Morality Relativism & the Concerns it RaisesI want to g.docx

Morality Relativism & the Concerns it Raises
“I want to give moral relativism the good spanking it deserves.”
Peter Kreef philosophy professor, Boston College
Does “relativism” need a spanking?2005 new Pope Benedict
warned of the “onslaught of moral relativism”He “has
characterized it as the major evil. Some observers believe he is
taking a stance in the tense cultural wars in the United States.”
(NPR radio, 2005)Mormons agree: “moral relativism/militant
atheism”Culture wars?
*
Source:
http://www.npr.org/templates/story/story.php?storyId=4618049
Defining the Terms: RelativismMoral relativism: morality is
purely culturalMoral differences & disagreements are
irreconcilableFor example, Inuit Eskimos practice infanticide:
one woman had borne 20 children but killed 10 at birth.Eskimos
also practice euthanasia: when the elderly become too feeble to
travel, they’re left to freeze.Hence, there’s no one universal
moral truth for all times, places, peoples and culturesThe only
possible good is toleration & mutual respect of pluralistic
values
*

James Rachels, “The Challenge of Cultural Relativism” (Fifty
Readings, 2nd Ed.), 397.
Defining the Terms: AbsolutismMoral absolutism: there are
clear moral truths to govern all ethical issues regardless of
situation.Immoral to accept the justifiability of two conflicting
positions on any given ethical issueFor example: with this
position, it would be unacceptable for Bush (pro-life) to say
Eskimo infanticide practices are understandable and permissible
among EskimosOr if polygamy or underage marriage is wrong,
it is wrong everywhere and at all times.But what is “underage
marriage”?
Moral Absolutism and Human KnowledgeName some fields of
human knowledge where we deal with facts and have made great
progress.Scientific theory must deal with hard dataNo science
that claims absolute knowledge;Fallibility is the hallmark of
scienceBut fallibility does not mean all theories are equal.Why
should ethics be any different?If moral truths are not absolute,
why should that prove that all moral values are equal?We can
measure progress in science but what about ethics?
Illogic of Extreme Moral RelativismIn extreme relativism, no
one can rightly pass judgment on others’ values/social
practicesConsider Afghan Taliban Culture & Values:Ban on
women's work outside the homeBan on women's presence in
radio or televisionBan on women at schools or universitiesEthic
of absolute relativism is self-contradictory:If I pass judgment
on others for passing any judgment, am I not passing judgment
on others?

Relativism with Norms Normative relativism: while cultural
values clearly differ, nevertheless there are some general
purposes shared by all moral codes.A socially accepted way of
regulating conflicts of interests in society to preserve that
people and culture with rules shaped by situations to that end. A
socially accepted way of regulating conflicts of interests within
an individual that can’t be equally satisfied at the same time
(example: crime victim’s desire for vengeance vs. desire for
justice)Morality is for social preservation & concern for others
Stable but Situationally SensitiveTo meet conflicts of interests
in a changing world, moral codes need two things:reliable
stability and relative adaptabilityIf the rules are constantly
changing, they lose credibilityWhen we refuse to change rules
that no longer serve the social good, the rules also lose
credibilityTao Te Ching: “We’re born soft & supple; the dead
are immobile & hard. The stiff and inflexible then are disciples
of death.”
On Moral JudgmentPassing Judgment vs. Acting on
Judgment:Normative relativism can and does pass judgment on
others with different values (terrorism is wrong whether in
America, Iraq, India, or China). But what we’re entitled to DO
about those judgments is another matter.Efforts to make all
things right can make more things worse.
Acting in JudgmentThere is no general rule that tells us what to

do when another culture is contradicting the value of their own
social good.Example: suppose a country is destroying its own
environment & endangering its people’s own welfareIt’s
justifiable to condemn their environmental destructiveness,but
the moral right to condemn does not indicate what action, if
any, should be taken against that particular evil.
*
Source:
http://www.npr.org/templates/story/story.php?storyId=4618049
*
James Rachels, “The Challenge of Cultural Relativism” (Fifty
Readings, 2nd Ed.), 397.
Journal of Interpersonal Violence
18(1) 92 –120
© The Author(s) 2013
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DOI: 10.1177/0886260512448847
http://jiv.sagepub.com
448847 JIV18110.1177/0886260512448847Barn
es and JacobsJournal of Interpersonal Violence
© The Author(s) 2011
Reprints and permission: http://www.
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1The University of Texas at Dallas, Richardson, TX, USA

Corresponding Author:
J. C. Barnes, School of Economic, Political & Policy Sciences,
The University of Texas at Dallas,
800 West Campbell Road, Richardson, TX 75080, USA
Email: [email protected]
Genetic Risk for
Violent Behavior and
Environmental Exposure
to Disadvantage and
Violent Crime: The Case
for Gene–Environment
Interaction
J. C. Barnes1 and Bruce A. Jacobs1
Abstract
Despite mounds of evidence to suggest that neighborhood
structural factors
predict violent behavior, almost no attention has been given to
how these
influences work synergistically (i.e., interact) with an
individual’s genetic
propensity toward violent behavior. Indeed, two streams of
research have,
heretofore, flowed independently of one another. On one hand,
criminolo-
gists have underscored the importance of neighborhood context
in the etiol-
ogy of violence. On the other hand, behavioral geneticists have
argued that
individual-level genetic propensities are important for
understanding violence.
The current study seeks to integrate these two compatible
frameworks by
exploring gene–environment interactions (GxE). Two GxEs

were examined
and supported by the data (i.e., the National Longitudinal Study
of Adolescent
Health). Using a scale of genetic risk based on three dopamine
genes, the
analysis revealed that genetic risk had a greater influence on
violent behavior
when the individual was also exposed to neighborhood
disadvantage or when
Article
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12448847&domain=pdf&date_stamp=2012-07-24
Barnes and Jacobs 93
the individual was exposed to higher violent crime rates. The
relevance of
these findings for criminological theorizing was considered.
Keywords
genetic risk, neighborhood context, violence, gene–environment
interaction
(GxE)
Biosocial research has blossomed over the past decade (Moffitt,
Ross, &
Raine, 2011). There are now hundreds of studies that
incorporate biological
or genetic perspectives into their theoretical propositions
(Moffitt, 1993;
Walsh, 2002), their statistical models (Barnes, Beaver, &
Boutwell, 2011;

Burt, 2009; Schilling, Walsh, & Yun, 2011), or their discussion
of the future
of criminology (Cullen, 2011; DeLisi & Piquero, 2011; Piquero,
2011). As a
result, there is now little doubt that biological and genetic risk
factors play a
key role in the etiology of delinquent, criminal, and antisocial
behavior
(Raine, 1993). Although biosocial criminology draws on many
different,
albeit related, perspectives such as evolutionary psychology
(Campbell,
2009), biological criminology (Mazur, 2009), and
neurocriminology (Raine
et al., 2003; Weber, Habel, Amunts, & Schneider, 2008), one
focus has gener-
ated an impressive body of evidence: behavioral and molecular
genetics
research (Craig & Halton, 2009; Ferguson, 2010).
As summarized in a number of recent meta-analyses, genetic
factors
account for a significant portion of the variance in antisocial
behavior
(Ferguson, 2010; Mason & Frick, 1994; Miles & Carey, 1997;
Moffitt, 2005;
Raine, 1993; Rhee & Waldman, 2002; Schilling et al., 2011).
These analyses
are impressive in the consistency with which they estimate the
genetic influ-
ence on antisocial behavior. To be specific, they reveal that
genes are respon-
sible for roughly half of the variance in antisocial behavior,
with the remaining
variance being attributed primarily to the nonshared
environment. Spurred by

these findings, scholars have begun to explore genetic factors in
more detail;
researchers are now analyzing the link between specific genes
(i.e., genetic
polymorphisms) and antisocial behavior (Beaver, DeLisi,
Vaughn, & Barnes,
2010; Burt & Mikolajewski, 2009; Craig & Halton, 2009).
The current study seeks to extend contemporary biosocial
research by ana-
lyzing the link between three genetic polymorphisms and
violent criminal
behavior. Although this is an important element of the current
focus, perhaps
just as important is our focus on gene–environment interactions
(GxE). The
question of whether genetic effects are contingent on
environmental
94 Journal of Interpersonal Violence 18(1)
experiences has been under investigation by recent cutting-edge
scholarship
(Caspi et al., 2002). It is to this literature that we now turn.
Gene–Environment Interaction
Behavioral genetics research typically decomposes the variance
in a pheno-
type into three separate components (i.e., heritability, shared
environment,
and nonshared environment). As noted above, behavioral
geneticists have
consistently reported that criminal behavior is around 50%
heritable. One

limitation of this research, however, is that the specific genetic
factors that
comprise this heritability estimate are unidentified. In other
words, behav-
ioral genetics cannot tell us which genes are driving the
heritability estimate.
With the recent mapping of the human genome, researchers are
beginning
to “pull back the heritability curtain” to identify links between
measured
genes and phenotypic outcomes. This line of research—referred
to as molec-
ular genetics—has already produced a wealth of insights into
these links
(Carey, 2003). For example, certain genetic polymorphisms
have been linked
to various antisocial behaviors such as ADHD (Faraone, Doyle,
Mick, &
Biederman, 2001), childhood conduct disorder (Beaver, 2009a;
Beaver et al.,
2007), and adulthood violent behavior (Burt & Mikolajewski,
2009; Craig &
Halton, 2009). Perhaps more importantly, molecular genetics
research has
identified the importance of the environment in triggering
genetic effects—a
process known as GxE (Shanahan & Hofer, 2005).1 Findings
from GxE
research show that certain genetic effects are more likely to
manifest when
combined with environmental risk factors (Beaver, 2008;
Cadoret, Yates,
Troughton, Woodworth, & Stewart, 1995; Caspi et al., 2002;
Moffitt, Caspi,
& Rutter, 2006; Thapar, Harold, Rice, Langley, & O’Donovan,

2007).
The logic of GxE notes that the effects of a genetic risk factor
on the
development of a phenotype (e.g., antisocial behavior) will
differ across indi-
viduals according to their exposure to environmental risk
factors or vice
versa. In other words, GxE calls for a nonadditive effect
between an environ-
mental risk factor and a genetic risk factor in the etiology of
antisocial behav-
ior. For example, a genetic risk factor may have a small or
negligible effect on
criminal behavior when a low level of environmental risk is
present. However,
when environmental risk is increased, the effects of the genetic
risk factor are
substantially increased.
There is now a sizable body of research examining GxEs in the
develop-
ment of antisocial phenotypes (Beaver et al., 2007; Caspi et al.,
2002; Foley
et al., 2004; Haberstick et al., 2005; Jaffee et al., 2005; Kim-
Cohen et al.,
2006; Vaske, 2009). Caspi and colleagues (2002) were some of
the first to test
the GxE hypothesis—in regards to a behavioral phenotype—
with a measured
gene. Their findings revealed that males with a particular

genotype (i.e.,
alleles for the MAOA gene linked with low MAOA activity)
who were also
maltreated as children (i.e., the environmental risk factor) were
more likely to
have been convicted of a violent offense compared to males who
were mal-
treated but did not have the genetic risk factor (i.e., respondents
who had the
high MAOA activity allele). Although only 12% of the sample
was exposed
to both risk factors (i.e., childhood maltreatment and low
MAOA activity
allele), these respondents accounted for approximately 44% of
all violent
convictions in the sample. A recent meta-analysis supported the
GxE between
MAOA genotype and childhood maltreatment in the prediction
of antisocial
behavior (Kim-Cohen et al., 2006; Taylor & Kim-Cohen, 2007).
It is worth noting that a slightly different interpretation of GxE
findings
has been proffered by Belsky, Bakermans-Kranenburg, and van
Ijzendoorn
(2007). Briefly, Belsky and colleagues (2007) reviewed much of
the research
into GxE and brought an important point to bear; much of the
evidence
reveals that individuals with more “risk” alleles are impacted to
a greater
degree by bad environments. But—and this is where their
differential suscep-
tibility hypothesis differs from the standard GxE framework—
there is also a
great deal of evidence revealing that individuals with more risk

alleles are
impacted to a greater degree by good environments. In other
words, Belsky
et al. proposed a “plasticity alleles” hypothesis which states that
individuals
carrying more plasticity alleles (previously referred to as risk
alleles) are
more influenced by the environment; whether that is for better
or for worse.
Caspi et al.’s (2002) landmark study reported evidence in
support of this
hypothesis. Although individuals carrying the low activity
MAOA allele dis-
played more antisocial behavior when maltreatment was high;
they also dis-
played fewer antisocial behaviors when not exposed to
maltreatment as
compared to individuals carrying the high-activity MAOA
allele.
Research has tested GxE (and plasticity allele) hypotheses using
genetic
polymorphisms other than MAOA. One emerging line of
research has exam-
ined the link between certain dopaminergic genes and antisocial
behavior
(Beaver, 2009b). Dopamine is a chemical (neurotransmitter)
that is found in
the brain and is believed to be part of the body’s pleasure and
reward center
(Beaver, 2009a). Thus, geneticists have hypothesized that
polymorphisms in
certain dopamine genes may be linked to antisocial behavior via
pleasure/
reward pathways in the brain (Rutter, 2006).

Scholars have identified three dopamine genes (DAT1, DRD2,
and DRD4)
related to antisocial behavior with empirical regularity (Beaver,
2009b;
Boutwell & Beaver, 2008; Burt & Mikolajewski, 2009; Guo,
Roettger, &
Shih, 2007; Guo, Roettger, & Cai, 2008), and other researchers
have begun to
identify GxEs with these polymorphisms (Bakermans-
Kranenburg & van
IJzendoorn, 2006; Sheese, Voelker, Rothbart, & Posner, 2007).
For example,
Bakermans-Kranenburg and van IJzendoorn (2006) reported that
children
with the risk allele on DRD4 who were also exposed to
insensitive care from
their mothers were much more likely to display externalizing
behavioral
problems compared to other children. Sheese et al. (2007) found
that children
with the risk allele for DRD4 displayed higher levels of
sensation seeking
when low-quality parenting was also present. Parenting quality
did not affect
sensation seeking for children without the risk allele. These
findings suggest
that the effect of dopaminergic genes in the etiology of
antisocial behavior is
salient, but that these effects may be contingent on
environmental experi-
ences. To date, criminological research has not considered the

full range of
possible GxEs. One group of environmental influences that
deserves atten-
tion is neighborhood and structural factors.
Structural Factors and Violent Behavior
Neighborhood Impacts on Violent Behavior
Criminology has a long history of studying how neighborhood
and structural
factors affect a person’s propensity for violence (Chauhan,
Reppucci, &
Turkheimer, 2009; Cloward & Ohlin, 1960; Kornhauser, 1978;
Leventhal &
Brooks-Gunn, 2003; Sampson, Raudenbush, & Earls, 1997;
Shaw & McKay,
1972 [though it must be noted that Shaw & McKay, 1972 spoke
only to
neighborhood effects and these findings may not apply at the
individual
level]). Among the most consistent results is that neighborhood
disadvantage
predicts violent criminal behavior (Pratt & Cullen, 2005).
Sampson and his
colleagues (1997), drawing on data from Chicago residents and
neighbor-
hoods, reported that neighborhood measures of concentrated
disadvantage
were strongly related to an individual’s level of self-reported
violent behavior.
Importantly, the impact of neighborhood disadvantage on
violent behavior
was not fully mediated by measures of immigrant concentration,
residential
stability, or collective efficacy. This finding suggests that
neighborhood fac-

tors are important in the etiology of violent behavior.
Building on the work of Sampson et al. (1997; as well as a
foundation of
other research such as Miller, 1958; Shaw & McKay, 1972;
Sutherland,
1939), one might expect that individuals who live in areas
plagued by high
crime rates will have a greater propensity toward violence. This
hypothesis is
consistent with the above work which has shown that crime
rates covary with
neighborhood disadvantage. Thus, crime rates are associated
with neighbor-
hood disadvantage, and neighborhood disadvantage has been
shown to pre-
dict individual-level crime propensity (Sampson et al., 1997).
Working
backwards from these findings, it is logical to assume that crime
rates in a
given area will also be predictive of a person’s criminal
propensity.
Unfortunately, to our knowledge, no study has directly
addressed this issue
(although it may be argued that Anderson, 1999 highlighted
these possibili-
ties). Put differently, as obvious as the association appears,
criminologists
have yet to consider whether aggregate crime rates are
predictive of an indi-
vidual’s criminal involvement or whether violent crime rates

interact with
individual propensities to predict criminal behavior. This
represents an obvi-
ous limitation with the extant literature.
GxE and Neighborhood Influences
Despite the consistency with which neighborhood and structural
factors have
been correlated with an individual’s violent behavior,
researchers have long
noted that not everyone who grows up in a “bad” neighborhood
becomes an
offender (Anderson, 1999; Piquero, 2011). Juxtaposing the
research on struc-
tural factors against the research on GxE, a provocative
question begins to
emerge (Plomin & Daniels, 1987): Do genetic risk factors
interact with
neighborhood and county-level risk factors to predict violent
criminal behav-
ior and if so, what are the underlying mechanisms of this
relationship?
Although there is little doubt that neighborhood factors matter,
for whom,
when, and why they matter is still shrouded in mystery.
Biosocial research
suggests several mechanisms by which neighborhood factors
may interact
with genetic risk for offending. Particularly relevant is research
that focuses
on criminal opportunities and offending likelihood (Cohen &
Felson, 1979).
In short, a crime can only take place if the opportunity for crime
exists.
Although it may be argued that criminal opportunities are

omnipresent,
opportunities for violent crime may be more numerous in
neighborhoods that
are marked by higher crime rates and greater disadvantage.
Typically, these
neighborhoods are defined by social structures that may not be
as adept at
controlling violent crime (Sampson et al., 1997) and may even
encourage
law-breaking behavior (Keizer, Lindenberg, & Steg, 2008). If
individuals are
more or less likely to engage in violent behavior as a function
of genetic dif-
ferences, it is also logical to expect that differences in violent
behavior will
emerge as a function of the interaction between genetic
propensities and
neighborhood exposure to opportunities.
As of this writing, only one study offers any insight on this
issue. Beaver,
Gibson, DeLisi, Vaughn, and Wright (2011) analyzed the effect
of two dopa-
mine genes (DRD2 and DRD4) on violent delinquency for two
sets of respon-
dents: (a) respondents who lived in an adequate neighborhood
(measured as
living at or below the 75th percentile on a measure of
neighborhood disadvan-
tage); and, (b) respondents who lived in a disadvantaged
neighborhood (i.e.,
living above the 75th percentile). The authors reported that risk

alleles on two
dopamine genes had a significant influence on violent
delinquency, but only if
the respondent lived in a disadvantaged neighborhood. The
dopamine genes
conferred no risk for respondents who lived in an adequate
neighborhood. In
short, Beaver et al. (2011) identified a compelling relationship
between genetic
factors and neighborhood disadvantage. Whether this
relationship holds when
different measures of genetic risk are examined and when
different types of
environmental measures are utilized remains to be seen.
Current Study
In his recent Sutherland Address to the American Society of
Criminology,
Francis Cullen (2011, p. 311) warned that, “we can no longer
pretend that
biology is not intimately implicated in human behavior and thus
in criminal
behavior.” He added that the challenge for traditional
criminologists is “to
put out the welcome mat to crime scientists and to understand
that the future of
criminology will be advanced by exploring systematically the
nexus between
propensity and opportunity—between offender and situation”
(p. 315). The
current study takes up this challenge by examining whether
genetic influ-
ences on violent behavior are contingent on exposure to
environmental
structures that are conducive to violence.

Our efforts advance similar work spearheaded by Beaver et al.
(2011) but
differ from that research in three important respects. First, we
employ an
alternate strategy for measuring genetic risk. Unlike Beaver et
al.—who
examined the individual effects of two genes—we compile a
genetic risk
index based on three genetic markers. This is important because
genetic
effects may act in concert to confer an increased risk toward
violent behavior;
an effect that will not be captured by exploring the genes
individually.
Second, we specifically examine the interaction effect between
genetic risk
and violent crime rates. Beaver et al. examined only the
interaction between
genetic risk and neighborhood disadvantage which is useful but
limited
because neighborhood disadvantage may not capture the same
variance as a
measure of local crime rates. It is worth pointing out that no
research (to our
knowledge) has considered the relationship between macro-level
crime rates
and an individual’s criminal (or violent) behavior. And third,
we employed an
alternative strategy for testing GxE by using split samples as
well as multipli-
cative interaction terms. This strategy avoids the problems of

multicollinear-
ity (by using split samples) and reduced sample sizes (by using
the
multiplicative interaction). In short, we capitalized on the
benefits of both
strategies in order to ensure that the findings were not sensitive
to one meth-
odological approach.
The present study examines three specific hypotheses:
Hypothesis 1: Respondents who carry a genetic risk toward
violent
behavior will be more likely to report violent behavior as
compared
to those who do not carry the genetic risk.
Hypothesis 2: The effect of genetic risk on violent behavior will
be
contingent upon the respondent’s exposure to neighborhood
disad-
vantage. When disadvantage is high, genetic risk will have a
larger
effect on violent behavior.
Hypothesis 3: The effect of genetic risk on violent behavior will
be
contingent upon the respondent’s exposure to violent crime. For
respondents who live in areas with high violent crime rates,
genetic
risk will have a larger effect on violent behavior.
Method
Data
The data for this study were drawn from the National

Longitudinal Study of
Adolescent Health (Add Health; Harris, 2009). These data have
been
described at length elsewhere (Harris et al., 2009; Kelly &
Peterson, 1997)
and need not be repeated here. Briefly, the Add Health is a
nationally repre-
sentative, longitudinal survey of American adolescents who
were enrolled in
middle and high school during the 1994-1995 academic year.
The study
began by interviewing all students enrolled in roughly 130
schools across the
United States (N ~ 90,000). From this sample of respondents, a
subsample of
approximately 20,000 were drawn and were administered a more
lengthy
follow-up survey that was completed in the respondents’ homes
(i.e., wave 1).
These surveys addressed myriad topics such as the respondent’s
health, their
personal relationships, and their involvement in delinquent and
criminal
behaviors. Four waves of data have been collected thus far.
Because the cur-
rent efforts draw only on wave 1 data, waves 2 through 4 are
not discussed.
Two unique features of the Add Health design are utilized by
the current
analysis. First, the Add Health researchers included a host of
neighborhood

and county-level measures that can be linked with the individual
respondents.
These data allow for the examination of contextual effects that
may influence
violent behavior. The second design feature of the Add Health
was the inclu-
sion of certain genetic markers for a subset of the participants.
Respondents
who had a twin or a full sibling participating in the Add Health
study were
asked to provide buccal cell samples so that they may be
genotyped.
Originally, 2,574 respondents were genotyped (Cohen et al.,
n.d.). After elim-
inating one twin from each MZ pair (to avoid artificially
deflating standard
errors), and after eliminating females from the sample (because
of males’
over involvement in violence [Beaver et al., 2011]), a final
analytic sample of
1,078 was obtained.
Measures
Dependent Variable
Violent behavior. During wave 1 interviews, respondents were
asked to
report the frequency with which they had been involved in a
number of vio-
lent behaviors over the past 12 months. To be specific, each
respondent was
asked to indicate how often they had used a weapon to get
something from
someone, gotten into a group fight, gotten into a serious fight,
hurt someone
badly enough that they required medical attention, used a

weapon in a fight,
and taken a weapon to school. Responses to the first four items
were coded
so that 0 = never, 1 = 1 or 2 times, 2 = 3 or 4 times, and 3 = 5
or more times.
Responses to the last two items (weapon fight and weapon
school) were
coded so that 0 = no and 1 = yes. Factor analysis indicated that
all six items
hung together on a single factor. Thus, to create the violent
behavior scale,
responses to the six items were summed together so that higher
values indi-
cated more involvement in violent behavior (α = .75).
Descriptive statistics
for this and all other variables utilized in the analysis can be
found in Table 1.
Genetic Risk Variable
Dopamine risk. A rich line of evidence suggests certain genetic
markers
related to dopamine activity are associated with criminal and
antisocial
behavior (Beaver, 2009b; Craig & Halton, 2009). The Add
Health included
genotypic information for three dopamine polymorphisms:
DAT1, DRD2,
and DRD4. To create the dopamine risk scale, a series of four
steps was fol-
lowed. First, DAT1 is a dopamine transporter gene that has been
linked with
myriad antisocial behaviors (Schilling et al., 2011). The two
most common

alleles are the 9-repeat and the 10-repeat, with the 10-repeat
allele being the
risk allele (Gill, Daly, Heron, Hawi, & Fitzgerald, 1997; Rowe
et al., 2001).
Following prior research (Beaver, Wright, DeLisi, & Vaughn,
2008) we
coded each allele so that 0 = 9-repeat allele and 1 = 10-repeat
allele. Respon-
dents with any other allele were assigned a missing value and
were omitted
from the analyses (Hopfer et al., 2005).
Second, DRD2 is a dopamine receptor polymorphism that has
two differ-
ent alleles: the A1 allele and the A2 allele. The A1 allele has
been identified
as the risk allele (Guo et al., 2007) and was, therefore, coded as
1. The A2
allele was coded as 0. Third, the DRD4 polymorphism is a
dopamine receptor
gene that has been implicated in antisocial conduct with the 7-
repeat allele
conferring increased risk as compared to the 4-repeat allele
(Faraone et al.,
2001; Rowe et al., 2001). Following prior researchers (Beaver
et al., 2011),
the DRD4 polymorphism was coded so that the 7-repeat allele
(along with
the 8-, 9-, and 10-repeat alleles) = 1 and the 4-repeat allele
(along with the
2-, 3-, 5-, and 6-repeat alleles) = 0.
The fourth step to creating the dopamine risk scale was to sum

each
respondent’s number of risk alleles for DAT1, DRD2, and
DRD4 into a
single scale. The polymorphisms were coded codominantly,
meaning that
Table 1. Descriptive Statistics for Add Health Males
Frequency Mean SD Minimum Maximum
Violent behavior 1.48 2.23 0 14
Dopamine risk 2.50 1.02 0 6
0 risk alleles 13
1 risk alleles 162
2 risk alleles 371
3 risk alleles 367
4 risk alleles 140
5 risk alleles 22
6 risk alleles 3
Neighborhood disadvantage 0.00 0.94 –1.19 4.61
County violent crime rate 735.19 624.58 24.73 3007.91
Age 15.66 1.69 12 20
Race .18 .38 0 1
Black 201
Non Black 928
the value for each polymorphism reflected the number of risk
alleles pres-
ent in the respondent. Humans carry two copies of every gene
(with the
exception of genes located on the sex chromosomes for males—
none of

the dopamine genes analyzed here are located on a sex
chromosome).
Thus, when summed together, the dopamine risk scale ranged
from a mini-
mum of 0 (i.e., no dopamine risk alleles) to a maximum of 6
(i.e., six
dopamine risk alleles).
Environmental Variables
Neighborhood disadvantage. A compelling line of research has
shown that
neighborhood indicators of disadvantage are salient predictors
of violent
criminal activity (Sampson et al., 1997). To account for these
influences,
we created an indicator of neighborhood disadvantage that was
measured at
the block-group level. The block-group level is the smallest
level of aggre-
gation, making it the most appropriate unit of analysis for
estimating neigh-
borhood effects. To create the neighborhood disadvantage scale,
the
following measures (taken from the 1990 U.S. Census) were
factor ana-
lyzed: the percentage of Black residents, the percentage of
female headed
households, the percentage of residents with an income under
US$15,000,
the percentage of residents on public assistance, and the
unemployment
rate. Factor analysis revealed that the five items were tapping
an underlying
latent construct. All factor loadings were greater than or equal
to .69 and the

reliability coefficient was .80. The scale was created using
regression scor-
ing based on the factor analysis results. Higher values reflected
more neigh-
borhood disadvantage.
Violent crime rate. The Uniform Crime Reports (UCR) is
produced by the
Federal Bureau of Investigation each year. These data reflect
the total amount
of reported crime in each of the 50 states. The Add Health
sample included
county-level crime rate data drawn from these UCR statistics
(1993 data).
The violent crime rate variable is a composite variable
reflecting the number
of robberies, aggravated assaults, rapes, and homicides per
100,000 residents
in each county.
Control Variables
Age. To control for age differences in violent behavior, the
respondent’s
age was included in the statistical analysis. The age variable
was coded con-
tinuously in years.
Race. To control for any potential race differences in violent
behavior, the
respondent’s race was controlled with a dichotomous variable
coded 0 = non-
Black and 1 = Black.

Analytic Strategy
The analysis unfolded in two interlocking steps. The first step
estimated the
influence of dopamine risk on self-reported violent behavior,
while also
including a multiplicative interaction term between the
dopamine risk scale
and the two environmental measures (i.e., neighborhood
disadvantage and
violent crime rate). The multiplicative terms were created by
mean centering
the dopamine risk scale, the neighborhood disadvantage scale,
and the violent
crime rate and then multiplying the dopamine risk scale by the
neighborhood
disadvantage scale and by the violent crime rate. Thus, two
multiplicative
terms were generated: dopamine risk X neighborhood
disadvantage and dopa-
mine risk X violent crime rate. Two negative binomial models
were estimated.
The first examined the interaction between dopamine risk and
neighborhood
disadvantage. The second examined the interaction between
dopamine risk
and violent crime rate. The coefficient estimate for the
interaction terms
indicated whether genetic risk was contingent upon
environmental exposure
to disadvantage/violent crime.
The second step in the analysis also investigated the interaction
between

the dopamine risk variable and the environmental variables.
This portion of
the analysis approached the interaction question using a slightly
different
strategy. To be specific, the effect of dopamine risk on violent
behavior was
examined after splitting the sample according to scores on the
environmental
variables. In the first analysis, respondents were split into two
groups: those
living at or above the 75th percentile on the neighborhood
disadvantage scale
(i.e., a high degree of disadvantage) and those living below the
75th percentile
(i.e., moderate to low levels of neighborhood disadvantage).
Once respon-
dents were split into the two groups, the negative binomial
models were rees-
timated, but this time the multiplicative term was omitted. The
same strategy
was followed in respect to the violent crime rate. The benefit of
these models
is twofold. First, these models offer a clean way to reconfirm
any findings
gleaned from the models that employ the multiplicative
interaction term.
Second, this approach is amenable to a graphical depiction of
the effect of
dopamine risk on violent behavior at different levels of
environmental risk.
Findings
Table 2 presents the findings gleaned from the first set of
negative binomial
models where self-reported violent behavior is the dependent
variable. As

can be seen, these two models explored the interaction between
dopamine
risk and the environmental measures by including multiplicative
interaction
terms. Model 1 analyzed the interaction between dopamine risk
and the
neighborhood disadvantage scale. Two findings emerged. First,
the coeffi-
cient estimate for the dopamine risk scale was positive and
statistically sig-
nificant.2 The incidence rate ratio revealed, for instance, that a
one unit
increase in the dopamine risk scale increased the rate of violent
behavior by
roughly 8% (when neighborhood disadvantage is set to zero—or
the mean).
The second key finding was that the multiplicative interaction
term was
moderately (p < .10) significant and the effect was positive.
This finding
deserves close attention because it suggests an interesting
relationship
between dopamine risk and neighborhood disadvantage. To be
specific, the
interaction term indicates that the effect of dopamine risk on
violent behavior
is contingent on the level of environmental risk that is present.
As the envi-
ronmental risk increases (i.e., gets more positive), the effect of
the dopamine
risk also increases.

Model 2 in Table 2 presents the findings from the regression
model which
explored the interaction between dopamine risk and violent
crime rates.
Similar to the findings from model 1, model 2 revealed that
dopamine risk
was positively related to the respondent’s self-reported violent
behavior.
Importantly, model 2 also revealed that dopamine risk and the
violent crime
rate interacted such that the effect of dopamine risk on violent
behavior was
stronger for respondents who lived in high crime counties.
Table 2. Negative Binomial Regression of Self-Reported
Violent Behavior on
Dopamine Risk and Environmental Risk for Add Health Males
Model 1 Model 2
b IRR SE b IRR SE
Age –.04 0.97 .03 –.05* 0.95 .03
Black (=1) .10 1.11 .17 .13 1.14 .13
Dopamine risk .08* 1.08 .05 .07** 1.07 .05
Neighborhood disadvantage .07 1.07 .06
Dopamine risk × neighborhood
disadvantage
.07** 1.07 .04
County violent crime rate .0001 1.0001 .0001
Dopamine Risk × county
violent crime rate

.0002* 1.0002 .0001
Note: b = unstandardized coefficient; IRR = incidence rate
ratio; SE = standard error; Standard
errors are clustered by block group in model 1 and by county in
model 2.
*p < .05, **p < .10 (one-tailed).
As outlined above, the second step of the analysis involved
splitting the
sample according to their level of exposure to the environmental
risk vari-
ables. Presented in Figure 1 is the first set of findings (with the
parameter
estimates presented in the figure caption) where the sample was
split accord-
ing to scores on the neighborhood disadvantage scale. The
findings from
these models are directly in line with the findings from model 1,
Table 2. Two
points are worth noting. First, the effect of dopamine risk on
violent behavior
is practically nonexistent for respondents living below the 75th
percentile on
0
0 1 2 3 4 5 6
0.5
1

1.5
2
2.5
3
3.5
4
Pr
ed
ic
te
d
Se
lf
-R
ep
or
te
d
V
io
le
nt
B

eh
av
io
r
Number of Dopamine Risk Alleles
At/Above 75th Percentile
Below 75th Percentile
Figure 1. Predicted scores on the self-reported violent behavior
scale for males
living at or above the 75th percentile and below the 75th
percentile for the
neighborhood disadvantage scale
Note: Standard errors are clustered by block group; Models
control for age, race, and
neighborhood disadvantage; Above 75th percentile coefficient
estimates: b
Dopamine Risk
= .23,
SE
Dopamine Risk
= .08, p < .05; Below 75th percentile coefficient estimates: b
Dopamine Risk
= .02,
SE
Dopamine Risk

= .05, p > .05.
the neighborhood disadvantage scale (i.e., moderate to low
levels of disad-
vantage). Indeed, the coefficient estimate for the dopamine risk
scale was not
statistically different from zero in this model (see note of
Figure 1). The sec-
ond finding to note is that the dopamine risk scale had a strong
positive effect
on violent behavior for respondents living at or above the 75th
percentile on
the neighborhood disadvantage scale. For respondents living at
or above the
75th percentile, there was an approximately 200% increase in
self-reported
violent behavior between respondents with minimal dopamine
risk (i.e., one
risk allele) and those with high dopamine risk (i.e., six risk
alleles). To be
specific, respondents with 1 risk allele were predicted to report
1.16 violent
acts while respondents with six risk alleles were predicted to
report 3.60 vio-
lent acts. A coefficient comparison test (Paternoster, Brame,
Mazerolle, &
Piquero, 1998) indicated that the coefficient estimate for the
dopamine risk
scale was statistically different across the two models (z = 2.23;
p < .05,
one-tailed).

The next set of findings is presented in Figure 2 (with the
parameter esti-
mates presented in the figure caption). This figure depicts the
findings from
the analyses where the sample was split according to scores on
the violent
crime rate measure. As before, respondents were split at the
75th percentile
and separate regression models were estimated. The findings in
Figure 2 are
consistent with the findings from Table 2, model 2. In
particular, dopamine
risk had no effect on violent behavior for respondents living in
moderate to
low crime counties (i.e., below the 75th percentile). Dopamine
risk did, how-
ever, increase violent behavior for respondents living in high-
crime areas
(i.e., at or above the 75th percentile). On one hand, respondents
living in
high-crime areas who had no risk alleles were predicted to
report less than
one violent act (predicted rate = .91). On the other hand,
respondents with
five risk alleles were predicted to report 3.13 violent acts (there
were no
respondents with six risk alleles who lived at or above the 75th
percentile for
the violent crime rate). A coefficient comparison test indicated
that the effect
of the dopamine risk scale was significantly different across the
two models
(z = 2.47; p < .05, one-tailed). Each of the findings outlined in
this section are
placed within the broader theoretical context in the next section.

Discussion
The current study tested three hypotheses. The first hypothesis
stated that
respondents who carry a genetic risk toward violent behavior
would be more
likely to report violent behavior as compared to those who did
not carry the
genetic risk. The results of the analysis supported this
hypothesis by revealing
that individuals with more risk alleles on the dopamine risk
scale were more
likely to report violent behavior. The second hypothesis argued
that the effect
of genetic risk on violent behavior would be contingent upon
the respon-
dent’s exposure to neighborhood disadvantage. This hypothesis
was tested in
two ways. First, a multiplicative interaction was entered into
the regression
model and the results supported the hypothesis; the interaction
term was
positive and statistically significant. Second, the sample was
split at the 75th
percentile and main effects models were estimated. These
models (the results
0
0.5
1

1.5
2
2.5
3
3.5
Pr
ed
ic
te
d
Se
lf
-R
ep
or
te
d
V
io
le
nt
B

eh
av
io
r
At/Above 75th Percentile
Below 75th Percentile
0 1 2 3 4 5 6
Number of Dopamine Risk Alleles
Figure 2. Predicted scores on the self-reported violent behavior
scale for males
living at or above the 75th percentile and below the 75th
percentile for the violent
crime rate
Note: Standard errors are clustered by county; Models control
for age, race, and violent crime
rate; Above 75th percentile coefficient estimates: b
Dopamine Risk
= .25, SE
Dopamine Risk
= .08, p < .05;
Below 75th percentile coefficient estimates: b
Dopamine Risk
= .003, SE
Dopamine Risk
= .06, p > .05.

of which were plotted in Figure 1) also revealed support for
Hypothesis 2:
respondents who lived in disadvantaged neighborhoods were
most likely to
report violence if they also had genetic risk factors.
The third hypothesis noted that the effect of genetic risk on
violent behav-
ior would be contingent on the respondent’s exposure to violent
crime. This
hypothesis was tested using two different approaches. First, a
multiplicative
interaction term was used and the results gleaned from this
model supported
the notion that respondents who lived in violent areas and had a
genetic risk
toward violent behavior were most likely to report violence.
The second way
in which Hypothesis 3 was tested was with split sample models
(split at the
75th percentile) and the results obtained from these models
were also sup-
portive of the hypothesis (results are plotted in Figure 2). In
summary, the
current study revealed evidence to support a GxE between a
dopamine risk
scale (a scale which indexed the number of risk alleles on three
dopamine
genes carried by each respondent) and the respondent’s
exposure to neigh-
borhood disadvantage and county-level violent crime rates.

It is worth mentioning, however, that the current findings did
not conform
to the differential susceptibility hypothesis (Belsky et al.,
2007), although
this does not disprove that hypothesis. The differential
susceptibility hypoth-
esis suggests that a “cross-over” effect will be observed in the
data (see
Simons et al., 2011, 2012). Although our data reveal a cross-
over effect, this
effect necessitates a different interpretation: those with the
lowest genetic risk
who lived in the worst neighborhoods displayed less violence as
compared to
all other respondents. The differential susceptibility hypothesis
expects indi-
viduals with the highest genetic risk to display more violent
behavior in high-
risk environments but less violent behavior in low-risk
environments as
compared to other individuals carrying less or no genetic risk.
This hypothe-
sis would have been supported had the main effect term (in the
multiplicative
models) for the dopamine risk scale emerged as having a
negative impact on
violent behavior. Alternatively, this hypothesis would have been
supported
had the dopamine risk scale exhibited a negative effect for
individuals living
below the 75th percentile and a positive effect for individuals
living at or
above the 75th percentile. Note, however, that the current study
was not
intended to be a test of the differential susceptibility
hypothesis, so the lack

of supportive evidence should not be taken as negative evidence
for that per-
spective. This is important because the differential
susceptibility hypothesis
necessitates an environmental continuum ranging from “good”
to “bad.” Our
environmental measures, however, were operationalized as
“bad” (i.e., at or
above 75th percentile) and “not bad” (below 75th percentile)
which may
reflect a different phenomenon.
Limitations to the analysis must be discussed. First, although a
link
between dopamine genes and violent behavior has been
highlighted by prior
work (e.g., Guo et al., 2008), and some scholars have reported
dopamine X
environment interactions (Beaver et al., 2011), the exact
mechanisms under-
lying these relationships are not well understood. Research
suggests that
dopamine is part of the body’s pleasure/reward center (Rutter,
2006), but this
does not describe why dopaminergic activity is related to
violent behavior.
Researchers should prioritize studies that seek to answer this
question. A sec-
ond limitation is that only three dopamine genes were analyzed.
Indeed, the
human genome is believed to include approximately 25,000
genes, leaving

much to be learned about the integration of genetics into
criminology. Finally,
we were unable to directly specify the interactional nature of
dopamine risk
and the two environmental risk measures (Shanahan & Hofer,
2005). In other
words, how does exposure to neighborhood disadvantage and
violent crime
affect genetic risk? Future work must seek to understand the
forces behind this
relationship. The remainder of our discussion is devoted largely
to this issue.
If predisposition to violence is a switch that must be “tripped”
by contex-
tual factors before it can exert an influence over behavior
(Pinker, 2002), the
precise mechanism by which neighborhood disadvantage trips
this switch is
unclear. Prior research suggests that it may be rooted in one of
two dynamics:
contextual triggering or social context as a social control
(Beaver et al.,
2011). The contextual triggering explanation holds that stressful
environ-
ments cause specific genotypes to be expressed. Thus, Caspi et
al. (2002)
found that severe child maltreatment was associated with
particular genetic
expressions not discovered among participants who were not
maltreated;
both the maltreatment and the genotypes were subsequently
associated with
antisocial conduct. The implication is that the stress of severe
maltreatment
caused the effects of the particular genotype to surface, which

in turn facili-
tated the antisocial conduct. The second explanation, rooted in
social control,
implies that the effects of particular genotypes are inhibited
from formation
when predisposed persons are adequately monitored and
supervised. In the
absence of adequate monitoring, the genotype has a greater
likelihood of sur-
facing, resulting in antisocial behavior.3
Although our research cannot pinpoint the precise mechanism
for the
observed relationship between violent crime, neighborhood
disadvantage,
and genotypes, deconstructing the relationship may be more
important to
developing insights into its etiology than anything else. Only
then can
researchers better specify the factors that moderate and mediate
the relation-
ship. We believe microstructural, subcultural, situational, and
existential pro-
cesses to be implicated.4
Beginning with microstructure, we make the simple observation
that dis-
advantaged neighborhoods are generally not pleasant places to
be. Physical
decay abounds. Disorder is visual and widespread. The threat of
predation is
palpable. Singly and in combination, these and related forces

foment fear
(see, for example, Winkel, 1998). Fear breeds insularity, and
insularity likely
encourages residents to retreat to within-network ties generally
high in cohe-
sion (for an overview of the relationship between social ties and
crime fear,
see Gibson, Zhao, Lovrich, & Gaffney, 2002). But strong ties
are not neces-
sarily protective. Some of the most lethal violence the world
over is commit-
ted between people who know one another, often intimately. As
retreat to
such ties increases, the frequency and duration of contact
increases and so
does the likelihood of disputatiousness (on disputatiousness, see
Luckenbill
& Doyle, 1989). Undermining disputants’ ability to cope is
resource depriva-
tion (Agnew, 1992), thereby lowering the flashpoint for
violence. Risk ver-
sions of dopaminergic alleles may amplify this problem by
promoting low
frustration tolerance, agitation, insensitivity, poor problem-
solving skills, and
hostile attribution bias (LaHoste et al., 1996). The ability of
affected indi-
viduals to resolve conflicts nonviolently may be compromised
just as the
need for peaceful conflict resolution rises.
The link between genotypes, neighborhood disadvantage, and
violence is
likely also rooted in subculture. Prior research suggests that
residents in
highly disadvantaged communities feel a profound sense of

procedural and
distributive injustice (Downing, 2011). More specifically, acute
resource
shortfalls give rise to perceptions of absolute and relative
deprivation. Relative
deprivation is especially probative of violence in disadvantaged
neighbor-
hoods because it promotes displays of one-upmanship to assert
status among
similarly positioned others (Anderson, 1999). Such displays
constitute both a
putdown and a provocation to those on the receiving end by
casting those
persons as inferior (Jacobs, Topalli, & Wright, 2000). Such
displays convey
double rejection: Not only has one failed in mainstream society,
she or he also
cannot “measure up” against similarly situated others.
Subcultural norms,
epitomized by the “code of the street” (Anderson, 1999), take
natural sensi-
tivity to rejection and amplify it by directing those slighted to
respond vigor-
ously to all affronts, big and small. Respect is currency in
disadvantaged
neighborhoods, so one must not only advance respect whenever
possible but
defend it vigorously whenever it comes into question. Because
rejection may
promote imbalances in brain chemistry governed partially by the
genes
explored here (see generally, Way, Taylor, & Eisenberger,
2009), affected
individuals may be more likely to engage in compensatory
behavior. In dis-
advantaged neighborhoods, such behavior frequently involves

aggression,
which permits affected individuals to “slough off” negative
affect and
restore a semblance of equilibrium, however temporary it may
be (Brown &
Gershon, 1993).
Situational forces also are implicated in the
genes/disadvantage/violence
link. Criminologists have consistently found that neighborhoods
with high
levels of disadvantage present widespread opportunities for
predatory vio-
lence (Sampson, Morenoff, & Gannon-Rowley, 2002). Part of
the reason is
rooted in weak informal social control, which creates attractive,
guardian-
free targets among impulsive offenders looking for a quick
score. Many such
opportunities emerge serendipitously, and serendipity hinges on
the situated
recognition and exploitation of novel cues (Jacobs, 2010). The
desire for nov-
elty seeking appears to be mediated by dopaminergic genes
(Zald et al., 2008;
but see Burt, McGue, Iacono, Comings, & MacMurray, 2002),
which dictates
how cues are perceived, how they are assessed, and how they
promote action
to exploit them. Moreover, persistent need in disadvantaged
settings erodes

the ability of affected individuals to exercise inhibition over
time in response
to novelty (see Gottfredson & Hirschi, 1990). This is important
because
dopamine genes have been linked to deficits in inhibition and
conduct disor-
der (Beaver et al., 2007; Boutwell & Beaver, 2008; Retz,
Rosler, Supprian,
Retz-Junginger, & Thome, 2003). Lowered inhibitions coupled
with desires
for novelty likely reduce the deterrent effect of threatened
sanctions
(Gottfredson & Hirschi, 1990; Wilson & Herrnstein, 1985),
especially in
localities where formal authority is not respected. This certainly
is the case in
many disadvantaged communities, where defiance often is more
common
than deterrence. Disrespect for authority promotes
noncompliance to threat-
ened sanctions (Sherman, 1993) which in combination with a
target-rich
environment, likely liberates a nontrivial amount of predatory
violence.
Then there are existential processes. Numerous studies have
shown that
dopamine levels are correlated with violent behavior (Ferguson
& Beaver,
2009). Indeed, violence can promote a seductive thrill that
produces measur-
able rises in mood-enhancing brain chemicals (Raine, 1993).
Some violent
offenders operating in disadvantaged communities specifically
implicate “the
rush” of aggression as a motivating factor in their behavior

(Jacobs, 2000).
Explosive violence is then applauded and encouraged by the
street code,
which bolsters its reinforcement potential (Anderson, 1999).
The more obvi-
ous existential link between dopaminergic genes, violence, and
neighborhood
disadvantage is indirect—coming through illicit drug use itself.
Few socio-
logical variables have been more robustly linked to illegal drug
consumption
than neighborhood disadvantage (Boardman, Finch, Ellison,
Williams, &
Jackson, 2001; Galea, Ahern, & Vlahov, 2003). The extent to
which an
available, accessible, and relatively cheap supply of illicit drugs
“creates”
drug use is unclear, but in the presence of at-risk dopamine
alleles, the nega-
tive synergy cannot be ignored. Dopamine imbalances have long
been impli-
cated in addictive drug use. Scholars suggest that drugs may
compensate for
biological deficits in dopamine production or uptake for some
users (Filbey
et al., 2008; Volkow, Fowler, Wang, & Swanson, 2004). Once
use begins and
escalates, the propensity for psychopharmacological,
economically compul-
sive, and systemic aggression rises proportionately (Goldstein,
1985). Because

most of this violence occurs, by definition, among criminal
disputants, griev-
ances will tend to be resolved informally through reprisal.
Retaliation has a
strong tendency to promote counter-retaliation, which promotes
conflict spi-
rals and a self-reinforcing cycle of instability that compounds
neighborhood
disadvantage and heightens the importance of reacting violently
to conditions
that violate (Anderson, 1999). This feedback loop then weakens
collective
efficacy and leads to yet more instability. Even if a small
proportion of
affected individuals turn to drug use because of some
combination of disad-
vantage and genotype, the synergy between drugs and violence
and the pro-
clivity for discrete disputes to trigger retaliation and then to
spread in
contagion-like fashion across disadvantaged communities likely
promotes a
disproportionate amount of serious violent crime.
The link between biological risk factors, neighborhood
disadvantage, and
violence is multisourced. Whether context or biology triggers
them is less
important than their symbiotic and contingent relationship. Such
factors
interact to potentiate violence in multiple and complex ways.
Acknowledgments
This research uses data from Add Health, a program project
directed by Kathleen

Mullan Harris and designed by J. Richard Udry, Peter S.
Bearman, and Kathleen
Mullan Harris at the University of North Carolina at Chapel
Hill, and funded by grant
P01-HD31921 from the Eunice Kennedy Shriver National
Institute of Child Health
and Human Development, with cooperative funding from 23
other federal agencies
and foundations. Special acknowledgment is due Ronald R.
Rindfuss and Barbara
Entwisle for assistance in the original design. Information on
how to obtain the Add
Health data files is available on the Add Health website
(http://www.cpc.unc.edu/
addhealth). No direct support was received from grant P01-
HD31921 for this
analysis.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research,
authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research,
authorship, and/or pub-
lication of this article.
Notes
1. It should be noted that GxEs are not limited to triggering

effects. As discussed by
Shanahan and Hofer (2005), GxEs can also work by blunting
genetic influences,
by allowing genetic influences to flourish, and by enhancing
adaptation.
2. It is important to point out that statisticians disagree over
whether the main effect
terms should be interpreted in a multiplicative interaction model
(McClendon,
2002). We have included an interpretation of the main effects
here because the
coefficient estimates were consistent with a model that did not
include the mul-
tiplicative term (i.e., the coefficient estimate reported in the
tables was simi-
lar [within .01] to the coefficient estimate retrieved from a main
effects only
model—although the standard errors were slightly smaller in the
interaction
model). Also, the bivariate correlation between the dopamine
risk scale and the
violent behavior scale was r = .06 (p < .10).
3. We acknowledge an anonymous reviewer, who insightfully
pointed out that mal-
treated individuals could be appropriately monitored—creating
the simultaneous
operation of contextual triggers and inhibiting forces.
Furthermore, it is unclear
from previous theoretical statements whether parental
maltreatment is concep-
tually distinct from inappropriate monitoring (i.e., could
parental maltreatment
manifest in the form of inappropriate monitoring?). It is our
position that parental

maltreatment and inappropriate monitoring represent distinct
behavioral phenom-
ena that, therefore, may occur simultaneously, or independently.
We advocate for
more refined measures of parental control and maltreatment so
that future research-
ers can address this more precisely. As noted by the blind
reviewer, one could
divide a sample into those who were both maltreated and
subjected to inappropri-
ate monitoring, and those who were subjected to only one or the
other in order to
disentangle which of these two conditions are more prominent
in their effect.
4. We thank an anonymous reviewer for pointing out that these
four areas are not
necessarily mutually exclusive. We offer them simply to
sensitize readers to the
nuanced variation in the mechanisms that likely
mediate/moderate the GxE rela-
tionship. Such nuances permit theoretical refinement by future
research.
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Bios
J. C. Barnes is an assistant professor in the Criminology
Department at The
University of Texas at Dallas. His research seeks to understand
how genetic and
environmental factors combine to impact criminological

phenomena.
Bruce A. Jacobs is a professor in the Criminology Department
at The University of
Texas at Dallas. He studies violent crime and offender decision-
making.

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