-
Title
-
An Imaging Gene by Environment Interaction (IG×E) Approach to Understanding Youth Antisocial Behavior
-
Author
-
Waller, Rebecca
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Dotterer, Hailey L.
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Hyde, Luke W.
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Research Area
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Special Areas of Interdisciplinary Study
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Topic
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Genetics, the Individual and Society
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Abstract
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An examination of the complex interplay of genes, environmental experience, and the brain is critical to understanding psychopathology, violence, and aggression. This essay reviews the gene–environment (G×E) interaction and imaging genetics literature relating to the development of youth antisocial behavior (AB). A model is proposed that bridges these approaches within an imaging gene×environment (IG×E) interaction framework. The potential application of an IG×E framework to youth AB is outlined and ongoing research challenges are discussed.
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Identifier
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etrds0012
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extracted text
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An Imaging Gene by Environment
Interaction (IG×E) Approach to
Understanding Youth Antisocial
Behavior
REBECCA WALLER, HAILEY L. DOTTERER, and LUKE W. HYDE
Abstract
An examination of the complex interplay of genes, environmental experience, and
the brain is critical to understanding psychopathology, violence, and aggression. This
essay reviews the gene–environment (G×E) interaction and imaging genetics literature relating to the development of youth antisocial behavior (AB). A model is proposed that bridges these approaches within an imaging gene×environment (IG×E)
interaction framework. The potential application of an IG×E framework to youth AB
is outlined and ongoing research challenges are discussed.
INTRODUCTION
Using an imaging gene×environment framework, this essay outlines an
approach that builds on traditional gene×environment interactions using
neuroimaging to examine how the brain may be the mechanism linking the
interaction of biology and experience to behavior. We use youth antisocial
behavior (AB) as an example to illustrate how gene×environment interactions can be examined in the context of neuroimaging studies and build
a model for understanding the conditional mechanisms that underlie the
development of psychopathology.
FOUNDATIONAL RESEARCH
DEFINITIONS OF YOUTH ANTISOCIAL BEHAVIOR
AB refers to a range of behaviors that cause harm and are costly to individuals, communities, and society as a whole. A variety of definitions for youth
AB and violence exist across disciplines, including diagnoses in psychology
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.
1
2
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
and psychiatry [e.g., conduct disorder (CD)] and legal terms (e.g., delinquency); however, in general, these behaviors include rule-breaking,
aggression, and other dangerous behaviors, such as early drug use. One of
the most consistent predictors of adult antisocial and criminal behavior is a
history of disruptive behavior problems starting in childhood (e.g., Campbell, Shaw, & Gilliom, 2000). Beyond later AB outcomes, children with these
early-starting behavior problems are also at risk of developing a wide range
of other adverse mental health problems in adulthood, including substance
use and depression (Odgers et al., 2008). As such, a large body of literature
has examined risk factors that are associated with the development of youth
AB. A better understanding of how different risk factors interact is of particular importance as such research has the potential to inform how, when, and
for whom we should intervene (Olds et al., 1998) and is thus instrumental in
designing more effective treatment and prevention programs.
ROLE OF “E”—ENVIRONMENT
Etiological models of AB have benefited from adopting an ecological
perspective, which emphasizes an examination of risk across levels (e.g.,
from communities to schools to parents to children), considering how these
levels interact over time (Bronfenbrenner, 1986; Shaw & Gross, 2008). While
individual factors, such as low intelligence, difficult temperament, and
poor executive functioning, have been identified as risks for AB (Loeber
& Farrington, 2000), environmental factors have also been robustly related
to the development of AB. In particular, strong evidence supports links
between the development of youth AB and parenting practices (e.g., lack
of supervision or involvement and harsh parenting; Loeber, Farrington,
Stouthamer-Loeber, & Van Kammen, 1998), child maltreatment (e.g., abuse
and neglect; Widom, 1989), parental criminality (Loeber et al., 1998), socioeconomic status (Sadeh et al., 2010), neighborhood dangerousness (Barnes
& Jacobs, 2013), and deviant peer affiliation (Dishion & Patterson, 2006).
However, as is the case for many psychological phenomena, although these
findings are robust, a majority of children who are exposed to these specific
environmental risk factors do not experience poor behavioral outcomes
(Kim-Cohen et al., 2006). It appears that various factors render some individuals more or less susceptible to the same experiences, making it difficult to
define single, causal, or overwhelming “risk environments” (Ellis & Boyce,
2011; Pluess & Belsky, 2013). Thus, recently researchers have begun to focus
their attention on identifying reasons why some individuals are more or less
susceptible to harsh or risky environments.
IG×E Approach to Understanding Youth Antisocial Behavior
3
ROLE OF “G”—GENETICS
Genetic variation is a key factor that may play a role in differences in susceptibility and put certain individuals at greater risk of developing AB. Some
portion of individual genetic risk stems from common genetic polymorphisms,
which are regions of the genome for which there exist two or more different
versions (i.e., alleles). Functional genetic polymorphisms can reflect changes
in a single (or multiple) base pairs of DNA that affect transcription of a gene
and/or the structure of the resulting translated protein. These changes may
then lead to differential protein function, which can in turn affect neurotransmitters key to brain function and subsequent behavior (Hyde, Swartz, Waller,
& Hariri, 2014; Meaney, 2010). Many candidate genes for AB are polymorphisms that have been directly linked to youth AB or related constructs in
behavioral studies (e.g., impulsivity, low fear, and aggression) or are polymorphisms that have been shown to affect neurotransmitter systems that
influence key brain structures or functioning linked to aggression, violence,
and AB.
For example, the enzyme monoamine oxidase A (MAOA) supports the
breakdown of monoamine neurotransmitters, including serotonin and
norepinephrine. In a seminal paper, researchers identified a rare mutation in
this MAOA gene that created an MAOA protein deficiency and was related
to high rates of aggressive, violent, and criminal behavior in a Dutch kindred
(Brunner, Nelen, Breakefield, Ropers, & Van Oost, 1993). Later studies have
built on this finding and reliably linked a more common polymorphism in
MAOA (a “low activity allele”) to AB, particularly among males and in the
context of maltreatment (e.g., Byrd & Manuck, 2013; Caspi et al., 2002; see
next section).
In more recent years, studies investigating the genetic basis of child
and adolescent AB have continued to focus on gene variants related to
neurotransmitter pathways involving dopamine and serotonin. This focus
stems from the fact that these neurotransmitters and their neural targets are
implicated in a variety of behaviors including emotion, reward, and learning, which are all areas that are recognized as being disrupted in AB (Hyde,
Shaw, & Hariri, 2013). In particular, research has explored DRD4, DRD2,
5-HTTLPR, TPH, and COMT genes in relation to AB (Belksy & Pluess, 2009).
At the same time, it is important to note that direct gene-behavior studies
have not yielded consistent answers (Viding & Frith, 2006), emphasizing
that it is unlikely that we will find specific “AB genes” that are invariant
across environments.
4
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
THE INTERACTION BETWEEN AN INDIVIDUAL’S GENES AND THEIR ENVIRONMENT
(G×E INTERACTIONS)
Background to G×E Interactions. To address weak and inconsistent findings
in gene-behavior links, including a lack of replication across studies and the
failure of genome-wide approaches to yield genetic variants that explain the
expected amount of “heritability” from twin studies (Maher, 2008), some
research has shifted to an examination of gene×environment (G×E) interactions. Specifically, G×E interaction research demonstrates that the effect an
environmental experience has an outcome (e.g., youth AB) is conditional on
genetic background (i.e., genotype). Similarly, G×E interaction research also
supports the idea that the effect of genotype on behavior is contingent on
experience (Moffitt, Caspi, & Rutter, 2005, 2006).
Examples of G×E Studies. In one of the earliest examples of a G×E interaction,
the low activity allele of the MAOA gene was found to predict AB in boys, but
only in the context of childhood maltreatment (Caspi et al., 2002). Since this
novel finding, many studies have replicated and extended research examining whether variation in genotype moderates the relationship between environmental risk and AB. Table 1 provides a summary of 45 studies to date
that have examined interactions between genes and experiences of environmental adversity in predicting youth AB. The majority of these studies (27 of
45) have investigated MAOA allelic variants, while the remainder has examined other candidate genes previously linked to AB, including those linked
to dopamine and/or serotonin neurotransmission.
The most consistent evidence of G×E interactions has been found with
MAOA. Specifically, the low activity allele of MAOA (MAOA-L) appears
to be related to increased risk for AB, but only in the context of childhood
adversity and typically more strongly in samples of boys/men (Byrd &
Manuck, 2013). In addition, genes affecting the dopamine (e.g., DRD4 and
DRD2) and serotonin (e.g., 5-HTTLPR) neurotransmitter systems appear to
interact with environmental context (particularly parenting and prenatal
risk) to predict youth AB (e.g., Kieling et al., 2013; Zohsel et al., 2014). Finally,
in addition to genetic effects being stronger in the context of harsh environments, a majority of studies have found that experience has a direct main
effect on AB. That is, harsh environments are robust predictors of youth AB,
whereas specific genetic variants do not consistently have a main effect on
AB. However, taken together, the findings from these 45 studies imply that
genes affecting relevant brain systems can help identify individuals more
sensitive to the environmental effects on AB.
IG×E Approach to Understanding Youth Antisocial Behavior
5
Table 1
G×E Studies of Youth Antisocial Behavior
References
N
(%
female)
Age
rangea
Allele
Environmentb
Measure
of youth
AB
Choe et al. (in
press)
189
(0%)
1.5–20a
MAOA-L
Punitive
disciplineb
Violent
Yes
attitude;
juvenile
arrests; ASB
Zohsel et al.
(2014)
308
(51)
8, 11, 15
DRD4-L
Prenatal stressb CD, ODD
Yes
Haberstick et al.
(2014)
4316
(0)
12–18
13–20
24–34
MAOA
Childhood
maltreatment
No
AB; CP;
violent
convictions;
disposition
toward
violence
G×E
(sig?)
Citationc
Choe, D.E., Shaw, D.S., Hyde, L.W., &
Forbes, E.E. (2014). Interactions
between MAOA and Punitive Discipline
in African American and Caucasian
Men’s Antisocial Behavior Clinical
Psychological Science, 2, 591–601.
Zohsel, K., Buchmann, A.F., Blomeyer, D.,
Hohm, E., Schmidt, M.H., Esser, G.,
Brandeis, D., Banaschewski, T., Laucht,
M. (2014). Mothers’ prenatal stress and
their children’s antisocial outcomes- a
moderating role for the dopamine
receptor D4 (DRD4) gene. Journal of
Child Psychology and Psychiatry, 55,
69–76.
Haberstick, B.C., Lessem, J.M., Hewitt, J.K.,
Smolen, A., Hopfer, C.J., Halpern, C.T., …
Harris, K.M. (2014). MAOA genotype,
childhood maltreatment, and their
Interaction in the etiology of adult
antisocial behaviors. Biological Psychiatry,
75, 25–30.
(continued overleaf)
6
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Table 1
(Continued)
References
N
(%
female)
Age
rangea
Allele
Environmentb
Measure
of youth
AB
G×E
(sig?)
Citationc
Willoughby et al.
(2013)
171
(0)
6 mo,
1, 2, 3
BDNF-met
Harsh
parentingb
CU, ODD
Yes
Kieling et al.
(2013)
4101
(51)
0, 11, 15
DAT1 &
MAOA
Prenatal
smokingb,
Maltreatment
CP
No
Barnes and
Jacobs (2013)
1078
(0)
12–18
DAT1-10R, Neighborhood Violent
DRD2-A1,
disadvantageb behavior
,
DRD2,
DRD4-L Crimeb
Willoughby, M.T., Mills-Koonce, R., Propper,
C.B., Waschbusch, D.A. (2013). Observed
parenting behaviors interact with a
polymorphism of the brain-derived
neurotrophic factor gene to predict the
emergence of oppositional defiant and
callous–unemotional behaviors at age 3
years. Development and Psychopathology,
25, 903–917.
Kieling, C., Hutz, M.H., Genro, J.P.,
Polanczyk, G.V., Anselmi, L., Camey, S.,
et al. (2013). Gene–environment
interaction in externalizing problems
among adolescents: evidence from the
Pelotas 1993 Birth Cohort Study.
Journal of Child Psychology and
Psychiatry, 54, 298–304
Barnes, J.C. & Jacobs, B.A. (2013).
Genetic risk for violent behavior and
environmental exposure to
disadvantage and violent crime: The
case for gene-environment interaction.
Journal of Interpersonal Violence, 18,
92–120
Yes
(continued overleaf)
IG×E Approach to Understanding Youth Antisocial Behavior
7
Table 1
(Continued)
Measure
of youth
AB
G×E
(sig?)
Citationc
MAOA-L,
Maltreatmentb
5-HTTLPR-S;
TPH-T
AB
Yes
10–11
15–16
DRD2-L,
DRD4,
COMT-A
Parental
separationb
Ext behavior
Yes;
DRD2
- no
12–26
MAOA-L
Traumatic life
events
Weapon use,
Public
fighting
Yes
Cicchetti, D., Rogosch, F., Thibodeau, E.L.
(2012). The effects of child maltreatment
on early signs of antisocial behavior:
Genetic moderation by tryptophan
hydroxylase, serotonin transporter and
monoamine oxidase A genes.
Development and Psychopathology, 24,
907–928.
Nederhoff, E., Belsky, J., Ormel, J.,
Oldehinkel, A.J. (2012). Effects of divorce
on Dutch boys’ and girls’ externalizing
behavior in Gene-Environment
perspective: Diathesis stress or differential
susceptibility in the Dutch Tracking
Adolescents’ Individual Lives Survey
study? Development and
Psychopathology, 24, 929–939.
McDermott, R., Dawes, C., Prom-Wormley,
E., Eaves, L., Haterni, P.K. (2012). MAOA
and Aggression: A Gene–Environment
Interaction in Two Populations. Journal of
Conflict Resolution, 57, 1043–1064.
References
N
(%
female)
Age
rangea
Allele
Cicchetti et al.
(2012)
62
(50)
10–12
Nederhoff et al.
(2012)
1134
(52)
McDermott et al.
(2012)
2665
(0)
Environmentb
(continued overleaf)
8
E MERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Table 1
(Continued)
Environmentb
References
N
(%
female)
Age
rangea
Allele
Conway et al.
(2012)
381
(61)
15, 20
5-HTTLPR-S Chronic life
stress
Fergusson et al.
(2012)
351
(0)
15–30a
MAOA-L
Lahey et al.
(2011)
162
(40)
4–6
9–11
DAT1-10R
Measure
of youth
AB
G×E
(sig?)
Citationc
Aggression
Yes
Conway, C., Keenan-Miller, D., Hammen, C.,
Lind, P., Najman, J., & Brennan, P. (2012).
Coaction of stress and serotonin
transporter genotype in predicting
aggression at the transition to adulthood.
Journal of Clinical Child & Adolescent
Psychology, 41, 53–63.
Fergusson, D.M., Boden, J.M., Horwood,
L.J., Miller, A., Kennedy, M.A. (2012).
Moderating role of the MAOA genotype in
antisocial behaviour. British Journal of
Psychiatry, 200, 116–123.
Lahey, B.B., Rathouz, P.J., Lee, S.S.,
Chronis-Tuscano, A., Pelham, W.E.,
Waldman, I.D., Cook, E.H. (2011).
Interactions between early parenting and a
polymorphism of the child’s dopamine
transporter gene in predicting future child
conduct disorder symptoms. Journal of
Abnormal Psychology, 120, 33–45.
Maltreatmentb, Property
Prenatal
offenses;
smokingb,
violent
Deprivationb,
offenses;
Leaving school
CP; hostility
CD
Negative and
positive
parentingb
Yes
Yes
(continued overleaf)
IG×E Approach to Understanding Youth Antisocial Behavior
9
Table 1
(Continued)
References
N
(%
female)
Age
rangea
Allele
Environmentb
Measure
of youth
AB
G×E
(sig?)
Citationc
Dick et al. (2011)
374
(50)
10–17a
CHRM2
Low parental
monitoringb
Ext behavior
Yes
Latendresse et al. 452
(2011)
(52)
12–22a
CHRM2
Deviant peer
affiliationb
Ext behavior
Yes
Beaver et al.
(2011a)
12–18
13–20
18–28
DRD2,
DRD4-L
Neighborhood Adolescent
disadvantageb victimization;
delinquent
peers;
violent
delinquency
Dick, D.M., Meyers, J.L., Latendresse, S.J.,
Creemers, H.E., Lansford, J.E., Pettit,
G.S., … & Huizink, A.C. (2011). CHRM2,
Parental Monitoring, and Adolescent
Externalizing Behavior Evidence for
Gene-Environment Interaction.
Psychological Science, 22, 481–489.
Latendresse, S.J., Bates, J.E., Goodnight,
J.A., Lansford, J.E., Budde, J. P., Goate,
A., … & Dick, D. M. (2011). Differential
susceptibility to adolescent externalizing
trajectories: Examining the interplay
between CHRM2 and peer group
antisocial behavior. Child Development,
82, 1797–1814.
Beaver, K.M., Gibson, C.L., Delisi, M.,
Vaughn, M.G., Wright, J.P. (2011a). The
interaction between neighborhood
disadvantage and genetic factors in the
prediction of antisocial outcomes. Youth
Violence and Juvenile Justice, 10, 25–40.
2574
(0)
Yes
(continued overleaf)
10
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Table 1
(Continued)
References
N
(%
female)
Age
rangea
Allele
Environmentb
Measure
of youth
AB
G×E
(sig?)
Citationc
Lee (2011)
672
(0)
12–18
13–20
20–24
MAOA-H
Deviant peer
behaviorb
Covert and
overt AB
Yes
Beaver et al.
(2011b)
913
(54)
12–18
28–34
MAOA-L
Protective risk Incarceration;
anger;
factor indexb
hostility
Yes
Fergusson et al.
(2011)
398
(0)
16–30a
MAOA-L
Physical and
sexual
abuse,
Inter-parental
violence
Property
offenses;
violent
offenses;
CP; hostility
Yes
Enoch et al.
(2010)
7158
(56)
0, 0–1,
2–3,
3–4, 7
MAOA-L
Family
adversityb,
Stressful
eventsb
CP
Yes
Lee, S.S. (2011). Deviant peer affiliation and
antisocial behavior: Interaction with
monoamine oxidase a (MAOA) genotype.
Journal of Abnormal Child Psychology, 39,
321–332.
Beaver, K.M., Nedelec, J.L., Wilde, M.,
Lippoff, C., Jackson, D. (2011b).
Examining the association between MAOA
genotype and incarceration, anger and
hostility: The moderating influences of risk
and protective factors. Journal of
Research in Personality, 45, 279–284.
Fergusson, D.M., Boden, J.M., Horwood,
J.L., Miller, A.L., Kennedy, M.A. (2011).
MAOA, abuse exposure and antisocial
behavior: 30-year longitudinal study.
British Journal of Psychiatry, 198,
457–463.
Enoch, M.A., Steer, C.D., Newman, T.K.,
Gibson, N., Goldman, D. (2010). Early life
stress, MAOA, and gene-environment
interactions predict behavioral
disinhibition in children. Genes, Brain, &
Behavior, 9, 65–74.
(continued overleaf)
IG×E Approach to Understanding Youth Antisocial Behavior 11
Table 1
(Continued)
References
N
(%
female)
Age
rangea
Allele
Environmentb
Measure
of youth
AB
G×E
(sig?)
Citationc
Wakschlag et al.
(2010)
177
(56)
0, 15
MAOA-L
Prenatal
smokingb
CD; hostile
attribution
bias
Yes
Edwards et al.
(2010)
186
(0)
5–22a
MAOA-L
Physical
disciplineb
Delinquency
Yes
Sadeh et al.
(2010)
118
(58)
12–16
5-HTTLPR-L Socioeconomic APSD, CU
statusb
Wakschlag, L.S., Kistner, E.O., Pine, D.S.,
Biesecker, G., Pickett, K.E., Skol, A.D.,
et al. (2010). Interaction of prenatal
exposure to cigarettes and MAOA
genotype in pathways to youth
antisocial behavior. Molecular
Psychiatry, 15, 928–937.
Edwards, A.C., Dodge, K.A., Latendresse,
S.J., Lansford, J.E., Bates, J.E., Pettit,
G.S., et al. (2010). MAOA-uVNTR and early
physical discipline interact to influence
delinquent behavior. Journal of Child
Psychology and Psychiatry, 51, 679–687.
Sadeh, N., Javdani, S., Jackson, J.J.,
Reynolds, E.K., Potenza, M.N.,
Gelernter, J., Lejuez, C.W. (2010).
Serotonin Transporter Gene
Associations With Psychopathic Traits
in Youth Vary as a Function of
Socioeconomic Resources. Journal of
Abnormal Psychology, 11, 604–609.
Some
(continued overleaf)
12
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Table 1
(Continued)
References
N
(%
female)
Age
rangea
Allele
Environmentb
Measure
of youth
AB
G×E
(sig?)
Citationc
Derringer et al.
(2010)
841
(29)
11, 14,
15, 17,
21, 25
MAOA-L
Harsh
discipline,
Sexual abuse
Substance
problems;
AB; CD
Yes
Li & Lee (2010)
2488
(52)
12–18
18–26
5-HTTLPR-S Maltreatment
AB
Yes
Weder et al.
(2009)
114
(34)
5–15
MAOA-L
Aggression
Yes
Derringer, J., Krueger, R.F., Irons, D.E.,
Iacono, W.G. (2010). Harsh discipline,
childhood sexual assault, and MAOA
genotype: an investigation of main and
interactive effects on diverse clinical
externalizing outcomes. Behavior
Genetics, 40, 639–648.
Li, J.J. & Lee, S.S. (2010). Latent Class
Analysis of Antisocial Behavior: Interaction
of Serotonin Transporter Genotype and
Maltreatment. Journal of Abnormal Child
Psychology, 38, 789–801.
Weder, N., Yang, B., Douglas-Palumberi, H.,
Massey, J., Krystal, J.H., Gelernter, J.,
Kaufman, J. (2009). MAOA genotype,
maltreatment, and aggressive behavior:
The changing impact of genotype at
varying levels of trauma. Biological
Psychiatry, 65, 417–424.
Maltreatmentb
(continued overleaf)
IG×E Approach to Understanding Youth Antisocial Behavior 13
Table 1
(Continued)
Environmentb
Measure
of youth
AB
G×E
(sig?)
Citationc
CP
Yes
Maltreatmentb
CD
Yesd
Maltreatmentb
Ext behavior
No
Sonuga-Barke, E.J.S., Oades, R.D.,
Psychogiou, L., Chen, W., Franke, B.,
Buitelaar, J., & Faraone, S.V. (2009).
Dopamine and serotonin transporter
genotypes moderate sensitivity to
maternal expressed emotion: The case of
conduct and emotional problems in
attention deficit/ hyperactivity disorder.
Journal of Child Psychology and
Psychiatry, 50, 1052–1063.
Prom-Wormley, E.C., Eaves, L.J., Foley, D.L.,
Gardner, C.O., Archer, K.J., Wormley, B.K.,
et al. (2009). Monoamine oxidase A and
childhood adversity as risk factors for
conduct disorder in females.
Psychological Medicine, 39, 579–590.
Van der Vegt, E.J.M., Oostra, B.A.,
Arias-Vásquez, A., van der Ende, J.,
Verhulst, F.C., Tiemeier, H. (2009). High
activity of monoamine oxidase A is
associated with externalizing behavior in
maltreated and non-maltreated adoptees.
Psychiatric Genetics, 19, 209–211.
References
N
(%
female)
Age
rangea
Allele
Sonuga-Barke,
et al. (2009)
708
(0)
5–17
DAT1-10R, Maternal
5-HTTLPR-S
expressed
positive
emotionb
Prom-Wormley
et al. (2009)
721
(100)
8–17
MAOA-H
10–15
MAOA-L
van der Vegt et al. 239
(2009)
(0)
(continued overleaf)
14
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Table 1
(Continued)
References
N
(%
female)
Age
rangea
Allele
Environmentb
G×E
(sig?)
Citationc
Hart &
Marmorstein
(2009)
672
(100)
11–23
MAOA-L
High % of
Aggression
children in
neighborhoodb
Yes
MAOA-H
Fraudulent
Delinquent
behavior
peersb
Family riskb,
Parental
incarcerationb
Yesd
Hart, D. & Marmorstein, N.R., (2009).
Neighborhoods and genes and everything
in between: Understanding adolescent
aggression in social and biological
contexts. Development and
Psychopathology, 21, 961–973.
Beaver, K.M., & Holtfreter, K. (2009).
Biosocial influences on fraudulent
behaviors. The Journal of Genetic
Psychology, 170, 101–114.
Beaver &
Holtfreter
(2009)
818
(0)
12–18
13–20
18–28
DeLisi et al.
(2009)
232
(100)
12–18
13–20
DRD2
Delinquency;
Parental
incarcerationb violent
delinquency;
police
contact
Yes
Measure
of youth
AB
DeLisi, M., Beaver, K.M., Vaughn, M.G.,
Wright, J.P. (2009). All in the family:
Gene×environment interaction between
DRD2 and criminal father is associated
with five antisocial phenotypes. Criminal
Justice and Behavior, 36, 1187–1197.
(continued overleaf)
IG×E Approach to Understanding Youth Antisocial Behavior 15
Table 1
(Continued)
References
N
(%
female)
Age
rangea
Allele
Environmentb
Measure
of youth
AB
BakermansKranenburg
et al. (2008)
157
(45)
13mo-2
1–3
2–4
4–5
DRD4
Positive
parentingb
Ext and
Yes
oppositional
behavior
Langley et al.
(2008)
266
(nr)
7–11
DRD5-5R,
Prenatal
DAT1-10R smokingb,
Low birth
weightb
ODD; CD
Yes
Propper et al.
(2007)
72
(50)
6 mo, 1,
1.5, 2,
2.5
DRD4-L
Ext behavior
Yesd
Negative &
positive
parentingb
G×E
(sig?)
Citationc
Bakermans-Kranenburg, M.J., Van
IJzendoorn, M.H., Pijlman, F.T.A., Mesman,
J., & Juffer, F. (2008). Experimental
evidence for differential susceptibility:
Dopamine D4 receptor polymorphism
(DRD4 VNTR) moderates intervention
effects on toddlers’ externalizing behavior
in a randomized control trial.
Developmental Psychology, 44, 293–300.
Langley, K., Turic, D., Rice, F., Holmans, P.,
van den Bree, M.B.M., Craddock, N., et al.
(2008). Testing for gene×environment
interaction effects in Attention-Deficit
Hyperactivity Disorder and associated
antisocial behavior. American Journal of
Medical Genetics Part B (Neuropsychiatric
Genetics), 147B, 49–53.
Propper, C., Willoughby, M., Halpern, C.T.,
Cox, M., & Carbone, M.A. (2007).
Parenting quality, DRD4, and the
prediction of externalizing and
internalizing behaviors in early
childhood. Developmental
Psychobiology, 49, 619–632.
(continued overleaf)
16
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Table 1
(Continued)
References
N
(%
female)
Age
rangea
Allele
Environmentb
G×E
(sig?)
Citationc
Sjoberg et al.
(2007)
117
(100)
16–19
MAOA-L
Maltreatmentb, Criminal
Type of
activity
residenceb
Yes
DRD4-S
Ext behavior
Maternal
insensitivityb
Yes
MAOA-L
Maltreatmentb, Emotional
Intimate
problems,
partner
ADHD
violenceb
Yes
Sjoberg, R.L., Nilsson, K.W., Wargelius, H.L.,
Leppert, J., Lindstrom, L., & Oreland, L.
(2007). Adolescent girls and criminal
activity: Role of MAOA-LPR genotype and
psychosocial factors. American Journal of
Medical Genetics Part B: Neuropsychiatric
Genetics, 144B, 159–164.
Bakermans-Kranenburg, M.J. & Van
IJzendoorn, M.H. (2006).
Gene-environment interaction of the
dopamine D4 receptor (DRD4) and
observed maternal insensitivity predicting
externalizing behavior in preschoolers.
Developmental Psychobiology, 48,
406–409.
Kim-Cohen, J., Caspi, A., Taylor, A.,
Williams, B., Newcombe, R., Craig, I.W,
Moffitt, T.E. (2006). MAOA,
maltreatment, and gene–environment
interaction predicting children’s mental
health: New evidence and a
meta-analysis. Molecular Psychiatry,
11, 903–913.
BakermansKranenburg
et al. (2006)
47
(51)
10 mo, 2,
3
Kim-Cohen et al.
(2006)
975
(0)
5, 7
Measure
of youth
AB
(continued overleaf)
IG×E Approach to Understanding Youth Antisocial Behavior 17
Table 1
(Continued)
References
N
(%
female)
Age
rangea
Allele
Environmentb
Measure
of youth
AB
G×E
(sig?)
Citationc
Young et al.
(2006)
247
(0)
12–18
MAOA
Maltreatment
CD
No
Huizinga et al.
(2006)
277
(0)
11–15
14–17
24–28
MAOA
Maltreatmentb
No
Widom &
Brzustowicz
(2006)
631
(48)
0–11
31–35 41
MAOA-L
Maltreatmentb
CD; violent
offense
arrest;
disposition
toward
violence,
APSD
AB
Young, S.E., Smolen, A., Hewitt, J.K.,
Haberstick, B.C., Stallings, M.C., Corley,
R.P., Crowley, T.J. (2006). Interaction
between MAO-A genotype and
maltreatment in the risk for conduct
disorder: Failure to confirm in adolescent
patients. American Journal of Psychiatry,
163, 1019–1025.
Huizinga, D., Haberstick, B.C., Smolen, A.,
Menard, S., Young, S.E., Corley, R.P., et al.
(2006). Childhood maltreatment,
subsequent antisocial behavior, and the
role of monoamine oxidase A genotype.
Biological Psychiatry, 60, 677–683.
Yes
Widom, C.S., Brzustowicz, L.M. (2006).
MAOA and the “Cycle of Violence”:
Childhood abuse and neglect, MAOA
genotype, and risk for violent and
antisocial behavior. Biological Psychiatry,
60, 684–689.
(continued overleaf)
18
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Table 1
(Continued)
References
N
(%
female)
Age
rangea
Allele
Environmentb
Measure
of youth
AB
G×E
(sig?)
Citationc
Nilsson et al.
(2006)
79
(0)
16–19
MAOA-L
Maltreatment,
Type of
residence
Criminal
activity
Yes
Haberstick et al.
(2005)
772
(0)
12–18
13–30
18–28
MAOA
Maltreatment, CP; violent
conviction
Adolescent
victimizationb
No
8–17
MAOA-L
Maltreatment
Yes
Nilsson, K.W., Sjoberg, R.L., Damberg, M.,
Leppert, J., Ohrvik, J., Alm, P.O., et al.
(2006). Role of monoamine oxidase A
genotype and psychosocial factors in
male adolescent criminal activity.
Biological Psychiatry, 59, 121–127.
Haberstick, B.C., Lessem, J.M., Hopfer, C.J.,
Smolen, A., Ehringer, MA., Timberlake, D.,
Hewitt, J.K. (2005). Monoamine oxidase A
(MAOA) and antisocial behaviors in the
presence of childhood and adolescent
maltreatment. American Journal of
Medical Genetics Part B: Neuropsychiatric
Genetics, 135, 59–64.
Foley, D.L., Eaves, L.J., Wormley, B., Silberg,
J.L., Maes, H.H., Kuhn, J., Riley, B. (2004).
Childhood adversity, monoamine oxidase
a genotype, and risk for conduct disorder.
Archives of General Psychiatry, 61,
738–744.
Foley et al. (2004) 514
(0)
CD
(continued overleaf)
IG×E Approach to Understanding Youth Antisocial Behavior 19
Table 1
(Continued)
Age
rangea
Allele
Environmentb
Measure
of youth
AB
Kahn et al. (2003) 161
(52)
6 mo, 1,
1.5, 2,
3, 4, 5
DAT1
Prenatal
smokingb
Hyperactive,
No
inattentive,
oppositional
Caspi et al. (2002) 671
(34)
3–11
26
MAOA-L
Maltreatmentb
CD; violent
conviction;
APSD;
violence
disposition
References
N
(%
female)
G×E
(sig?)
Yes
Citationc
Kahn, R.S., Khoury, J., Nichols, W.C., &
Lanphear, B.P. (2003). Role of dopamine
transporter genotype and maternal
prenatal smoking in childhood
hyperactive–impulsive, inattentive, and
oppositional behaviors. Journal of
Pediatrics, 143, 104–110.
Caspi, A., McClay, J., Moffitt, T.E., Mill, J.,
Martin, J., Craig, I.W., Taylor, A.,
Poulton, R. (2002). Role of genotype in
the cycle of violence in maltreated
children. Science, 297, 851–854.
Note:
a Annual assessment.
b Prospective assessment of environment.
c Bold citations = references that appear in the text and are thus listed in the references section. All other citations only appear in this table.
d Interaction significant but association with lower AB.
CD, conduct disorder; ODD, oppositional defiance disorder; CP, conduct problems; APSD, antisocial personality disorder; CU, callous unemotional traits; AB,
antisocial behavior; Ext, externalizing behavior.
20
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
CUTTING-EDGE RESEARCH
IMAGING GENETICS
Although G×E interaction research has increased the complexity of our
understanding of how experience and the genome interact in the development of AB, G×E interaction research alone cannot reveal specific biological
mechanisms linking genes and experience to behavior. These variables must
ultimately affect brain structure and function if they are to affect behavior
and increase risk for AB (Hyde et al., 2014). In mapping pathways from
genes to behavior, we first need to understand how genetic polymorphisms
affect brain structure and function, particularly since many candidate genes
for youth AB (and psychopathology more broadly) are genes that affect
neurotransmitter systems.
Several highly connected brain areas have been identified within neuroimaging studies as regions of interest in relation to youth AB. Among
neuroimaging studies that have used magnetic resonance imaging (MRI)
to assess the function and structure of brain areas related to youth AB,
research has clearly identified robust links with the amygdala. Emerging
studies have also begun to implicate other areas, including the orbitofrontal
cortex (and the broader ventralmedial prefrontal cortex), dorsolateral and
dorsomedial prefrontal cortex, anterior cingulate cortex, and insula. These
areas are broadly implicated in the development of AB likely owing to their
collective roles in affective processing, responsivity to reward/punishment,
learning, integration of sensory information, monitoring of internal states
and motivation, execution of planned behavior, and working memory (for
a review, see Hyde et al., 2013). However, questions remain as to how and
why these individual differences in neural structure and function arise.
In order to address questions relating to the origins and mechanisms
through which differences in brain structure and function emerge and
develop, we adopt an imaging genetics approach that links common genetic
polymorphisms to individual differences in brain structure and function
(Hariri, Drabant, & Weinberger, 2006). An imaging genetics approach has
several advantages when used in studies to understand the development
of psychopathology. First, by connecting genetic variation to biological
phenotypes in the brain, a mechanism is provided through which genes
can affect behavior. Second, by focusing on neural and genetic variables,
imaging genetics enables greater synergy with animal models and other
neuroscience approaches, which can, in concert, advance our understanding of the molecular and cellular pathways linking genetic variation to
differences in brain structure and function, and ultimately to differences
in behavior (e.g., Caspi, Hariri, Holmes, Uher, & Moffitt, 2010). Third, the
IG×E Approach to Understanding Youth Antisocial Behavior
21
dimensional and relatively objective intermediate phenotypes within imaging genetics (e.g., brain activation) are advantageous relative to other forms
of psychopathology research that can be hindered by broad nosological
definitions. In particular, diagnostic criteria have typically been plagued
by heterogeneity within diagnosis and comorbidity across diagnoses (Burt,
2012; Frick, Ray, Thornton, & Kahn, 2014; Moffitt, 1993; also see Key Issues
for Future Research), and using the brain as an outcome may lead to more
precise, homogenous, and dimensional phenotypes.
Thus, imaging genetics has proven to be a fruitful approach for understanding how differences in genotype lead to individual differences in brain
structure and function, which can, in turn, be linked to differences in behaviors. However, despite the fact that imaging genetics holds much promise for
understanding the development of youth AB, little work has applied imaging genetics to this outcome and no research to date has examined how G×E
interactions may fit into this pathway.
THE INTEGRATION OF IMAGING GENETICS AND G×E: IG×E
A cutting edge approach to examine how genetic and environmental variables interact to “get under the skin” involves integrating a G×E interaction
approach with an imaging genetics approach. This recent strategy of examining the brain as a mediator between G×E interactions has been termed
“imaging gene–environment interactions” (IG×E; Hyde, Bogdan, & Hariri, 2011)
and fits within a broader neurogenetics approach that seeks to understand
pathways through which genes, environments, and the brain interact to predict behavior and risk for psychopathology (Bogdan, Hyde, & Hariri, 2012).
In particular, we believe that linking these pieces together (i.e., G×E interactions predicting brain function) helps to test the inherent complexity in
models of youth AB (see Figure 1; Hyde et al., 2011). In an IG×E interaction
model, genes have an effect on the brain, as demonstrated in imaging genetics
studies (see pathway b, Figure 1), and the environment interacts with these
genes to predict behavior, as demonstrated by G×E interaction studies (pathway a). In combining these approaches, IG×E models emphasize that the
environment interacts with genetic variability to predict brain function and
structure, and ultimately behavior (see pathways b–e), specifying conditional
mechanisms among genes, context, the brain, and behavior.
APPLICATION OF IG×E TO YOUTH AB
IG×E interaction studies are exciting, but they remain at the cutting edge
of empirical research as the approach has been recently proposed and few
empirical studies exist to test the full model (i.e., G×E interactions that predict
22
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Neural functioning
b
c
Genes
d
e
a
a
rGE
Gene×Environment (G×E)
Antisocial behavior
a
Environment
Figure 1 Theoretical Model of IG×E. A theoretical model of IG×E highlighting
how IG×E can be modeled conceptually and statistically. Relationships of the
variables are shown for more traditional G×E and imaging genetics paths, as well
as new paths possible in IG×E. The “a” paths model typical G×E relationships; “b
& e” paths model traditional imaging genetics links; “d” paths show direct effects
of the environment on neural functioning; “c & e” paths model gene–environment
interactions predicting behavior via neural functioning. Within this model, the
covariance between a genetic variant and an environment is modeled and reflects
the correlation (rGE) between specific genetic variant and specific environment.
behavior via their effect on the brain). However, several studies have published results in which G×E interactions predict brain function, a critical first
step in this emerging field. For example, in a study testing portions of an
IG×E interaction model, Canli and colleagues (2006) found that that variability in a gene affecting the serotonin system (5-HTTLPR) interacted with life
stress to predict amygdala reactivity during a resting period in the MRI scanner. More recently, Bogdan, Williamson, & Hariri (2012) showed that variation a gene affecting HPA axis functioning (key to stress-reactivity) predicted
amygdala reactivity but only in the context of previous emotional neglect.
These studies support the notion that genetic effects on the brain are likely to
be contingent on experience and that modeling IG×E pathways may lead to
a better understanding of the development of neural circuits, key to understanding psychopathology. However, studies to date have focused primarily
on the brain, and are yet to examine an outcome of broader psychopathology.
Thus, IG×E studies of youth AB are needed that link G×E interaction results
to behavior through their effect on the brain. These studies are likely to focus
IG×E Approach to Understanding Youth Antisocial Behavior
23
on G×E interactions involving genetic polymorphisms related to serotonin
and dopaminergic neurotransmitter systems that can be reliably linked to
alterations in the functioning of key regions of the brain relevant to youth AB.
Figure 2 presents a summary of these potentially important variables and
how they could interact based on evidence to date, providing a testable
Cutting-edge research
Neural functioning
2
Affective processing: amygdala; OFC; ACC; insula
Reward processing: striatum
Inhibitory/cognitive control: dlPFC; dmPFC
Intermediate behavior phenotypes
Affective processing: emotion regulation;
empathy; pain/distress recognition; response to
threat
Foundational research
Reward processing: learning; response to
reward/punishment stimuli; error detection and
correction
1
Candidate genes
Serotonin: MAOA; TPH; 5-HTTLPR
Dopamine: DAT1; DRD2; DRD4; COMT
Inhibitory/cognitive control: working memory;
decision-making; inhibition; planned behavior
Gene×Environment (G×E)
Environment
Early insult: Prenatal smoking; perinatal
complications
Proximal risk: Maltreatment; abuse;
neglect; harsh parenting; parental
psychopathology
Distal risk: Neighborhood danger;
poverty
Antisocial behavior
Development
Subtypes: presence vs absence of
callous unemotional traits;
aggressive vs rule-breaking behavior;
proactvie vs reactive aggression; age
of onset (i.e., early vs late)
Figure 2 Summary of potential IG×E targets toward understanding youth
antisocial behavior. A preliminary hypothetical model outlining potential targets for
future IG×E studies examining AB development among youth. The model
emphasizes the interaction between the environment and biology (genes and
neural reactivity) as these variables predict antisocial phenotypes or AB.
Consistent with other works in this series, we differentiate between foundational
research, including traditional G×E paths, and cutting-edge research, comprising
newly proposed paths specific to IG×E models (Hyde et al., 2014). Note. 1 We
highlight gene variants that have seen the bulk of research to date. For simplicity
we have separate these genes according to their influence on serotonin versus
dopamingeric systems. However, several genes presented have multiple and
complex effects on multiple neurotransmitter systems (e.g., MAOA affects all
monoamines, not just serotonin). 2 We highlight regions of interest in relation to
youth AB that have received the most research focus to date. The brain areas
listed are involved in multiple dimensions and thus could be listed in more than
one way (e.g., the ACC is involved both affective and error processing). The
complex inter-connectivity between these different regions should therefore not be
overlooked (Hyde et al., 2013). Finally, we have not modeled the moderating effects
of the environment and genes on paths from neural functioning to intermediate
behavioral phenotypes or from intermediate behavior phenotypes to antisocial
behavior, but again, the potential for this additional complexity should be noted.
24
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
framework of multiple possible IG×E interaction pathways. Previous
studies have hinted at these effects. For example, in one study male carriers
of the MAOA-L allele, which has been linked in G×E interaction studies
to greater risk for aggressive behavior, showed greater amygdala activation to emotionally arousing stimuli and reduced activity in regulatory
prefrontal regions compared to individuals with the high expressing allele
(Meyer-Lindenberg et al., 2006). The same neural profile has been linked
to AB in youth and adults (Hyde et al., 2013). The results of this imaging
genetics study, taken in conjunction with findings from G×E interaction
studies linking MAOA-L to increased risk for AB following maltreatment
or other environmental adversity, suggests a potential IG×E pathway from
gene to brain to youth AB (Buckholtz & Meyer-Lindenberg, 2008). Specifically, MAOA-L, in the context of maltreatment, appears to lead to greater
amygdala reactivity, which may lead to risk for AB, particularly impulsive
and reactive aggression (Viding & Frith, 2006). Nevertheless, it should be
emphasized that MAOA cannot be considered an “AB gene,” but rather as
one of many allelic variants that confers risk for specific vulnerability in
neural processing, which could lead to the development of AB given the
“right” (or wrong) environmental context.
KEY ISSUES FOR FUTURE RESEARCH
While IG×E interaction studies hold much promise, no empirical studies
have tested full IG×E interaction models pertaining to youth AB and future
research in this field faces a number of theoretical and methodological
challenges.
DEFINITION OF ENVIRONMENT?
First, controversy remains surrounding the use and definition of “environment.” Typically, environment refers to both experiential phenomena (e.g.,
harsh parenting) and exposure to physical forces (e.g., natural disasters).
However, these potential influences differ in how much an individual’s
genotype could contribute to their own experience. Factors such as natural
disasters are less likely to be correlated with genotype, whereas experiences
such as parenting received by a child may be influenced, at least to some
extent, by a child’s genotype (and the genes shared with parents). A wealth
of research suggests that many “experiences” appear to be correlated with
genotype such that it is difficult to determine their “causal” nature (Jaffee,
2011). For example, children with difficult temperaments have been shown
to “evoke” harsher parenting. However, these youth also share their parents’
genes, which may influence their difficult temperament, their parent’s
IG×E Approach to Understanding Youth Antisocial Behavior
25
harshness, and/or their (or their parent’s) subsequent AB. This scenario
demonstrates both passive (sharing genes) and evocative (“evoking” or
eliciting a different environment) gene–environment correlations (rGE).
G×E and IG×E interaction studies may thus be biased by rGE because many
“experiences” are correlated with genotype meaning that G×E interactions
could actually be reflecting G×G interactions (Jaffee, 2011). Studies can
minimize biases from rGE using genetically informed approaches (i.e., twin
or adoption studies), examining the effects of natural disasters/experiments,
or using randomized controlled trial designs (Hyde et al., 2011).
G×E×E AND G×G×E
Second, although G×E interaction research has the potential to increase the
complexity of our understanding of risk factors for AB, even greater complexity likely exists in the form of G×E×E and G×G×E interactions (Rutter &
Dodge, 2011). For example, in an interesting G×E×E study, a well replicated
G×E interaction finding that 5-HTTLPR genotype interacts with maltreatment to influence depression risk (Caspi et al., 2003) was found to be further
moderated by social support. Specifically, only those with the “risk allele” in
the 5-HTTLPR with both a history of childhood maltreatment and low social
support showed increased depressive symptoms (Kaufman et al., 2004). This
finding emphasizes the complex and multifaceted nature of the relationship
among genes, experiences, and behavior, in which some environments exacerbate risk (e.g., maltreatment), while others appear to be protective (e.g.,
high social support). Moreover, this study illustrates the complex interactions
that multiple experiences and genetic polymorphisms likely have in shaping
behavior across development.
MEASUREMENT
Third, measurement approaches and methods differ substantially across
G×E interaction studies (Table 1). For example, across the 45 reviewed
G×E interaction studies of youth AB, complex environments such as harsh
parenting and child maltreatment were captured via single item questions
and general questionnaire items, with only a few studies using observations
of experience. These methodological differences make it difficult to compare
findings. In addition, many studies relied on self-report questionnaires to
assess both risk and outcome, which can bias the results. Further, many
G×E interaction studies of youth AB measured childhood maltreatment
employed retrospective assessment of the environment. Research has shown
that the incidence of childhood traumatic events varies depending on informant, point of assessment, and developmental stage (Shaffer, Huston, &
Egeland, 2008). Reliance on self-reported, retrospective data may thus result
26
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
in the magnitude of associations being underestimated (i.e., difficulties
recalling) or overestimated (i.e., negative bias affecting reporting of current
behavior and past events). Future G×E and IG×E studies are needed that
adopt prospective longitudinal designs to examine environmental risk
factors and behavioral outcomes as they occur, develop, and interact, using
a variety of assessment methods (e.g., Choe et al., 2014).
SAMPLE SIZE AND COMPOSITION
Fourth, IG×E interaction models require especially large samples to gain
acceptable levels of power. Models that test whether the effects of gene
on behavior via the brain are further moderated by environment require
large sample sizes. Specifically, 500–1000 subjects is likely the minimum
sample size range required in order to examine expected small to moderate effects of individual variables modeled in a moderated mediation
framework (Preacher, Rucker, & Hayes, 2007). Future IG×E studies need
to adopt creative strategies to increase sample size and power, including
piecing together smaller convenience samples (e.g., Yan, Craddock, Zuo,
Zang, & Milham, 2013), using consortium models (e.g., Thyreau et al.,
2012), or conducting neuroimaging meta-analysis (e.g., Jahanshad et al.,
2013). Furthermore, many studies utilize samples of convenience, such as
college students in subject pools or community volunteers who respond to
a flier. These kinds of samples are likely to vary in a number of important
dimensions that could affect the consistency and replicability of findings
and distort the relationship between individual differences in brain and
behavior. Thus, future IG×E studies must be thoughtful about sampling
and who the study will generalize to, with representative samples offering
particular advantages (Falk et al., 2013).
DEMOGRAPHIC FACTORS
Fifth, demographic variables including age (Lenroot & Giedd, 2011), gender
(Wakschlag et al., 2010), race/ethnicity (Propper et al., 2007), and genetic substructure (Cardon & Palmer, 2003) are likely to influence findings, and require
careful control and examination as additional moderators in future studies
of youth AB.
DEVELOPMENTAL STAGE
Sixth, development is likely to play an important role in the unfolding of
gene–environment–brain–behavior relationships. Many genetic variants
relevant to AB (e.g., MAOA and 5-HTTLPR) are likely to have influences
IG×E Approach to Understanding Youth Antisocial Behavior
27
on neural functioning in utero or very early in development. Environmental
experiences also likely differ in their impact depending on developmental
stage (Sroufe & Rutter, 1984) and during particular “sensitive periods” of
development (Meaney, 2010). For example, in relation to IG×E interactions
and youth AB, harsh parenting may only moderate the effect of certain genotypes when harsh parenting is measured in early childhood and when AB
is measured in adolescence. In contrast, interactions between genotype and
deviant peer experiences may only relate to AB when both peer experiences
and AB are measured in adolescence. Studies that test IG×E interactions longitudinally across multiple developmental periods are likely to help uncover
these more complex interactions (i.e., IG×E×development – “IG×E×D”;
Hyde et al., 2014).
SUBTYPES OF AB
Finally, thoughtfulness about phenotype is critical to the study of youth
AB adopting an IG×E interaction framework, as a high level of precision
is needed to find smaller or complex effects. Converging evidence across
multiple methods suggests that antisocial youth are not a homogenous
group, but rather may include several subgroups with different etiologies.
For example, AB that begins early (before age 10) is associated with greater
early risk, including neurocognitive deficits, harsher parenting, more difficult temperament, and higher comorbidity (Moffitt et al., 2002; Patterson,
Reid, & Dishion, 1992); a more chronic and escalating trajectory of behavior
(Shaw & Gross, 2008); and worse outcomes in adulthood (Moffitt et al., 2002).
In contrast, AB that starts in adolescence has been linked to deviant peer
affiliation (Dishion, Patterson, Stoolmiller, & Skinner, 1991), fewer proximal
family risks, and a less elevated and less chronic trajectory of AB with
fewer problematic outcomes during adulthood (Moffitt, Caspi, Dickson,
Silva, & Stanton, 1996). Thus, we may expect these subgroups to have very
different neural and genetic correlates, though few studies have addressed
this question.
In addition, research has examined callous-unemotional (CU) traits among
a subgroup of antisocial youth, characterized by diminished displays of
empathy toward others. Empirical studies using this subtyping approach
demonstrate that youth with high levels of CU traits and AB display
more severe, chronic, and highly heritable forms of AB (Frick et al., 2014).
Moreover, this research has emphasized that subgroups differ in their neurocognitive vulnerability: youth with AB and high levels of CU traits have
less amygdala reactivity to threat, whereas those with AB but with low levels
CU traits have been shown to have greater amygdala reactivity to threat. In
sum, an examination of both age of onset and/or the presence of CU traits
28
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
may help to uncover important subgroups of antisocial youth who differ in
genetic risk profile, contingent experiential risk, and developmental course.
CONCLUSION
G×E interaction studies emphasize that genotype interacts with environment across development to influence risk for psychopathology, particularly
youth AB. IG×E interaction studies can ultimately elucidate conditional
mechanisms by which genes (e.g., MAOA) and experience (e.g., maltreatment) interact to affect neural structure and function (e.g., amygdala
reactivity), and resulting psychopathology (e.g., youth AB). Recent research
suggests that several genes, experiences, and behaviors are most promising
for understanding the development of AB and adopting these variables into
an IG×E approach may help us better understand the complex development
of these costly and dangerous behaviors.
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REBECCA WALLER SHORT BIOGRAPHY
Rebecca Waller, PhD is a Postdoctoral Research Fellow who joined the Michigan Neurogenetics and Developmental Psychopathology Laboratory at the
University of Michigan in 2013 after receiving her doctorate from the University of Oxford. She has an MA in Experimental Psychology and MSc in
Evidence-Based Social Intervention, also from the University of Oxford. Her
research interests focus on examining behavioral and personality precursors
of psychopathy and antisocial behavior from a developmental psychopathology perspective.
HAILEY L. DOTTERER SHORT BIOGRAPHY
Hailey L. Dotterer, BA earned her degree in Psychology from the University of Michigan and is currently working in the Michigan Neurogenetics
and Developmental Psychopathology Laboratory. Her research interests are
in psychopathy, antisocial behavior, and associated risk factors, such as childhood maltreatment and exposure to violence.
LUKE W. HYDE SHORT BIOGRAPHY
Luke W. Hyde, PhD is the Director of the Michigan Neurogenetics and
Developmental Psychopathology (MiND) Laboratory. He earned his BA
in psychology and religion from Williams College. He received his PhD in
IG×E Approach to Understanding Youth Antisocial Behavior
33
psychology from the University of Pittsburgh with training in the clinical
and developmental psychology areas. He also received a concentration in
cognitive neuroscience from the Center for the Neural Basis of Cognition
at the University of Pittsburgh and Carnegie Mellon University and did
his clinical psychology residency at Western Psychiatric Institute of the
University of Pittsburgh Medical Center. He is currently an Assistant
Professor in Psychology at the University of Michigan, as well as a Research
Assistant Professor at the Center for Human Growth and Development and
a Research Affiliate at the Survey Research Center of the Institute for Social
Research. His research interests focus on specifying models of the interaction
of biology and experience in the development of psychopathology with
specific interests in youth externalizing behaviors such as conduct disorder
and psychopathy. His past and current research incorporates approaches
from imaging genetics and developmental psychopathology to inform
mechanistic models that link genes, experience, brain, and behavior from
early childhood to adulthood.
Lab Website/URL: The Michigan Neurogenetics and Developmental Psychopathology (MiND) Lab: mindlab.psych.lsa.umich.edu
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