Genes in the Dopaminergic System and Delinquent Behaviors Across the Life Course

Abstract

This article examines the interaction between social control and social risk mechanisms, and genes within the dopaminergic system (DAT1 and DRD2) as related to serious and violent forms of delinquent behavior among adolescents and young adults. We use nine waves of data from the National Youth Survey Family Study (NYSFS) to examine the relevance of protective or risky social factors at four social levels, including school, neighborhood, friends, and family within the gene–environment interaction framework. We extend previous work in this area by providing a testable typology of gene–environment interactions derived from current theories in this area. We find consistent evidence that the associations between putatively risky genotypes and delinquent behavior are suppressed within protective social environments. We also provide some evidence that supports the differential susceptibility hypothesis for these outcomes. Our findings largely confirm the conclusions of previous work and continue to highlight the critical role of the social environment within candidate gene studies of complex behaviors.

Keywords

gene–environment interaction problem behavior DAT1 DRD2

Introduction

In the wake of decades of research, there is consensus among social scientists that variation in nearly all behavioral traits is the product of genetic and environmental factors (Ferguson, 2010; Rhee & Waldman, 2002; Turkheimer, 2000). The strongest evidence for this perspective is that heritability estimates for most traits vary considerably across environments (Moffitt, 2005). Stated differently, genetic influences on a given trait can—and often do—depend on forces in the environment, a phenomenon referred to as gene–environment interaction (G×E; Rutter, 2006). While variation in heritability estimates capture the latent influences of genes, scholars have recently focused their attention on uncovering the specific genes that might interact with measured environments to predict various phenotypic outcomes. Along these lines, a landmark achievement occurred more than a decade ago when Caspi et al. (2002) reported the most widely cited measured G×E in the prediction of violent and antisocial behavior.

In the wake of the Caspi et al. (2002) study, researchers have begun to examine the relevance of the gene–environment interplay more widely, with growing interest aimed at further illuminating the contribution of G×Es as sources of variance in delinquent behavior (Beaver, DeLisi, Wright, & Vaughn, 2009; Guo, Roettger, & Cai, 2008; Simons et al., 2011). Emergent findings in this area suggest that an individual’s likelihood of engaging in delinquent behavior as a result of environmental triggers might increase depending on their genes. Because delinquent behavior is a highly polygenic trait, it stands to reason that single genes confer only a minor increase in the odds of committing a given delinquent act (Plomin, DeFries, Craig, & McGuffin, 2008). Despite exerting rather small main effects, the influence of certain genotypes may become magnified when coupled with risky environments (or vice versa). These general associations (G×Es) continue to be demonstrated in the literature with increasing frequency in a diverse range of samples (Caspi et al., 2002; Freese & Shostak, 2009; Guo et al., 2008; Kim-Cohen et al., 2006; Simons et al., 2011). However, there is also evidence that some of the most “established” G×E associations do not replicate across independent samples. Specifically, Risch et al. (2009) examine the link between the 5HTTLPR genotype and depression as a function of stressful life events similar to those reported in Caspi et al. (2003) using 14 independent samples and do not find evidence for a G×E association in this well powered (N = 14,250) meta-analysis. As such, it is critical to evaluate previously published G×E associations with new sources of data and to expand on this previous work with additional phenotypes and environmental moderators.

In this article, we use data from nine waves of the National Youth Survey Family Study (NYSFS) to examine gene–environment interactions in the prediction of antisocial behavior. We examine whether the specific alleles within two genes in the dopaminergic pathway (DRD2 and DAT1) interact with neighborhood, familial, school, and peer factors to predict serious and violent delinquency during adolescence and young adulthood. The NYSFS provides a rich set of repeated measures across multiple social domains from a national sample of respondents. Most importantly, we also elaborate on previous research (see Guo et al., 2008) by providing a testable typology of gene–environment interactions derived from existing theory that help to frame the results of this and other articles in this area.

Gene–Environment Interaction: A Brief Overview

The growing body of G×E scholarship (Shanahan & Boardman, 2009; Shanahan & Hofer, 2005) has outlined four distinct ways in which genes and the environment might coalesce nonadditively to influence delinquent phenotypes: (a) diathesis-stress, (b) differential susceptibility, (c) social push, and (d) social distinction. Each model is described graphically in Figure 1. The diathesis-stress hypothesis suggests that unobserved genetic factors may predict delinquent behaviors for individuals who encounter adverse environments of some variety. In this regard, risky social contexts may be required to trigger genetic tendencies for adverse behaviors (Shanahan & Hofer, 2005), and it represents the primary G×E model within the biological and social sciences (Reiss & Leve, 2007). According to this perspective, environmental triggers remain the fundamental cause (Link & Phelan, 1995). The social control model (Shanahan & Hofer, 2005) is statistically indistinguishable from the diathesis-stress model but emphasizes the presence of social resources rather than exposure to stress. In other words, social control mechanisms reduce genetic risks, and social risks exacerbate genetic risks. As such, the difference between the social control model and the diathesis stress model is simply the focus on the healthy or unhealthy end of the environmental spectrum. To illustrate, previous research suggests that residing in a state with high levels of taxes on cigarettes reduces the relative influence of genes on the likelihood that an individual will become a regular smoker (Boardman, 2009), which is taken as a form of social control.

Figure 1:

Conceptual Gene–Environment Interaction Models

Second, Belsky and Pluess (2009) have argued that the explicit focus on risky environments overlooks the possibility that particular individuals are more susceptible than the typical person to the influences of their social environments in a “for better and for worse” fashion (for a recent review of this perspective, see Ellis, Boyce, Belsky, Bakermans-Kranenburg, & Van Ijzendoorn, 2011). As shown in Figure 1, not only will susceptible individuals experience the worst outcomes in the worst environments, but they will also have the best outcomes in the best environments (Belsky & Beaver, 2011). Simons and his colleagues (2011), for instance, demonstrated that carriers of “plasticity alleles” (e.g., the 7R allele in DRD4 and the “short” allele in 5HTTLPR) are significantly more likely to exhibit signs of aggression when they are in the most adverse environments and significantly more likely to have the lowest symptoms of aggression when they are in the most favorable social environments. This is precisely the anticipated association of the differential susceptibility model, and their work highlights the need to consider the full range of social environments rather than just risky versus nonrisky social contexts because this model anticipates no association between genotype and delinquent behavior for those in typical environments.

The third model has been called the social distinction model and predicts a very different association than the diathesis stress model. The key difference of this model is that it does not necessarily anticipate a causal gene–environment interaction; the environment is not “causing” genes to operate differently. Rather, environmental variation in more risky contexts makes it difficult to identify genetic associations. As such, it is within the most protective environments in which genotype becomes the most salient. For example, Boardman, Barnes, Wilson, Evans, and Mendes de Leon (2012) use data from the Chicago Health and Aging Project to examine the link between the e4 allele in the APOE gene and cognitive decline among older adults in Chicago. They show that the risky allele is only weakly associated with change in cognitive functioning for residents of the most disorganized neighborhoods (e.g., those with trash in the streets, the presence of vandalism, poorly maintained sidewalks, etc.). The effect becomes much more pronounced, however, for residents in the most stable, organized, and ordered neighborhoods.

The last interaction model in Figure 1 has been called the social push model (Raine, 2002), and it emphasizes the difference between normal and atypical social environments rather than socially stressful environments, per se. This model hypothesizes that genetic factors are the most salient within the average or typical environments; on either extreme, the social environment is pushing the phenotype, and the genotype does less to distinguish among individuals. Boardman, Roettger, et al. (2012) have shown this with social factors related to obesity. They show that the heritability of body mass index (BMI) is the highest among students who attend schools with typical norms about body size but is significantly reduced in schools that have strong norms about very high or very low levels of BMI because the environment is driving BMI in those extreme contexts and genotype has little room to differentiate among individuals.

To examine the relevance of these gene–environment interaction models, we focus the current study on two genes in the dopaminergic system that have been linked to delinquent behavior in previous research (Beaver et al., 2007; Guo et al., 2008): DRD2 and DAT1. DRD2 is a gene that encodes the dopamine D2 receptor and plays a fundamental role in the complex circuitry of the human brain. Variations within DRD2 are associated with attention deficit hyperactivity disorder (ADHD), impulsivity, antisocial behavior, and addiction (Colzato, van den Wildenberg, Van der Does, & Hommel, 2010; Groman & Jentsch, 2011; Guo, Roettger, & Shih, 2007; Marino et al., 2004). The Taq1A locus (rs1800497) is 9.4kb downstream from the DRD2 gene, which is located on chromosome 11 (11q23). Individuals who possess the A1 allele are found to be at higher risk for behavioral issues relative to those with the A2 variant of the gene, and previous research has shown that the A1 allele is not related to delinquency for those with higher levels of closeness within their families (Guo et al., 2008). Vaske, Wright, and Beaver (2011) show that increasing number of A1 alleles in DRD2 is significantly associated with delinquency but only among those who had been victims of crimes themselves. Specifically, only those who had recently reported being shot at, stabbed, jumped, or had someone pull a knife or gun on them demonstrated an increased risk of delinquency as a function of their DRD2 genotype. These results support the diathesis-stress perspective. DAT1 is a gene that is involved in the regulation of the neurotransmitter dopamine in the central nervous system where individuals with the 10R variant of this gene are found to have an increased risk for a number of behavioral issues, including antisocial behavior, ADHD, and substance abuse (Cornish et al., 2005; Guo, Cai, Guo, Wang, & Harris, 2010). We examine the number of copies of the 10R variant of DAT1 in G×E analyses. Evidence for the social distinction G×E perspective with respect to the 10R allele in DAT1 has been shown by Vaughn, DeLisi, Beaver, and Wright (2009) who find that the 10R allele is significantly associated with the number of police contacts among adolescents who interact with the least number of delinquent peers.

In sum, we focus on these two polymorphisms because they are implicated in same neurological pathway (the dopaminergic system), are fairly common in the population, have shown main effects for delinquent behavior (Guo et al., 2007), and have shown interactive effects with various aspects of the environment (Guo et al., 2008; Simons et al., 2011; Vaske et al., 2011).

Method

Data

We use data from the NYSFS, a prospective longitudinal study of 1,725 respondents from adolescence through adulthood. The NYSFS is based on a nationally representative multistage probability sample of households in the continental United States (Elliot, Huizinga, & Menard, 1989). Originally selected in 1976, respondents were between the ages of 12 and 17 and characterize a multi-cohort sequential longitudinal design. Annual interviews were conducted between 1977 and 1981 and in 3-year intervals from 1984 to 1993. In 2002 and 2003, follow-up interviews were conducted (age range of participants: 37-44) during which additional behavioral and DNA data were collected from the original respondents. Data collection was conducted primarily through confidential, in-person interviews, although some interviews at later waves were conducted by telephone. Genomic DNA data were voluntarily collected from 80% of the respondents who provided buccal (cheek) swab DNA samples pre-amplified using a well-established method (Zhang et al., 1992). Data obtained using this DNA are high-quality, and these methods are reliable for genotyping (Anchordoquy, McGeary, Liu, Krauter, & Smolen, 2003; Haberstick & Smolen, 2004). Of the 1,725 original respondents at Wave 1, 1,677 (97% of the original sample) were located and were eligible for interviews at Wave 10. At Wave 10, 1,266 (73% of the original sample) were interviewed and 988 (57% of the original sample and 78% of those interviewed) provided DNA. After eliminating individuals with poor DNA quality and those with missing values on the key dependent variables, our final sample includes 724 respondents.

Measures

Dependent Variables

Serious delinquency is measured with an 11-item scale of self-reports for items that could lead to arrest and incarceration. Overall, we use 11 items that were asked in a consistent manner over a period of 9 waves. These questions were the number of times a respondent engaged in the following acts during the prior 12 months: (a) attacking someone, (b) deliberately damaging another person’s property, (c) using force to obtain things from others, (d) carrying a hidden weapon, (e) stealing anything worth less than $5, (f) stealing anything worth between $5 and $50, (g) stealing anything worth more than $50, (h) selling marijuana, (i) selling hard drugs, (j) hitting a parent, and (k) having forced sex against another’s will. Frequencies were collapsed such that 0 = occurred 0 times, 1 = occurred once or twice, 2 = occurred 3 or 4 times, and 3 = occurred 5 or more times. At each wave, scores from self-reports were summed together to create a general score for serious delinquency. Violent delinquency is the total number of times an individual reported attacking someone and having forced sex against another’s will. Table 1 presents descriptive statistics for the dependent variables, plus age, across the nine waves of the NYSFS used in the present study.

Table 1:

Serious and Violent Delinquency Across the Life Course: National Youth Survey Family Study, Waves 1-9

Wave	Serious (ln)	Violent (ln)	Age
1	.49 (.75)	.11 (.38)	13.77 (1.96)
2	.43 (.72)	.10 (.45)	14.77 (1.96)
3	.46 (.73)	.08 (.38)	15.77 (1.96)
4	.43 (.75)	.17 (.60)	16.77 (1.96)
5	.42 (.72)	.19 (.66)	17.77 (1.96)
6	.36 (.67)	.16 (.58)	20.86 (1.98)
7	.33 (.63)	.28 (.77)	24.22 (2.04)
8	.25 (.55)	.15 (.57)	26.96 (2.02)
9	.24 (.54)	.24 (.75)	30.17 (2.05)
Total	.38 (.68)	.17 (.59)	19.96 (5.78)

Note. Data come from nine waves of the National Youth Survey Family Study (NYSFS). N = 724. Cell entries represent means and standard deviations (in parantheses).

Protective and Risk Factors

We emphasize four protective and risk factors that are multi-level (e.g., families, schools, peers, and neighborhoods) and multi-dimensional (e.g., social control and social risk). All environmental measures are assessed at Wave 1 of the study. Family closeness is a self-report measure that includes five summed items and asks whether respondent feels like an outsider (reverse-coded), family listens if respondent has a problem, respondent is lonely when with family (reverse-coded), respondent feels close to family, and family does not take interest in problems (reverse-coded). Each item ranged from 1 (strongly disagree) to 5 (strongly agree; Cronbach’s α = .72).

School attachment is a self-report measure that includes 6 reverse-coded and summed items ranging from 1 (strongly disagree) to 5 (strongly agree) and asks the extent to which a respondent is not often asked to take part in school activities, a respondent is not called on, a respondent feels that nobody at school cares, a respondent does not feel as if he or she belongs at school, a respondent often feels lonely at school, and teachers do not ask the respondent to work on projects (Cronbach’s α = .65).

Peer delinquency is a self-report measure that includes 6 summed items, asking how many friends had cheated on school tests, damaged property, used marijuana, stole something worth less than $5 and more than $50, hit or threatened to hit, used alcohol, broke into vehicles or buildings to steal, sold drugs, and suggested that respondent break the law. Each item ranged from 1 (none of them) to 5 (all of them; Cronbach’s α = .75).

Neighborhood disorganization is a parent-report measure that includes six items, summed together, asking whether the following problems occurred in the parent’s (and respondent’s) neighborhood: vandalism or buildings broken into, winos and junkies, abandoned houses, burglaries and thefts, run down and poorly kept buildings, or assaults and muggings. Each response ranged from 1 (not a problem), 2 (somewhat a problem), or 3 (a big problem; Cronbach’s α = .77).

Control Variables

All five of the following control variables were measured at baseline, Wave 1. Parent-reported control variables include parent unemployed, which is coded 1 if the principal wage earner is unemployed and 0 if otherwise. In addition, parental poverty is coded 1 if the family received any public assistance including food stamps or welfare payments during the past year and 0 if otherwise. Parent attended college is coded 1 if the parent respondent attended college and 0 if otherwise. Respondent’s household size is a continuous measure indicating the number of persons currently residing in the household including family and nonfamily members. Self-reported grades from the first four waves of data are used to create a continuous measure of grade point average (GPA). We also control for respondent’s age (and age squared) at each wave of interview (Wave 1 through Wave 9) and race (two groups, with minority respondents consisting of those classified as Black, Hispanic, Native American, Asian, or other race/ethnicity, and with White as the reference group). Descriptive statistics for all variables used in the analyses are presented in Table 2.

Table 2:

Descriptive Statistics for all Variables Used in the Analysis

	M/%	SD/N
Genotype
DAT1 (10R/10R)	0.61	441
(10R/9R)	0.34	248
(9R/9R)	0.05	34
DRD2 (A1/A1)	0.06	43
(A1/A2)	0.34	246
(A2/A2)	0.60	435
Sociodemographic and background controls
Male	0.59	427
Non-White	0.16	116
Parent college graduate	0.35	253
Parent unemployed	0.06	43
Parent below poverty	0.16	116
Household size	5.10	1.69
GPA	2.68	0.71
Social control mechanisms
Family closeness	20.04	2.90
School attachment	21.96	3.31
Social risk mechanisms
Neighborhood disorganization	7.29	1.85
Peer delinquency	16.65	5.73

Note. Data come from nine waves of the National Youth Survey Family Study (NYSFS). N = 724. Cell entries represent means and standard deviations (for continuous variables) and percents and N size for categorical variables. GPA = grade point average.

Analytic Strategy

We nest wave-specific observations within individuals using a simple multi-level model (random intercept) using the xtmixed package in STATA 12.0. This model is described in great detail elsewhere (Rabe-Hesketh and Skrondal 2005) and it allows us to describe the delinquency (y) of the jth individual (n=724) at time i (n=9) as follows:

y_{i j} = b_{0 j} + b_{1} x_{i j} + ε_{i j} + u_{j},

This simple model only presents one wave specific covariate (x_ij) but it can be extended to include a variety of different control and explanatory variables. The most important aspect of this model is the differentiation between within (ϵ_ij) and between (u_j) person error. The standard errors are adjusted for the nesting of observations within individuals and this allows for traditional inference regarding parameter estimates. All models adjust for the control variables described above and we include an interaction between each environmental variable and each genetic polymorphism. To examine the different GxE models described above, we distinguish between three levels of risk (or protection). If we do not observe an association between genotype and delinquency in the most protective environments but do in the riskier environments, then our findings would support the social trigger model. Again, these models are detailed in Figure 1. The most important aspect of this categorization is the fact that we will have an “average” or “typical” environment that is characterized by the center of the distribution. If the social push perspective is accurate, then we should see the largest genetic associations among those with the average level of risk exposure.

Results

Tables 3 and 4 present the results from 16 multi-level (random intercept) regression models. For parsimony, only the main genetic, main environment, and gene-by-environment interaction terms for each regression are presented in the table (full results available on request). Of the 16 models, we find evidence of significant environmental moderation (p < .05) in seven cases, which is far above the number that we would observe due to chance alone. Most importantly, six of these seven interactions occurred with protective forms of social control as opposed to social risks. Specifically, three of the four models for family closeness and three of the four models for school attachment indicate that the presence of social resources reduces the influence of genotype on serious and violent forms of delinquency, and this is consistent for DRD2 and DAT1. Evidence for the social trigger or diathesis stress model only appears in 1 of the 16 models. In this case, DAT1 is not associated with violent delinquency for those who reside in the least risky neighborhoods but this polymorphism significantly predicts violent behaviors for those in the most disorganized communities.

Table 3:

DRD2 Genotype, Delinquency, and Gene–Environment Interactions

	Family closeness	Peer delinquency	Neighborhood disorganization	School attachment
Environment [low]	Serious delinquency
Average	0.01 (0.05)	0.14** (0.05)	0.01 (0.04)	−0.03 (0.05)
High	−0.03 (0.05)	0.38*** (0.00)	0.04 (0.06)	−0.04 (0.05)
DRD2 genotype	0.13* (0.05)	−0.01 (0.05)	−0.04 (0.04)	0.04 (0.05)
G×E estimates
DRD2 × Average	−0.17* (0.07)	−0.04 (0.07)	0.05 (0.06)	−0.06 (0.06)
DRD2 × High	−0.21** (0.05)	0.08 (0.07)	0.05 (0.08)	−0.11 (0.07)
Environment [low]	Violent delinquency
Average	−0.04 (0.03)	0.03 (0.03)	−0.02 (0.02)	−0.02 (0.02)
High	−0.05* (0.02)	0.06* (0.03)	0.02 (0.03)	0.01 (0.02)
DRD2 Genotype	0.06* (0.02)	0.00 (0.03)	−0.01 (0.02)	0.05* (0.02)
G×E estimates
DRD2 × Average	−0.07* (0.03)	−0.01 (0.03)	0.02 (0.03)	−0.05+ (0.03)
DRD2 × High	−0.08* (0.03)	0.02 (0.03)	0.04 (0.04)	−0.07* (0.03)

Note. Cell entries represent coefficients from a multi-level model in which observations are nested within individuals across nine waves of data from the National Youth Survey (N = 724). Standard errors are provided in parentheses below. All models control for gender, age, age squared, race/ethnicity, parental education, parental employment status, parental poverty status, family size, and grade point average. Boxed estimates are presented graphically in Figure 2.

p < .10. *p < .05. **p < .01. ***p < .001.

Table 4:

DAT1 Genotype, Delinquency, and Gene–Environment Interactions

	Family closeness	Peer delinquency	Neighborhood disorganization	School attachment
Environment [low]	Serious delinquency
Average	0.15 (0.11)	0.16 (0.11)	−0.15 (0.09)	−0.01 (0.10)
High	0.10 (0.11)	0.35** (0.11)	−0.16 (0.12)	0.20+ (0.11)
DAT1 genotype	0.12* (0.05)	0.03 (0.05)	−0.05 (0.04)	0.07 (0.05)
G×E estimates
DAT1 × Average	−0.15* (0.07)	−0.03 (0.07)	0.11* (0.05)	−0.02 (0.06)
DAT1 × High	−0.14* (0.06)	0.03 (0.07)	0.15* (0.07)	−0.18** (0.06)
Environment [low]	Violent delinquency
Average	−0.02 (0.05)	0.01 (0.06)	−0.07 (0.04)	0.02 (0.05)
High	−0.05 (0.05)	0.09 (0.05)	0.03 (0.59)	0.11* (0.05)
DAT1 genotype	0.02 (0.02)	0.01 (0.03)	−0.01 (0.02)	0.04 (0.02)
G×E estimates
DAT1 × Average	−0.04 (0.03)	0.01 (0.04)	0.04 (0.03)	−0.04 (0.03)
DAT1 × High	−0.02 (0.03)	−0.01 (0.03)	0.00 (0.04)	−0.07* (0.03)

p < .10. *p < .05. **p < .01. ***p < .001.

To better differentiate social control (as a protective mechanism) compared with the other G×E models, we plot the fitted values from four of the regression models using the margins command in STATA 12.0. We focus on family closeness and school attachment because as shown in Tables 3 and 4, these environmental factors demonstrate the most consistent evidence for G×E associations. These interactions are shown in Figure 2. The dashed line represents the estimated level of delinquency as a function of genotype for those with the lowest level of social resources. For the top left panel, this line represents those with the lowest level of reported family closeness. The solid heavy line is for those with the highest level of family closeness (or school attachment) and the lighter solid line is the average. Several important relationships are evident in this figure. First, in all four cases, genotype for both genes in the dopaminergic system is only associated with an increased risk of both forms of delinquent behavior for those without attachment to family or to school. In other words, the level of control is less relevant than the absence or presence of control mechanisms. This is important theoretically because it suggests that for most individuals (those who are exposed to average and high levels of social control mechanisms), genotype is not significantly linked to either form of delinquency (as evidenced by the flat lines). Second, there is no evidence to support the social push or distinction models. In no case is genotype for those from the most supportive environments (the dark solid black lines) positively associated with delinquency (e.g., no support for social distinction) and no evidence to indicate that genotype is only linked to delinquent behavior in the average environments (e.g., no support for the social push model). In the case of school attachment, DAT1, and serious delinquency (upper right corner of Figure 1), we present evidence in support of the differential susceptibility model. Specifically, the weakest genetic association is for those in the average level of school attachment but most importantly, DAT1 genotype confers a risk for those in the riskiest environments (those with low school attachment) and a protective factor among those with the strongest attachment to their schools.

Figure 2:

Gene–Environment Interactions: Family Closeness, School Attachment, Dopamine-Related Genotypes, and Serious and Violent Delinquency

To further explore the nature of the G×E associations presented above, we created a cumulative measure of dopaminergic risk (sum of genotype scores for DRD2 and DAT1) and environmental risk (total number of domains in which individuals have the lowest level of support or the highest level of risk). As shown in the previous figure, most of the environmental moderation seems to be in the extreme cases in which persons have the lowest level of family closeness and school attachment. We took the extreme values for each of the four measures of the environment and summed these together. The regression results are presented in Table 5, and they are graphically presented in Figure 3. The dashed line indicates those that have the riskiest level of school attachment, neighborhood disorganization, peer delinquency, and family attachment; the solid black line represents those that have one or none of these risks. Again, we show that the strongest link between genotype and both forms of delinquency is among those with no forms of social control available to them. There is very little genetic association for those in the average levels for both outcomes, but in the case of serious delinquency, there is additional support for the idea that those in the most integrated, safe, and supported contexts demonstrate a strong and negative association between their dopaminergic profile and their risk of engaging in serious delinquency. This cross-over association is in line with the differential susceptibility model, and it indicates that some people do quite well in the best environments and incredibly poorly in the worst and that this characteristic (vulnerability) may have its source in genes related to the dopaminergic system (Simons et al., 2011). The differential susceptibility model is important for social and genetic epidemiologists because it provides yet another physiological mechanism through which the environment gets “under the skin” via socially sanctioned behaviors.

Table 5:

Cumulative Environmental by Cumulative Dopaminergic Risk for Serious and Violent Forms of Delinquency

	Serious (ln)	Violent (ln)
Environmental risk [ref = 0]
1	0.16 (0.11)	0.14* (0.05)
2	0.16 (0.11)	0.09 (0.06)
3	0.18 (0.13)	0.13* (0.06)
Dopaminergic risk (G)	0.14*** (0.04)	0.07** (0.02)
G×E [Env risk = 0]
1	−0.14** (0.05)	−0.08** (0.03)
2	−0.18** (0.05)	−0.07** (0.03)
3	−0.22*** (0.06)	−0.09** (0.03)

p < .10. *p < .05. **p < .01. ***p < .001.

Figure 3:

Cumulative Genetic and Environmental Risk for Serious and Violent Forms of Delinquency

Discussion

Summary

The objectives of this study were twofold—to build on previous research in this area and to examine a typology of gene–environment interactions. As such, our article frames the emerging empirical G×E results using the latest gene–environment interaction theory (Belsky & Pluess, 2009; Ellis et al., 2011). We also highlight the need for researchers to consider differences among individuals regarding their likelihood of responding to a particular environmental factor and in which direction they may respond. Inquiry into the interaction between genetic characteristics of individuals and the social, physical, and cultural characteristics of their local environments has grown considerably in the past decade (Boardman, Blalock, & Pampel, 2010; Freese & Shostak, 2009; Simons et al., 2011). Efforts to distill a generalizable typology of gene–environment interaction effects (Shanahan & Hofer, 2005) has made significant progress, and we are at a point where we can summarize the patterns of main and interactive effects to gauge the utility of the models within the existing G×E paradigm. To date, however, very little efforts have been made to synthesize this large and diverse body of work in a way that allows us to know the specific phenotypes, environments, genotypes, and the timing of gene–environment interactions that are the most relevant for understanding who, how, and when certain individuals are at the greatest risk of engaging in delinquent behaviors.

In large part, our results support previous work that makes the following point: To understand the extent to which genetic polymorphisms are associated with complex behaviors such as delinquency, it is critical that people and their genes are properly contextualized. Absent a full accounting of the environments in which delinquent behaviors are initiated, it is very difficult to make a general statement about the “effect” of any specific polymorphism. We examined four alternative G×E models and the bulk of our results tend to support the social control version of the stress-diathesis model. Conceptually, the social control and diathesis-stress models are quite similar, but the weaker moderating role played by stressful social environments compared with the strong and consistent role played by controlled and supportive social environments suggests that this specific phenotype (e.g., delinquent behavior) is best understood with the social control model. In other words, the bulk of our results suggests that some genetic effects may be less pronounced when individuals are raised, socialized, and educated in the most stable and healthy social environments. Importantly, while these models generally support the social control perspective, there is some tentative evidence for the differential susceptibility hypothesis with respect to DRD2 and serious delinquency. Carriers of two copies of the A1 allele at DRD2 are significantly more likely than those with one or no copies of the A1 allele to engage in serious delinquency when there is a low level of family closeness but are significantly less likely than those with no copies of the A1 allele to report higher delinquency when they perceive a high level of family closeness.

One of the main contributions of our article is importance of considering the full range of environments when examining different models in the G×E typology. Most G×E studies use a binary indicator or a continuous measure of environmental exposure that makes it impossible to evaluate the relevance of the social push model. Furthermore, the ordinal approach that we used provides information about the functional form of environmental moderation. Importantly, this approach led us to conclude that it is the most risky environment in which genotype emerges as a factor related to problem behavior. This is the strong version of the social trigger (Shanahan & Hofer, 2005) model that is somewhat different than the additive model provided by Caspi et al. (2002).

Additional Considerations

Although our research adds to the existing G×E literature, there are several points that readers should consider when interpreting the results of our study. First, previous research has shown that the composition of National Youth Survey participants changes over time such that the later waves of the study may not be generalizable to the entire population (Brame & Paternoster, 2003). Specifically, the most likely to offend are also more likely to drop out of the study. As such, our persistent offending group may be composed of an increasingly select group of offenders for reasons that are different from the social and genetic moderators that we present. Second, as discussed above, there is growing skepticism in the social and behavioral sciences about the interpretation of p-values from G×E estimates as evidence for “significant associations.” Specifically, Duncan and Keller (2011) make a strong case that most candidate gene-by-environment interaction results are likely false positives. One would have to make unreasonable assumptions about the magnitude of the main genetic effects and the environmental moderation to have a distribution of G×E estimates found in the literature, and they argue that most studies are grossly underpowered to detect the associations. In ancillary analyses (results available on request), we simulated data based on the effect sizes and covariance estimates across environmental levels using distributions comparable with our dependent variables, and we calculated power to detect G×E associations of .791 and .585 for our high and average environmental levels, respectively. These estimates support the power of the high environment category and provide fairly weak support for the power of the average environmental category. However, both estimates are well above the very low power estimates described by Duncan and Keller (2011). Furthermore, Pluess and Belsky (2012) challenge their findings because their model assumes that there must be a main genetic association in the population; if the differential susceptibility model is applicable, then the slope should be zero in the population. As we show here, that may indeed be the case with these two loci in the dopaminergic system.

This point is exacerbated when you consider the fact that we examined 16 statistical models with 2 G×E estimates in each model. We continue to rely on a traditional p < .05 threshold despite the multiple testing. If we used the most conservative Bonferroni adjustment, then our level of significance would drop to .0015 and we would only have one interaction that met this criterion. It is important to note, however, that the likelihood of observing 11 significant interactions out of the 32 tests (assuming independence) is roughly 2.5*10⁻⁷. Taken together, we believe that this approach provides strong support that most or all of our G×E associations are not statistical artifacts of multiple testing. That said, we encourage readers to consider these three important limitations when considering the meaning of our findings.

Some have made very strong arguments that G×E studies should emphasize exogenous sources of environmental influence (Fletcher & Conley, 2013). If genotype and environmental exposure are correlated, then it is possible that the G×E parameter estimates could be biased. We examined the possibility of gene–environment correlation by comparing the mean genotype across the three levels for each of our environmental measures. For both genes, we only observe one small departure from independence with respect to the minor allele frequency across environmental levels. In the case of DRD2, we estimated a mean genotype of .39 in the low school measure but .52 and .48 in the average and high school levels, respectively. This difference was not statistically significant (p < .053) but still worth reporting. To further investigate this point, we estimated regression models with main environmental effects that included genotype and compared those with models without genotype. If gene–environment correlation was contaminating our environmental scores, then the effect of the environment would be reduced on the inclusion of these controls. In no case did we observe this pattern. As such, we are confident that our G×E associations are not systematically biased by the subjective nature of our environmental measures.

Finally, while this article uses repeated assessments of delinquent behavior, we did not investigate the extent to which social and genetic risks combine to place individuals on separate trajectories of delinquent behavior throughout the life course. We prioritized testing the different G×E models that provided important information, but it compromised our ability to test specific turning points, critical times during the life course, and the extent to which persistence or desistence are best captured by G×E associations rather than mean delinquency levels (Sakai et al., 2010). To briefly demonstrate the significance of this perspective, consider the values presented in Figure 4. Here, we estimated comparable multi-level models and examined wave specific estimates for four groups: those with (a) protective social factors (average or low risk in the previous analyses) and low dopaminergic risk (0, 1, or 2 dopaminergic alleles), (b) protective social factors and high dopaminergic risk (3 or 4 dopaminergic alleles), (c) no protective social factors and low dopaminergic risk, and (d) no protective factors and high dopaminergic risk. Three important relationships are evident in Figure 4 and are worth describing. First, the absence of protective social factors is an important factor related to the risk of serious delinquency in adolescence regardless of genotype. Both of the darker lines are significantly higher than the corresponding lines below. Second, as we saw in models predicting differential susceptibility, those with the highest dopaminergic score in the controlled environments have the lowest levels of serious delinquency for most of the study. The third point, however, is the most obvious and perhaps the most important. While those from the least supportive environments do not differ from one another as a function of genotype in the first three waves of the study (as evidence by the confidence interval estimates), by Wave 4 (average age ~17), those with lower dopaminergic scores show strong desistence patterns and are then eventually indistinguishable from those from less risky social contexts. However, those from risk environments with higher dopaminergic scores demonstrate significantly higher levels of serious delinquency that persists through young adulthood. Subtle and seemingly insignificant differences early in the life course that are due to genetic antecedents have tremendous consequences for one of two paths: (a) desisting from delinquent behaviors in a way that is concordant with most youth or (b) maintaining these behaviors for a large part of one’s adult life. Given the timing of this departure with on time graduation from high school, we encourage future researchers to examine specific combinations of social and genetic risk factors that may place children on certain academic tracks or increase their likelihood of dropping out of school altogether.

Figure 4:

Serious Delinquency Across the Life Course as a Function of Social Controls and Genetic Risk

Conclusion

In sum, our article provides an opportunity to extend G×E studies linking social context, genetic factors, and delinquent behavior (Beaver et al., 2009; Guo et al., 2008; Simons et al., 2011; Vaske et al., 2011; Vaughn et al., 2009) using an independent sample with a long assessment period and detailed information across the typical delinquent life course. For the most part, our results are in line with this previous work. That is, we show evidence for social control (Guo et al., 2008) and some support for the differential susceptibility model (Simons et al., 2011), and we show evidence for an interactive risk with the 10R allele in the DAT1 gene that was not reported in previous work. Although very similar to one another, the diathesis-stress and social control models differ from one another in terms of whether they focus on “good” (controlled) or “bad” (stressful) environments. The most consistent finding to emerge was that the absence of protective factors from schools and families, rather than the presence of risk factors from friends and neighbors, appears to have the most important influence on genetic associations for delinquent behavior. As such, we find more evidence for the importance of “good” social factors for reducing the link between genotype and undesirable behavior rather than the importance of “bad” environments as a trigger for latent genetic tendencies. This contribution is somewhat subtle but has important implications for G×E research because very little attention is paid to genetic associations across the environmental spectrum.

It is our hope that future studies examine the extent to which environments change over time to see whether the apparently weaker interactions involving, for example, neighborhood and peer group are a result of attenuated relationships—findings that may have resulted from excluding the dynamic aspect of changing environments over the life course in our analysis. Acknowledging that there is much more to be done, this article nonetheless highlights the importance of evaluating previously published associations within the social sciences using independent samples, which, to date, remain less common compared with other disciplines. Even reports of null and alternative findings are critical to the process of identifying genes and pathways by which social environments may interact with genes to create delinquent behavior. We hope that future work will build on this and other emerging studies in this important area of research.

Footnotes

Authors’ Note:

This article is part of a larger study funded by the National Institute of Child Health and Human Development (R01HD060726). Research funds were also provided by the NIH/NICHD-funded CU Population Center (R24HD066613).

Jason D. Boardman, PhD, is an associate professor in the Department of Sociology and a research associate in the Population Program in the Institute of Behavioral Science at the University of Colorado. He is a social demographer whose research focuses on the influence of school and neighborhood-level factors on healthy development among adolescents and young adults. He is specifically interested in the moderation of social influences by individual differences in genotype.

Scott Menard, PhD, is a professor in the Department of Criminal Justice and Criminology at Sam Houston State University. He is the most recent principal investigator on the National Youth Survey Family Study (NYSFS), a national study of prosocial behavior, substance use, criminal victimization, and offending over the life course and across generations. His publications include books, monographs, and articles on quantitative methods and statistics (particularly logistic regression and longitudinal research), criminological theory testing, and studies of victimization, illicit substance use, and criminal behavior in adolescents and adults.

Michael E. Roettger, PhD is a research associate at the Edna Bennett Pearce Prevention Research Center at the Pennsylvania State University. He holds a doctorate in sociology, with a focus on applied quantitative methods and analysis of panel data. His research focuses on a number of areas, including gene–environment interactions related to delinquency and other problem behaviors, long-term risks associated with parental incarceration, and the relationship between prenatal stress and subsequent behavioral problems in children.

Kelly E. Knight, PhD, is an assistant professor in the Department of Criminal Justice and Criminology at Sam Houston State University. She received her PhD from the Department of Sociology at the University of Colorado at Boulder and has held positions at the Center for the Study and Prevention of Violence and at the National Council on Crime and Delinquency. Her primary research interests include life-course criminology, victimology, and the family.

Brian B. Boutwell, PhD is an assistant professor in the Department of Criminal Justice and Criminology at Sam Houston State University. Currently, his research interests include behavioral genetics, human intelligence, life-course criminology, and evolutionary psychology.

Andrew Smolen, PhD, is the director of the Institute for Behavioral Genetics’ (IBG) Genotyping and Sequencing Laboratory, senior research associate, and faculty fellow of IBG. Trained as a biochemist and pharmacologist, he has developed expertise in the design, implementation, and interpretation of genotyping analyses for large scale human genetic studies.

References

Anchordoquy

H. C.

McGeary

Liu

Krauter

K. S.

Smolen

(2003). Genotyping of three candidate genes following whole genome preamplification of DNA collected from buccal cells. Behavior Genetics, 33, 73-78.

Beaver

K. M.

DeLisi

Wright

J. P.

Vaughn

M. G.

(2009). Gene-environment interplay and delinquent involvement: Evidence of direct, indirect and interactive effects. Journal of Adolescence Research, 24, 147-168.

Beaver

K. M.

Wright

J. P.

DeLisi

Daigle

L. E.

Swatt

M. L.

Gibson

C. L.

(2007). Evidence of a gene × environment interaction in the creation of victimization. International Journal of Offender Therapy and Comparative Criminology, 51, 620-645.

Belsky

Beaver

K. M.

(2011). Cumulative-genetic plasticity, parenting and adolescent self-regulation. Journal of Child Psychology and Psychiatry, 52, 619-626.

Belsky

Pluess

(2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135, 885-908.

Boardman

J. D.

(2009). State-level moderation of genetic tendencies to smoke. American Journal of Public Health, 99, 480-486.

Boardman

J. D.

Barnes

L. L.

Wilson

R. S.

Evans

D. A.

Mendes de Leon

C. F.

(2012). Social disorder, APOE-E4 genotype, and change cognitive function among older adults living in Chicago. Social Science & Medicine, 74, 1584-1590.

Boardman

J. D.

Blalock

C. L.

Pampel

F. C.

(2010). Trends in the genetic influences on smoking. Journal of Health and Social Behavior, 51, 108-123.

Boardman

J. D.

Roettger

M. E.

Domingue

B. W.

McQueen

M. B.

Haberstick

B. C.

Harris

K. M.

(2012). Gene-environment interactions related to body mass: School policies and social context as environmental moderators. Journal of Theoretical Politics, 24, 370-388.

10.

Brame

Paternoster

(2003). Missing data problems in criminological research: Two case studies. Journal of Quantitative Criminology, 19, 55-78.

11.

Caspi

McClay

Moffitt

T. E.

Mill

Martin

Craig

I. W.

. . . Poulton

(2002). Role of genotype in the cycle of violence in maltreated children. Science, 297, 851-854.

12.

Caspi

Sugden

Moffitt

T. E.

Taylor

Craig

I. W.

Harington

. . . Poulton

(2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301, 386-389.

13.

Colzato

L. S.

van den Wildenberg

W. P.

Van der Does

A. J.

Hommel

(2010). Genetic markers of striatal dopamine predict individual differences in dysfunctional, but not functional impulsivity. Neuroscience, 170, 782-788.

14.

Cornish

K. M.

Manly

Savage

Swanson

Morisano

Butler

. . . Hollis

C. P.

(2005). Association of the dopamine transporter (DAT1) 10/10-repeat genotype with ADHD symptoms and response inhibition in a general population sample. Molecular Psychiatry, 10, 686-698.

15.

Duncan

L. E.

Keller

M. C.

(2011). A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. American Journal of Psychiatry, 168, 1041-1049.

16.

Elliot

D. S.

Huizinga

Menard

(1989). Multiple problem youth: Delinquency, drugs and mental health. New York, NY: Springer-Verlag.

17.

Ellis

B. J.

Boyce

W. T.

Belsky

Bakermans-Kranenburg

M. J.

Van Ijzendoorn

M. H.

(2011). Differential susceptibility to the environment: An evolutionary-neurodevelopment theory. Development and Psychopathology, 23, 7-28.

18.

Ferguson

C. J.

(2010). Genetic contributions to antisocial personality and behavior: A meta-analytic review from an evolutionary perspective. Journal of Social Psychology, 150, 160-180.

19.

Fletcher

J. M.

Conley

(2013). The challenge of causal inference in gene-environment interaction research: Leveraging research designs from the social sciences. American Journal of Public Health, 103(Suppl. 1), S42-5.

20.

Freese

Shostak

(2009). Genetics and social inquiry. Annual Review of Sociology, 35, 107-128.

21.

Groman

S. M.

Jentsch

J. D.

(2011). Cognitive control and the dopamine D2-like receptor: A dimensional understanding of addiction. Depression and Anxiety, 29, 295-306.

22.

Guo

Cai

Guo

Wang

Harris

K. M.

(2010). The dopamine transporter gene, a spectrum of most common risky behaviors, and the legal status of the behaviors. PLoS ONE, 5, e9352.

23.

Guo

Roettger

M. E.

Cai

(2008). The integration of genetic propensities into social-control models of delinquency and violence among male youths. American Sociological Review, 73, 569-588.

24.

Guo

Roettger

M. E.

Shih

J. C.

(2007). Contributions of the DAT1 and DRD2 genes to serious and violent delinquency among adolescents and young adults. Human Genetics, 126, 125-136.

25.

Haberstick

B. C.

Smolen

(2004). Genotyping of three single nucleotide polymorphisms following whole genome preamplification of DNA collected from buccal cells. Behavioral Genetics, 34, 541-547.

26.

Kim-Cohen

Caspi

Taylor

Williams

Newcombe

Craig

I. W.

Moffitt

T. E.

(2006). MAOA, early adversity, and gene-environment interaction predicting children’s mental health: New evidence and a meta-analysis. Molecular Psychiatry, 11, 903-913.

27.

Link

B. G.

Phelan

(1995). Social conditions as fundamental causes of disease. Journal of Health and Social Behavior, 35, 80-94.

28.

Marino

Vanzin

Giorda

Frigerio

Lorusso

M. L.

Nobile

Molteni

(2004). Measures of childhood problem behaviors and DRD2/Taq1 and DRD4/48bp-repeat polymorphisms. Behavior Genetics, 34, 495-502.

29.

Moffitt

T. E.

(2005). The new look of behavioral genetics in developmental psychopathology: Gene-environment interplay in antisocial behavior. Psychological Bulletin, 131, 533-554.

30.

Plomin

DeFries

J. C.

Craig

I. W.

McGuffin

(2008). Behavioral genetics in the postgenomic era. Washington, DC: American Psychological Association.

31.

Pluess

Belsky

(2012). Conceptual issues in psychiatric gene-environment interaction research: A comment. American Journal of Psychiatry, 169, 222-223.

32.

Rabe-Hesketh

Skrondal

(2005). Multilevel and longitudinal modeling using Stata. College Station, TX: Stata Press Publication.

33.

Raine

(2002). Biosocial studies of antisocial and violent behavior in children and adults: A review. Journal of Abnormal Child Psychology, 30, 311-326.

34.

Reiss

Leve

L. D.

(2007). Genetic expression outside the skin: Clues to mechanisms of genotype × Environment interaction. Development and Psychopathology, 19, 1005-1027.

35.

Rhee

S. H.

Waldman

I. D.

(2002). Genetic and environmental influences on antisocial behavior: A meta-analysis of twin and adoption studies. Psychological Bulletin, 128, 490-529.

36.

Risch

Herrell

Lehner

Liang

K. Y.

Eaves

Hoh

. . . Merikangas

K. R.

(2009). Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: A meta-analysis. Journal of the American Medical Association, 301, 2462-2471.

37.

Rutter

(2006). Genes and behavior: Nature-nurture interplay explained. Malden, MA: Blackwell.

38.

Sakai

J. T.

Boardman

J. D.

Gelhorn

Smolen

Corley

R. P.

Menard

. . . Stallings

M. C.

(2010). Utilizing trajectory analyses to refine phenotype for genetic associations: Conduct problems and 5HTTLPR. Psychiatric Genetics, 20, 199-206.

39.

Shanahan

M. J.

Boardman

J. D.

(2009). Genetics and behavior in the life course: A promising frontier. In Elder

G. H.

Jr. Giele

J. Z.

(Eds.), The craft of life course research (pp. 215-235). New York, NY: Guilford.

40.

Shanahan

M. J.

Hofer

S. M.

(2005). Social context in gene-environment interactions: Retrospect and prospect. Journals of Gerontology: Series B, 60B, 65-76.

41.

Simons

R. L.

Lei

M. K.

Beach

S. R. H.

Brody

G. H.

Philibert

R. A.

Gibbons

F. X.

(2011). Social environment, genes, and aggression: Evidence supporting the differential susceptibility perspective. American Sociological Review, 76, 883-912.

42.

Turkheimer

(2000). Three laws of behavior genetics and what they mean. Current Directions in Psychological Science, 9, 160-164.

43.

Vaske

Wright

J. P.

Beaver

K. M.

(2011). A dopamine gene (DRD2) distinguishes between offenders who have and have not been violently victimized. International Journal of Offender Therapy and Comparative Criminology, 55, 251-267.

44.

Vaughn

M. G.

DeLisi

Beaver

K. M.

Wright

J. P.

(2009). DAT1 and 5HTT are associated with pathological criminal behavior in a nationally representative sample of youth. Criminal Justice and Behavior, 36, 1113-1124.

45.

Zhang

Cui

Schmitt

Hubert

Navidi

Arnheim

(1992). Whole-genome amplification from a single cell: Implications for genetic analysis. Proceedings of the National Academy of Sciences of the United States of America, 89, 5847-5851.