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01-01-2018

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

Authors: Rashelle J. Musci, Brian Fairman, Katherine E. Masyn, George Uhl, Brion Maher, Danielle Y. Sisto, Sheppard G. Kellam, Nicholas S. Ialongo

Published in: Prevention Science | Issue 1/2018

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.

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRef

Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135, 885–908. doi:10.1037/a0017376.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRef

Canter, L., & Canter, M. (1991). Parents on your side: A comprehensive parent involvement program for teachers. Santa Monica: Lee Canter & Associates.

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.PubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRef

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.CrossRefPubMedPubMedCentral

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.

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRef

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.CrossRef

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.CrossRefPubMedCentral

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.CrossRefPubMed

Lupica, C., Riegel, A., & Hoffman, A. (2004). Marijuana and cannabinoid regulation of brain reward circuits. British Journal of Pharmacology, 143, 227–234.CrossRefPubMedPubMedCentral

MacCoun, R., & Reuter, P. (1997). Interpreting Dutch cannabis policy: Reasoning by analogy in the legalization debate. Science, 278, 47–52.CrossRefPubMed

Maher, B. (2015). Polygenic scores in epidemiology: Risk prediction, etiology, and clinical utility. Current Epidemiological Reports, 2, 239–244.CrossRef

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.

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.CrossRefPubMedPubMedCentral

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.CrossRef

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.

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.

Muthén, B., & Masyn, K. (2005). Discrete-time survival mixture analysis. Journal Of Educational and Behavioral Statistics, 30, 27–58.CrossRef

Muthén, B., & Muthén, L. (1998–2013). Mplus users guide. Los Angeles: Author.

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.CrossRefPubMed

Okoli, C. T. C., Kelly, T., & Hahn, E. J. (2007). Second hand smoke and nicotine exposure: A brief review. Addictive Behaviors, 32, 1977–1988.CrossRefPubMed

Pacula, R. L. (2010). Examining the impact of marijuana legalization on marijuana consumption.

Patterson, G.R., Reid J., & Dishion, T. (1992). A social learning approach: IV. Antisocial boys. Eugene, OR: Castalia.

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

Plomin, R., Haworth, C., & Davis, O. (2009). Common disorders are quantitative traits. Nature Reviews Genetics, 10, 872–878.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

Rutter, M., & Silberg, J. (2002). Gene-environment interplay in relation to emotional and behavioral disturbance. Annual Review of Psychology, 53, 463–490.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.

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.CrossRef

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRef

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.CrossRefPubMed

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.CrossRefPubMed

Title: Polygenic Score × Intervention Moderation: an Application of Discrete-Time Survival Analysis to Model the Timing of First Marijuana Use Among Urban Youth
Authors: Rashelle J. Musci
Brian Fairman
Katherine E. Masyn
George Uhl
Brion Maher
Danielle Y. Sisto
Sheppard G. Kellam
Nicholas S. Ialongo
Publication date: 01-01-2018
Publisher: Springer US
Published in: Prevention Science / Issue 1/2018
Print ISSN: 1389-4986
Electronic ISSN: 1573-6695
DOI: https://doi.org/10.1007/s11121-016-0729-1

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

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