Dopaminergic Polymorphisms, Academic Achievement, and Violent Delinquency.

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research-article2014

IJOXXX10.1177/0306624X14554381International Journal of Offender Therapy and Comparative CriminologyYun et al.

Article

Dopaminergic Polymorphisms, Academic Achievement, and Violent Delinquency

International Journal of Offender Therapy and Comparative Criminology 2015, Vol. 59(13) 1409–1428 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0306624X14554381 ijo.sagepub.com

Ilhong Yun1, Julak Lee2, and Seung-Gon Kim1

Abstract Recent research in the field of educational psychology points to the salience of selfcontrol in accounting for the variance in students’ report card grades. At the same time, a novel empirical study from molecular genetics drawing on the National Longitudinal Study of Adolescent Health (Add Health) data has revealed that polymorphisms in three dopaminergic genes (dopamine transporter [DAT1], dopamine D2 receptor [DRD2], and dopamine D4 receptor [DRD4]) are also linked to adolescents’ grade point averages (GPAs). Juxtaposing these two lines of research, the current study reanalyzed the Add Health genetic subsample to assess the relative effects of these dopaminergic genes and self-control on GPAs. The results showed that the effects of the latter were far stronger than those of the former. The interaction effects between the dopaminergic genes and a set of environmental factors on academic performance were also examined, producing findings that are aligned with the “social push hypothesis” in behavioral genetics. Finally, based on the criminological literature on the link between academic performance and delinquency, we tested whether dopaminergic effects on violent delinquency were mediated by GPAs. The results demonstrated that academic performance fully mediated the linkage between these genes and violent delinquency. Keywords dopamine, genetics, academic performance, delinquency

1Chosun 2Kyonggi

University, Gwangju, South Korea University, Suwon-si, South Korea

Corresponding Author: Julak Lee, Department of Protection and Security Management, Kyonggi University, 154-42 Gwanggyosan-ro Yongtong-gu, Suwon-si, Gyonggi-do 443-760, South Korea. Email: [email protected]

Downloaded from ijo.sagepub.com at EMORY UNIV on February 20, 2016

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International Journal of Offender Therapy and Comparative Criminology 59(13)

Reviews of the literature on crime and delinquency consistently reveal evidence speaking to an inverse association between cognitive ability and delinquency (Hirschi & Hindelang, 1977; McGloin & Pratt, 2003; McGloin, Pratt, & Maahs, 2004). While some researchers argued that deficits in cognitive ability are the main cause of crime and delinquency (Herrnstein & Murray, 1994), extant literature does not support such a direct relationship hypothesis. Rather, the majority of research concludes that the cognitive ability–delinquency link is indirect, especially when cognitive ability is measured as IQ, mediated by low academic achievement (Ellis, Beaver, & Wright, 2009; McGloin et al., 2004; Ward & Tittle, 1994). Accordingly, considerable research attention has been paid to identifying the risk factors for low academic achievement (Meeks & Murrell, 2001; Steinberg, Brown, & Dornbusch, 1996). Empirical research has so far revealed that academic performance is a superordinate construct multifactorially determined by a wide range of predictors, such as intelligence, self-control, social support, and even random life events (Richardson, Abraham, & Bond, 2012; Wolfe & Johnson, 1995). To this body of research, Beaver and colleagues recently made a notable addition by demonstrating that specific genetic polymorphisms also affect academic performance (Beaver, Vaughn, Wright, DeLisi, & Howard, 2010). Specifically, their analyses of the genetic subsample of the National Longitudinal Study of Adolescent Health (Add Health) revealed that polymorphisms in three dopamine-related genes (dopamine transporter [DAT1], dopamine D2 receptor [DRD2], and dopamine D4 receptor [DRD4]) predicted English, math, history, and science grades of middle and high school youths in statistically meaningful ways. Specifically, adolescents who possessed at least one risk allele1 among the three genes received significant lower grades in at least one of the four subject matters than those who did not possess risk alleles. When a genetic index was used reflecting the total number of risk alleles combined, the effects of the index on cumulative grade point average (GPA) was obviously not trivial with its standardized regression coefficient (β) of −.12 (p < .001). When the total sample was divided by gender, there was an 11% reduction in GPA for males and a 14% reduction for females, for those with three risk alleles as opposed to those with zero risk alleles. These differences, according to Beaver et al. (2010), can be substantial enough for a student to be accepted into a college or denied admission. The Beaver et al.’s (2010) study is particularly worthwhile in that it identified specific candidate genes underpinning academic performance from a relatively large genetic sample of youths. Prior to this, much quantitative genetic research on academic performance was limited to estimating the effect size of the overall genetic effects (heritability), rather than trying to locate specific genetic polymorphisms, although the dopaminergic system has commonly been suspected as contributing to academic performance due to its widespread effects on learning and memory (Previc, 1999; Qiang et al., 2010). Given that Beaver et al.’s (2010) study is the first study that provided evidence linking measured dopaminergic polymorphisms and academic achievement, their research finding can be further expanded in more nuanced ways or otherwise applied to the criminological literature.


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In the current study, we attempt to expand Beaver et al.’s (2010) study in the following directions. First, the behavioral genetics literature unequivocally demonstrates that neither genes nor the environment operate in a vacuum in creating human behavioral phenotypes; instead, they work interactively or in a mediational way following certain processual pathways (Beaver et al., 2010; Caspi et al., 2002; Rutter, 2006). Given that Beaver et al.’s (2010) study examined only the main and direct effects of dopaminergic genes on academic achievement, the present study further examines whether these genetic effects are mediated and/or modified by certain key environmental factors. Second, recent research in the field of educational psychology emphasizes the predictive efficacy of self-control on academic achievement, especially when academic achievement is measured in terms of report card grades (Duckworth, Quinn, & Tsukayama, 2012; Duckworth & Seligman, 2005, 2006). In view of the nascent findings on dopaminergic genes’ effects on academic achievement (Beaver et al., 2010), it seems to be a natural course of inquiry to examine the comparative effects exerted by self-control and dopaminergic genes on academic achievement. Third, a large body of criminological research has shown that academic performance is implicated in the etiology of juvenile delinquency (Ellis et al., 2009; McGloin et al., 2004; Ward & Tittle, 1994). In addition, the behavioral genetics literature has demonstrated that the dopaminergic system is also associated with a list of antisocial outcomes including juvenile delinquency (Chen et al., 2005; Gunter, Vaughn, & Philibert, 2010; Guo, Roettger, & Shih, 2007). Given Beaver et al.’s (2010) findings on genetic effects on academic achievement, we therefore investigate whether the effects of dopaminergic genes on violent delinquency are mediated by academic performance.

Mediation and Interaction of Genetic and Environmental Factors Research findings in the field of developmental psychology indicate that a host of developmental outcomes, including a child’s behavioral/emotional problems and social adjustment, are the results of multifarious interplay of genetic and environmental factors acting independently and interactively (Rutter & Silberg, 2002). An important research area is therefore to identify specific processes by which genetic and environmental factors operate together to produce identifiable outcomes. In regard to the association between dopaminergic polymorphisms and academic achievement, one potential explanation of this association is a mediational hypothesis, whereby genetic effects on academic performance are mediated by certain environmental factors. Beaver et al.’s (2010) original work, however, stopped at examining only main effect models rather than delving into examining the potential mediating mechanisms. Thus, it seems a reasonable research endeavor to delve into whether the genetic effects on academic performance revealed in Beaver et al.’s (2010) analysis are simply main effects or indirect effects mediated by certain salient environmental factors. Another productive area of research on gene and environment interplay centers around the interaction of genetic and environmental factors. In the parlance of behavioral genetics, the gene–environment interaction (G × E) refers to the phenomenon


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where genetic effects on human phenotypes are conditioned by environmental factors (Caspi et al., 2002; Plomin, DeFries, & Loehlin, 1977; Rutter, 2006). This G × E denotes that certain genes have effects only when they are paired with certain environments. Existence of a G × E means that a single statistic is not adequate to describe a whole population because genetic effects diverge depending on environmental factors (Purcell, 2002). Beaver et al.’s (2010) original study did not investigate possible G × E effects; yet, they left an exhortative statement urging future researchers to examine G × Es involving environmental factors.

Relative Effects of Dopaminergic Genes and Self-Control on Academic Achievement The three dopaminergic polymorphisms’ effects on academic performance found in Beaver et al.’s (2010) study appear nontrivial. Nevertheless, a more elucidating account may be obtained when these genetic effects are juxtaposed and compared with the effects of other salient predictors of academic performance, which have been frequently identified in prior empirical research. One such prominent predictor is selfcontrol (aka self-discipline). A continuing line of research has demonstrated self-control’s robust effect on educational achievement (Duckworth & Seligman, 2005, 2006; Wolfe & Johnson, 1995). Duckworth and Seligman’s (2005) study, for instance, revealed that self-control explained more than twice as much variance in eighth-grade students’ GPAs as did IQ. Wolfe and Johnson’s (1995) meta-analysis also found that self-control predicted college GPA better than 32 other personality-related variables. Adjusting for gender and race only, Beaver and colleagues (2010) did not examine the independent effects of dopaminergic genes on academic achievement while controlling for self-discipline.

Dopaminergic Genes, Academic Achievement, and Violent Delinquency The neurotransmitter dopamine exerts substantial effects on learning, memory, motivation, and reward (Previc, 1999; Qiang et al., 2010). Therefore, certain dopaminergic genes responsible for the production, transportation, and degradation of dopamine can at least partially account for individual variation in cognitive abilities, learning, and academic achievement (Beaver et al., 2010; Qiang et al., 2010). Given that dopamine is an excitatory neurotransmitter, these genes are also associated with a variety of antisocial personality traits and behaviors. For instance, a variant of a gene responsible for the transportation of dopamine (DAT1) has been reported to be associated with deficiency in self-regulation (Wright, Schnupp, Beaver, DeLisi, & Vaughn, 2012), substance abuse (Hopfer et al., 2005), and violent criminal behaviors (Guo et al., 2007; Vaughn, DeLisi, Beaver, & Wright, 2009). On a different note, as discussed earlier, the criminology literature indicates that cognitive abilities’ effects—at least when measured as IQ—on delinquency are mediated by academic performance (Ellis et al., 2009; McGloin et al., 2004; Ward & Tittle,


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1994). Awareness of this mechanism underlying IQ, academic performance, and delinquency may produce a parallel, but novel, hypothesis in view of the newly revealed dopaminergic genes’ effects on academic performance (Beaver et al., 2010). That is, similar to the causal process in which academic performance mediates IQ’s effects on delinquency, academic performance may also mediate dopaminergic genes’ effects on delinquency. To our knowledge, criminological research thus far has never tested this possibility. A confluence of the criminological literature with recent research findings demonstrating genetic effects on both delinquency and academic performance, however, suggests that such a meditational hypothesis is not only possible, but plausible. In the current study, therefore, we examine the specific nature of associations among the dopaminergic genes, GPAs, and violent delinquency. Specifically, this study explores whether the effects of the dopaminergic genes on academic performance are mediated by three salient environmental factors—namely, peer affiliation, poverty, or parental level of education—whose linkages to academic performance have been repeatedly demonstrated in the literature (Eamon, 2005; Hochschild, 2003; Majoribanks, 1996). We then assess the relative power of the dopamine genes and selfdiscipline in predicting academic achievement. In view of prevalent G × Es in the etiology of various human traits, we further examine whether the genes’ effects on academic performance are conditioned by delinquent peer affiliation, poverty, and parental education. Finally, considering the criminological literature showing the link between academic performance and delinquency, we assess whether academic performance mediates the genetic effects on violent delinquency.

Method Sample Data for the present study were drawn from the Add Health, a longitudinal and nationally representative sample of American youths (Udry, 2003). Initial data collection began in 1994-1995, when the respondents were enrolled in 7th through 12th grades. In total, 132 schools in the nation were sampled through stratified cluster sampling techniques. Students attending these schools were asked to complete a self-report questionnaire. More than 90,000 students completed the survey instrument. To obtain more detailed information from the respondents, a stratified subsample was selected and reinterviewed at home. In all, 20,745 adolescents and 17,700 of their primary caregivers were interviewed in the in-home survey. Adolescents were queried on involvement in delinquent conduct, peer relations, and a host of other issues related to adolescent development (Harris et al., 2003). Subjects also reported their most recent report card grades in English or language arts, mathematics, history, and science. The second wave of data was collected from 14,738 of the respondents in 1996. Most of the items in Wave 1, including the report card grades in the four subjects, were also included in Wave 2 interviews. The third wave of data was collected in 2001-2002, when most participants had reached between 18 and 26 years of age. As a consequence, the items in the survey instruments were redesigned to include more age-appropriate questions. Overall, 15,197 participants were interviewed successfully.


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A distinctive feature that sets the Add Health study apart from other studies of youths is inclusion of the DNA subsample. At Wave 3 interviews, respondents who had a sibling included in the Add Health study were asked to submit their buccal cells for genotyping. The genetic subsample consists of 2,574 monozygotic (MZ) twins (11%), dizygotic (DZ) twins (18%), and siblings (70%). Genotyping was conducted for five candidate polymorphisms linked to critical neurotransmitters: DAT1, DRD2, DRD4, the serotonin transporter (5HTT), and monoamine oxidase A (MAOA; Harris, Halpern, Smolen, & Haberstick, 2006). The inclusion of genetic measures along with academic performance measures and other correlates of delinquency in the Add Health study provide a rare opportunity to examine the research questions of the current study. Among the three waves of data collection, only the first two waves include measures of academic performance in the form of report card grades. There is a noticeable discrepancy in the volume of missing data on these measures between the two waves, with Wave 2 data containing far more missing values. This occurred because a sizable number of students graduated from high school between Waves 1 and 2, and also not all the remaining students were uniformly enrolled in the four subjects within each wave. To maintain sufficient statistical power, thus, our analyses are restricted to Wave 1 academic achievement measure, while excluding the Wave 2 measure. Thus, except for genetic measures culled from Wave 3 data, all the measures for current analyses were drawn from Wave 1 data collection. The DNA sample of the Add Health study includes an 11% of MZ twins. Because MZ twins are genetically identical, including both MZ twins would essentially result in double counting them from a genetic perspective. For this reason, we randomly eliminated one twin from each MZ twin pair from the analyses. Furthermore, as the DNA sample includes siblings from the same households, the regression assumption of independent observations is violated, potentially leading to biased standard errors. To rectify this problem, Huber/White robust standard errors were calculated in all multivariate equations. These two separate techniques have been frequently employed by researchers utilizing the Add Health DNA sample (Beaver, Wright, DeLisi, & Vaughn, 2012; Haberstick et al., 2005). Our final analytical sample consists of the youths in the genetic subsample who reported their report card grades at Wave 1. All the tables provide the exact sample sizes that were used in the calculation of statistical estimates, following the list-wise deletion method for missing data.

Measures Academic performance. To measure academic performance, respondents were asked during Wave 1 interviews to report their most recent report card grades in English or language arts, mathematics, history, and science. Responses were coded as follows: 1 = D, 2 = C, 3 = B, and 4 = A. These four items were summed and divided by four to create the Wave 1 GPA measure (α = .75). Violent delinquency. Molecular genetics research on antisocial behaviors indicates that genes are more likely to have an influence on serious and violent delinquency than on


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minor delinquent behaviors (Guo, Roettger, & Cai, 2008; Rutter, 2006). To assess the effects of the dopaminergic genes on delinquency, we created a seven-item Wave 1 violent delinquency scale, similar to the one used by prior researchers (Guo et al., 2008). The items included pertain to a range of violent acts including how many times in the past 12 months they had hurt someone badly enough to need medical attention, used or threatened to use a weapon to get something from someone, participated in a group fight, deliberately damaged other’s property, pulled a knife or gun on, shot or stabbed someone, or carried a weapon to school. Most of the items were coded as 0 = never, 1 = once or twice, 2 = 3 or 4 times, and 3 = 5 or more times, while two items were coded dichotomously. Following Guo et al. (2008), dichotomously coded items were recoded to match the scaling of the rest of the items, reflecting the seriousness of respective behaviors. Responses to the items were then summed together to form a violent delinquency scale (α = .77). Genetic polymorphism measure. It is worth noting that many genes are polymorphic— that is, they have different variants. These variants, known as alleles, are inherited from one’s parents (one copy from the biological father and another from the biological mother). Certain alleles are known as “risk alleles” because they have been linked to negative outcomes, be it low academic performance, a personality trait, or a type of behavior (Rutter, 2006). In the current study, measures of three dopaminergic polymorphisms—the DAT1, the DRD2, and the DRD4—were utilized. The DAT1 is a gene that regulates the level and duration of dopamine receptor activation. The DAT1 gene contains 6, 7, 8, 9, 10, and 11 repeat alleles. The 10R allele is associated with reduced dopaminergic function, because it facilitates more efficient reuptake of dopamine in the synaptic cleft than the other repeats (Mill, Asherson, Browes, Souza, & Craig, 2002). With the Add Health sample, more than 96% had one of the following combinations of alleles: (a) two 10R alleles, (b) two 9R alleles, or (c) one 10R allele and one 9R allele. In line with previous studies (Beaver et al., 2010; Hopfer et al., 2005), adolescents who had an allele other than a 10R and a 9R were removed from analyses. Prior research suggests that the 10R allele is associated with the development of attention deficit hyperactivity disorder (ADHD), pathological gambling, depression, and other maladaptive outcomes (Comings et al., 2001; Rowe et al., 1998; Swanson et al., 2000). Given that the 10R allele is considered the risk allele, the allele combinations are coded in a way that a score of 0 was assigned when there existed no 10R allele and a score of 1 was given if at least one 10R allele was present. The DRD2 gene consists of two alleles: the A1 allele and the A2 allele. The A1 allele is typically considered the risk allele for a wide range of maladaptive behaviors, such as alcoholism, antisocial personality traits, and pathological gambling (Noble, 2003). We also dichotomously coded the DRD2 gene in such a way that a score of 1 was given if the allelic combination involved at least one A1 allele, and 0 was given if the combination involved only A2 alleles; 45.1% of the sample had at least one A1 risk allele. The DRD4 is one of the most examined polymorphisms in genetic research (Faraone, Doyle, Mick, & Biederman, 2001). Ten different alleles, consisting of 2 to 11 repeats, are found at the DRD4 locus. Following Hopfer et al. (2005), alleles that


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had repeat sequences greater than or equal to 7 were coded as risk alleles. These risk alleles have been reported to be associated with a list of negative psychological and behavioral outcomes including ADHD, binge drinking, and conduct disorder (Beaver et al., 2007; Faraone et al., 2001; Vaughn et al., 2009). Adolescents who had at least one risk allele were given a value of 1, while those who did not have a risk allele were assigned a value of 0. It is reasonable to assume that complex human traits such as academic performance and violent delinquency are polygenic phenotypes (Rutter, 2006), meaning multiple genes are involved in manifesting themselves in such phenotypes. The vast majority of single genes, therefore, have only small effects on these complex human traits. Correspondingly, previous studies have revealed that genetic effects on complex behavioral traits are more consistent and easier to identify when individual genetic polymorphisms are combined together to form an additive genetic scale (Beaver et al., 2010; Belsky & Beaver, 2011). Following the prior researchers’ practice, we also created a dopamine index by summing the scores on the three dopaminergic polymorphisms. The values of the resultant dopamine index ranged from 0 to 3 (M = 1.78, SD = .71), where the value of 3 denotes that each of the three dopamine genes has at least one risk allele, while the value of 0 indicates the absence of any risk allele. Self-control. To compare the effects of dopamine genes and self-control on academic performance, a five-item measure was created following Gottfredson and Hirschi’s (1990) description of low self-control. They argued that low self-control is characterized by variations in factors such as impulsivity, preference for simple tasks, risktaking, avoidance of mental activities, preference for physical activities, and self-centeredness (Gottfredson & Hirschi, 1990). Youths at Wave 1 responded on a 5-point Likert-type scale to statements asking whether they had had problems or trouble “keeping their mind on what they were doing,” “getting their homework done,” “paying attention in school,” and “getting along with their teachers.” These items tapped into preference for simple tasks and physical activities, impulsivity, and temper components of low self-control. To tap the self-centeredness component, respondents were asked whether they felt they were “doing everything just about right.” Responses to these items were summed to form a self-control scale (α = .63). The same measure of self-control has been used by previous researchers analyzing the Add Health data (Beaver, 2010; Perrone, Sullivan, Pratt, & Margaryan, 2004). The self-control scale was coded in a way that high scores indicated higher levels of self-control. Delinquent peers. Associating with delinquent peers is not only a robust correlate of delinquency but also of poor academic performance (Maguin & Loeber, 1996). A measure of delinquent peers was created to examine the potential mediating and interactive effects exerted by delinquent peers on academic performance. Criminologists have typically constructed a measure of delinquent peers by asking respondents how many of their close friends engaged in certain types of delinquent acts during a certain time period. Yet, recent studies suggest that this type of indirect measure of peer delinquency may incorrectly reflect the true state of delinquent peer association due to


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“assumed similarity” (Haynie, 2001; Haynie & Osgood, 2005). That is, when reporting their friends’ delinquency, respondents tend to impute their own delinquent qualities to their close friends. Furthermore, this type of indirect measure reflects only perceived peer delinquency rather than the actual level of delinquent acts committed by peers. Haynie (2001) argued, to correct for this problem, that a direct measure of peer delinquency employing peers’ own reporting of their delinquent involvement should be used. Pursuant to Haynie’s suggestion, we constructed a direct measure of peer delinquency taking advantage of the peer network data available in the Add Health study. Specifically, during the Wave 1 in-school data collection, adolescents were asked to report their own involvement in six minor delinquent acts during the past 12 months, including smoking cigarettes, drinking alcohol, getting drunk, racing on a bike or car, having been in danger due to dare, and having skipped school without an excuse. Adolescents were then asked to identify their close friends, and then the nominated friends (respondents) were located and interviewed. By connecting the respondents’ interview data to their peers’ self-reports of delinquency, the Add Health team was able to create a peer network database (Udry, 2003). Based on these data, we calculated the mean delinquency for each of the six delinquent items in a respondent’s peer network. The means for the six items were summated to create a peer delinquency index (α = .75), which represents the average level of delinquent involvement of the respondent’s close friends. Public assistance. Poverty or low income is a predictor of educational attainment, with its effect size being about .30 (White, 1982). There is emerging evidence that poverty is also correlated with certain genes, which often engender chronic poverty across generational lines (Rowe & Rodgers, 1997). To assess the extent to which the effects of dopaminergic genes are mediated and/or conditioned by poverty, we tapped the level of poverty of the household in which the adolescent resides. To this purpose, a single item at Wave 1 asking the primary caregiver whether he or she receives any form of public assistance was employed and coded dichotomously (1 = yes, 0 = no). Parental education. The literature on child’s educational achievement shows that parental education is also an important predictor (Haveman & Wolfe, 1995). In the Add Health study, primary caregivers reported at Wave 1 the level of their educational attainment as well as that of their current spouse’s or partner’s. The original response categories were recoded to range from 0 (no education) to 8 (postgraduate). The scores on parents’ education levels were summed and divided by two to create a parental education scale (α = .63). Control variables. To reduce the possibility that any statistically significant relationships could be explained away in terms of spuriousness, three demographic control variables were employed. The control variables were race (0 = White, 1 = non-White including Hispanics), age (in years), and sex (0 = female, 1 = male). Race was included to prevent population stratification effects, and age and sex took into account the facts that genetic effects may vary across age and sex (Beaver et al., 2012).


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Table 1. Bivariate Correlation Matrix for Variables (N = 2,252). Variable 1. GPA 2. Dopamine index 3. Self-control 4. Delinquent peers 5. Public assistance 6. Parent education 7. Race 8. Age 9. Sex

1

2

1 −.12* .35* −.20* −.18* .24* −.15* −.02* −.15*

1 .01 −.01 .02 −.12* .13* .02 −.04

3

1 −.19* −.04 .01 .03 −.01 −.06*

4

1 .01 −.09* −.24* .15* .05

5

1 −.19* .06* −.04 −.03

6

1 −.06* −.07* .05

7

8

1 .04 −.01

1 .02

Note. GPA = grade point average. *p < .05 (two-tailed).

Plan of Analysis This study proceeded in several related analytical steps. First, a bivariate correlation coefficient matrix was constructed to compare the magnitudes of associations between predictor variables and academic performance. Then a series of ordinary least squares (OLS) regression equations were calculated to assess the relative predictive power between the dopaminergic genes and self-control on GPA as well as to examine whether dopaminergic genes’ effects on academic performance were mediated by environmental variables. To investigate interaction effects between the genes and environmental factors, a set of split-sample OLS models were estimated. Finally, an additional set of OLS equations assessed whether dopaminergic genes have direct effects on violent delinquency or the effects are mediated by academic performance.

Results The analytical sample consists of slightly more female respondents (52.04%), and the majority of the sample was White (67.37%). The mean number of delinquent peers was .92. About 7% of the sample lived in a household in which the primary caregiver received some form of public assistance. Table 1 displays a bivariate correlation matrix for all the variables and scales used in the analyses. Consistent with Beaver et al.’s (2010) study, the dopamine index has a statistically significant inverse association with GPA, indicating that adolescents with more dopaminergic risk alleles are likely to attain lower GPAs. Self-control is also significantly related to GPA. Note that the magnitude of the association between GPA and self-control (r = .36) is almost 3 times the size of the correlation between GPA and the dopamine index (r = −.13). We conducted a correlation coefficient difference test following the approach delineated by Meng, Rosenthal, and Rubin (1992). The result showed that the two correlations were significantly different from each other and


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Table 2. OLS Equations Examining the Effects of Dopaminergic Risk Alleles and Self-Control on Academic Achievement. Model 1 Variable Dopamine index Race Age Sex Delinquent peers Public assistance Parental education Self-control R2 n

Model 2

Model 3

b

SE

β

b

SE

β

b

SE

β

−.12* −.20* −.01 −.21*

.02 .04 .01 .03

−.12 −.14 −.01 −.15

−.09* −.25* −.02 −.24* −.29* −.38* .08*

.03 .04 .01 .04 .04 .08 .01

−.09 −.18 −.04 −.17 −.24 −.14 .21

−.07* −.23* −.01 −.19* −.22* −.34* .08* .06*

.03 .04 .01 .04 .04 .08 .01 .01 .28 1,118

−.08 −.16 −.02 −.14 −.18 −.13 .22 .29

.06 1,749

.21 1,118

Note. Huber/White standard errors are presented. OLS = ordinary least squares. *p < .05 (two-tailed).

self-control was more strongly associated with GPA than was the dopamine index (t = 11.09, p < .001). Table 2 presents the results of three OLS models predicting academic achievement. Model 1 includes the key predictor variable, the dopamine index, and three demographic control variables. Model 2 addresses the question of whether the dopamine index has a direct effect on academic achievement or whether its effect is mediated by the three potential mediating variables. Following Baron and Kenny’s (1986) lead, if the dopamine index loses significance or its significance is reduced substantially upon inclusion of an environmental variable, then it can be concluded that the environmental variable mediates the association between the dopamine index and GPAs. Finally, Model 3 compares the relative predictive power of the dopamine index and self-control, net of controls and mediating variables. Model 1 reveals that the dopamine index has a significant inverse effect, net of demographic controls. The dopamine index’s effect maintains statistical significance in Model 2 even with the inclusion of the three potentially mediating variables. Also note that the change in standardized regression coefficients is only minimal from −.12 to −.09. These findings converge to suggest that dopaminergic risk alleles in combination have a direct, rather than indirect, effect on academic achievement. In Model 3, self-control is added to Model 2 to assess the relative contribution of the dopamine index and self-control. The results show that self-control accounts for more than 3 times as much variance in GPA (β = .29, p < .001) as did the dopamine index (β = −.08, p < .001). Having estimated the main effects models thus far, we now turn to examine the extent to which the effect of the dopamine index is conditioned by the level of delinquent peer association, public assistance, and parental education. The split-sample


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Table 3. Effect of Dopaminergic Risk Alleles on GPA by Delinquent Peer Association, Public Assistance, and Parental Education. Low delinquent peers Variable Dopamine index Self-control Delinquent peers Public assistance Parental education Race Age Sex R2 n

b

High delinquent peers

β

b

−.09* −.10 .06* —

−.05

β

.08* —

−.41* −.17

−.21*

−.08

.09

.25

.18

−.28* −.21 .01 .01 −.21* −.16 .26 456

b

β

Public assistance b

High parental education

β

b

β

Low parental education b

−.06 −.07* −.08 −.10 −.11 −.10* −.11 −.04

.25 —

.07 *

No public assistance

β −.04

.34 .06* .29 .06 .27 .08* .36 .05* .24 — −.24* −.20 −.02 −.01 −.21* −.18 −.27* −.21 —

—

—

— −.57* −.17 −.32* −.16

.08*

.22

.07

.15

—

—

—

—

−.08 −.05 −.27* −.19 .09 .07 −.34* −.24 −.17* −.12 .02 .03 .01 .02 .01 .01 .02 .04 −.01 −.03 −.18 * −.13 −.18* −.14 −.11 −.08 −.16* −.13 −.24* −.18 .22 .26 .17 .28 .20 369 322 540 679 54a

Note. GPA = grade point average; OLS = ordinary least squares. aThe OLS estimates in the model for those who receive public assistance can be unstable in view of the weak power stemming from the small ratio of predictor variables to sample size (Tabachnick & Fidell, 2007). We addressed this issue employing two different approaches. First, we ran an OLS equation by reducing the number of predictors by dropping demographic control variables. Second, we increased the sample size by imputing the missing values of GPA using the mean imputation method and reran the equation. Neither of the approaches revealed a notably different pattern from the one exhibited in Table 3. *p < .05 (two-tailed).

method was employed by splitting the full sample at the mean of each of the three variables, respectively. An interaction would be detected if the dopamine index is significantly associated with academic achievement in one sample but not in the other. The split-sample method has been used previously by delinquency researchers for detecting interactions among biological and environmental predictors in the etiology of deviant behaviors (Beaver, Wright, & DeLisi, 2008; Turner, Hartman, & Bishop, 2007). Table 3 presents the results of the three sets of split-sample analyses. For each set, the left column represents a benign environment, while the right column embodies a negative environment. The first set of split samples contrasts regression estimates for a sample of youths who reported associating with fewer than the mean number of delinquent peers with regression estimates for those who associated with more than mean number of delinquent peers. The dopamine index holds statistical significance under the low delinquent peers sample, while its significance disappears in the high


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Yun et al. Table 4. OLS Equations Examining the Effect of Dopaminergic Risk Alleles on Violent Delinquency. Model 1 Variable Dopamine index Race Age Sex Self-control Wave 1 GPA R2 n

Model 2

Model 3

b

SE

β

b

SE

β

b

SE

β

.18* .24* −.04 .85*

.07 .11 .03 .10

.05 .05 −.03 .18

.17* .28* −.04 .79* −.18*

.07 .11 .03 .09 .02

.05 .06 −.03 .17 −.24

.08 .21 −.07* .63* −.17* −.18*

.07 .11 .03 .10 .02 .08 .11 1,734

.03 .05 −.05 .14 −.24 −.06

.04 2,252

.10 2,252

Note. Huber/White standard errors are presented. OLS = ordinary least squares; GPA = grade point average. *p < .05 (two-tailed).

delinquent peers sample. Surprisingly, the same pattern appears in the second as well as the third column. The dopamine index holds statistical significance in the sample of households that did not receive public assistance and in which parental education was above the mean. It loses significance, however, in the samples with converse conditions, which represent adverse environments. These findings clearly suggest that the dopaminergic influences on academic achievement are more salient in benign environments than in poor environments, offering evidence of gene and environment interactions. As a final step in our analyses, we estimated three additional OLS equations to examine whether the dopamine index predicted violent delinquency and, if so, whether the association was mediated by academic achievement. The results are presented in Table 4. The dopamine index in Model 1, where only demographic control variables are included, exerts significant influences on violent delinquency. When self-control is added to the equation in Model 2, it still maintains its effect. Yet, the relative magnitude of influence is far smaller for the dopamine index (β = .05) when compared with that for self-control (β = −.24). Model 3 assesses whether the genetic influences on violent delinquency are mediated by academic performance, similar to the mediating role of academic performance in the cognitive ability–delinquency relation found in the delinquency literature (McGloin et al., 2004). If the magnitude of the coefficient for the dopamine index is markedly reduced or the coefficient loses its significance when GPA is added to the model, it provides evidence of a mediation effect of academic performance (Baron & Kenny, 1986). When GPA scores were added into Model 3, the dopamine index lost significance, thereby signifying that the relationship between the dopaminergic polymorphisms and violent delinquency is mediated by academic achievement.


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Discussion An accumulated body of research has revealed that genetic factors are involved in a wide array of human behavioral traits (Rutter, 2006). Along this line of research, Beaver et al. (2010) recently showed that youths’ academic achievement is influenced by three dopaminergic genetic polymorphisms. The present study attempted to expand their main effect models by reanalyzing the Add Health DNA subsample. The results of our analyses are summarized as follows. First, our study reconfirmed Beaver et al.’s (2010) findings by revealing that the dopaminergic genes’ effects on academic achievement are direct and not mediated by certain key sociological factors, such as delinquent peers, parental education, or poverty. Our analyses, however, indicated that although the three dopaminergic genes do affect students’ GPAs, their effects are far less potent than the effects of self-control, which essentially points to the very real possibility that discipline outperforms talent. Thus, despite Beaver et al.’s (2010) novel findings demonstrating measured genes’ effects on GPA, if a student underachieves academically, we may justifiably assume that it is due more to lack of self-control than to unfavorable dopaminergic allelic combinations. In a similar vein, Duckworth and Seligman (2005) maintained that the royal road to increased academic achievement is to apply programs that are conducive to building children’s self-control. Fortunately, evidence is accumulating that selfcontrol can be learned as well as improved by employing suitable techniques, such as mental contrasting (Duckworth, Grant, Loew, Oettingen, & Gollwitzer, 2011), or by even experiencing negative outcomes after engaging in risky activities (Romer, Duckworth, Sznitman, & Park, 2010). Pursuant to Beaver et al.’s (2010) exhortative statement for further research on G × Es, we assessed whether the genetic effects on academic performance were conditioned by three environmental factors. The results maintained a surprisingly consistent pattern in which the dopamine index exerted significant effects on academic performance only in positive and benign environments, while its effects were consistently null in poor and adverse environments. This pattern of findings is not surprising in view of the “social push hypothesis” in the behavioral genetics literature (Raine, 2002). According to this hypothesis, when adverse environmental factors “push” negative outcomes, such as delinquency or low academic achievement, then genetic factors’ effects become harder to identify. This occurs because, in a disadvantaged environment, socioenvironmental factors derived from the adverse environment overwhelm genetic factors in producing the outcomes. Conversely, genetic effects are highlighted in advantaged environment as such benign environments minimize the “noise” of social factors that might overwhelm genetic expression. The current study further expands Beaver et al.’s (2010) study by placing it in the context of delinquency causation. Prompted by the empirical documentation that academic performance mediates the effects of cognitive abilities on delinquency, we assessed whether a similar causal process operated among the dopaminergic genes, academic achievement, and violent delinquency. As presented in Table 4, the dopaminergic effects on violent delinquency were fully mediated by GPA. To our knowledge,


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Yun et al.

this is the first study that has empirically found the mediating role of academic performance linking the dopaminergic genes and violent delinquency. Complex human traits are rarely a product of either biology or environment alone, but they result from genetic and environmental factors working interactively (Raine, 2002; Rowe & Rodgers, 1997; Rutter, 2006). For instance, aggressive behaviors are likely to result from interactions between predisposing genetic vulnerabilities and potentiating environmental risk factors. As DeLisi, Beaver, Wright, and Vaughn (2008) argued, human behaviors are not bound by artificial disciplinary boundaries. The results of our analyses advise researchers to place keen attention on the interplay between genes and environment. Had we not examined G × E, we would probably have been left without the contextualized knowledge that the dopaminergic genes affect GPAs in certain environmental conditions, but not in others. Although science pursues parsimony, main effect models should be viewed with caution as nature and nurture operate in an interactive fashion most of the time. Although the current study provides evidence relating dopaminergic genes, academic performance, and violent delinquency, the results should be interpreted with caution due to the following limitations. First, although the Add Health study involves a nationally representative sample of American youths, the DNA subsample is not representative. Thus, the extent to which our findings can be generalized beyond the Add Health DNA sample is left open. In a similar vein, there is a glaring paucity of criminological research employing genetic samples of races and ethnicities other than Whites. Thus, it is an open empirical question whether the genes in the present study will have the same effects across different racial and ethnic groups. A second limitation pertains to the measure of self-control used in the current study. Although researchers analyzing the Add Health data have frequently employed the same self-control scale as used in this study, it should be acknowledged that its psychometric validity and reliability are not as well established as other proven scales such as that proposed by Grasmick, Tittle, Bursik, and Arneklev (1993). Similarly, the items constituting the self-control scale of this study such as “getting homework done” or “getting along with teachers” may capture self-control’s dimensions that differ from the typical dimensions of self-control assessed in prior studies such as avoiding risktaking, being impulsive, or having delayed gratification tendencies (Grasmick et al., 1993; Wolfe & Johnson, 1995). In this regard, the items comprising the current selfcontrol scale seem to relate more to “self-disciplined obedience” than to “having strong internally motivated willpower,” which can directly contribute to academic achievement. Third, in relation to the measure of self-control, we would have used a better and, possibly, multimethod measure of self-control, if one was available. Our measure, while capturing the characteristics of low self-control as delineated by Gottfredson and Hirschi (1990), did not tap other important dimensions, such as school attendance, study habits, time spent for study, and so forth. Fourth, we did not examine each specific dopamine gene’s effects on academic performance or violent delinquency. Instead, we used a cumulated genetic risk index, pooling the individual effects together. It is quite plausible, however, that individual genes have unique and independent effects on the phenotypes.


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Finally, we only utilized Wave 1 cross-sectional data of the Add Health study. Thus, the significant findings revealed in our analyses should be understood as correlational rather than causational. Although genes always come before behavioral phenotypes, other mediating and interactive variables could be operating simultaneously with the dependent variables. A fruitful avenue for future researchers is, thus, to replicate the present study adopting a life course perspective, preferably using genetic samples of other racial and ethnic groups. Acknowledgment Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design.

Authors’ Note Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 West Franklin Street, Chapel Hill, North Carolina 27516-2524 ([email protected]).

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by research fund from Chosun University, 2015. This research used data from the National Longitudinal Study of Adolescent Health (Add Health), a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by Grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies.

Note 1. In genetics, an allele simply refers to a variant form of a gene, while a risk allele refers to a variant form of a gene that confers an increased risk for a particular phenotype (e.g., diseases) as opposed to other variants that do not confer such a risk. In molecular genetic studies, 10-repeat alleles, A1 alleles, and alleles with more than 6 repeats are considered to be risk alleles for the dopamine transporter (DAT1), the dopamine D2 receptor (DRD2), and the dopamine D4 receptor (DRD4) genes, respectively.

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