Neighborhood Disadvantage Alters the Origins of Children’s Nonaggressive Conduct Problems

Consideration of the Individual: A Key Missing Ingredient

Critically, however, the role of individual genetic and biologic risk has not been incorporated in the above models in any meaningful way. The sole (albeit brief) exception to this can be found in Jencks and Mayer (1990):

Epidemic models must allow for individual differences in susceptibility to neighborhood or school influences. Epidemic models of antisocial or self-destructive behavior usually impute differential susceptibility differences in upbringing, but the model works the same way if we impute individual differences to heredity or to chance. (p. 114)

One way to incorporate individual differences in susceptibility into all of the above models is via the gene-environment interaction (G×E). G×E is defined as differential responsiveness to environmental risk as a function of genetic variability (Plomin, DeFries, & Loehlin, 1977; Rutter, Silberg, O’Connor, & Simonoff, 1999a, 1999b) and is thought to constitute a fundamental mechanism through which genes influence human behavior and mental health (Johnson, 2007; Moffitt, Caspi, & Rutter, 2006; Rutter, Moffitt, & Caspi, 2006).

There is provocative, if limited, support for possible G×E between youth antisocial behavior and structural characteristics of the neighborhood (Cleveland, 2003; Tuvblad, Grann, & Lichtenstein, 2006). Cleveland (2003) examined the heritability of adolescent aggression (AGG) by neighborhood in more than 2,000 sibling pairs from the National Longitudinal Study of Adolescent Health. In classifying neighborhoods, he created a composite of neighborhood disadvantage (i.e., proportion of single-parent homes, proportion of households with annual incomes of less than $15,000, and the unemployment rate). Neighborhoods were then dichotomized into disadvantaged (defined as the 25% most disadvantaged neighborhoods) and adequate (the remaining 75% of neighborhoods). Heritability estimates for AGG were calculated for each of the two neighborhood types. Results revealed that although AGG was genetically influenced regardless of neighborhood type, shared environmental influences (i.e., those that create similarities across family members regardless of the proportion of genes shared) were significant only for those individuals residing in disadvantaged neighborhoods. Similarly, Tuvblad et al. (2006) examined how contextual and familial risk (defined via parental education and occupation and socioeconomic conditions in the neighborhood) moderated the heritability of general antisocial behavior in a population-based Swedish study of 1,133 adolescent twin pairs. As with Cleveland, Tuvblad et al. found that shared environmental influences on antisocial behavior were more important for adolescents residing in disadvantaged environments. Genetic influences, by contrast, were more important in advantaged environments.

These findings thus collectively suggest that shared environmental influences on antisocial behavior are more influential for those individuals living in disadvantaged neighborhoods. The specific processes underlying this pattern of moderation remain unclear, but one possibility is that some experiences are so risky that they can elicit psychopathological outcomes even in the absence of genetic risk, which is a phenomenon referred to as a bioecological G×E. The bioecological model of G×E (Bronfenbrenner & Ceci, 1994; Pennington et al., 2009) harkens back to early notions that genetic influences may sometimes be most strongly expressed in “average, expectable environments” (Scarr, 1992), whereas deleterious environments amplify environmental influences (Lewontin, 1995; Pennington et al., 2009; Raine, 2002). The logic of this model was best illustrated by Lewontin (1995) through his analogy of genetically variable seeds that are planted in either a nutrient-rich or a nutrient-deprived field. The environmental adversity conferred by the deprived soil should eventuate in a field populated largely by short plants. By contrast, because all plants received adequate nutrition in the nutrient-rich soil, the plants would be able to fully express their genetic endowment for height, thereby making height more heritable in this environment. Put differently, some adverse experiences provide such a strong “social push” for a given outcome that the importance of genetic factors in these environments is diminished (Raine, 2002; Turkheimer, Haley, Waldron, D’Onofrio, & Gottesman, 2003). Only in the absence of these risks can genetically mediated individual differences fully manifest. If true, such findings would have key implications for the treatment and prevention of child antisocial behavior, as well as future genome-wide association studies of antisocial behavior, as they would suggest that antisocial behavior is more or less genetic in origin across various contexts.

Unfortunately, these findings of shared environmental (and possibly genetic) moderation by neighborhood disadvantage are less conclusive than one would like for two key reasons. First, although Cleveland (2003) examined AGG as the outcome variable, neither the Cleveland nor the Tuvblad et al. (2006) study differentiated between or considered both aggressive and nonaggressive rule-breaking forms of antisocial behavior. This is potentially problematic because there is converging evidence that although AGG (e.g., assaulting others, bullying) and nonaggressive rule breaking (RB; e.g., lying, stealing, vandalism) are moderately to strongly correlated, they nevertheless constitute meaningfully distinct dimensions of antisocial behavior. As reviewed previously (Burt, 2012; Tremblay, 2010), AGG and RB evidence distinctive developmental trajectories, demographic correlates, personological underpinnings, and, importantly, etiologic differences. In particular, AGG is a highly heritable (65%) behavioral dimension that emerges in early childhood and exhibits specific ties to negative emotionality and executive dysfunction. Although the frequency of aggressive behavior decreases after early childhood, those individuals who are most aggressive early in life typically continue to aggress at relatively high rates across the life span. By contrast, RB demonstrates particularly strong associations with impulsivity, is most frequent during adolescence, and evidences more moderate levels of genetic influences (48%) and stronger shared environmental influences as compared with AGG (18% vs. 5%, respectively). It is thus entirely possible that neighborhood poverty differentially moderates AGG and RB.

Second, and more importantly, neither the Cleveland (2003) nor the Tuvblad et al. (2006) study evaluated whether the increase in shared environmental influences with neighborhood disadvantage reflected the increasing importance of environmental experiences on adolescent antisocial behavior or whether it instead reflected an increasing role for passive gene-environment correlation (rGE) in antisocial behavior. Passive rGE refers to the fact that the environment parents provide to their biological children likely reflects the genetically influenced preferences/tendencies of the parent. And because parents also share genes with their biological children, the child’s genes are then correlated with his or her environmental experiences (thereby mimicking shared environmental influences; Neiderhiser et al., 2004). In this case, what appears to be an increasing effect of the shared environment on youth antisocial behavior with increasing neighborhood disadvantage could reflect an increasing role for passive rGE, such that parents with a tendency toward antisocial behavior themselves are both selecting into more disadvantaged neighborhoods and passing on genes of risk for antisocial behavior to their children. In short, it is as yet entirely unclear whether the increase in shared environmental influences with increasing neighborhood disadvantage does in fact reflect the increasing influence of actual environmental experiences in youth antisocial behavior.

Assortative mating can also inflate estimates of shared environmental influences. Assortative mating is thought to reflect a largely active rGE process in which individuals seek out and mate with others similar to themselves. To the extent that these phenotypic similarities between spouses reflect genetic similarities, assortative mating in the parents would increase the proportion of genes shared by dizygotic (DZ) twins but not by monozygotic (MZ) twins who are already genetically identical. By doing so, assortative mating can serve to artifactually inflate shared environmental estimates. This point is critically important here, given that it is now well known that there are at least modest levels of assortative mating for antisocial behavior (Krueger, Moffitt, Caspi, Bleske, & Silva, 1998). Should assortative mating for antisocial behavior vary with neighborhood (which is as yet unknown), it could be possible that prior findings of shared environmental moderation reflect increases in assortative mating.

How might we disambiguate actual shared environmental influences from passive rGE and assortative mating? The answer lies in the methodologic design. Both Tuvblad et al. (2006) and Cleveland (2003) made use of the classical twin design, evaluating the extent to which twin similarity varied by zygosity in advantaged versus disadvantaged neighborhoods, respectively. Although useful in many ways, this design is unable to disambiguate passive rGE or assortative mating from estimates of the shared environment and, thus, is considered to be a less optimal design for the study of shared environmental influences (Burt, 2014). Fortunately, there is a straightforward solution to this dilemma: Namely, we could also include data on the twins’ parents. This extension of the classical twin design is referred to as the nuclear twin family design. The nuclear twin family model provides two additional pieces of information, over and above the covariance between the twins, on which to base parameter estimates: the covariance between parents and the covariance between parents and children. This additional information allows the nuclear twin family model to, among other things, disambiguate shared environmental influences shared only by siblings (S or sibling level) from those shared by parents and children (F or family level; see Table 1 for definitions of the parameters obtained via twin modeling). The model then capitalizes on the newfound individuation of the family-level environment by further modeling its covariance with genetic influences, thereby allowing researchers to both explicitly estimate passive rGE and disambiguate it from sibling-level shared environmental influences. Comparison of these various estimates across advantaged and disadvantaged neighborhoods, respectively, thus allows us to explicitly evaluate which specific components of the shared environment vary by neighborhood status. In other words, we would be able to more definitively evaluate whether the increase in shared environmental influences reflects actual increases in the importance of the environment on child antisocial behavior.

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Table 1.

Definitions of the Parameters Obtained via Twin Modeling

Parameter	Model	Definition
A	CTM NTFM	Additive genetic variance = the effect of individual genes summed over loci; acts to increase familial correlations (between twin siblings and between parents and their biological children) relative to the proportion of genes shared; if acting alone, creates monozygotic correlations that are twice the magnitude of dizygotic correlations.
C	CTM	Shared environmental variance = environmental influences common to family members that act to make them similar to each other regardless of the proportion of genes shared; if acting alone, creates equivalent monozygotic and dizygotic correlations.
S	NTFM	Sibling environmental influences = those shared environmental influences that create similarity between siblings (e.g., exposure to common peers, schools, and parenting styles) but not between parents and their children.
F	NTFM	Familial environmental variance = shared environmental influences passed via vertical “cultural transmission” between parents and their offspring (e.g., socioeconomic status, social mores); creates similarities between siblings and between parents and their children.
E	CTM NTFM	Nonshared environmental variance = environmental influences that serve to differentiate family members regardless of the proportion of genes shared; measurement error is also included here.
w	NTFM	Covariance between additive genetic and familial environmental effects = reflects the extent to which the familial environment is a function of the genetically influenced preferences/tendencies of the parent; also referred to as a passive gene-environment correlation.
μ	NTFM	Assortative mating copath = spousal similarity on the trait in question; is assumed to be a function of primary phenotypic assortment, whereby mates choose each other on the basis of phenotypic similarity.

Note: Some parameters can be obtained only in the classical twin model (CTM), others can be obtained only in the nuclear twin family model (NTFM; see Fig. S2 in the Supplemental Material available online), and others can be obtained in both models. Dominant or nonadditive genetic influences are not estimated in the current study and are thus omitted here.

In the current study, we sought to do just this by making use of the nuclear twin family model to examine whether and how neighborhood disadvantage moderates the etiology of aggressive and nonaggressive antisocial behavior, respectively. Our sample consisted of 1,027 child twin pairs, half of whom were oversampled to reside in modestly-to-severely impoverished neighborhoods. Given the results of existing twin studies (Cleveland, 2003; Tuvblad et al., 2006), we expected to find evidence of shared environmental moderation of child antisocial behavior by neighborhood disadvantage. We further expected this moderation to be more pronounced for RB, given meta-analytic and nuclear twin family studies that have indicated that shared environmental influences are far more salient for RB than for AGG (Burt, 2009; Burt & Klump, 2012), as well as evidence that RB may be particularly affected by broader societal processes (Breslau et al., 2011). We further anticipated that this shared environmental moderation would be a function of actual shared environmental experiences rather than passive rGE or assortative mating, given the important role of shared environmental influences on youth RB noted in other studies (Burt & Klump 2012).

Method

Participants were recruited as part of the Twin Study of Behavioral and Emotional Development in Children (TBED-C), a study within the population-based Michigan State University Twin Registry (MSUTR; Burt & Klump, 2013; Klump & Burt, 2006). The TBED-C includes two independent samples: a population-based sample of 1,054 twins from 527 families recruited from across lower Michigan and an “at-risk” sample of 1,000 twins from 500 families residing in modestly-to-severely disadvantaged neighborhoods in the same recruitment area. These two samples were combined for our primary analyses, thereby allowing us to capture both severity and variability in neighborhood poverty and to maximize our sample size. To be eligible for participation in the TBED-C, neither twin could have a cognitive or physical condition that would preclude completion of the assessment (as assessed via parental screen; e.g., a significant developmental delay). Children provided informed assent, and parents provided informed consent for themselves and their children. The twins were 48.7% female and ranged in age from 6 to 10 years (M = 8.03, SD = 1.49; although 30 of the 1,027 pairs had turned 11 by the time the family participated).

Although virtually all mothers participated with their twins during the in-person assessment, roughly 5% of fathers completed their questionnaires via the mail. In keeping with the parameterization of the nuclear twin family model (described later), parental self-report data were omitted for those parent figures who did not share 50% of their genes with the twins (i.e., grandmothers and stepfathers), although their reports of the twins were retained. The self-reports of divorced or separated biological parents with joint custody arrangements or who were otherwise involved in their twins’ lives were retained for analysis (note that their exclusion from analysis did not alter our conclusions). Our final sample thus included self-reports from 992 biological mothers and 822 biological fathers.

Recruitment procedures are detailed in prior work (Burt & Klump, 2013). In brief, families were recruited directly from birth records, or from a population-based registry that was itself recruited via birth records, via anonymous recruitment mailings in conjunction with the Michigan Department of Health and Human Services. Recruitment procedures for the at-risk sample were identical except that mailings were restricted to those families residing in neighborhoods with census-level poverty data above the 2008 mean of 10.5% (additional information on neighborhood poverty rates is provided later). This recruitment strategy yielded overall response rates of 62% for the population-based sample and 57% for the at-risk sample. Families participating in the population-based sample endorsed ethnic group memberships at rates comparable to area inhabitants (e.g., White = 86.4%, Black = 5.4%; Burt & Klump, 2013). Compared with the population-based sample, the at-risk sample was significantly more racially diverse (15% Black, 75% White) and reported lower family incomes (the means were $72,027 and $57,281, respectively; Cohen’s d = −0.38), higher paternal felony convictions (d = 0.30), and higher rates of twin conduct problems and hyperactivity (d = 0.34 and 0.27, respectively).

It is important to note that both samples appear representative of recruited families as indexed via a brief questionnaire administered to approximately 85% of nonparticipating families (Burt & Klump, 2013). As compared with nonparticipating twins, participating twins were experiencing equivalent levels of conduct problems and hyperactivity (d ranged from −0.08 to 0.01 in the population-based sample and from 0.01 to 0.09 in the at-risk sample; all n.s.). Participating families also did not differ from nonparticipating families in paternal felony convictions (d = −0.01 and 0.13 for the population-based and the at-risk samples, respectively), rate of single-parent homes (d = 0.10 and −0.01 for the population-based and the at-risk samples, respectively), paternal years of education (both d ≤ .12), or maternal and paternal alcohol problems (d ranged from 0.03 to 0.05 across the two samples). However, participating mothers reported more years of education (d = 0.17 and 0.26 in the population-based and at-risk samples, respectively) than did nonparticipating mothers. Maternal felony convictions were also more common in participating than in nonparticipating families but only in the population-based sample (d = −0.20 in the population-based sample and 0.02 in the at-risk sample).

Zygosity was established by using physical similarity questionnaires administered to the twins’ primary caregiver (Peeters, Van Gestel, Vlietinck, Derom, & Derom, 1998). On average, the physical similarity questionnaires used by the MSUTR have accuracy rates of 95% or better. The population-based study included 259 MZ pairs (137 male-male and 122 female-female) and 268 DZ pairs (125 male-male, 111 female-female, and 32 opposite-sex pairs). The at-risk study included 165 MZ pairs (86 male-male and 79 female-female) and 335 DZ pairs (85 male-male, 95 female-female, and 155 opposite-sex pairs).

Results

Descriptive statistics for AGG and RB are presented in Table S1 in the Supplemental Material. Paired-samples t tests indicated that fathers were engaging in higher levels of RB relative to mothers (p < .001) and somewhat higher levels of AGG (p = .077). Independent-samples t tests similarly indicated that boys evidenced higher rates of RB and AGG than did girls (Cohen’s d = 0.29 and 0.31, respectively; both p < .001). Twin RB and AGG also varied by ethnicity, such that they were less common in White participants than in non-White participants (d = −0.39 and −0.35, respectively; both p < .001). RB also decreased slightly with age (r = −.10, p < .001). Ethnicity, sex, and age were regressed out of the twin data prior to analysis (McGue & Bouchard, 1984). Neighborhood poverty (as measured continuously) was modestly correlated with maternal and paternal self-reports of their own RB (r = .08 and .10, respectively; both p < .01) but not with their AGG (both r = .00). Neighborhood poverty was also associated with teacher reports of child RB and AGG (r = .17 and .10, respectively; both p < .001).

G×E model results

We confirmed these impressions via formal tests of etiologic moderation (Purcell, 2002). Model fit statistics are reported in Table 2. The no moderation model provided a better fit to the AGG data by three of the five fit indices, which argues against the presence of significant etiologic moderation. Moreover, even when estimated, the pattern of (nonsignificant) moderation pointed to increasing genetic influences and decreasing shared environmental influences with increasing poverty (the genetic and shared environmental moderators were estimated at 0.25 and −0.26, respectively); these results are not in keeping with those of prior studies.

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Table 2.

Gene-Environment Interaction Fit Indices

Model	−2lnL	df	χ² (df)	AIC	BIC	SABIC	DIC
Moderation of child AGG
Linear ACE moderation	4,155.84	1519	—	1,117.84	−3,020.57	−608.68	−1,624.70
No moderation	4,165.64	1522	9.81 (3)†	1,121.64	−3,025.74	−609.09	−1,627.12
Moderation of child RB
Linear ACE moderation	4,164.96	1519	—	1,126.96	−3,016.01	−604.12	−1,620.14
No moderation	4,179.99	1522	15.03 (3)†	1,135.99	−3,018.57	−601.91	−1,619.94
Linear A moderation only	4,167.89	1521	2.93 (1)	1,125.89	−3,021.26	−606.19	−1,623.55
Linear C moderation only	4,165.08	1521	0.12 (1)	1,123.08	−3,022.66	−607.60	−1,624.96
Linear E moderation only	4,177.63	1521	12.67 (1)†	1,135.63	−3,016.39	−601.32	−1,618.68

Note: Aggression (AGG) and rule breaking (RB) represent aggressive and nonaggressive antisocial behavior, respectively. Daggers indicate a significant change in chi-square at p < .05. The best-fitting model for a given set of analyses is highlighted in boldface and is indicated by a nonsignificant change in chi-square relative to the linear ACE moderation model (additive genetic, shared environmental influences, and nonshared environmental influences represented with A, C, and E, respectively) and/or the lowest or most negative AIC (Akaike’s information criterion), BIC (Bayesian information criterion), SABIC (sample-size-adjusted Bayesian information criterion), and DIC (deviance information criterion) values for at least three fit indices.

By contrast, the linear ACE moderation model (additive genetic, shared environmental influences, and nonshared environmental influences represented with A, C, and E, respectively) provided a better fit than the no moderation to child RB data model using four of the five fit indices. We then examined specific submodels of the linear moderation model. The C moderation only model provided the best fit to the child RB data relative to both the full ACE moderation model and the A and E moderation only models by all five fit indices. Parameter estimates for the best-fitting C moderation model indicated that genetic and nonshared environmental parameter estimates were moderate to large in magnitude regardless of neighborhood type (the unstandardized genetic and nonshared environmental variances were 0.50 and 0.41, respectively; both p < .05). Moreover, as their moderators were constrained to be zero, these genetic and nonshared environmental influences did not shift with the level of neighborhood poverty (note that even when estimated, their moderators were very small, 0.04 and −0.02, respectively). Shared environmental influences, by contrast, were estimated at 0.00 (n.s.) in wealthy and middle-class neighborhoods and increased dramatically in impoverished neighborhoods (the shared environmental moderator was estimated to be 0.56 with a 95% confidence interval, CI, of 0.24 to 0.99, thereby indicating an unstandardized shared environmental variance estimate of 0.31). This rather large C moderator was observed even in the full ACE moderation model, although it was not significant in that less restrictive model (the moderator was 0.53).

Nuclear twin family model results

For our primary analyses, we sought to better understand the shared environmental moderation observed for child RB via a series of nuclear twin family models. Parameter estimates for the full ASFE models are presented in Table 3. Genetic influences on child RB were observed to be moderate to large in magnitude regardless of the level of neighborhood poverty. Assortative mating also appeared to be present regardless of the level of neighborhood poverty. Shared sibling environmental influences (S) were small and nonsignificant in wealthy and middle-class neighborhoods but significant and moderate in magnitude in impoverished neighborhoods. Alternately, family environmental (F) and passive rGE effects contributed significantly to RB in wealthy and middle-class neighborhoods. It is interesting that the passive rGE effect in wealthy and middle-class neighborhoods was negatively signed, which indicates that increases in the genetic variance (A) in child RB are associated with decreases in the importance of vertical cultural transmission (F).

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Table 3.

Unstandardized Nuclear Twin Family Design Heritability Estimates for Nonaggressive Rule Breaking by Level of Neighborhood Poverty and Community Resource Availability

Measure	Model	A	S	F	Passive rGE	Assortative mating	E
Level of neighborhood poverty
0% to 19.9%	ASFE	0.68* [0.39, 0.88]	0.02 [0.00, 0.16]	0.12* [0.03, 0.20]	−0.19* [−0.26, −0.08]	0.19* [0.12, 0.23]	0.41* [0.34, 0.50]
	AFE	0.72* [0.56, 0.88]	—	0.13* [0.07, 0.20]	−0.20* [−0.26, −0.14]	0.19* [0.12, 0.27]	0.40* [0.34, 0.49]
20% or more	ASFE	0.55* [0.13, 0.98]	0.30* [0.10, 0.52]	0.03 [0.00, 0.15]	−0.08~ [−0.22, 0.009]	0.15~ [−0.003, 0.31]	0.39* [0.29, 0.54]
	ASE	0.30* [0.13, 0.46]	0.40* [0.26, 0.56]	—	—	0.15~ [−0.006, 0.31]	0.45* [0.35, 0.58]
Community resource availability
More	ASFE	0.82* [0.35, 1.24]	0.08 [0.00, 0.30]	0.17* [0.02, 0.36]	−0.24* [−0.40, −0.06]	0.27* [0.14, 0.41]	0.31* [0.22, 0.44]
	AFE	0.98* [0.71, 1.26]	—	0.24* [0.12, 0.38]	−0.31* [−0.40, −0.20]	0.27* [0.14, 0.41]	0.30* [0.22, 0.43]
Less	ASFE	0.30~ [0.00, 0.87]	0.33* [0.10, 0.58]	0.00[0.00, 0.11]	−0.02 [−0.19, 0.06]	0.21* [0.06, 0.36]	0.43* [0.29, 0.64]
	ASE	0.23* [0.07, 0.40]	0.35* [0.21, 0.51]	—	—	0.21* [0.06, 0.36]	0.45* [0.34, 0.59]

Note: 95% confidence intervals are presented below the point estimate in brackets. Additive genetic, environmental influences shared between siblings, family environmental influences, and nonshared environmental influences are represented with A, S, F, and E, respectively. Because A, S, F, and E are variances, neither their estimates nor their confidence intervals can be negatively signed. The passive rGE (gene-environment correlation) and assortative mating estimates can be either positively or negatively signed. Bold font and an asterisk indicate that the parameter is significantly greater than zero at p < .05. Bold font and a tilde (~) indicate that the parameter is marginally significant at p < .10.

To clarify whether these differences in the etiology of child RB across level of neighborhood poverty were statistically significant, we ran two sets of analyses. We first evaluated which nuclear twin family model (i.e., ASFE, AFE, or ASE) provided the best fit to the RB data at low and high levels of neighborhood poverty, respectively. Model fit results are presented in Table 4. As shown in the table, the AFE model provided the best fit to the data in wealthy and middle-class neighborhoods (i.e., 0%–19.9% poverty) by all five fit indices. In sharp contrast, the ASE model provided the best fit to the data in impoverished (i.e., 20% or more poverty) neighborhoods, again by all five fit indices. Such results confirmed our overall impressions from Table 3, namely, that the etiology of child RB varies significantly with level of neighborhood poverty.

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Table 4.

Nuclear Twin Family Model Fit Indices for Nonaggressive Rule Breaking by Level of Neighborhood Poverty and Community Resource Availability

Model	−2lnL	df	χ² (df)	AIC	BIC	SABIC	DIC
0% to 19.9% neighborhood poverty
ASFE model	6,255.33	2300	—	1,655.33	−4,381.17	−729.76	−2,267.61
ASE model	6,268.59	2301	13.26 (1)†	1,666.59	−4,377.80	−724.80	−2,263.32
AFE model	6,255.45	2301	0.12 (1)	1,653.45	−4,384.37	−731.37	−2,269.89
20% or more neighborhood poverty
ASFE model	2,948.53	1013	—	922.53	−1,453.68	152.88	−522.80
ASE model	2,950.20	1014	1.67 (1)	922.20	−1,455.74	152.42	−523.93
AFE model	2,956.77	1014	8.24 (1)†	928.77	−1,452.45	155.70	−520.65
More resource availability
ASFE model	2,098.91	767	—	564.91	−1,049.16	166.42	−344.33
ASE model	2,106.51	768	7.60 (1)†	570.51	−1,048.10	169.06	−342.35
AFE model	2,099.46	768	0.55 (1)	563.46	−1,051.62	165.54	−345.88
Less resource availability
ASFE model	2,205.45	783	—	639.45	−1,038.02	202.90	−318.49
ASE model	2,205.53	784	0.08 (1)	637.53	−1,040.72	201.79	−320.27
AFE model	2,213.91	784	8.46 (1)†	645.91	−1,036.52	205.98	−316.08

Note: Additive genetic, environmental influences shared between siblings, family environmental influences, and nonshared environmental influences are represented with A, S, F, and E, respectively. Analyses were conducted separately for neighborhood poverty and community resource availability. Chi-square values indicate the change in chi-square relative to the full ASFE model. Daggers indicate a significant change at p < .05. The best-fitting model for a given set of analyses is highlighted in boldface and is indicated by a nonsignificant change in chi-square and/or the lowest or most negative AIC (Akaike’s information criterion), BIC (Bayesian information criterion), SABIC (sample-size-adjusted Bayesian information criterion), and DIC (deviance information criterion) values for at least three of the five fit indices.

We also ran a series of constraint models (see Table S2 in the Supplemental Material). As Table S2 shows, the fully unconstrained ASFE model provided a better fit to the data than did the fully constrained ASFE model, in which all parameter estimates were constrained across neighborhood type, by all five fit indices. Such results again point to significant etiologic differences in child RB with level of neighborhood poverty. Additional constraint analyses further revealed that neither F nor S could be individually constrained across neighborhood type without a significant decrement in fit, which was consistent with the model fit results presented earlier. Passive rGE, A, and E, by contrast, could each be constrained across level of neighborhood poverty without a significant decrement in fit. Interestingly, however, visual inspection of the parameter estimates for the best-fitting ASE/AFE models revealed the presence of nonoverlapping CIs for the genetic estimates (i.e., 0.72, 95% CI = [0.56, 0.88] vs. 0.30, 95% CI = [0.13, 0.46]). Such results indicate that genetic influences on RB may in fact be more important in wealthy and middle-class neighborhoods than in impoverished neighborhoods.

Comment

In the current study, we evaluated whether and how neighborhood disadvantage shaped the etiology of child AGG and RB. Although we did not find evidence that neighborhood poverty moderated the etiology of child AGG, we observed consistent differences in the etiology of RB by level of neighborhood poverty and lack of community resources. Familial environmental influences (i.e., cultural transmission from parents to children) were observed almost exclusively in wealthy and middle-class neighborhoods, as were passive gene-environment correlations. There was also some evidence that genetic influences on child RB are particularly prominent in wealthy and middle-class neighborhoods. In sharp contrast, sibling-level shared environmental influences were estimated to be near zero in the wealthiest neighborhoods and increased several fold with increasing levels of neighborhood poverty. This pattern of results persisted even when examining neighbor informant-reports of community resource availability as our measure of neighborhood disadvantage—findings that serve to bolster our results for neighborhood poverty and also suggest that our conclusions may extend to neighborhood disadvantage more broadly. Such findings collectively indicate that pervasive neighborhood disadvantage substantively alters the etiology of child RB, serving both to enhance sibling-level environmental influences and suppress the otherwise important roles of genetic influences and vertical cultural transmission on these outcomes.

These results constructively replicate, and meaningfully extend, those of prior studies. Cleveland (2003) and Tuvblad et al. (2006) both reported that shared environmental influences on adolescent antisocial behavior increased with increasing neighborhood disadvantage—results that are fully compatible with those reported here despite the fact that both Tuvblad et al. and Cleveland examined samples of adolescents whereas we examined participants in middle childhood. Similarly, Tuvblad et al. found evidence that genetic influences on antisocial behavior decrease with increasing neighborhood disadvantage—results that were partially replicated in the current study. That said, Cleveland reported shared environmental moderation of AGG in particular, for which we did not find evidence of moderation. Although it remains unclear what might account for this discrepancy, the developmental stage of our respective samples is one possibility (middle childhood vs. adolescence). That said, Cleveland did not examine RB; thus, we cannot know how those results might look.

The current study also substantively adds to our understanding of the ways in which neighborhood disadvantage moderates the etiology of child antisocial behavior. Neither Tuvblad et al. (2006) nor Cleveland (2003) made use of nuclear twin family models and, thus, were unable to disambiguate passive rGE from shared environmental influences. Because of this, it was possible that the increase in shared environmental influences (C) observed in those studies reflected the increasing importance of passive rGE. The results of the current study strongly argue against this possibility. Sibling-level shared environmental influences were found to increase rather dramatically with increasing levels of neighborhood poverty and decreasing levels of community resources. Moreover, passive rGE and familial shared environmental influences were all but exclusive to wealthy and middle-class neighborhoods, which further argues against the notion that increasing levels of passive rGE underlay the increase in shared environmental influences observed in prior work. The negative direction of the passive rGE we observed in wealthy and middle-class neighborhoods also represents a novel result: Namely, our results indicate that in wealthy and middle-class neighborhoods, children with a lower genetic loading for RB have a higher cultural propensity to engage in RB (and vice versa). Although it remains unclear what that cultural propensity might be, one possibility is that familial socioeconomic status and social mores exert more of an effect on child RB in the absence of genetic risk for RB. In future work, researchers should examine this possibility directly.

Finally, the results of the current study dovetail nicely with prior suggestions that AGG and RB constitute meaningfully different, if correlated, dimensions of youth antisocial behavior (Burt, 2012). Only child RB was consistently moderated by neighborhood disadvantage, whereas etiologic influences on child AGG were relatively unaffected. Such results clearly imply that the etiologic differences between AGG and RB extend beyond previously reported differences in their basic genetic and environmental architectures (Burt, 2009) to also include differential responsiveness to particular environmental experiences. Given this, we would argue that researchers interested in the causal processes underlying antisocial behavior should be disambiguating the two dimensions whenever possible.

Limitations

There are some limitations to be considered. First, given the role of development in the etiology of conduct problems (Burt & Neiderhiser, 2009), the current results should be considered specific to middle childhood and should not be applied to other developmental periods. That said, similar results were reported for adolescent samples in both Cleveland (2003) and Tuvblad et al. (2006), suggesting a general environmental effect of neighborhood disadvantage on youth antisocial behavior. Of note, the finding of a consistent pattern of moderation across child and adolescent studies of antisocial behavior has not been shown for other environmental risk factors (Burt, 2015), which suggests that neighborhood may represent a more potent, or at least longer term, moderator. Similarly, it remains unclear how child sex might further moderate these associations, although extant work has not shown evidence of etiologic differences in antisocial behavior across sex (Burt, 2009). Nonetheless, future work should seek to confirm the absence of additional moderation by sex. Next, although the phenotypic associations between parent and child RB and neighborhood poverty were in the expected direction, they were small. However, the presence of a small phenotypic association between the moderator and the outcome has no bearing on the extent of etiologic moderation, with the exception of reducing concerns about possible rGE confounds (van der Sluis et al., 2012).

Finally, although the results presented here highlight distinctions within the overarching construct of antisocial behavior, it is worth noting that AGG and RB demonstrate considerable overlap as well. Previous studies have shown that of those individuals with childhood-onset AGG, roughly 50% also exhibit clinically significant RB (Hudziak et al., 2003), results that were replicated here (r = .64 for teacher report of twin; when squared to index the coefficient of determination, this corresponds to 41% overlap across AGG and RB, respectively). Although this level of overlap may appear incompatible with meaningful differences between these two dimensions of antisocial behavior, AGG and RB are in fact differentially predictive of a wide variety of youth outcomes (as reviewed in prior work; see Burt, 2012). Moreover, we would argue that the presence of significant overlap between AGG and RB is in fact to be expected. The comorbidity of mental disorders, once thought to be the exception, now seems to be the rule (Clark, Watson, & Reynolds, 1995) and has been conceptualized as evidence that core psychopathological processes link separate mental disorders (Kendler, Prescott, Myers, & Neale, 2003; Krueger et al., 2002). In addition to these common processes, however, there is evidence of causal processes that are disorder specific (see especially Krueger et al., 2002). Accordingly, although our results highlight etiological distinctions between AGG and RB, they are not inconsistent with common etiological influences that contribute both to their covariation with each other and to the comorbidity between antisocial behavior and other externalizing spectrum disorders.

Conclusions

Our findings have several important implications for understanding the etiology of antisocial behavior in both advantaged and disadvantaged neighborhoods, as well as for understanding G×E processes more generally. Indeed, although much has been made of the moderation of genetic influences by measured aspects of the environment, the current study indicates that this process of etiological moderation is not unique to genetic influences. And although the moderation of environmental influences is somewhat less straightforward to interpret than the moderation of genetic influences, prior literature points to two possible routes for environmental moderation. One possibility is that effects of parenting on child outcomes are accentuated in disadvantaged neighborhoods (Gorman-Smith, Tolan, & Henry, 1999; Gorman-Smith, Tolan, Zelli, & Huesmann, 1996), given that exposure to a common parenting style is thought to load on S or the sibling-level shared environment rather than on F or the family-level shared environment (because parents are not themselves being parented anymore; see Table 1). This effect of parenting may take the form of protection, in which the parent-child relationship is able to buffer children from the consequences of neighborhood poverty (e.g., increased crime and joblessness), or the form of increased risk, in which the parent-child relationship mirrors and accentuates the experiences in the broader community.

Another possibility (which may or may not co-occur with the first) involves early exposure to environmental contaminants, which are experienced disproportionately by those individuals living in impoverished environments (Perera et al., 2002). Decades of research have highlighted the damaging effects of prenatal and early childhood exposure to common environmental toxicants (e.g., lead, cigarette smoke) on later health outcomes, including youth antisocial behavior (DiFranza, Aligne, & Weitzman, 2004; Mansi et al., 2007; Needleman, Schell, Bellinger, Leviton, & Allred, 1990). Fetuses and children are particularly sensitive to such exposure, both because early disruptions in development can have long-lasting effects (Rice & Barone, 2000) and because many neurotoxicants are transferred across the blood-brain barrier (Neubert & Tapken, 1988; Rodier, 2004). These exposures could contribute to the increase in RB in disadvantaged neighborhoods and may exert powerful enough main effects to obviate the contributions of individual genes. Moreover, we would a priori expect such early exposure to create similarities between siblings (e.g., fetal exposure would be experienced by both twins), but not necessarily between children and their parents, and thus load on S (the sibling-level shared environment).

The current results also speak to the unique etiology of child RB in wealthy and middle-class neighborhoods and in those with high levels of community resources. Namely, genetic influences appear to be particularly important to the etiology of RB in advantaged neighborhoods, as are familial environmental influences. Moreover, these genetic and family-level shared environmental influences are negatively correlated. Such findings point to a distinctive etiology of child RB in advantaged neighborhoods—namely, RB appears to be a function of either high levels of genetic risk or high levels of family-level shared environmental influence but not both. Future work should seek to identify the environmental experiences that contribute to RB in advantaged neighborhoods in particular and to evaluate their negative correlation with genetic risk for RB.