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Adolescent Criminal Justice Involvement, Educational Attainment, and Genetic Inheritance: Testing an Integrative Model Using the Add Health Data

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Abstract

This study integrates developmental and life-course criminology with advances in socio-genomics to investigate the complex relationships among criminal justice (CJ) involvement (e.g., arrest, conviction, and incarceration), educational attainment, and genetic inheritance. Using data from the National Longitudinal Study of Adolescent to Adult Health, we conduct an analysis based on a whole-genome polygenic score for educational attainment. We find that participants with lower polygenic scores for educational attainment were significantly more likely to report CJ involvement during adolescence. We then show that the association between the education polygenic score and adolescent CJ involvement risk may be attributed to gene-environment correlation mechanisms that operate via both individual factors (e.g., psychopathic personality traits and delinquency) and social factors (e.g., family characteristics and school experiences). Finally, we find evidence that adolescent CJ involvement mediates the association between the education polygenic score and male participants’ actual educational attainment. Results also indicate that the influence of CJ involvement on education was partially confounded by genetic factors. Findings in this paper not only enrich existing criminological theories on the causes and consequences of CJ involvement in the life-course process but also help to improve causal inference in the study of the impact of CJ involvement on later-in-life outcomes.

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Notes

  1. In GWA studies, genomes from a large sample of individuals are scanned to search for genetic variants that can be used to predict the outcome. The GWA analysis typically involves two steps. First, each genetic variant is used to predict the phenotype and the corresponding p value is recorded. Because the prediction involves a large number of genetic variables, it is likely that some small p values are due to chance. To address the multiple testing issue, a conservative p value threshold is set as 5 × 10−8 or smaller. Second, even extremely smaller p values do not completely rule out all possible false-positives, thus replication using independent data is required to establish validity of a GWA study.

  2. Over 93% of Add Health participants completed high school or earned a high school equivalency degree.

  3. Each genotype typically has only two alleles. There are three possible combinations of two alleles in a population (e.g., AA, CC, and AC). Effect allele is the one positively associated with the outcome.

  4. Population stratification refers to the presence of a systematic difference in the genetic distribution between subpopulations. Population stratification is a primary consideration in genome-wide association studies. Failure to control for it may lead to false results. Principal component analysis has been developed as a standard approach to address population stratification in regression analysis.

References

  • Barnes, J. C., Boutwell, B. B., Beaver, K. M., Gibson, C. L., & Wright, J. P. (2014a). On the consequences of ignoring genetic influences in criminological research. Journal of Criminal Justice, 42, 471–482.

    Article  Google Scholar 

  • Barnes, J. C., Wright, J. P., Boutwell, B. B., Schwartz, J. A., Connolly, E. J., et al. (2014b). Demonstrating the validity of twin research in criminology. Criminology, 52, 588–626.

    Article  Google Scholar 

  • Barnes, J. C., Jorgensen, C., Beaver, K. M., Boutwell, B. B., & Wright, J. P. (2015). Arrest prevalence in a national sample of adults: The role of sex and race/ethnicity. American Journal of Criminal Justice, 40, 457–465.

    Article  Google Scholar 

  • Barnes, J. C., Liu, H., Motz, R. T., Tanksley, P. T., Kail, R. M., et al. (2019). The propensity for aggressive behavior and lifetime incarceration risk: A test for gene-environment interaction (G×E) using whole-genome data. Aggression and Violent Behavior, 49, 1–11.

    Article  Google Scholar 

  • Beaver, K. M., Boutwell, B. B., Barnes, J. C., Vaughn, M. G., & DeLisi, M. (2017). The association between psychopathic personality traits and criminal justice outcomes: Results from a nationally representative sample of males and females. Crime and Delinquency, 63, 708–730.

    Article  Google Scholar 

  • Belsky, D. W., & Harden, K. P. (2019). Phenotypic annotation: Using polygenic scores to translate discoveries from genome-wide association studies from the top down. Current Directions in Psychological Science, 28, 82–90.

    Article  Google Scholar 

  • Belsky, D. W., Moffitt, T. E., Corcoran, D. L., Domingue, B., Harrington, HonaLee, et al. (2016). The genetics of success: How single-nucleotide polymorphisms associated with educational attainment relate to life-course development. Psychological Science, 27, 957–972.

    Article  Google Scholar 

  • Belsky, D. W., Domingue, B. W., Wedow, R., Arseneault, L., Boardman, J. D., et al. (2018). Genetic analysis of social-class mobility in five longitudinal studies. Proceedings of the National Academy of Sciences of the United States of America, 115, E7275–E7284.

    Google Scholar 

  • Bernburg, J. G., & Krohn, M. D. (2003). Labeling, life chances, and adult crime: The direct and indirect effects of official intervention in adolescence on crime in early adulthood. Criminology, 41, 1287–1318.

    Article  Google Scholar 

  • Billy, J. O. G., Wenzlow, A. T., & Grady, W. R. (1998). User documentation for the add health public use contextual database. Available from http://www.cpc.unc.edu/projects/addhealth/codebooks/wave1/index.html.

  • Boardman, J. D., Domingue, B. W., & Daw, J. (2015). What can genes tell us about the relationship between education and health? Social Science and Medicine, 127, 171–180.

    Article  Google Scholar 

  • Brame, R., Bushway, S. D., Paternoster, R., & Turner, M. G. (2014). Demographic patterns of cumulative arrest prevalence by ages 18 and 23. Crime & Delinquency, 60, 471–486.

    Article  Google Scholar 

  • Branigan, A. R., McCallum, K. J., & Freese, J. (2013). Variation in the heritability of educational attainment: An international meta-analysis. Social Forces, 92, 109–140.

    Article  Google Scholar 

  • Broidy, L., & Agnew, R. (1997). Gender and crime: A general strain theory perspective. Journal of Research in Crime and Delinquency, 34, 275–306.

    Article  Google Scholar 

  • Conley, D., & Domingue, B. (2016). The bell curve revisited: Testing controversial hypotheses with molecular genetic data. Sociological Science, 3, 520–539.

    Article  Google Scholar 

  • Conley, D., & Fletcher, J. M. (2017). The genome factor: What the social genomics revolution reveals about ourselves, our history, and the future. Princeton University Press.

    Book  Google Scholar 

  • Conley, D., Domingue, B. W., Cesarini, D., Dawes, C., Rietveld, C. A., et al. (2015). Is the effect of parental education on offspring biased or moderated by genotype? Sociological Science, 2, 82–105.

    Article  Google Scholar 

  • De Li, S. (1999). Legal sanctions and youths’ status achievement: A longitudinal study. Justice Quarterly, 16, 394–396.

    Article  Google Scholar 

  • DeLisi, M., Beaver, K. M., Wright, J. P., & Vaughn, M. G. (2008). The etiology of criminal onset: The enduring salience of nature and nurture. Journal of Criminal Justice, 36, 217–223.

    Article  Google Scholar 

  • Domingue, B. W., Belsky, D. W., Conley, D., Harris, K., & Boardman, J. D. (2015). Polygenic influence on educational attainment: New evidence from the national longitudinal study of adolescent to adult health. AERA Open, 1, 1–13.

    Article  Google Scholar 

  • Domingue, B. W., Belsky, D. W., Fletcher, J. M., Conley, D., Boardman, J. D., et al. (2018). The social genome of friends and schoolmates in the national longitudinal study of adolescent to adult health. Proceedings of the National Academy of Sciences of the United States of America, 115, 702–707.

    Article  Google Scholar 

  • Donnellan, M. B., Trzesniewski, K. H., Robins, R. W., Moffitt, T. E., & Caspi, A. (2005). Low self-esteem is related to aggression, antisocial behavior, and delinquency. Psychological Science, 16, 328–335.

    Article  Google Scholar 

  • Dunn, L. M., & Dunn, D. M. (2007). Peabody picture vocabulary test (4th ed.). Pearson.

    Google Scholar 

  • Elwert, F., & Winship, C. (2014). Endogenous selection bias: The problem of conditioning on a collider variable. Annual Review of Sociology, 40, 31–53.

    Article  Google Scholar 

  • Farrington, D. P. (1989). Early predictors of adolescent aggression and adult violence. Violence and Victims, 4, 79–100.

    Article  Google Scholar 

  • Farrington, D. P., Kazemian, L., & Piquero, A. R. (2018). The oxford handbook of developmental and life-course criminology. Oxford Handbooks.

    Google Scholar 

  • Firth, D. (1993). Bias reduction of maximum likelihood estimates. Biometrika, 80, 27–38.

    Article  Google Scholar 

  • Fox, B. (2017). It’s nature and nurture: Integrating biology and genetics into the social learning theory of criminal behavior. Journal of Criminal Justice, 49, 22–31.

    Article  Google Scholar 

  • Freese, J. (2008). Genetics and the social science explanation of individual outcomes. The American Journal of Sociology, 114, S1–S35.

    Article  Google Scholar 

  • Freese, J. (2018). The arrival of social science genomics. Contemporary Sociology: A Journal of Reviews, 47, 524–536.

    Article  Google Scholar 

  • Giordano, P. C., Cernkovich, S. A., & Rudolph, J. L. (2002). Gender, crime, and desistance: Toward a theory of cognitive transformation. American Journal of Sociology, 107, 990–1064.

    Article  Google Scholar 

  • Goldberger, A. S. (1979). Heritability. Economica, 46, 327–347.

    Article  Google Scholar 

  • Gottfredson, Michael R., & Hirschi, Travis. (1990). A general theory of crime. Stanford University Press.

    Book  Google Scholar 

  • Guay, J.-P., Ouimet, M., & Proulx, J. (2005). On intelligence and crime: A comparison of incarcerated sex offenders and serious non-sexual violent criminals. International Journal of Law and Psychiatry, 28, 405–417.

    Article  Google Scholar 

  • Guo, G., Roettger, M. E., & Cai, T. (2008). The integration of genetic propensities into social-control models of delinquency and violence among male youths. American Sociological Review, 73, 543–568.

    Article  Google Scholar 

  • Harris, K. M. (1999). The health status and risk behavior of adolescents in immigrant families. In D. J. Hernandez (Ed.), Children of immigrants: Health, adjustment, and public assistance. (pp. 286–347). National Academy Press.

    Google Scholar 

  • Harris, K. M., Halpern, C. T., Hussey, J., Whitsel, E. A., Killeya-Jones, L., et al. (2013). Social, behavioral, and genetic linkages from adolescence into adulthood. American Journal of Public Health, 103, S25–S32.

    Article  Google Scholar 

  • Heinze, G. (2006). A comparative investigation of methods for logistic regression with separated or nearly separated data. Statistics in Medicine, 25, 4216–4226.

    Article  Google Scholar 

  • Highland, H., Avery, C., Duan, Q., Li, Y., & Harris, K. M. (2018). Quality control analysis of add health GWAS data. Available from https://addhealth.cpc.unc.edu/wp-content/uploads/docs/user_guides/AH_GWAS_QC.pdf.

  • Hirschfield, P. (2009). Another way out: The impact of juvenile arrests on high school dropout. Sociology of Education, 82, 368–393.

    Article  Google Scholar 

  • Hirschi, T. (1969). Causes of delinquency. University of California.

    Google Scholar 

  • Hirschi, T., & Hindelang, M. J. (1977). Intelligence and delinquency: A revisionist review. American Sociological Review, 42, 571–587.

    Article  Google Scholar 

  • Hjalmarsson, Randi, Department of, statistikInstitutionenförnationalekonomi med, Handelshögskolan, universitetGöteborgs, et al. (2008). Criminal justice involvement and high school completion. Journal of Urban Economics, 63, 613–630.

    Article  Google Scholar 

  • Janosz, M., LeBlanc, M., Boulerice, B., & Tremblay, R. E. (1997). Disentangling the weight of school dropout predictors: A test on two longitudinal samples. Journal of Youth and Adolescence, 26, 733–762.

    Article  Google Scholar 

  • Kirk, D. S., & Sampson, R. J. (2013). Juvenile arrest and collateral educational damage in the transition to adulthood. Sociology of Education, 86, 36–62.

    Article  Google Scholar 

  • Kirk, D. S., & Wakefield, S. (2018). Collateral consequences of punishment: A critical review and path forward. Annual Review of Criminology, 1, 171–194.

    Article  Google Scholar 

  • Kong, A., Thorleifsson, G., Frigge, M. L., Vilhjalmsson, B. J., Young, A. I., et al. (2018). The nature of nurture: Effects of parental genotypes. Science, 359, 424–428.

    Article  Google Scholar 

  • Lanctôt, N., Cernkovich, S. A., & Giordano, P. C. (2007). Delinquent behavior, official delinquency, and gender: Consequences for adulthood functioning and well-being. Criminology, 45, 131–157.

    Article  Google Scholar 

  • Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., et al. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50, 1112–1121.

    Article  Google Scholar 

  • Lewis, G. J., Asbury, K., & Plomin, R. (2017). Externalizing problems in childhood and adolescence predict subsequent educational achievement but for different genetic and environmental reasons. Journal of Child Psychology and Psychiatry, 58, 292–304.

    Article  Google Scholar 

  • Li, Yi., Liu, H., & Guo, G. (2015). Does marriage moderate genetic effects on delinquency and violence? Journal of Marriage and Family, 77, 1217–1233.

    Article  Google Scholar 

  • Liu, H. (2018). Social and genetic pathways in multigenerational transmission of educational attainment. American Sociological Review, 83, 278–304.

    Article  Google Scholar 

  • Liu, H. (2019). Genetic architecture of socioeconomic outcomes: Educational attainment, occupational status, and wealth. Social Science Research, 82, 137–147.

    Article  Google Scholar 

  • Liu, H., & Guo, G. (2016). Opportunities and challenges of big data for the social sciences: The case of genomic data. Social Science Research, 59, 13–22.

    Article  Google Scholar 

  • Liu, H., Li, Yi., & Guo, G. (2015). Gene by social-environment interaction for youth delinquency and violence: Thirty-nine aggression-related genes. Social Forces, 93, 881–903.

    Article  Google Scholar 

  • Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., et al. (2017). Human demographic history impacts genetic risk prediction across diverse populations. American Journal of Human Genetics, 100, 635–649.

    Article  Google Scholar 

  • Mears, D. P., Cochran, J. C., & Bales, W. D. (2012). Gender differences in the effects of prison on recidivism. Journal of Criminal Justice, 40, 370–378.

    Article  Google Scholar 

  • Mednick, S. A., Gabrielli, W. F., & Hutchings, B. (1984). Genetic influences in criminal convictions: Evidence from an adoption cohort. Science, 224, 891–894.

    Article  Google Scholar 

  • Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100, 674–701.

    Article  Google Scholar 

  • Moffitt, T. E. (2001). Sex differences in antisocial behaviour: Conduct disorder, delinquency, and violence in the dunedin longitudinal study. Cambridge University Press.

    Book  Google Scholar 

  • Motz, R. T., Barnes, J. C., Caspi, A., Arseneault, L., Cullen, F. T., et al. (2020). Does contact with the justice system deter or promote future delinquency? Results from a longitudinal study of British adolescent twins. Criminology, 58, 307–335.

    Article  Google Scholar 

  • Okbay, A., Beauchamp, J. P., Fontana, M. A., Lee, J. J., Pers, T. H., et al. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature, 533, 539–542.

    Article  Google Scholar 

  • Okbay, A., Turley, P., Benjamin, D., Visscher, P., Braudt, D., et al. (2018). SSGAC polygenic scores (PGSs) in the national longitudinal study of adolescent to adult health (Add Health). Available from https://addhealth.cpc.unc.edu/wp-content/uploads/docs/user_guides/SSGAC-PGS_UsersGuide.pdf.

  • Pappa, I., Pourcain, B. S., Benke, K., Cavadino, A., Hakulinen, C., et al. (2016). A genome-wide approach to children’s aggressive behavior: The eagle consortium. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 171, 562–572.

    Article  Google Scholar 

  • Pearl, J. (2009). Causality: Models, reasoning, and inference. Cambridge University Press.

    Book  Google Scholar 

  • Perry, B. L. (2016). Gendering genetics: Social and biological contingencies in the protective effects of social integration for men and women. American Journal of Sociology, 121, 1655–1696.

    Article  Google Scholar 

  • Plomin, R., DeFries, J. C., & Loehlin, J. C. (1977). Genotype-environment interaction and correlation in the analysis of human behavior. Psychological Bulletin, 84, 309–322.

    Article  Google Scholar 

  • Preacher, K. J., & Hayes, A. F. (2004). Spss and sas procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717–731.

    Article  Google Scholar 

  • Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., et al. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 38, 904–909.

    Article  Google Scholar 

  • Price, A. L., Zaitlen, N. A., Reich, D., & Patterson, N. (2010). New approaches to population stratification in genome-wide association studies. Nature Reviews Genetics, 11, 459–463.

    Article  Google Scholar 

  • Ramey, D. M. (2016). The influence of early school punishment and therapy/medication on social control experiences during young adulthood. Criminology, 54, 113–141.

    Article  Google Scholar 

  • Reich, D. E., Cargill, M., Bolk, S., Ireland, J., Sabeti, P. C., et al. (2001). Linkage disequilibrium in the human genome. Nature, 411, 199–204.

    Article  Google Scholar 

  • Rietveld, C. A., Agrawal, A., Eriksson, J. G., Albrecht, E., Alizadeh, B. Z., et al. (2013). Gwas of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 340, 1467–1471.

    Article  Google Scholar 

  • Rietveld, C. A., Esko, T., Davies, G., Pers, T. H., Turley, P., et al. (2014). Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proceedings of the National Academy of Sciences of the United States of America, 111, 13790–13794.

    Article  Google Scholar 

  • Ross, C. E., & Broh, B. A. (2000). The roles of self-esteem and the sense of personal control in the academic achievement process. Sociology of Education, 73, 270–284.

    Article  Google Scholar 

  • Rowe, D. C., & Osgood, D. W. (1984). Heredity and sociological theories of delinquency: A reconsideration. American Sociological Review, 49, 526–540.

    Article  Google Scholar 

  • Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6, 34–58.

    Article  Google Scholar 

  • Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype → environment effects. Child Development, 54, 424–435.

    Google Scholar 

  • Simons, R. L., Lei, M. K., Beach, S. R. H., Brody, G. H., Philibert, R. A., et al. (2011). Social environment, genes, and aggression: Evidence supporting the differential susceptibility perspective. American Sociological Review, 76, 883–912.

    Article  Google Scholar 

  • Sweeten, G. (2006). Who will graduate? Disruption of high school education by arrest and court involvement. Justice Quarterly, 23, 462–480.

    Article  Google Scholar 

  • Sweeten, G., Bushway, S. D., & Paternoster, R. (2009). Does dropping out of school mean dropping into delinquency? Criminology, 47, 47–91.

    Article  Google Scholar 

  • Tanner, J., Davies, S., & O’Grady, B. (1999). Whatever happened to yesterday’s rebels? Longitudinal effects of youth delinquency on education and employment. Social Problems, 46, 250–274.

    Article  Google Scholar 

  • Tielbeek, J. J., Johansson, A., Polderman, T. J. C., Rautiainen, M.-R., Jansen, P., et al. (2017). Genome-wide association studies of a broad spectrum of antisocial behavior. JAMA Psychiatry, 74, 1242–1250.

    Article  Google Scholar 

  • VanderWeele, Tyler. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.

    Google Scholar 

  • Vigil-Colet, A., & Morales-Vives, F. (2005). How impulsivity is related to intelligence and academic achievement. Spanish Journal of Psychology, 8, 199–204.

    Article  Google Scholar 

  • Wagner, B., Li, J., Liu, H., & Guo, G. (2013). Gene-environment correlation: Difficulties and a natural experiment-based strategy. American Journal of Public Health, 103, S167–S173.

    Article  Google Scholar 

  • Wedow, R., Zacher, M., Huibregtse, B. M., Harris, K. M., Domingue, B. W., et al. (2018). Education, smoking, and cohort change: Forwarding a multidimensional theory of the environmental moderation of genetic effects. American Sociological Review, 83, 802–832.

    Article  Google Scholar 

  • Wertz, J., Caspi, A., Belsky, D. W., Beckley, A. L., Arseneault, L., et al. (2018). Genetics and crime: Integrating new genomic discoveries into psychological research about antisocial behavior. Psychological Science, 19, 791–803.

    Article  Google Scholar 

  • Westfall, J., & Yarkoni, T. (2016). Statistically controlling for confounding constructs is harder than you think. PLoS ONE, 11, e0152719.

    Article  Google Scholar 

  • White, J. L., Moffitt, T. E., Caspi, A., Bartusch, D. J., Needles, D. J., et al. (1994). Measuring impulsivity and examining its relationship to delinquency. Journal of Abnormal Psychology, 103, 192–205.

    Article  Google Scholar 

  • Widdowson, A. O., Siennick, S. E., & Hay, C. (2016). The implications of arrest for college enrollment: An analysis of long-term effects and mediating mechanisms. Criminology, 54, 621–652.

    Article  Google Scholar 

  • Winship, C., & Mare, R. D. (1984). Regression models with ordinal variables. American Sociological Review, 49, 512–525.

    Article  Google Scholar 

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Funding

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. This research uses Add Health GWAS data funded by NICHD grants R01 HD073342 and R01 HD060726. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). This research benefits from GWAS results and polygenic scores made publicly available by the Social Science Genetic Association Consortium (SSGAC).

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Authors and Affiliations

  1. School of Criminal Justice, University of Cincinnati, Cincinnati, OH, USA

    Hexuan Liu, Ryan T. Motz & J. C. Barnes

  2. Institute for Interdisciplinary Data Science, University of Cincinnati, Cincinnati, OH, USA

    Hexuan Liu

  3. Population Research Center, University of Texas at Austin, Austin, TX, USA

    Peter T. Tanksley

  4. Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Kathleen Mullan Harris

  5. Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Kathleen Mullan Harris

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  1. Hexuan Liu
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Appendix

Appendix

Table 5 Logistic regression models predicting different types of criminal justice involvement during adolescence
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Table 6 Linear regression models predicting educational attainment at Wave IV using different types of criminal justice involvement during adolescence
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Table 7 Logistic regression models predicting high school completion and tertiary education participation at Wave IV
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Liu, H., Motz, R.T., Tanksley, P.T. et al. Adolescent Criminal Justice Involvement, Educational Attainment, and Genetic Inheritance: Testing an Integrative Model Using the Add Health Data. J Dev Life Course Criminology 7, 195–228 (2021). https://doi.org/10.1007/s40865-021-00166-8

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