG – 15 .49 .47 (.17) .28 (.19) .44 (.11) .00 (--) .40 (.26) .59
Estimates from Model for AFD18
AFD18 .17 .08 (.16) .32 (.09) .23 (.08) .52 (.13) .52 (.12) .54
RB – 18 .45 .48 (.22) .42 (.13) .22 (.07) .46 (.26) .00 (--) .58
O-AGG – 18 .80 -.05 (.23) .72 (.20) .52 (.14) .15 (.46) .00 (--) .42
P-AGG – 18 .54 .52 (.24) .45 (.14) .26 (.09) .01 (31.86) .30 (.28) .61
Note. Sample size of analyses includes 854 MZ twins, 643 DZ twins, and 501 singletons. There is no SE estimate for the specific
component of “E,” as it was calculated based off of R-square. “A” represents additive genetic component. “C” represents variance
from shared environmental “E” represents variance from individual environment.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Figure 1. Depiction of Common Phenotype (Equated Loadings)
(Estimated Loadings) models.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Common Phenotype (Equated Loadings) and Common Phenotype
44
and Common Phenotype
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Figure 2. Depiction of Independent Pathway
Note. The subscript “IP” refers to the genetic and environmental variance shared
between each ASB factor and AFD. The subscript “RB” refers to the variance specific
to RB. The subscript “O” refers to the variance specific to O
refers to the variance specific to P
specific to AFD.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Depiction of Independent Pathway model.
. The subscript “IP” refers to the genetic and environmental variance shared
between each ASB factor and AFD. The subscript “RB” refers to the variance specific
to RB. The subscript “O” refers to the variance specific to O-AGG. The subscript “P”
refers to the variance specific to P-AGG. The subscript “D” refers to the variance
45
. The subscript “IP” refers to the genetic and environmental variance shared
between each ASB factor and AFD. The subscript “RB” refers to the variance specific
subscript “P”
AGG. The subscript “D” refers to the variance
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Figure 3. Percent of males and females in sample who endorsed a number of ASB items
age 15.
0
2
4
6
8
10
12
14
16
18
20
RB - Males RB - Females
Percent Endrosed
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
males and females in sample who endorsed a number of ASB items
Females O-AGG - Males O-AGG -
Females
P-AGG - Males P-AGG
Females
ASB Factor Group
4 Symptoms Endrosed
3 Symptoms Endrosed
2 Symptoms Endrosed
1 Symptom Endrosed
46
males and females in sample who endorsed a number of ASB items by
AGG -
Females
4 Symptoms Endrosed
3 Symptoms Endrosed
2 Symptoms Endrosed
1 Symptom Endrosed
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Figure 4. Percent of males and females in sample who endorsed a number of ASB items by
age 18.
0
5
10
15
20
25
RB - Males RB - Females
Percent Endorsed
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Percent of males and females in sample who endorsed a number of ASB items by
Females O-AGG - Males O-AGG -
Females
P-AGG - Males P-AGG
Females
ASB Factor Group
4 Symptoms Endrosed
3 Symptoms Endrosed
2 Symptoms Endrosed
1 Symptom Endrosed
47
Percent of males and females in sample who endorsed a number of ASB items by
AGG -
Females
4 Symptoms Endrosed
3 Symptoms Endrosed
2 Symptoms Endrosed
1 Symptom Endrosed
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Figure 5. Percent of individuals in MM
a number of items per ASB factor.
0
10
20
30
40
50
60
70
80
90
100
0 1
Percent
Number of ASB Items Endorsed
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
individuals in MM pairs who reported an AFD prior to age 15
a number of items per ASB factor.
2 3 4
Number of ASB Items Endorsed
RB
O-AGG
P-AGG
48
who reported an AFD prior to age 15 and endorsed
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Figure 6. Percent of individuals in MM
a number of items per ASB factor.
0
10
20
30
40
50
60
70
80
90
100
0 1
Percent
Number of ASBs Items Endorsed
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Percent of individuals in MM pairs who reported an AFD prior to age 18 and endorsed
a number of items per ASB factor.
2 3 4
Number of ASBs Items Endorsed
RB
O-AGG
P-AGG
49
who reported an AFD prior to age 18 and endorsed
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
50
Figure 7. Cumulative distribution of drinking onset by number of antisocial behaviors and sex in
combined sample.
0
10
20
30
40
50
60
70
80
90
100
10 11 12 13 14 15 16 17 18 19 20 21 22 23
% Onset
Onset Age
Males 1+ Sxs
Females 1+ Sxs
Males 0 Sxs
Females 0 Sxs
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Figure 8. Depiction of shared and specific genetic and environmental variance
associated with each ASB factor and AFD
Note. The subscript “IP” refers to the genetic and environmental variance shared
between each ASB factor and AFD. The subsc
to RB. The subscript “O” refers to the variance specific to O
refers to the variance specific to P
specific to AFD.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
. Depiction of shared and specific genetic and environmental variance proportions
associated with each ASB factor and AFD15.
. The subscript “IP” refers to the genetic and environmental variance shared
between each ASB factor and AFD. The subscript “RB” refers to the variance specific
to RB. The subscript “O” refers to the variance specific to O-AGG. The subscript “P”
refers to the variance specific to P-AGG. The subscript “D” refers to the variance
51
proportions
. The subscript “IP” refers to the genetic and environmental variance shared
ript “RB” refers to the variance specific
AGG. The subscript “P”
AGG. The subscript “D” refers to the variance
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
Figure 9. Depiction of shared and specific genetic and environmental variance proportions
associated with each ASB factor and AFD18.
Note. The subscript “IP” refers to the genetic and environmental variance shared
between each ASB factor and AFD. The subscript “RB” refers t
to RB. The subscript “O” refers to the variance specific to O
refers to the variance specific to P
specific to AFD.
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
red and specific genetic and environmental variance proportions
associated with each ASB factor and AFD18.
. The subscript “IP” refers to the genetic and environmental variance shared
between each ASB factor and AFD. The subscript “RB” refers to the variance specific
to RB. The subscript “O” refers to the variance specific to O-AGG. The subscript “P”
refers to the variance specific to P-AGG. The subscript “D” refers to the variance
52
red and specific genetic and environmental variance proportions
. The subscript “IP” refers to the genetic and environmental variance shared
o the variance specific
AGG. The subscript “P”
AGG. The subscript “D” refers to the variance
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
53
Appendix
CHILDHOOD BEHAVIOR
K.12 Now I’d like to ask some questions about things you might have done when you were
younger, that is, before your 18
th
birthday.
First, before the age of 18, did you play hooky a lot from school?
YES NO GO TO K.13
K.12a How old were you when this began? _____ YEARS
K.13 Before the age of 18, did you run away from home overnight more than once?
YES NO GO TO K.14
K.13a How old were you when this began? _____ YEARS
K.14 Before the age of 18, did you tell a lot of lies?
YES NO GO TO K.15
K.14a How old were you when this began? _____ YEARS
K.15 Before the age of 18, did you more than once steal things from a store or form someone
you knew?
YES NO GO TO K.16
K.15a How old were you when this began? _____ YEARS
K.16 (Before the age of 18), did you ever deliberately start a fire?
YES NO GO TO K.17
K.16a How old were you when this began? _____ YEARS
K.17 (Before the age of 18), did you ever deliberately destroy someone else’s property (other
than by setting a fire)?
YES NO GO TO K.18
ANTISOCIAL BEHAVIOR FACTORS AND ALCOHOL INITIATION
54
K.17a How old were you when this began? _____ YEARS
K.18 (Before the age of 18), did you physically hurt animals on a number of occasions?
YES NO GO TO K.19
K.18a How old were you when this began? _____ YEARS
K.19 (Before the age of 18), did you often start physical fights?
YES NO GO TO K.20
K.19a How old were you when this began? _____ YEARS
K.20 (Before the age of 18), did you use a weapon in a fight more than once?
YES NO GO TO K.21
K.20a How old were you when this began? _____ YEARS
K.21 (Before the age of 18), did you physically hurt other people a number of times?
YES NO GO TO K.22
K.21a How old were you when this began? _____ YEARS
K.22 (Before the age of 18), did you ever rob or mug someone?
YES NO GO TO SECTION M
K.22a How old were you when this began? _____ YEARS
Abstract (if available)
Abstract
Behavioral subtypes of antisocial behavior, such as Rule‐Breaking and Aggression, are consistent predictors of early alcohol use. The goal of the current research is to better understand the etiology of early adolescent alcohol use by using data from twins to examine the genetic and environmental overlap between antisocial behavior factors and alcohol initiation. Data were collected by structured interview from 6,812 participants in the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders. Assessment included retrospective reports of age at first drink and how many of the 11 antisocial behaviors corresponding to DSM-III-R symptoms for Conduct Disorder were engaged in prior to age 18. A 3‐factor model best fit the structure of the antisocial behaviors. These factors were termed Rule‐Breaking, Object Aggression, and Personal Aggression. Higher scores on all three factors were associated with early alcohol initiation. In multivariate twin analyses, 27% of the variance in risk for early alcohol initiation (drinking by age 15) was shared with the antisocial behavior factors, but the basis for this overlap differed across the factors. Most of the genetic overlap between early alcohol initiation and the antisocial behavior factors was accounted for by Rule‐Breaking, whereas, the overlap with Object Aggression was attributed to environments shared by siblings. Personal Aggression had significant genetic and individual environmental overlap with early alcohol initiation. These differences in the nature of the associations between specific antisocial behavior factors and early drinking contribute to a more nuanced understanding of their shared association.
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Khoddam, Rubin
(author)
Core Title
Different genetic and environmental structures for the overlap of three antisocial behavior factors with alcohol initiation
School
College of Letters, Arts and Sciences
Degree
Master of Arts
Degree Program
Psychology
Publication Date
06/20/2014
Defense Date
03/28/2014
Publisher
University of Southern California
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Tag
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Prescott, Carol A. (
committee chair
), Baker, Laura A. (
committee member
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