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Prevention Science

, Volume 19, Issue 1, pp 6–14 | Cite as

Polygenic Score × Intervention Moderation: an Application of Discrete-Time Survival Analysis to Model the Timing of First Marijuana Use Among Urban Youth

  • Rashelle J. MusciEmail author
  • Brian Fairman
  • Katherine E. Masyn
  • George Uhl
  • Brion Maher
  • Danielle Y. Sisto
  • Sheppard G. Kellam
  • Nicholas S. Ialongo
Article

Abstract

The present study examines the interaction between a polygenic score and an elementary school-based universal preventive intervention trial and its effects on a discrete-time survival analysis of time to first smoking marijuana. Research has suggested that initiation of substances is both genetically and environmentally driven (Rhee et al., Archives of general psychiatry 60:1256–1264, 2003; Verweij et al., Addiction 105:417–430, 2010). A previous work has found a significant interaction between the polygenic score and the same elementary school-based intervention with tobacco smoking (Musci et al., in press). The polygenic score reflects the contribution of multiple genes and has been shown in prior research to be predictive of smoking cessation, tobacco use, and marijuana use (Uhl et al., Molecular Psychiatry 19:50–54, 2014). Using data from a longitudinal preventive intervention study (N = 678), we examined age of first marijuana use from sixth grade to age 18. Genetic data were collected during emerging adulthood and were genotyped using the Affymetrix 6.0 microarray (N = 545). The polygenic score was computed using these data. Discrete-time survival analysis was employed to test for intervention main and interaction effects with the polygenic score. We found main effect of the polygenic score approaching significance, with the participants with higher polygenic scores reporting their first smoking marijuana at an age significantly later than controls (p = .050). We also found a significant intervention × polygenic score interaction effect at p = .003, with participants at the higher end of the polygenic score benefiting the most from the intervention in terms of delayed age of first use. These results suggest that genetics may play an important role in the age of first use of marijuana and that differences in genetics may account for the differential effectiveness of classroom-based interventions in delaying substance use experimentation.

Keywords

Survival analysis Polygenic Universal intervention 

Notes

Acknowledgments

This work used data from the Center for Prevention and Early Intervention at the Johns Hopkins Bloomberg School of Public Health. We are grateful for the collaboration of the Baltimore City Public Schools, teachers, parents, and students who participated in the study.

Compliance with Ethical Standards

Funding

This research was supported by grants to Nicholas Ialongo from the National Institute of Mental Health (MH57005, T32 MH18834) and the National Institute on Drug Abuse (R37DA11796, R01DA036525).

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study. The Institutional Review Board at the Johns Hopkins Bloomberg School of Public Health approved the study.

References

  1. Barrish, H. H., Saunders, M., & Wolf, M. M. (1969). Good behavior game: Effects of individual contingencies for group consequences on disruptive behavior in a classroom. Journal of Applied Behavior Analysis, 2, 119–124. doi: 10.1901/jaba.1969.2-119.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Beach, S., Brody, G. H., Lei, M.-K., & Philbert, R. (2010). Differential susceptibility to parenting among African American youths: Testing the DRD4 hypothesis. Journal of Family Psychology, 24, 513–521. doi: 10.1037/a0020835.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Belsky, J., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2007). For better and for worse: Differential susceptibility to environmental influences. Current Directions in Psychological Science, 16, 300–304. doi: 10.1111/j.1467-8721.2007.00525.x.CrossRefGoogle Scholar
  4. Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135, 885–908. doi: 10.1037/a0017376.CrossRefPubMedGoogle Scholar
  5. Bradshaw, C. P., Zmuda, J. H., Kellam, S. G., & Ialongo, N. S. (2009). Longitudinal impact of two universal preventive interventions in first grade on educational outcomes in high school. Journal of Educational Psychology, 101, 926.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Brody, G. H., Beach, S. R. H., Philibert, R. A., Chen, Y.-f., & Murray, V. M. (2009). Prevention effects moderate the association of 5-HTTLPR and youth risk behavior initiation: Gene × environment hypotheses tested via a randomized prevention design. Child Development, 80, 645–661. doi: 10.1111/j.1467–8624.2009.01288.x.CrossRefPubMedGoogle Scholar
  7. Brody, G., Chen, Y., Yu, T., Beach, S. R. H., Kogan, S. M., Simons, R. L., Windle, M., & Philibert, R. A. (2012). Life stress, the dopamine receptor gene, and emerging adult drug use trajectories: A longitudinal, multilevel, mediated moderation analysis. Development & Psychopathology, 24, 941–951.CrossRefGoogle Scholar
  8. Canter, L., & Canter, M. (1991). Parents on your side: A comprehensive parent involvement program for teachers. Santa Monica: Lee Canter & Associates.Google Scholar
  9. Cleveland, H. H., Wiebe, R. P., & Rowe, D. C. (2005). Sources of exposure to smoking and drinking friends among adolescents: A behavioral-genetic evaluation. The Journal of Genetic Psychology, 166, 153.PubMedGoogle Scholar
  10. Corrigall, W. A., Coen, K. M., & Adamson, K. L. (1994). Self-administered nicotine activates the mesolimbic dopamine system through the ventral tegmental area. Brain Research, 653, 278–284.CrossRefPubMedGoogle Scholar
  11. Covey, D. P., Wenzel, J. M., & Cheer, J. F. (2015). Cannabinoid modulation of drug reward and the implications of marijuana legalization. Brain Research, 1628, 233–243.CrossRefPubMedGoogle Scholar
  12. Duncan, L. E., Pollastri, A. R., & Smoller, J. W. (2014). Mind the gap: Why many geneticists and psychological scientists have discrepant views about gene-environment interaction (G X E) research. American Psychologist, 69, 249–268.CrossRefPubMedGoogle Scholar
  13. Elkins, I. J., McGue, M., & Iacono, W. (2007). Prospective effects of attention-deficit/hyperactivity disorder, conduct disorder, and sex on adolescent substance use and abuse. Archives of General Psychiatry, 64, 1145–1152.CrossRefPubMedGoogle Scholar
  14. Ensminger, M. E., Forrest, C. B., Riley, A. W., Kang, M., Green, B. F., & Starfield, B. (2000). The validity of measures of socioeconomic status of adolescents. Journal of Adolescent Research, 15, 392–419. doi: 10.1177/0743558400153005.CrossRefGoogle Scholar
  15. Fowler, T., Lifford, K., Shelton, K., Rice, F., Thapar, A., Neale, M. C., & Van Den Bree, M. (2007). Exploring the relationship between genetic and environmental influences on initiation and progression of substance use. Addiction, 102, 413–422.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Guan, Y.-F., Li, G.-R., Wang, R.-J., Yi, Y.-T., Yang, L., Jiang, D., & Peng, Y. (2012). Application of next-generation sequencing in clinical oncology to advance personalized treatment of cancer. Chinese Journal of Cancer, 31, 463–470. doi: 10.5732/cjc.012.10216.
  17. Haberstick, B. C., Zeiger, J. S., Corley, R. P., Hopfer, C. J., Stallings, M. C., Rhee, S. H., et al. (2011). Common and drug-specific genetic influences on subjective effects to alcohol, tobacco and marijuana use. Addiction, 106, 215–224.CrossRefPubMedGoogle Scholar
  18. Hall, W. (2015). What has research over the past two decades revealed about the adverse health effects of recreational cannabis use? Addiction, 110, 19–35.CrossRefPubMedGoogle Scholar
  19. Ialongo, N. S., Werthamer, L., Kellam, S. G., Brown, C. H., Wang, S., & Lin, Y. (1999). Proximal impact of two first-grade preventive interventions on the early risk behaviors for later substance abuse, depression, and antisocial behavior. American Journal of Community Psychology, 27, 599–641.CrossRefPubMedGoogle Scholar
  20. Ialongo, N., Poduska, J., Werthamer, L., & Kellam, S. (2001). The distal impact of two first grade preventive interventions on conduct problems and disorder in early adolescence. Journal of Emotional and Behavioral Disorders, 9, 146–160.CrossRefGoogle Scholar
  21. Kellam, S. G., Brown, C. H., Poduska, J. M., Ialongo, N., Wang, W., Toyinbo, P., Petras, H., Ford, C., Windham, A., & Wilcox, H. C. (2008). Effects of a universal classroom behavior management program in first and second grades on young adult behavioral, psychiatric, and social outcomes. Drug Alcohol Dependance, 95, S5–S28. doi: 10.1016/j.drugalcdep.2008.01.004.CrossRefGoogle Scholar
  22. Kellam, S. G., Wang, W., Mackenzie, A. C., Brown, C. H., Ompad, D. C., Or, F., & Windham, A. (2014). The impact of the Good Behavior Game, a universal classroom-based preventive intervention in first and second grades, on high-risk sexual behaviors and drug abuse and dependence disorders into young adulthood. Prevention Science, 15, 6–18.CrossRefPubMedCentralGoogle Scholar
  23. Laviolette, S. R., & Van de Kooy, D. (2004). The neurobiology of nicotine addiction: Bridging the gap from molecules to behavior. Nature Reviews Neuroscience, 5, 55–65.CrossRefPubMedGoogle Scholar
  24. Lupica, C., Riegel, A., & Hoffman, A. (2004). Marijuana and cannabinoid regulation of brain reward circuits. British Journal of Pharmacology, 143, 227–234.CrossRefPubMedPubMedCentralGoogle Scholar
  25. MacCoun, R., & Reuter, P. (1997). Interpreting Dutch cannabis policy: Reasoning by analogy in the legalization debate. Science, 278, 47–52.CrossRefPubMedGoogle Scholar
  26. Maher, B. (2015). Polygenic scores in epidemiology: Risk prediction, etiology, and clinical utility. Current Epidemiological Reports, 2, 239–244.CrossRefGoogle Scholar
  27. Masyn, K.E. (2014). Discrete-time survival analysis in prevention science. In Z. Sloboda, & H. Petras (Eds.), Defining prevention science, Advances in prevention science, (513–535). New York, NY: Springer Science + Business Media. doi:  10.1007/978-1-4899-7424-2_22.
  28. Montana, G., & Pritchard, J. K. (2004). Statistical tests for admixture maping with case control and cases only. American Journal of Human Genetics, 75, 771–789.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Musci, R., Masyn, K., Uhl, G., Maher, B., Kellam, S., & Ialongo, N. (2015a). Polygenic score x intervention moderation: An application of discrete-time survival analysis to modeling the timing of first tobacco use among urban youth. Development & Psychopathology, 27, 111–122.CrossRefGoogle Scholar
  30. Musci, R., Masyn, K., Maher, B., Benke, K., Uhl, G., & Ialongo, N. (2015b). The effects of the interplay of genetics & early environmental risk on the course of internalizing symptoms from late childhood through adolescence. Development & Psychopathology. Google Scholar
  31. Musci, R., Uhl, G., Maher, B., & Ialongo, N. (2015c). Testing gene x environment moderation of tobacco and marijuana use trajectories in adolescence and young adulthood. Journal of Consulting & Clinical Psychology. Google Scholar
  32. Muthén, B., & Masyn, K. (2005). Discrete-time survival mixture analysis. Journal Of Educational and Behavioral Statistics, 30, 27–58.CrossRefGoogle Scholar
  33. Muthén, B., & Muthén, L. (1998–2013). Mplus users guide. Los Angeles: Author.Google Scholar
  34. Muthén, B., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463–469. doi: 10.1111/j.0006-341X.1999.00463.x.CrossRefPubMedGoogle Scholar
  35. Okoli, C. T. C., Kelly, T., & Hahn, E. J. (2007). Second hand smoke and nicotine exposure: A brief review. Addictive Behaviors, 32, 1977–1988.CrossRefPubMedGoogle Scholar
  36. Pacula, R. L. (2010). Examining the impact of marijuana legalization on marijuana consumption.Google Scholar
  37. Patterson, G.R., Reid J., & Dishion, T. (1992). A social learning approach: IV. Antisocial boys. Eugene, OR: Castalia.Google Scholar
  38. Petras, H., Schaeffer, C. M., Ialongo, N., Hubbard, S., Muthén, B., Lambert, S. F., & Kellam, S. (2004). When the course of aggressive behavior in childhood does not predict antisocial outcomes in adolescence and young adulthood: An examination of potential explanatory variables. Development and Psychopathology, 16, 919–941.CrossRefPubMedGoogle Scholar
  39. Petras, H., Masyn, K., & Ialongo, N. (2011). The developmental impact of two first grade preventive interventions on aggressive/disruptive behavior in childhood and adolescence: An application of latent transition growth mixture modeling. Prevention Science, 12, 300–313.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Plomin, R., Haworth, C., & Davis, O. (2009). Common disorders are quantitative traits. Nature Reviews Genetics, 10, 872–878.CrossRefPubMedGoogle Scholar
  41. Pritchard, J. K., & Rosenberg, N. A. (1999). Use of unlinked genetic markers to detect population stratification in association studies. American Journal of Human Genetics, 65, 220–228. doi: 10.1086/302449.CrossRefPubMedPubMedCentralGoogle Scholar
  42. Rose, J. E., Behm, F., Drgon, T., Johnson, C., & Uhl, G. R. (2010). Personalized smoking cessation: Interactions between nicotine dose, dependence and quit-success genotype score. Molecular Medicine, 16, 247–253.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Rutter, M., & Silberg, J. (2002). Gene-environment interplay in relation to emotional and behavioral disturbance. Annual Review of Psychology, 53, 463–490.CrossRefPubMedGoogle Scholar
  44. Sankararaman, S., Sridhar, S., Kimmel, G., & Halperin, E. (2008). Estimating local ancestry in admixed populations. American Journal of Human Genetics, 82, 290–303. doi: 10.1016/j.ajhg.2007.09.022.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177. doi: 10.1037/1082-989X.7.2.147.CrossRefPubMedGoogle Scholar
  46. Tapper, A. R., Nashmi, R., & Lester, H. A. (2006). Neuronal nicotinic acetylcholine receptors and nicotine dependence. In B. K. Madras, C. M. Colvis, J. D. Pollock, J. L. Rutter, D. Shurtleff, & M. von Zastrow (Eds.), Cell biology of addiction (pp. 179–190). Cold Spring Harbor: Cold Spring Harbor Laboratory Press.Google Scholar
  47. Uhl, G. R., Drgon, T., Johnson, C., Ramoni, M., Behm, F. M., & Rose, J. E. (2010a). Genome-wide association for smoking cessation success in a trial of precessation nicotine replacement. Molecular Medicine, 16, 512–526.CrossRefGoogle Scholar
  48. Uhl, G. R., Drgon, T., Johnson, C., Walther, D., David, S. P., Aveyard, P., Murphy, M., Johnstone, E. C., & Munafo, M. R. (2010b). Geomone-wide association for smoking cessation success: Participants in the Patch in Practice trial of nicotine replacement. Pharmacogenomics, 11, 357–367.CrossRefPubMedPubMedCentralGoogle Scholar
  49. Uhl, G., Walther, D., Musci, R., Fisher, C., Anthony, J., Storr, C., Behm, F., Eaton, W., Ialongo, N., & Rose, J. (2014). Smoking quit success genotype score v1.0 predicts quit success and distinct patterns of developmental involvement with common addictive substances. Molecular Psychiatry, 19, 50–54. doi: 10.1038/mp.2012.155.CrossRefPubMedGoogle Scholar
  50. Vlachou, S., & Panagis, G. (2014). Regulation of brain reward by the endocannabinoid system: A critical review of behavioral studies in animals. Current Pharmaceutical Design, 20, 2072–2088.CrossRefPubMedGoogle Scholar
  51. Volkow, N. D., Baler, R. D., Compton, W. M., & Weiss, S. R. (2014). Adverse health effects of marijuana use. New England Journal of Medicine, 370, 2219–2227.CrossRefPubMedPubMedCentralGoogle Scholar
  52. Wang, Y., Browne, D., Petras, H., Stuart, E., Wagner, F., Lambert, S., Kellam, S., & Ialongo, N. (2009). Depressed mood and the effect of two universal first grade preventive interventions on survival to the first tobacco cigarette smoked among urban youth. Drug and Alcohol Dependence, 100, 194–203.CrossRefPubMedGoogle Scholar
  53. Wang, Y., Storr, C., Green, K., Zhu, S., Stuart, E., Lynne-Landsman, Petras, H., Kellam, S., & Ialongo, N. (2012). The effect of two elementary school-based prevention interventions on being offered tobacco and the transition to smoking. Drug & Alcohol Dependence, 120, 202–208.CrossRefGoogle Scholar
  54. Webster-Stratton, C. (1984). Randomized trial of two parent-training programs for families with conduct disordered children. Journal of Consulting and Clinical Psychology, 52, 666–678.CrossRefPubMedGoogle Scholar
  55. Werthamer-Larsson, L., Kellam, S., & Wheeler, L. (1991). Effect of first-grade classroom environment on shy behavior, aggressive behavior, and concentration problems. American Journal of Community Psychology, 19, 585–602.CrossRefPubMedGoogle Scholar

Copyright information

© Society for Prevention Research 2016

Authors and Affiliations

  • Rashelle J. Musci
    • 1
    Email author
  • Brian Fairman
    • 1
  • Katherine E. Masyn
    • 2
  • George Uhl
    • 3
  • Brion Maher
    • 1
  • Danielle Y. Sisto
    • 1
  • Sheppard G. Kellam
    • 1
  • Nicholas S. Ialongo
    • 1
  1. 1.Department of Mental Health, Bloomberg School of Public HealthJohns Hopkins UniversityBaltimoreUSA
  2. 2.School of Public HealthGeorgia State UniversityAtlantaUSA
  3. 3.New Mexico VA Healthcare SystemAlbuquerqueUSA

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