Genetic and Environmental Overlap Between Delinquent Peer Association and Delinquency in Adolescence

Abstract

Delinquent peer association and criminal/delinquent behaviors are highly intertwined. The directionality and mechanisms underlying this relationship, however, have been debated in the literature for decades. The current study seeks to further inform this debate by examining whether individual differences at the level of the genome can help to explain the association between delinquent peer affiliation and delinquency. Using the twin and full-sibling subsample from the National Longitudinal Study of Adolescent Health (Add Health), behavioral genetic methodology is used to examine whether delinquent peer affiliation and delinquency in adolescence covary as the result of common genetic factors. Results indicate that delinquent peer association and delinquency are moderately influenced by additive genetic factors, and that common genes are in fact influencing the covariance between the two outcomes. The importance of incorporating genetic explanations into traditional theories of delinquency is discussed.

Keywords

Add Health behavioral genetics delinquent peers delinquency

Introduction

Delinquent peer association has been linked to a variety of adverse behaviors, including substance use, externalizing behaviors, and criminal behaviors (Loeber & Dishion, 1983; Warr, 2002). The directionality and mechanisms underlying this relationship, however, have been debated in the criminological literature for decades. First, social process theorists (e.g., differential association and social learning theories) argue that delinquent peer affiliation precedes delinquency and that friends resemble each other behaviorally because of the influence that one has on the other after their friendship has formed. Put simply, an adolescent’s behavior influences the behavior of his or her friends as the result of peer pressure, social learning, or imitation (Akers, 2009). From this perspective, individual differences are of no consequence to delinquent peer formation and delinquency; both are hypothesized to result from purely environmental factors.

Proponents of self-control theory, however, place individual differences at the heart of the relationship by arguing that individuals select themselves into delinquent peer groups and criminal involvement (Gottfredson & Hirschi, 1990). As such, friends will resemble each other because they have self-selected one another due to preexisting characteristics that each possesses. In fact, research has shown that peers tend to resemble each other based on a variety of characteristics, including race/ethnicity, attitudes and beliefs, substance use, political orientation, school performance, educational aspirations, behavioral problems, and delinquent and criminal behaviors (Eiser, Morgan, Gammage, Brooks, & Kirby, 1991; Ennett & Bauman, 1994; Hallinan & Williams, 1989, 1990; Hogue & Steinberg, 1995; Kandel, 1978). From this perspective, friendships do not form randomly but are the result of self-selection. Furthermore, research suggests that this selection process is at least partially driven by genetic factors (Scarr & McCartney, 1983).

The Nature of Nurture

Criminologists have traditionally viewed delinquent peer groups as a purely environmental condition. The idea that the environment can be influenced by individual differences at the level of the genome has yet to be completely accepted and incorporated into traditional criminological theories. Yet, examining the nature of nurture (as it is often referred to) is not a new area of study. For more than 30 years, researchers have been applying behavioral genetic methodologies to environmental measures, including parenting (Plomin, 1994; Rowe, 1981, 1983), sibling relationship (Baker & Daniels, 1990; Daniels & Plomin, 1985; Rende, Slomkowski, Stocker, Fulker, & Plomin, 1992), and most important to the current study, peer group characteristics (Manke, McGuire, Reiss, Hetherington, & Plomin, 1995; Pike, Manke, Reiss, & Plomin, 2000). In fact, in a systematic review of the literature (N = 55 studies) on the genetic influence on various measures of the environment (e.g., parenting, social support, marital quality, and peer interactions), Kendler and Baker (2007) reported an overall weighted heritability of 0.27. Put differently, existing research suggests that genetic factors account for roughly 27% of the observed variance concerning exposure to different environmental factors.

There are at least three explanations as to how genetic factors can influence environmental measures. Known as gene–environment correlations (rGEs), these include passive rGE, evocative rGE, and active rGE. Together, rGEs refer to the likelihood that individuals with a genetic predisposition for a particular behavior/trait will be found in an environment conducive to its expression. First, passive rGEs occur as the result of parents providing their children with their genes and the environment in which they are raised. Passive rGEs can help to explain the relationship between various parenting measures and child behavior (Scarr & McCartney, 1983). For example, parents who read to their children are said to produce more literate and intelligent children. Yet intelligence is a highly heritable trait (Plomin, 1990). As such, parents are not only passing down their genetic material for intelligence but are also providing an environment (e.g., reading, cognitive stimulation) that promotes intellectual achievement.

Second, evocative rGEs occur due to an individual’s genetic predispositions that can elicit or provoke a response from people in the environment. Evocative rGEs can be applied to various relationships across various settings (e.g., home, school, and peer groups). For example, research has shown that parental aggravation and child neglect can lead to behavioral problems in children (Hildyard & Wolfe, 2002). Yet, a temperamental child may evoke feelings of frustration in his or her parent(s) who may then withdraw from the child, resulting in neglect. The relationship between parental aggravation, neglect, and child behavior may be partly explained by an evocative rGE, as child temperament is highly heritable (Jaffee et al., 2004). Evocative rGEs can also be applied to the relationship between delinquent peer affiliation and delinquency. For example, an individual’s genetic predispositions toward delinquent behaviors might elicit rejection from prosocial individuals thereby leaving fewer alternatives for companionship (Burt & Klump, 2013).

Finally, active rGEs occur as the result of individuals seeking out particular environments that accord closely with their own genetic predispositions. An antisocial individual, for example, may seek out others who share similar characteristics. In fact, evidence from the assortative mating literature suggests that active rGEs may help to explain why antisocial individuals tend to mate with other antisocial individuals (Boutwell, Beaver, & Barnes, 2012; Caspi & Herbener, 1990; Quinton, Pickles, Maughan, & Rutter, 1993). Related to delinquent peer affiliation, individuals may seek out peers who resemble themselves on a host of characteristics, including delinquent involvement. As such, if an individual’s genetic predispositions influence friend selection, then measures of peer group characteristics will be heritable.

Delinquent Peers

Early research using correlation analyses from adopted and twin sibling samples have shown that peer group formation is partly due to genetic factors (Baker & Daniels, 1990; Daniels & Plomin, 1985). More recent and advanced genetic model-fitting studies further reveal varying genetic and environmental estimates on friendship quality and peer group characteristics. For example, Pike and Atzaba-Poria (2003) reported that variation in most of the common dimensions of friendship quality¹ (e.g., conflict resolution, help and guidance, companionship and recreation, intimate exchange, validation, and caring) was influenced by genetic factors, with heritability estimates ranging from 0.29 to 0.56. Manke and colleagues (1995) also examined friendship quality by measuring positive and negative interactions between participants and their closest friend. Their results revealed that 31% of the variance in the self-reported measure of positive interactions was due to genetic factors, but the genetic effects were negligible for negative interactions. Manke et al. (1995) also assessed characteristics of the adolescent’s peer group (e.g., college orientation, delinquency, and popularity) using parent reports. The results of these analyses showed that peer group characteristics are strongly heritable with genetic estimates ranging between 0.49 and 0.85. Similarly, Pike and Eley (2009) reported moderate genetic effects (0.26-0.47) on prosocial and antisocial peer group characteristics as well as friendship quality.

When investigating deviant peer groups more specifically, again varying genetic and environmental estimates emerge. For example, Iervolino et al. (2002) examined two samples of twins derived from the Nonshared Environment in Adolescent Development (NEAD) study and the Colorado Adoption Project (CAP). Their results differed based on the sample used showing a strong genetic influence on peer delinquency in the CAP but negligible genetic effects in the NEAD. In 2005, using data from the National Longitudinal Study of Adolescent Health (Add Health), Cleveland, Wiebe, and Rowe reported that 64% of the variance in substance-using peers was attributed to genetic factors with the remaining variance being due to the nonshared environment. Kendler et al. (2007) also examined peer group deviance using a sample of male twins from the Virginia Twin Registry and found that genetic estimates increased with age from about 0.30 at ages 8 to 11 to about 0.50 across the past three time periods (15-17, 18-21, and 22-25 years old). Button et al. (2007) further reported that 21%, 40%, and 39% of the variance in delinquent peer affiliation was due to genetic, shared, and nonshared environmental factors, respectively. Similarly, Gillespie, Neale, Jacobson, and Kendler (2009) reported heritability estimates ranging between 0.23 and 0.37 for measures of peer group deviance using a sample of 15- to 25-year-old male twins. Although the genetic estimates appear to vary across the studies, a review of the literature (N = 6 studies) by Kendler and Baker (2007) reveals a weighted heritability estimate of 0.21 for measures of peer interactions. Furthermore, genetic factors have also been shown to help explain, at least in part, the stability of delinquent peer association over time (Beaver et al., 2011). Overall, it is evident that variation in various measures of delinquent peers, traditionally viewed as purely an environmental factor, can also be attributed to genetic factors.

Relationship Between Delinquent Peers and Delinquency

While the genetic influence on delinquent peer association is starting to receive empirical attention in the criminological literature (Beaver, 2011; Beaver et al., 2009; Beaver et al., 2011; Beaver, Wright, & DeLisi, 2008; Wright, Beaver, DeLisi, & Vaughn, 2008), research on the heritability of delinquent and criminal behaviors is well-established, with estimates averaging between 0.40 and 0.50 (for a review, see Moffitt, 2005; Rhee & Waldman, 2002). Taken together, a strong argument can be made that individual genetic differences may help to explain the association between delinquent peer affiliation and delinquency (Scarr & McCartney, 1983). To date, only a couple of genetically informative studies have examined this possibility using outcomes similar to those used in the current study. For example, one of the first studies to apply behavioral genetic methodology to the relationship between antisocial behavior and delinquent peer association was conducted by Rowe and Osgood in 1984. Their results revealed that 61% to 64% of the covariance between self-reported antisocial behavior and the delinquency of peers was attributed to the same genes operating on both.

In 2007, Button and colleagues also examined whether common genes influenced the relationship between delinquent peer associations and conduct problems in a sample of adolescents twins (N = 1,071 pairs). Their bivariate genetic analyses revealed that 86% of the correlation between delinquent peer affiliation and conduct problems is explained by common genetic effects, with the remaining 14% being attributed to common nonshared environmental factors. These two studies suggest that common genes may be important factors to consider when discussing the relationship between delinquent peers and delinquency. There remains a need, however, for additional replication studies using behavioral genetic methodologies (with different samples and measures) to further advance this area of research within criminology.

Purpose of Study

The present study seeks to examine the relationship between delinquent peer affiliation and delinquency in adolescence by using a genetically informative design with a sample of twins and full-siblings. The purpose of the current study is twofold. First, the genetic and environmental effects on measures of delinquent peer affiliation and delinquency will be estimated at two time periods in adolescence. Next, and central to the current study, the extent to which the same genetic and environmental factors are explaining the co-occurrence of delinquent peer affiliation and delinquency will be calculated. This article extends the two behavioral genetic studies mentioned above in the following ways: (a) We use a specific measure of delinquent involvement rather than a general measure of conduct disorder (see Button et al., 2007) and (b) we use bivariate genetic analysis rather than correlation analysis (see Rowe & Osgood, 1984) to more accurately estimate the genetic and environmental overlap between delinquent peer association and delinquency. The current study is important to the field of criminology because a vast amount of research on delinquent peers and criminal involvement has traditionally excluded genetic factors. These factors may partially explain the process of self-selecting deviant peers among delinquent youths.

Method

Sample

A subsample of twins and full-siblings from the Add Health² was used in the current study (N = 1,900 sibling pairs). Add Health researchers began collecting data from a nationally representative sample of American adolescents in Grades 7 through 12 in September 1994. More than 90,000 students from 132 schools completed the Wave I in-school questionnaire. From this initial sample, a random subsample of 20,745 adolescents were then selected to complete the Wave I in-home questionnaire. The second wave of data collection occurred approximately a year later when participants were in Grades 8 through 12, and included 14,738 of the original participants from Wave I. As participants were still adolescents in 1995-1996, many of the questions asked during the Wave I interview were asked again during the Wave II interview. The third wave of data collection occurred approximately 6 years later in 2001-2002 when participants were entering adulthood. The fourth and most recent wave of data was collected in 2007-2008, when participants were between the ages of 24 and 34.

The Add Health researchers oversampled sibling pairs in Wave I by asking respondents whether they had a sibling residing within their household who was also enrolled in Grades 7 through 12. Identified siblings of varying degrees of genetic relatedness (e.g., twins, nontwin sibling of twins, full-siblings, half-siblings, cousins, and nonrelated siblings) were then included in the Wave I in-home interview. The current study only included information from twin and full-sibling pairs during adolescence (i.e., Waves I and II). Specifically, there were 289 monozygotic (MZ; identical) twin pairs, 452 dizygotic (DZ; fraternal) twin pairs, and 1,159 full-sibling pairs included in the analyses.³

Measures

Two measures of interest are included in the current study: delinquent peer associations and delinquency at Waves I and II. First, a measure of delinquent peer association was created at both waves by summing three items pertaining to participants perceptions of their closest friends’ involvement with substances. Specifically, participants reported how many of their three closest friends smoked at least one cigarette a day, drank alcohol at least once a month, and used marijuana at least once a month. Responses ranged from 0 to 3 friends for each of the three questions, and responses were summed together so that higher scores reflect greater delinquent peer associations (Wave I α = 0.75; Wave II α = 0.76). This measure of delinquent peers has been used in previous research (Beaver et al., 2009; Beaver et al., 2011; Beaver & Wright, 2005; Beaver et al., 2008; Bellair, Roscigno, & McNulty, 2003; Wright et al., 2008). The mean for the measure of delinquent peers at Wave I was 2.46 (SD = 2.61) and at Wave II was 2.78 (SD = 2.71). Due to the skewed distribution, the measures of delinquent peers at Waves I and II were log transformed, log (x + 1), prior to being included into the behavioral genetic analyses.

Delinquency was also measured at Waves I and II by asking respondents to report their level of involvement in various antisocial acts within the past 12 months (see appendix). These questions ranged from “you took something from a store without paying for it” to “you got into a serious physical fight.” The response categories across the 14 questions were summed together to create an overall measure of delinquency at both waves. The scores ranged from 0 to 40 at Wave I (M = 2.56, SD = 4.30) and from 0 to 37 at Wave II (M = 1.66, SD = 3.20). Higher values on the measure of delinquency at Waves I and II indicate greater delinquent involvement in adolescence (Wave I α = .84; Wave II α = .81). The positively skewed distributions at both waves required that the measures of delinquency also be log transformed prior to being included into the analyses.

Analytical Plan

The first step in the analyses was to examine the cross-sibling correlations of MZ and DZ/full-sibling pairs for the measures of delinquent peers and delinquency to assess whether additive and/or dominant genetic factors are influencing the measures. This is accomplished by first selecting only MZ twin pairs and correlating Twin 1’s score on a measure (e.g., delinquency) to that of his or her co-twin for the same measure. This is then repeated for DZ/full-sibling pairs. A comparison of the cross-sibling correlations for MZ twin pairs and DZ/full-sibling pairs will determine whether additive and/or dominant genetic factors are contributing to variation in the phenotype. If the correlation for MZ twins is greater than (but less than 2 times that of) DZ/full-siblings, this suggests that additive genetic factors are influencing the phenotype. If, however, the correlation for MZ twins is more than 2 times that of DZ/full-siblings, this suggests that additive and dominant genetic factors are influencing the phenotype under investigation (Neale & Cardon, 1992).⁴ Careful inspection of the correlations is important at this stage of analysis as it will dictate which model to use in the behavioral genetic modeling portion of the analysis (i.e., ACE or ADE).

The next step is to preliminarily examine whether delinquent peers and delinquency covary as the result of underlying genetic factors that are influencing both variables. This is first accomplished by comparing the cross-sibling/cross-trait correlations of MZ and DZ/full-sibling pairs for the measures of delinquent peers and delinquency. Again, selecting only MZ twin pairs, Twin 1’s score on the measure of delinquent peers is correlated to Twin 2’s score on delinquency, and Twin 2’s score on delinquent peers is correlated to Twin 1’s score on the measure of delinquency. These calculations are then repeated using only DZ/full-sibling pairs. If the cross-sibling/cross-trait correlation for MZ twin pairs is higher than that of DZ/full-sibling pairs, this suggests that common genetic factors are influencing delinquent peer association and delinquent involvement (Neale & Cardon, 1992). If the results from these two preliminary analyses suggest that genetic factors are influencing delinquent peer association and delinquency as well as their co-occurrence, the next stage is to conduct a bivariate genetic analysis in the program Mx.

Bivariate genetic modeling technique allows for the partitioning of the correlation between delinquent peer association and delinquency into the components that are due to common additive genetic, dominant genetic or shared environmental, and nonshared environmental factors.⁵ More specifically, bivariate genetic analyses can produce the following: (a) additive genetic $a_{1}^{2}$ , dominant genetic $d_{1}^{2}$ or shared environmental $c_{1}^{2}$ , and nonshared $e_{1}^{2}$ environmental estimates for the first variable (e.g., delinquent peers); (b) additive genetic $a_{2}^{2}$ , dominant genetic $d_{2}^{2}$ or shared environmental $c_{2}^{2}$ , and nonshared $e_{2}^{2}$ environmental estimates for the second variable (e.g., delinquency); and (c) the additive genetic (r_g), dominant genetic (r_d) or shared environmental (r_c), and nonshared (r_e) environmental correlations between delinquent peers and delinquency. These correlations estimate the degree to which the factors overlap between the two variables.

Once a full bivariate ADE model, for example, has been estimated, submodels⁶ are then fitted to the data and compared against it (e.g., AE and E). The best-fitting model will be the one that has a nonsignificant p value for the difference in chi-square test and the largest negative Akaike information criterion (AIC) value (Neale & Cardon, 1992). For example, an AE submodel sets the dominant genetic parameters (D) to zero. If the goodness-of-fit statistics indicate that setting D to zero does not significantly decrease the fit of the model (compared with ADE), then the effects of dominant genetics are considered negligible and can be dropped. Following this, the E submodel would then be compared with the ADE model by setting both genetic parameters (A and D) to zero.⁷ Again, if the goodness-of-fit statistics indicate that dropping the effects of genetic factors does not significantly reduce the fit of the model, then the E submodel is considered the best-fitting and most parsimonious model. Finally, if both submodels (AE and E) result in a significant difference in chi-square statistic, this implies that the ADE model is the best-fitting model.

The estimates from the best-fitting model (ACE/ADE, CE, AE, or E) are then used to calculate the extent to which the correlation between delinquent peers and delinquency is due to common genetic and environmental factors. If ADE is the best-fitting model, for example, calculating the proportion of the correlation that is due to common additive genetic factors involves multiplying the square root of the additive genetic estimates from both variables ( $\sqrt{a_{1}^{2}} \times \sqrt{a_{2}^{2}}$ ) by the additive genetic correlation between the two variables (r_g). This value is then divided by the correlation between delinquent peers and delinquency (r_p) to provide the proportion of the correlation that is due to common additive genetic factors. These calculations are then repeated for dominant genetic factors $[\frac{(\sqrt{d_{1}^{2}} \times \sqrt{d_{2}^{2}} \times r_{d})}{r_{p}}]$ and nonshared environmental factors $[\frac{(\sqrt{e_{1}^{2}} \times \sqrt{e_{2}^{2}} \times r_{e})}{r_{p}}]$ to determine the proportion of the correlation between delinquent peers and delinquency that is due to common dominant genetic and nonshared environmental influences, respectively.

Results

The results from the cross-sibling and cross-sibling/cross-trait correlations suggest that genetic factors are influencing the measures of delinquent peers and delinquency, as well as their relationship with one another. As shown in Table 1, the MZ cross-sibling correlations for the measures of delinquent peers and delinquency were larger than the DZ/full-sibling cross-sibling correlations at both waves. As the MZ cross-sibling correlations tended to be more than double those of the DZ/full-sibling pairs, an ADE model (compared with an ACE model) was considered most appropriate when conducting the bivariate genetic analyses. Also, the cross-sibling/cross-trait results further suggest a genetic overlap between delinquent peers and delinquency at both waves. Specifically, as seen in Table 1, the cross-sibling/cross-trait correlations for MZ twin pairs were, on average, larger than that of DZ/full-sibling pairs at both waves. Together, the results from the cross-sibling and cross-sibling/cross-trait analyses provided the necessary basis for conducting the bivariate genetic analyses by suggesting that common genes may help to explain why individuals select themselves into delinquent peer networks as well as delinquent activities during adolescence.

Table 1:

Cross-Sibling and Cross-Sibling/Cross-Trait Correlations

	MZ	DZ/full-sibling
Wave I
Delinquent peers	.69***	.32***
Delinquency	.54***	.25***
Delinquent peers and delinquency^a	.24***	.17***
Wave II
Delinquent peers	.63***	.29***
Delinquency	.40***	.23***
Delinquent peers and delinquency^a	.20**	.13**

Note. MZ = monozygotic; DZ = dizygotic.

The averages are presented here.

p < .01. ***p < .001.

The results from the bivariate genetic analyses between delinquent peers and delinquency at Waves I and II are presented in Table 2.⁸ As shown in the table, the AE model was the best-fitting model at Wave I. More specifically, the results reveal that 67% and 33% of the variance in the measure of delinquent peers at Wave I was due to additive genetic and nonshared environmental factors, respectively. For delinquency at Wave I, half of the variance was attributed to additive genetics with the other half being due to nonshared environmental factors. The additive genetic and nonshared environmental correlations between delinquent peers and delinquency at Wave I were .50 and .19, respectively. These estimates were then used to calculate the proportion of the correlation between delinquent peers and delinquency at Wave I (r = .37, p < .001) that is attributed to common genetic and nonshared environmental factors (see equations presented above). These calculations reveal that 79% and 21% of the correlation between delinquent peer association and delinquency at Wave I was due to common additive genetic and nonshared environmental influences operating on both variables, respectively.

Table 2:

Bivariate Analyses of Delinquent Peer Association and Delinquency at Waves I and II

Model fit		Change statistics				Variable 1			Variable 2			r
	−2lnl	df	Δχ²	p	AIC	A	D	E	A	D	E	a	d	e
						Delinquent peer I			Delinquency I
ADE	4843.73	7063			−9282.27	.66 (.00-.75)	.01 (.00-.73)	.33 (.25-.39)	.49 (.00-.56)	.01 (.00-.58)	.50 (.44-.58)	.49 (.49-1)	1 (−1-1)	.20 (.08-.29)
AE	4843.95	7066	0.22	.97	−9288.05	.67 (.62-.72)	—	.33 (.28-.38)	.50 (.44-.56)	—	.50 (.44-.56)	.50 (.43-.58)	—	.19 (.10-.27)
E	5317.52	7069	473.79	.00	−8820.48	—	—	1 (1-1)	—	—	1 (1-1)	—	—	.37 (.34-.40)
						Delinquent peer II			Delinquency II
ADE	3904.68	6518			−9131.32	.60 (.00-.66)	.00 (.00-.68)	.40 (.34-.46)	.37 (.00-.51)	.04 (.00-.51)	.59 (.49-.69)	.50 (−1-1)	1 (−1-1)	.21 (.10-.31)
AE	3905.17	6521	0.49	.92	−9136.83	.60 (.54-.66)	—	.40 (.34-.46)	.43 (.36-.50)	—	.57 (.50-.64)	.48 (.38-.57)	—	.21 (.12-.29)
E	4225.22	6524	320.54	.00	−8822.78	—	—	1 (1-1)	—	—	1 (1-1)	—	—	.34 (.31-.37)

Note. Boldfaced values represent the best-fitting models.

The results from the bivariate genetic analysis at Wave II are also presented in Table 2, revealing that the AE submodel was the best-fitting model. As shown in the table, additive genetic factors accounted for 60% of the variance in the measure of delinquent peer association at Wave II with the remaining 40% being due to the nonshared environment. For the measure of delinquency at Wave II, the results revealed that 43% and 57% of the variance was due to additive genetic and nonshared environmental factors, respectively. Furthermore, the additive genetic and nonshared environmental correlation between delinquent peers and delinquency at Wave II were estimated at .48 and .21, respectively. Using these estimates, calculations revealed that 71% of the correlation between delinquent peer association and delinquency at Wave II (r = .34, p < .001) was due to common additive genetic factors, and the remaining 29% was due to common nonshared environmental factors.

Discussion

Criminological theorizing is deeply rooted in sociological tradition. That the environment (in this case delinquent peers) influences criminal behaviors is widely accepted among scholars (Cooper, Walsh, & Ellis, 2010). And, there is no doubt that delinquent peer affiliation and criminal involvement are highly intertwined (Cottle, Lee, & Heilbrun, 2001). The mechanism that underlies this relationship, however, remains far more contentious. Some have argued that social processes clearly underlie the covariation of peers and behavior, while others have staunchly maintained selection factors are the dominant force (Fergusson, Swain-Campbell, & Horwood, 2002; Scarr & McCartney, 1983). While empirical evidence exists to support both sides, it is likely that a combination of both approaches best explains the relationship (Kendler, Jacobson, Myers, & Eaves, 2008). That is, individuals select themselves into deviant peer groups based on similar characteristics that they possess, at which point the behaviors and actions of their peers further influences their own behaviors. Within the peer selection process, however, a factor that has been traditionally ignored in the criminological literature is the influence of genes. Yet, variation at the level of the genome may help to explain why individuals select delinquent peer groups and criminal involvement (Scarr & McCartney, 1983).

The current study reveals three important findings that further add to the delinquent peer and delinquency literature. First, our finding that delinquency is moderately influenced by genetic factors is not surprising and has been reported elsewhere (Boisvert, Wright, Knopik, & Vaske, 2012).⁹ That genetic factors influence antisocial and delinquent behaviors has been demonstrated repeatedly over the past 50 years, using various samples, methodologies, and statistical techniques (Moffitt, 2005; Rhee & Waldman, 2002). At this point, it is undeniable that criminal behavior tends to “run in the family,” as the result of the environment and genetics. Our second finding that delinquent peer affiliation is heritable is also consistent with previous studies examining the nature of delinquent peer groups (Kendler & Baker, 2007). That delinquent peer affiliation is influenced by genetic factors further supports the argument that individuals select their peers (i.e., active rGE). That is, individuals tend to chose peer networks based on genetically driven characteristics that they also possess (Scarr & McCartney, 1983).

In addition to active rGE, it is also possible that evocative rGE may be influencing the delinquent peer and delinquency relationship. This may best be explained using Moffitt’s theoretical framework as an example. According to Moffitt (1993), the major factor influencing delinquency in many adolescents is social mimicry. Known as adolescent-limited offenders, these youths experience a transitional period in their teenage years where they are biologically mature (similar to adults) but society has not yet given them adult status. As a result, these youths begin to mimic the behaviors of life-course persistent offenders, who are already behaving as adults (e.g., smoking, drinking, and engaging in sexual intercourse). From this perspective, life-course persistent offenders may be evoking delinquent behaviors from individuals who are low to medium genetic risk for antisocial behaviors. Also, individuals at high genetic risk for antisocial behaviors have already been rejected by their prosocial peers and are therefore left with few alternatives for companionship (Burt & Klump, 2013).

Our results not only highlight the importance of genetic factors in the study of delinquent peer affiliation and delinquency, but they also inform our view of the environment. First, our results showed that the effects of the shared environment on delinquent peer association and delinquency are negligible.¹⁰ Shared environmental factors are those that tend to exert the same effect on siblings within the home. This can include general parenting styles, socioeconomic status (SES) of the family, SES of the neighborhood, and parental educational level, just to name a few. Research has shown that shared environmental factors tend to exert a stronger effect in childhood and early youth when individuals are spending most of their time within the home and are exposed to similar environmental factors as their siblings (Kendler et al., 2008; Loehlin, 1992; Plomin, 1986). As youths move into middle adolescence, however, becoming more independent and able to individuate themselves from their family members (Thornberry, 1987), the shared environment becomes less important, and the effects of genetics and nonshared environments become more pronounced (Plomin, 1986).

Interestingly, the influence of the nonshared environment has been largely overlooked in the field of criminology (Beaver, 2008). Perhaps this is a function of the methodologies used in the social sciences where the standard practice is to include only one child per home and generalize the findings back to other children residing in the same home. This approach assumes that all environmental factors are exerting the same effect on children in the home, which is a serious flaw in social science research (Beaver, 2008). As mentioned, not all environmental effects will exert the same effect on individuals, and, of the two types of environments (e.g., shared and nonshared), it is typically the nonshared environment that has the strongest effect on behaviors deemed antisocial (Boisvert et al., 2012; Haberstick, Schmitz, Young, & Hewitt, 2005; Rhee & Waldman, 2002).

The third and final finding derived from our study demonstrated that common genes are influencing the covariance between delinquent peer affiliation and delinquency. This finding further supports studies that have also shown a genetic contribution to the co-occurrence of delinquent peer association and antisocial behaviors, including conduct disorder and substance use (Button et al., 2007; Rowe & Osgood, 1984). Gillespie et al. (2009), for example, found that the majority of the genetic variance (50%-78%) in peer group deviance was explained by cannabis use in a sample of male twins. Harakeh et al. (2008) also reported a significant genetic overlap between peer characteristics (e.g., college orientation and peer delinquency) and tobacco use in young adults. Together, the empirical evidence suggests that common genes may put an individual at-risk for delinquent peer associations and delinquent and antisocial behaviors, at least in adolescence and young adulthood. Our results also suggest that common nonshared environmental factors can help explain the association between delinquent peer association and delinquency. These can include both unique experiences that are occurring within the home (e.g., specific parental treatment) and outside the home (e.g., school) that places an individual at-risk for associating with deviant peers and at-risk for delinquent involvement.

It has also been suggested that delinquent peer exposure may moderate the genetic influence on antisocial behaviors (Burt & Klump, 2013). In other words, studies have shown that the magnitude of the genetic effect on problem behaviors, such as conduct problems, antisocial behaviors, and substance use, is stronger when coupled with delinquent peer association (Beaver et al., 2009; Harden, Hill, Turkheimer, & Emery, 2008; Hicks, South, DiRago, Iacono, & McGue, 2009). For example, the magnitude of the genetic effect on conduct problems was greatest when delinquent peer affiliation was high (Button et al., 2007). These findings further highlight the dynamic nature of genetic expression, which has important implications for prevention and intervention efforts.

Antisocial peer association is considered one of the “big four” risk factors influencing the likelihood of reoffending (along with antisocial attitudes, history of antisocial behavior, and antisocial personality) identified by Andrews and Bonta (2010). Research has shown that intervention programs that target delinquent peer affiliation for change significantly decreases the likelihood of recidivism (Cullen, Wright, Gendreau, & Andrews, 2003). Indeed, one of the goals of cognitive-behavioral programming is to isolate offenders from other antisocial individuals, and research has shown that these types of programs tend to achieve higher reductions in recidivism than other treatment modalities (Cullen et al., 2003). The fact that genetic factors are influencing antisocial behaviors and delinquent peer associations should not negatively affect prevention and intervention efforts. Rather, it suggests that similar individual characteristics may be influencing delinquent peer association and delinquency; thus, targeting heritable psychological constructs (i.e., low self-control, antisocial attitudes) that place one at-risk for antisocial peers and antisocial behavior may have a cumulative effect on both outcomes. As such, there is a need for more tailored and individual treatment and intervention programs compared with the current “one size fits all” approach being used by most correctional facilities today (Harakeh et al., 2008).

Limitations

There are several limitations to the current study. First, our study takes a cross-sectional approach to the delinquent peer and delinquency relationship at two points in time during adolescence. A more developmental approach may be needed to better understand the complex interplay between delinquent peer group association and delinquency over time. Unfortunately, the Add Health only asks participants about their peers’ behaviors at Waves I and II, which are measured only 1 year apart. A study by Kendler and colleagues (2008), however, was able to examine the causal pathway between peer deviance and conduct disorder in a group of male twins (N = 746 pairs) from the ages of 8 to 17. The authors found support for a bidirectional causal relationship with the effects of genetics and shared environment affecting the association in different ways. More specifically, they found that genes influenced conduct disorder, which then (through social selection) lead to the association with delinquent peers. The strength of the genetic influence on this relationship remained constant across time. They also reported that shared environmental factors influenced delinquent peer affiliation, which then (through social influence) affected conduct disorder. The strength of the effects of the shared environment was particularly strong in early childhood and diminished over time. We recommend that continued longitudinal research using behavioral genetic methodologies be conducted to further inform the delinquent peer and delinquency literature.

Second, our measure of delinquent peers must be interpreted with caution, as it is limited to substance-using behaviors of peers from the study respondents’ perception. In other words, our measure of delinquent peers reflects the number of closest peers (maximum of three) who are using substances (e.g., cigarettes, alcohol, marijuana) from the participant’s point of view. It is possible that the respondent may over- or underestimate his or her peers’ involvement based on their own involvement in substances and personality characteristics. Although our measure of delinquent peers has been used in previous research (Beaver et al., 2009; Beaver et al., 2011; Beaver & Wright, 2005; Beaver et al., 2008; Bellair et al., 2003; Wright et al., 2008), it would be worthwhile to replicate these analyses using a more complete measure of peer delinquent involvement (e.g., stealing, fighting, vandalism).

Third, studies have shown a robust relationship between antisocial behaviors and substance use and abuse (Neighbors, Kempton, & Forehand, 1992; Reebye, Moretti, & Lessard, 1995). It is possible then that the observed relationship reported here between deviant/substance-using peers and delinquency is operating through the respondent’s own comorbid substance use and not necessarily through delinquency. While the current study did not seek to disentangle these behaviors, we recommend that future studies examine the co-occurrence of substance use/abuse, delinquency, and delinquent peer affiliation from a behavioral genetic perspective to better understand these complex relationships.

Finally, although the use of twins is critical to behavioral genetic studies, some have questioned the generalizability of results derived from studies that include twin pairs. Specifically, it has been argued that twins possess unique characteristics due to their shared environment and experiences, including their increased risk for birth complications and lower birth weight (Moilanen & Ebeling, 1998; Rutter, Simonoff, & Silberg, 1993), which have been linked to later antisocial behaviors (Tibbetts & Piquero, 1999). As such, even though the Add Health is a nationally representative sample of adolescents, using the twin subsample has been criticized for its ability to generalize findings back to the general population. Barnes and Boutwell (2013), however, addressed this exact issue in their recent study using the Add Health data. These authors found few significant differences across key variables when comparing twins and singletons. More importantly, twin status did practically nothing to alter the effects of key variables on outcomes such as delinquency (this includes the effect of delinquent peers on delinquent outcomes).

For decades now, criminologists have rightly pointed to the importance of peer dynamics in understanding the origins of antisocial and criminal behavior. Generally absent from the discussion, at least until more recent times, has been an effort to pull back the curtain regarding the underlying biological explanations for why certain humans affiliate with one another. To be sure, scholars in other fields have uncovered evidence regarding the importance of genetic effects on peer group formation (Kendler et al., 2007), with more recent research even suggesting specific genes that may correlate between friends (Boardman, Domingue, & Fletcher, 2012; Fowler, Settle, & Christakis, 2012). Our study adds to this already growing line of research.

A final point worth considering, though, is in regard to what happens in the peer group once it has been formed. J. R. Harris (1998) has suggested that peers are the primary socializing agent of children, and given the woeful lack of shared environmental effects (once genetic effects are held constant), there is much in favor of her argument (see Rowe, 1994, for a more general discussion). At the same time, given the overwhelming evidence that nonshared environments matter, it stands to reason that much is to be gained by studying the peer group and the litany of important effects it may exert on children and adolescents. The only caveat, given the results of our study and others, is that failing to control for genetic influences when examining peer effects is likely to yield uninterruptable results. Put in a different way, studies in this area lacking genetic controls will not accomplish anything other than reifying an already well-established correlation. Causality, should it exist in this regard, will fail to be established until properly specified models become more common.

Footnotes

Appendix

Acknowledgements

The authors would like to thank the anonymous reviewers for their insightful comments and suggestions.

This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (). No direct support was received from grant P01-HD31921 for this analysis.

Notes

Danielle Boisvert is an assistant professor in the College of Criminal Justice at Sam Houston State University. Her key research interests include life-course/developmental criminology, biosocial criminology, and behavioral genetics. Her research focuses mainly on the examination of genetic and environmental influences on a variety of delinquent and criminal behaviors throughout the life course.

Brian B. Boutwell is an assistant professor in the College of Criminal Justice at Sam Houston State University. His research examines the biological, environmental, and social contributors to antisocial and criminal behaviors. His recent publications examine the interplay between genetic and environmental risk factors in the prediction of behavioral problems.

Jamie Vaske is an assistant professor at Western Carolina University. She received her PhD from the University of Cincinnati in 2009. Her areas of interest include biosocial criminology, quantitative methods, and gender and antisocial behavior.

Jamie Newsome is an assistant professor in the Department of Criminal Justice at the University of Texas at San Antonio. Her primary research interests are in biosocial criminology and evolutionary psychology. Her recent research has examined genetic and environmental influences on resilience and vulnerability to risk for delinquency

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