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The Impact of Genes on Adolescent Substance Use: a Developmental Perspective

  • Adolescent/Young Adult Addiction (M Heitzeg, Section Editor)
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Abstract

Purpose of Review

This review discusses the importance of understanding the impact of genetic factors on adolescent substance use within a developmental framework. Methods for identifying genetic factors, relevant endophenotypes and intermediate phenotypes, and gene-environment interplay effects will be reviewed.

Recent Findings

Prior work supports the role of polygenic variation on adolescent substance use. Mechanisms through which genes impact adolescent phenotypes consist of differences in neural structure and function, early temperamental differences, and problem behavior. Gene-environment interactions are characterized by increased vulnerability to both maladaptive and adaptive contexts.

Summary

Developmental considerations in genetic investigations highlight the critical role that polygenic variation has on adolescent substance use. Yet, determining what to do with this information, especially in terms of personalized medicine, poses ethical and logistic challenges.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Adkins DE, Clark SL, Copeland WE, Kennedy M, Conway K, Angold A, et al. Genome-wide meta-analysis of longitudinal alcohol consumption across youth and early adulthood. Twin Res Hum Genet. 2015;18(4):335–47. https://doi.org/10.1017/thg.2015.36.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Trucco EM, Schlomer GL, Hicks BM. Alcohol use disorders. In: Fitzgerald HE, Puttler LI, editors. Alcohol use disorders. New York: Oxford University Press; 2018. p. 49–68.

    Google Scholar 

  3. Dodge KA, Malone PS, Lansford JE, Miller S, Pettit GS, Bates JE, et al. A dynamic cascade model of the development of substance-use onset. Monogr Soc Res Child Dev. 2009;74(3):119. https://doi.org/10.1111/j.1540-5834.2009.00528.x.

    Article  Google Scholar 

  4. Belsky DW, Moffitt TE, Baker TB, Biddle AK, Evans JP, Harrington H, et al. Polygenic risk and the developmental progression to heavy, persistent smoking and nicotine dependence: evidence from a 4-decade longitudinal study. JAMA Psychiatry. 2013;70(5):534–42. https://doi.org/10.1001/jamapsychiatry.2013.736.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Rose RJ, Dick DM, Viken RJ, Pulkkinen L, Kaprio J. Drinking or abstaining at age 14? A genetic epidemiological study. Alcohol Clin Exp Res. 2001;25(11):1594–604. https://doi.org/10.1111/j.1530-0277.2001.tb02166.x.

    Article  CAS  PubMed  Google Scholar 

  6. •• Russell MA, Schlomer GL, Cleveland HH, Feinberg ME, Greenberg MT, Spoth RL, et al. PROSPER intervention effects on adolescents’ alcohol misuse vary by GABRA2 genotype and age. Prev Sci. 2018;19(1):27–37. https://doi.org/10.1007/s11121-017-0751-yThis study is among the first to test gene × environment × development effects on adolescent alcohol misuse. A cutting-edge methodological approach, time-varying effect modeling is used to pinpoint periods during which differences in intervention effects byGABRA2genotype are most pronounced across adolescence.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Johnston LD, Miech RA, O'Malley PM, Bachman JG, Schulenberg JE, Patrick ME. Monitoring the future national survey results on drug use, 1975–2018: overview, key findings on adolescent drug use. 2019. p. 119.

  8. Moises HW, Yang L, Kristbjarnarson H, Wiese C, Byerley W, Macciardi F, et al. An international two-stage genome-wide search for schizophrenia susceptibility genes. Nat Genet. 1995;11(3):321–4. https://doi.org/10.1038/ng1195-321.

    Article  CAS  PubMed  Google Scholar 

  9. Dick DM. Commentary for special issue of prevention science “using genetics in prevention: science fiction or science fact?”. Prev Sci. 2018;19(1):101–8. https://doi.org/10.1007/s11121-017-0828-7.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Trucco EM, Hicks BM, Villafuerte S, Nigg JT, Burmeister M, Zucker RA. Temperament and externalizing behavior as mediators of genetic risk on adolescent substance use. J Abnorm Psychol. 2016;125(4):565–75. https://doi.org/10.1037/abn0000143.

    Article  PubMed  PubMed Central  Google Scholar 

  11. • Trucco EM, Villafuerte S, Hussong A, Burmeister M, Zucker RA. Biological underpinnings of an internalizing pathway to alcohol, cigarette, and marijuana use. J Abnorm Psychol. 2018;127(1):79–91. https://doi.org/10.1037/abn0000310This study demonstrates the utility of examining intermediate phenotypes through which specific genetic risk factors impact substance use in late adolescence. Findings indicate that early difficulties coping effectively with stressors and later depression may represent one pathway through which genetic risk factors impact adolescent substance use.

    Article  PubMed  Google Scholar 

  12. Beach SRH, Lei MK, Brody GH, Philibert RA. Prevention of early substance use mediates, and variation at SLC6A4 moderates, SAAF intervention effects on OXTR methylation. Prev Sci. 2018;19(1):90–100. https://doi.org/10.1007/s11121-016-0709-5.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Cope LM, Munier EC, Trucco EM, Hardee JE, Burmeister M, Zucker RA, et al. Effects of the serotonin transporter gene, sensitivity of response to alcohol, and parental monitoring on risk for problem alcohol use. Alcohol. 2017;59:7–16. https://doi.org/10.1016/j.alcohol.2016.12.001.

    Article  CAS  PubMed  Google Scholar 

  14. Brody GH, Chen Y, Beach SRH. Differential susceptibility to prevention: GABAergic, dopaminergic, and multilocus effects. J Child Psychol Psychiatry. 2013;54(8):863–71. https://doi.org/10.1111/jcpp.12042.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Macare C, Ducci F, Zhang Y, Ruggeri B, Jia T, Kaakinen M, et al. A neurobiological pathway to smoking in adolescence: TTC12-ANKK1-DRD2 variants and reward response. Eur Neuropsychopharmacol. 2018;28(10):1103–14. https://doi.org/10.1016/j.euroneuro.2018.07.101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Trucco EM, Villafuerte S, Heitzeg MM, Burmeister M, Zucker RA. Rule breaking mediates the developmental association between GABRA2 and adolescent substance use. J Child Psychol Psychiatry. 2014;55(12):1372–9. https://doi.org/10.1111/jcpp.12244.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Sarnyai Z. Oxytocin as a potential mediator and modulator of drug addiction. Addict Biol. 2011;16(2):199–201. https://doi.org/10.1111/j.1369-1600.2011.00332.x.

    Article  CAS  PubMed  Google Scholar 

  18. Korucuoglu O, Gladwin TE, Baas F, Mocking RJT, Ruhé HG, Groot PFC, et al. Neural response to alcohol taste cues in youth: effects of the OPRM1 gene. Addict Biol. 2017;22(6):1562–75. https://doi.org/10.1111/adb.12440.

    Article  CAS  PubMed  Google Scholar 

  19. Miranda R, Ray L, Justus A, Meyerson LA, Knopik VS, McGeary J, et al. Initial evidence of an association between OPRM1 and adolescent alcohol misuse. Alcohol Clin Exp Res. 2010;34(1):112–22. https://doi.org/10.1111/j.1530-0277.2009.01073.x.

    Article  CAS  PubMed  Google Scholar 

  20. Ray LA, Hutchison KE. A polymorphism of the mu-opioid receptor gene (OPRM1) and sensitivity to the effects of alcohol in humans. Alcohol Clin Exp Res. 2004;28(12):1789–95. https://doi.org/10.1097/01.ALC.0000148114.34000.B9.

    Article  CAS  PubMed  Google Scholar 

  21. van der Zwaluw CS, Otten R, Klleinjan M, Engels RCME. Different trajectories of adolescent alcohol use: testing gene-environment interactions. Alcohol Clin Exp Res. 2014;38(3):704–12. https://doi.org/10.1111/acer.12291.

    Article  CAS  PubMed  Google Scholar 

  22. Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet. 2010;42(5):436–40. https://doi.org/10.1038/ng.572.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Minică CC, Verweij KJH, van der Most PJ, Mbarek H, Bernard M, van Eijk KR, et al. Genome-wide association meta-analysis of age at first cannabis use. Addiction. 2018;113(11):2073–86. https://doi.org/10.1111/add.14368.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Edenberg HJ, Dick D, Xuei X, Tian H, Almasy L, Bauer LO, et al. Variations in GABRA2, encoding the alpha 2 subunit of the GABA(A) receptor, are associated with alcohol dependence and with brain oscillations. Am J Hum Genet. 2004;74(4):705–14. https://doi.org/10.1086/383283.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Heitzeg MM, Villafuerte S, Weiland BJ, Enoch M-A, Burmeister M, Zubieta J-K, et al. Effect of GABRA2 genotype on development of incentive-motivation circuitry in a sample enriched for alcoholism risk. Neuropsychopharmacology. 2014;39(13):3077–86. https://doi.org/10.1038/npp.2014.161.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kendler KS, Gardner C, Dick DM. Predicting alcohol consumption in adolescence from alcohol-specific and general externalizing genetic risk factors, key environmental exposures and their interaction. Psychol Med. 2011;41:1507–16. https://doi.org/10.1017/S003329171000190X.

    Article  CAS  PubMed  Google Scholar 

  27. Plomin R. Child development and molecular genetics: 14 years later. Child Dev. 2013;84:104–20. https://doi.org/10.1111/j.1467-8624.2012.01757.x.

    Article  PubMed  Google Scholar 

  28. Elam KK, Chassin L, Lemery-Chalfant K, Pandika D, Wang FL, Bountress K, et al. Affiliation with substance-using peers: examining gene-environment correlations among parent monitoring, polygenic risk, and children’s impulsivity. Dev Psychobiol. 2017;59(5):561–73. https://doi.org/10.1002/dev.21529.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Latendresse SJ, Musci R, Maher BS. Critical issues in the inclusion of genetic and epigenetic information in prevention and intervention trials. Prev Sci. 2018;19(1):58–67. https://doi.org/10.1007/s11121-017-0785-1.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Webb BT, Edwards AC, Wolen AR, Salvatore JE, Aliev F, Riley BP, et al. Molecular genetic influences on normative and problematic alcohol use in a population-based sample of college students. Front Genet. 2017;8:30. https://doi.org/10.3389/fgene.2017.00030.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Vrieze SI, McGue M, Iacono WG. The interplay of genes and adolescent development in substance use disorders: leveraging findings from GWAS meta-analyses to test developmental hypotheses about nicotine consumption. Hum Genet. 2012;131(6):791–801. https://doi.org/10.1007/s00439-012-1167-1.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461:747–53. https://doi.org/10.1038/nature08494.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88(1):76–82. https://doi.org/10.1016/j.ajhg.2010.11.011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yang J, Lee SH, Goddard ME, Visscher PM. Genome-wide complex trait analysis (GCTA): methods, data analyses, and interpretations. Methods Mol Biol. 2013;1019:215–36. https://doi.org/10.1007/978-1-62703-447-0_9.

    Article  CAS  PubMed  Google Scholar 

  35. Minică CC, Dolan CV, Hottenga J-J, Pool R, Fedko IO, Mbarek H, et al. Heritability, SNP- and gene-based analyses of cannabis use initiation and age at onset. Behav Genet. 2015;45(5):503–13. https://doi.org/10.1007/s10519-015-9723-9.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Vrieze SI, McGue M, Miller MB, Hicks BM, Iacono WG. Three mutually informative ways to understand the genetic relationships among behavioral disinhibition, alcohol use, drug use, nicotine use/dependence, and their co-occurrence: twin biometry, GCTA, and genome-wide scoring. Behav Genet. 2013;43(2):97–107. https://doi.org/10.1007/s10519-013-9584-z.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Kumar SK, Feldman MW, Rehkopf DH, Tuljapurkar S. Reply to Yang et al.: GCTA produces unreliable heritability estimates. Proc Natl Acad Sci U S A. 2016;113(32):E4581. https://doi.org/10.1073/pnas.1608425113.

    Article  CAS  Google Scholar 

  38. Gottesman II, Gould TD. The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry. 2003;160:636–45. https://doi.org/10.1176/appi.ajp.160.4.636.

    Article  PubMed  Google Scholar 

  39. • Glaser YG, Zubieta J-K, Hsu DT, Villafuerte S, Mickey BJ, Trucco EM, et al. Indirect effect of corticotropin-releasing hormone receptor 1 gene variation on negative emotionality and alcohol use via right ventrolateral prefrontal cortex. J Neurosci. 2014;34(11):4099–107. https://doi.org/10.1523/JNEUROSCI.3672-13.2014This work represents one of the few empirical studies that combine genetic, neurobiological, and social aspects of substance use risk in one model. A moderated mediation model was estimated whereby genetic risk factors to substance use behavior via brain function were examined, in addition to testing whether these associations may differ for those experiencing childhood stressors.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hyde LW. Developmental psychopathology in an era of molecular genetics and neuroimaging: a developmental neurogenetics approach. Dev Psychopathol. 2015;27(2):587–613. https://doi.org/10.1017/S0954579415000188.

    Article  PubMed  Google Scholar 

  41. Yang J, Lee SH, Wray NR, Goddard ME, Visscher PM. GCTA-GREML accounts for linkage disequilibrium when estimating genetic variance from genome-wide SNPs. Proc Natl Acad Sci U S A. 2016;113(32):E4580. https://doi.org/10.1073/pnas.1602743113.

    Article  CAS  Google Scholar 

  42. •• Bogdan R, Salmeron BJ, Carey CE, Agrawal A, Calhoun VD, Garavan H, et al. Imaging genetics and genomics in psychiatry: a critical review of progress and potential. Biol Psychiatry. 2017;82(3):165–75. https://doi.org/10.1016/j.biopsych.2016.12.030This article provides a comprehensive review of the current state of the neurogenetics field. This review discusses progress made in neurogenetics as well as potential pitfalls of novel approaches.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Edenberg HJ. The genetics of alcohol metabolism: role of alcohol dehydrogenase and aldehyde dehydrogenase variants. Alcohol Res Health. 2007;30:5–13.

    PubMed  PubMed Central  Google Scholar 

  44. Takeshita T, Morimoto K. Self-reported alcohol-associated symptoms and drinking behavior in three ALDH2 genotypes among Japanese university students. Alcohol Clin Exp Res. 1999;23(6):1065–9. https://doi.org/10.1097/00000374-199906000-00015.

    Article  CAS  PubMed  Google Scholar 

  45. McCarthy MI, Wall TL, Brown S, Carr LG. Integrating biological and behavioral factors in alcohol use risk: the role of ALDH2 status and alcohol expectancies in a sample of Asian Americans. Exp Clin Psychopharmacol. 2000;8(2):168–75. https://doi.org/10.1037//1064-1297.8.2.168.

    Article  CAS  PubMed  Google Scholar 

  46. Ehringer MA, Clegg HV, Collins AC, Corley RP, Crowley T, Hewitt JK, et al. Association of the neuronal nicotinic receptor β2 subunit gene (CHRNB2) with subjective responses to alcohol and nicotine. Am J Med Genet B Neuropsychiatr Genet. 2007;144B(5):596–604. https://doi.org/10.1002/ajmg.b.30464.

    Article  CAS  PubMed  Google Scholar 

  47. Shuckit MA. Genetics of the risk for alcoholism. Am J Addict. 2000;9(2):103–12. https://doi.org/10.1080/10550490050173172.

    Article  Google Scholar 

  48. Rothbart MK. Temperament, development, and personality. Curr Dir Psychol Sci. 2007;16(4):207–12. https://doi.org/10.1111/j.1467-8721.2007.00505.x.

    Article  Google Scholar 

  49. Ellingson JM, Richmond-Rakerd LS, Statham DJ, Martin NG, Slutske WS. Most of the genetic covariation between major depressive and alcohol use disorders is explained by trait measures of negative emotionality and behavioral control. Psychol Med. 2016;46:2919–30. https://doi.org/10.1017/S0033291716001525.

    Article  CAS  PubMed  Google Scholar 

  50. Hussong AM, Jones DJ, Stein GL, Baucom DH, Boeding S. An internalizing pathway to alcohol use and disorder. Psychol Addict Behav. 2011;25:390–404. https://doi.org/10.1037/a0024519.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Zucker RA, Hicks BM, Heitzeg MM. Alcohol use and the alcohol use disorders over the life course: a cross-level developmental review. In: Cicchetti D, editor. Developmental psychopathology: maladaptation and psychopathology. Hoboken: Wiley; 2016. p. 833–97.

    Google Scholar 

  52. Trucco EM, Cope LM, Burmeister M, Zucker RA, Heitzeg MM. Pathways to youth behavior: the role of genetic, neural, and behavioral markers. J Res Adolesc. 2018;28(1):26–39. https://doi.org/10.1111/jora.12341.

    Article  PubMed  PubMed Central  Google Scholar 

  53. •• Hines L, Morley KI, Mackie C, Lynskey M. Genetic and environmental interplay in adolescent substance use disorders. Curr Addict Rep. 2015;2(2):122–9. https://doi.org/10.1007/s40429-015-0049-8This article provides a comprehensive review of the interplay between genetic and environmental influences in the etiology of adolescent substance use. Moreover, the importance of incorporating a stage-sequential conceptualization of substance use in models when testing the independent and combined effects of genes and the environment are discussed.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Wang FL, Chassin L, Lee M, Haller M, King K. Roles of response inhibition and gene–environment interplay in pathways to adolescents’ externalizing problems. J Res Adolesc. 2017;27(2):258–77. https://doi.org/10.1111/jora.12270.

    Article  PubMed  Google Scholar 

  55. Pieters S, van der Zwaluw CS, Van Der Vorst H, Wiers RW, Smeets H, Lambrichs E, et al. The moderating effect of alcohol-specific parental rule-setting on the relation between the dopamine D2 receptor gene (DRD2), the μ-opioid receptor gene (OPRM1) and alcohol use in young adolescents. Alcohol Alcohol. 2012;47(6):663–70. https://doi.org/10.1093/alcalc/ags075.

    Article  CAS  PubMed  Google Scholar 

  56. Daw J, Boardman JD, Peterson R, Smolen A, Haberstick BC, Ehringer MA, et al. The interactive effect of neighborhood peer cigarette use and 5HTTLPR genotype on individual cigarette use. Addict Behav. 2014;39(12):1804–10. https://doi.org/10.1016/j.addbeh.2014.07.014.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Rabinowitz JA, Musci RJ, Milam AJ, Benke K, Uhl GR, Sisto DY, et al. The interplay between externalizing disorders polygenic risk scores and contextual factors on the development of marijuana use disorders. Drug Alcohol Depend. 2018;191:365–73. https://doi.org/10.1016/j.drugalcdep.2018.07.016doi:10.1016/j.drugalcdep.2018.07.016.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Fowler T, Lifford K, Shelton K, Rice F, Thapar A, Neale MC, et al. Exploring the relationship between genetic and environmental influences on initiation and progression of substance use. Addiction. 2007;102(3):413–22. https://doi.org/10.1111/j.1360-0443.2006.01694.x.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Brody GH, Beach SRH, Philibert RA, Chen Y-f, Murry VM. Prevention effects moderate the association of 5-HTTLPR and youth risk behavior initiation: gene x environment hypotheses tested via a randomized prevention design. Child Dev. 2009;80(3):645–61. https://doi.org/10.1111/j.1467-8624.2009.01288.x.

    Article  PubMed  Google Scholar 

  60. Beach SRH, Brody GH, Lei M-K, Philibert RA. Differential susceptibility to parenting among African American youths: testing the DRD4 hypothesis. J Fam Psychol. 2010;24(5):513–21. https://doi.org/10.1037/a0020835.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Belsky J, Pluess M. The nature (and nurture?) of plasticity in early human development. Perspect Psychol Sci. 2009;4:345–51. https://doi.org/10.1111/j.1745-6924.2009.01136.x.

    Article  PubMed  Google Scholar 

  62. Pluess M. Vantage sensitivity: environmental sensitivity to positive experiences as a function of genetic differences. J Pers. 2017;85(1):38–50. https://doi.org/10.1111/jopy.12218.

    Article  PubMed  Google Scholar 

  63. Bakermans-Kranenburg MJ, van IJzendoorn MH, Pijlman FT, Mesman J, Juffer F. Experimental evidence for differential susceptibility: dopamine D4 receptor polymorphism (DRD4 VNTR) moderates intervention effects on toddlers’ externalizing behavior in a randomized controlled trial. Dev Psychol. 2008;44:293–300. https://doi.org/10.1037/0012-1649.44.1.293.

    Article  PubMed  Google Scholar 

  64. Musci RJ, Masyn KE, Uhl G, Maher B, Kellam SG, Ialongo NS. Polygenic score × intervention moderation: an application of discrete-time survival analysis to modeling the timing of first tobacco use among urban youth. Dev Psychopathol. 2015;27(1):111–22. https://doi.org/10.1017/S0954579414001333.

    Article  PubMed  Google Scholar 

  65. Trucco EM, Villafuerte S, Heitzeg MM, Burmeister M, Zucker RA. Susceptibility effects of GABA receptor subunit alpha-2 (GABRA2) variants and parental monitoring on externalizing behavior trajectories: risk and protection conveyed by the minor allele. Dev Psychopathol. 2016;28:15–26. https://doi.org/10.1017/S0954579415000255.

    Article  PubMed  Google Scholar 

  66. Trucco EM, Villafuerte S, Burmeister M, Zucker RA. Beyond risk: prospective effects of GABA receptor subunit alpha-2 (GABRA2) × positive peer involvement on adolescent behavior. Dev Psychopathol. 2017;29:711–24. https://doi.org/10.1017/S0954579416000419.

    Article  PubMed  Google Scholar 

  67. Popejoy AB, Fullerton SM. Genomics is failing on diversity. Nature. 2016;538:161–4. https://doi.org/10.1038/538161a.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Walsh CG, Ribeiro JD, Franklin JC. Predicting risk of suicide attempts over time through machine learning. Clin Psychol Sci. 2017;5(3):457–69. https://doi.org/10.1177/2167702617691560.

    Article  Google Scholar 

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Acknowledgments

This publication was supported by the National Institute on Minority Health and Health Disparities (U54 MD012393 to E. M. Trucco) and the National Institute on Alcohol Abuse and Alcoholism (K08 AA023290 to E. M. Trucco) of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Trucco, E.M., Madan, B. & Villar, M. The Impact of Genes on Adolescent Substance Use: a Developmental Perspective. Curr Addict Rep 6, 522–531 (2019). https://doi.org/10.1007/s40429-019-00273-z

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