Elsevier

Journal of Criminal Justice

Volume 39, Issue 5, September–October 2011, Pages 378-384
Journal of Criminal Justice

Evidence of a gene × environment interaction between perceived prejudice and MAOA genotype in the prediction of criminal arrests

https://doi.org/10.1016/j.jcrimjus.2011.05.003Get rights and content

Abstract

Purpose

The current study builds on a large body of research that has revealed that both genetic and environmental factors contribute to the development of antisocial behaviors. While a number of studies have indicated that stressful environments interact with specific genetic polymorphisms to create antisocial phenotypes, studies have not yet examined whether perceived prejudice and specific genetic polymorphisms combine together to predict criminal arrests over the life course.

Methods

The current study builds on the existing gene × environment literature by using data from the National Longitudinal Study of Adolescent Health (Add Health) to examine the effects of MAOA and perceived prejudice on the probability of being arrested.

Results

The results of the multivariate models reveal a statistically significant gene × environment interaction between MAOA and perceived prejudice in the prediction of arrest for males.

Conclusions

The results indicate that the presence of both perceived prejudice and MAOA increase the likelihood of being arrested. The implications of these results are discussed and limitations are noted.

Highlights

► Recent studies have shown that stressful environments interact with genetic polymorphisms to predict antisocial outcomes. ► MAOA genotype and perceived prejudice are not related to the probability of arrest when examined independently. ► MAOA genotype and perceived prejudice do, however, interact to predict the probability of being arrested for males. ► These findings are consistent with the differential-susceptibility hypothesis.

Introduction

There is a rapidly growing body of interdisciplinary research indicating that all forms of human behaviors are the result of a complex arrangement of genetic and environmental factors (Moffitt, 2005, Plomin et al., 2008, Ridley, 2003, Rutter, 2006). For the most part, criminological theory and research has ignored the genetic side to this equation and focused almost exclusively on the environmental origins to antisocial behaviors (Walsh and Ellis, 2004, Wright et al., 2008). Although this trend still continues to dominate criminology, a handful of studies have recently penetrated the criminological literature showing that genetic factors and environmental factors are mutually interdependent and work synergistically to produce behavioral variation (Beaver, DeLisi, Wright and Vaughn, 2009, Beaver, Gibson, Jennings and Ward, 2009, Delisi et al., 2009, Ellis, 2005). These gene × environment studies hold the potential to increase the predictive ability of environmental factors because they rely on genetic information to help identify who is most likely to be affected by the environment (Belsky & Pluess, 2009). Seen in this way, gene × environment research is not only compatible with most criminological research, but also could add greater theoretical and empirical specificity to understanding the causes of antisocial behavior (Moffitt, 2005).

Most of the gene × environment research that has been published is produced by non-criminologists, meaning that it has yet to fully inform the criminological audience (Wright et al., 2008). The current study partially addresses this gap in the criminological literature by focusing on two main goals. First, we provide a brief overview of the gene × environment approach to the study of human behavior. Second, we examine whether an environmental factor extracted from one criminological perspective—general strain theory—is able to moderate the effect of genetics on antisocial behavior. To do so, we analyze data drawn from the National Longitudinal Study of Adolescent Health.

Section snippets

Evidence of gene × environment effects on antisocial behaviors

Evidence of genetic and gene × environment effects on antisocial behaviors has been garnered, in large part, from behavioral genetic studies. Behavioral genetic studies analyze samples of kinship pairs, usually twin pairs, to estimate the extent to which variance in a phenotype is influenced by genetic as well as environmental factors. To the extent that genetic factors are involved in creating phenotypic variance, the similarity on the phenotype will increase as genetic similarity increases.

Strain and gene × environment interactions

The landmark study conducted by Caspi et al. (2002) provided some of the first empirical support for gene × environment interactions in the creation of antisocial behaviors by focusing on the environmental pathogen of childhood maltreatment. Given the complexity of antisocial behaviors, there are likely other environmental pathogens that also moderate the effect of MAOA genotype. Overall, the class of environments that appear to be of particular importance to gene × environment research

The current study

Recently, Walsh (2002) argued that more and more criminological research should seek to integrate genetic findings into existing criminological theories. In particular, he singled out strain theory as being one criminological theory that could easily incorporate findings from genetic research (Walsh, 2000). The current study builds on this theoretical work by empirically examining whether one measure of strain interacts with MAOA to predict criminal arrests. Based on the results of prior

Data

The data utilized in this study are drawn from the National Longitudinal Study of Adolescent Health (Add Health), which is a prospective and nationally representative sample of youths (Udry, 2003). The first wave of data was collected in 1994 when approximately 90,000 adolescents, who were enrolled in middle or high schools, were interviewed. The youths attending the selected schools were asked to complete the wave I in-school survey with questions pertaining to social relationships, family

Ever arrested

Similar to past researchers analyzing the Add Health data (Beaver, DeLisi, Mears and Stewart, 2009, DeLisi et al., 2008), we employed a self-reported ever arrested measure. Self-report arrest data, while not immune to limitations, are expected to produce results similar to analyses using official arrest records (Kirk, 2006). During wave IV interviews, respondents were asked if they had ever been arrested. This item was coded dichotomously where 0 = never arrested and 1 = arrested at least once.

Monoamine Oxidase A

DNA

Plan of analysis

The analysis for this study proceeds in a number of steps. First, because MAOA is X-linked, all of the models were estimated separately for females and males. Second, two models were calculated for both sexes. The first model was a baseline binary logistic regression model where the direct main effects of MAOA and perceived discrimination were introduced into the equation. The second model was a binary logistic regression model where a multiplicative interaction term between MAOA and perceived

Results

The analysis begins by examining the effect of MAOA and perceived discrimination on the probability of being arrested for females and males. The results of the analyses are presented in Table 2. As can be seen in the left-hand columns, MAOA and perceived discrimination are both unrelated to the odds of being arrested when their effects are examined individually for females. In fact, the only covariate to emerge as a statistically significant predictor of being arrested is African American,

Discussion

Research spanning multiple disciplines has revealed that genetic and environmental influences have salient effects on the development of antisocial behaviors (Caspi et al., 2002, Caspi et al., 2003, Foley et al., 2004, Haberstick et al., 2005, Kim-Cohen et al., 2006, Widom and Brzustowicz, 2006). The most cutting-edge research, however, has moved away from simply estimating genetic and environmental effects and instead has explored the various ways in which genes and the environment combine

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    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 (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.

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