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A systematic review of gene-by-intervention studies of alcohol and other substance use

Published online by Cambridge University Press:  30 June 2020

Zoe E. Neale Open the ORCID record for Zoe E. Neale [Opens in a new window] ,
Sally I-Chun Kuo  and
Danielle M. Dick
Zoe E. Neale*
Affiliation:
Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
Sally I-Chun Kuo
Affiliation:
Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
Danielle M. Dick
Affiliation:
Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, USA
*
Author for correspondence: Zoe E. Neale, Department of Psychology, Box 842018, Richmond, VA23284-2018; E-mail: [email protected].
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Abstract

Alcohol and other substance use problems are common, and the efficacy of current prevention and intervention programs is limited. Genetics may contribute to differential effectiveness of psychosocial prevention and intervention programs. This paper reviews gene-by-intervention (G×I) studies of alcohol and other substance use, and implications for integrating genetics into prevention science. Systematic review yielded 17 studies for inclusion. Most studies focused on youth substance prevention, alcohol was the most common outcome, and measures of genotype were heterogeneous. All studies reported at least one significant G×I interaction. We discuss these findings in the context of the history and current state of genetics, and provide recommendations for future G×I research. These include the integration of genome-wide polygenic scores into prevention studies, broad outcome measurement, recruitment of underrepresented populations, testing mediators of G×I effects, and addressing ethical implications. Integrating genetic research into prevention science, and training researchers to work fluidly across these fields, will enhance our ability to determine the best intervention for each individual across development. With growing public interest in obtaining personalized genetic information, we anticipate that the integration of genetics and prevention science will become increasingly important as we move into the era of precision medicine.

Keywords

alcoholG×Igene-by-interventionpreventionsubstance use
Type
Regular Articles
Information
Development and Psychopathology , Volume 33 , Issue 4 , October 2021 , pp. 1410 - 1427
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Agrawal, A., & Lynskey, M. T. (2008). Are there genetic influences on addiction: evidence from family, adoption and twin studies. Addiction (Abingdon, England), 103, 1069–1081. https://doi.org/10.1111/j.1360-0443.2008.02213.xCrossRefGoogle ScholarPubMed
Ahrnsbrak, R., Bose, J., Hedden, S. L., Lipari, R. N., & Park-Lee, E. (2017). Key substance use and mental health indicators in the United States: Results from the 2016 National Survey on Drug Use and Health. Rockville, MD, USA: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration.Google Scholar
Albert, D., Belsky, D. W., Crowley, D. M., Latendresse, S. J., Aliev, F., Riley, B., … Dodge, K. A. (2015). Can genetics predict response to complex behavioral interventions? Evidence from a genetic analysis of the Fast Track Randomized Control Trial. Journal of Policy Analysis and Management, 34, 497–518.CrossRefGoogle ScholarPubMed
Anton, R. F., O'Malley, S. S., Ciraulo, D. A., Cisler, R. A., Couper, D., Donovan, D. M., … COMBINE Study Research Group. (2006). Combined pharmacotherapies and behavioral interventions for alcohol dependence: The COMBINE study: A randomized controlled trial. JAMA, 295, 2003–2017. https://doi.org/10.1001/jama.295.17.2003CrossRefGoogle ScholarPubMed
Arria, A. M., Garnier-Dykstra, L. M., Caldeira, K. M., Vincent, K. B., Winick, E. R., & O'Grady, K. E. (2013). Drug use patterns and continuous enrollment in college: results from a longitudinal study. Journal of Studies on Alcohol and Drugs, 74, 71–83.CrossRefGoogle ScholarPubMed
Austin, J. C. (2019). Evidence-based genetic counseling for psychiatric disorders: A road map. Cold Spring Harbor Perspectives in Medicine, a036608. http://dx.doi.org/10.1101/cshperspect.a036608Google Scholar
Balldin, J., Berglund, M., Borg, S., Månsson, M., Bendtsen, P., Franck, J., … Willander, A. (2003). A 6-month controlled naltrexone study: Combined effect with cognitive behavioral therapy in outpatient treatment of alcohol dependence. Alcoholism: Clinical and Experimental Research, 27, 1142–1149. https://doi.org/10.1097/01.ALC.0000075548.83053.A9CrossRefGoogle ScholarPubMed
Barr, P. B., Kuo, S. I.-C., Aliev, F., Latvala, A., Viken, R., Rose, R. J., … Dick, D. M. (2019). Polygenic risk for alcohol misuse is moderated by romantic partnerships. Addiction, 114, 1753–1762. https://doi.org/10.1111/add.14712CrossRefGoogle ScholarPubMed
Bates, B. R., Lynch, J. A., Bevan, J. L., & Condit, C. M. (2005). Warranted concerns, warranted outlooks: A focus group study of public understandings of genetic research. Social Science & Medicine (1982), 60, 331–344. https://doi.org/10.1016/j.socscimed.2004.05.012CrossRefGoogle ScholarPubMed
Bauer, L. O., Covault, J., Harel, O., Das, S., Gelernter, J., Anton, R., & Kranzler, H. R. (2007). Variation in GABRA2 predicts drinking behavior in project MATCH subjects. Alcoholism, Clinical and Experimental Research, 31, 1780–1787. https://doi.org/10.1111/j.1530-0277.2007.00517.xCrossRefGoogle ScholarPubMed
Beach, S. R., Brody, G. H., Lei, M. K., & Philibert, R. A. (2010). Differential susceptibility to parenting among African American youths: Testing the DRD4 hypothesis. Journal of Family Psychology, 24(5), 513–521. http://dx.doi.org/10.1037/a0020835CrossRefGoogle ScholarPubMed
Belsky, D. W., & Israel, S. (2014). Integrating genetics and social science: genetic risk scores. Biodemography and Social Biology, 60, 137–155. https://doi.org/10.1080/19485565.2014.946591CrossRefGoogle ScholarPubMed
Belsky, J., & van Ijzendoorn, M. H. (2015). What works for whom? Genetic moderation of intervention efficacy. Development and Psychopathology, 27, 1–6. https://doi.org/10.1017/S0954579414001254CrossRefGoogle ScholarPubMed
Bogdan, R., Baranger, D. A. A., & Agrawal, A. (2018). Polygenic risk scores in clinical psychology: Bridging genomic risk to individual differences. Annual Review of Clinical Psychology, 14, 119–157. https://doi.org/10.1146/annurev-clinpsy-050817-084847CrossRefGoogle ScholarPubMed
Border, R., Johnson, E. C., Evans, L. M., Smolen, A., Berley, N., Sullivan, P. F., & Keller, M. C. (2019). No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. American Journal of Psychiatry, 176, 376–387. https://doi.org/10.1176/appi.ajp.2018.18070881CrossRefGoogle ScholarPubMed
Brody, G. H., Chen, Y., & Beach, S. R. H. (2013). Differential susceptibility to prevention: GABAergic, dopaminergic, and multilocus effects. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 54, 863–871. https://doi.org/10.1111/jcpp.12042CrossRefGoogle ScholarPubMed
Brody, G. H., Chen, Y.-F., Beach, S. R. H., Kogan, S. M., Yu, T., Diclemente, R. J., … Philibert, R. A. (2014). Differential sensitivity to prevention programming: A dopaminergic polymorphism-enhanced prevention effect on protective parenting and adolescent substance use. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 33, 182–191. https://doi.org/10.1037/a0031253CrossRefGoogle ScholarPubMed
Brody, G. H., Yu, T., & Beach, S. R. H. (2015). A differential susceptibility analysis reveals the “who and how” about adolescents’ responses to preventive interventions: tests of first- and second-generation Gene x Intervention hypotheses. Development and Psychopathology, 27, 37–49. https://doi.org/10.1017/S095457941400128XCrossRefGoogle Scholar
Cabana-Domínguez, J., Shivalikanjli, A., Fernàndez-Castillo, N., & Cormand, B. (2019). Genome-wide association meta-analysis of cocaine dependence: Shared genetics with comorbid conditions. Progress in neuro-psychopharmacology & biological psychiatry, 94, 109667. https://doi.org/10.1016/j.pnpbp.2019.109667.CrossRefGoogle ScholarPubMed
Cardon, L. R., & Palmer, L. J. (2003). Population stratification and spurious allelic association. Lancet (London, England), 361, 598–604. https://doi.org/10.1016/S0140-6736(03)12520-2CrossRefGoogle ScholarPubMed
Chang, L.-H., Couvy-Duchesne, B., Liu, M., Medland, S. E., Verhulst, B., Benotsch, E. G., … GSCAN Consortium. (2019). Association between polygenic risk for tobacco or alcohol consumption and liability to licit and illicit substance use in young Australian adults. Drug and Alcohol Dependence, 197, 271–279. https://doi.org/10.1016/j.drugalcdep.2019.01.015CrossRefGoogle ScholarPubMed
Chapman, R., Likhanov, M., Selita, F., Zakharov, I., Smith-Woolley, E., & Kovas, Y. (2019). New literacy challenge for the twenty-first century: genetic knowledge is poor even among well educated. Journal of Community Genetics, 10(1), 73–84. http://dx.doi.org/10.1007/s12687-018-0363-7CrossRefGoogle ScholarPubMed
Cheng, Z., Zhou, H., Sherva, R., Farrer, L. A., Kranzler, H. R., & Gelernter, J. (2018). Genome-wide association study identifies a regulatory variant of RGMA associated with opioid dependence in European Americans. Biological Psychiatry, 84, 762–770. https://doi.org/10.1016/j.biopsych.2017.12.016CrossRefGoogle ScholarPubMed
Clarke, T.-K., Adams, M. J., Davies, G., Howard, D. M., Hall, L. S., Padmanabhan, S., … McIntosh, A. M. (2017). Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112 117). Molecular Psychiatry, 22, 1376–1384. https://doi.org/10.1038/mp.2017.153CrossRefGoogle Scholar
Cleveland, H. H., Griffin, A. M., Wolf, P. S. A., Wiebe, R. P., Schlomer, G. L., Feinberg, M. E., … Vandenbergh, D. J. (2018a). Transactions between substance use intervention, the oxytocin receptor (OXTR) gene, and peer substance use predicting youth alcohol use. Prevention Science: The Official Journal of the Society for Prevention Research, 19, 15–26. https://doi.org/10.1007/s11121-017-0749-5CrossRefGoogle Scholar
Cleveland, H. H., Schlomer, G. L., Vandenbergh, D. J., Feinberg, M., Greenberg, M., Spoth, R., … Hair, K. L. (2015). The conditioning of intervention effects on early adolescent alcohol use by maternal involvement and dopamine receptor D4 (DRD4) and serotonin transporter linked polymorphic region (5-HTTLPR) genetic variants. Development and Psychopathology, 27, 51–67. https://doi.org/10.1017/S0954579414001291CrossRefGoogle ScholarPubMed
Cleveland, H. H., Schlomer, G. L., Vandenbergh, D. J., Wolf, P. S. A., Feinberg, M. E., Greenberg, M. T., … Redmond, C. (2018b). Associations between alcohol dehydrogenase genes and alcohol use across early and middle adolescence: Moderation x Preventive intervention. Development and Psychopathology, 30, 297–313. https://doi.org/10.1017/S0954579417000633CrossRefGoogle Scholar
Colder, C. R., Scalco, M., Trucco, E. M., Read, J. P., Lengua, L. J., Wieczorek, W. F., & Hawk, L. W. (2013). Prospective associations of internalizing and externalizing problems and their co-occurrence with early adolescent substance use. Journal of Abnormal Child Psychology, 41, 667–677. https://doi.org/10.1007/s10802-012-9701-0CrossRefGoogle ScholarPubMed
Coleman, J. R. I., Peyrot, W. J., Purves, K. L., Davis, K. A. S., Rayner, C., Choi, S. W., … Breen, G. (2020). Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank. Molecular Psychiatry, Advance online publication. https://doi.org/10.1038/s41380-019-0546-6.Google ScholarPubMed
Coram, M. A., Fang, H., Candille, S. I., Assimes, T. L., & Tang, H. (2017). Leveraging multi-ethnic evidence for risk assessment of quantitative traits in minority populations. American Journal of Human Genetics, 101, 218–226. https://doi.org/10.1016/j.ajhg.2017.06.015CrossRefGoogle ScholarPubMed
Dahlgren, A., Wargelius, H.-L., Berglund, K. J., Fahlke, C., Blennow, K., Zetterberg, H., … Balldin, J. (2011). Do alcohol-dependent individuals with DRD2 A1 allele have an increased risk of relapse? A pilot study. Alcohol and Alcoholism (Oxford, Oxfordshire), 46, 509–513. https://doi.org/10.1093/alcalc/agr045CrossRefGoogle ScholarPubMed
Dar-Nimrod, I., & Heine, S. J. (2011). Genetic essentialism: On the deceptive determinism of DNA. Psychological Bulletin, 137(5), 800–818. http://dx.doi.org/10.1037/a0021860CrossRefGoogle ScholarPubMed
Dempfle, A., Scherag, A., Hein, R., Beckmann, L., Chang-Claude, J., & Schäfer, H. (2008). Gene–environment interactions for complex traits: Definitions, methodological requirements and challenges. European Journal of Human Genetics: EJHG, 16, 1164–1172. https://doi.org/10.1038/ejhg.2008.106CrossRefGoogle Scholar
Dick, D. M., Agrawal, A., Keller, M. C., Adkins, A., Aliev, F., Monroe, S., … Sher, K. J. (2015). Candidate gene-environment interaction research: Reflections and recommendations. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 10, 37–59. https://doi.org/10.1177/1745691614556682CrossRefGoogle ScholarPubMed
Dick, D. M., Barr, P. B., Cho, S. B., Cooke, M. E., Kuo, S. I., Lewis, T. J., … Su, J. (2018). Post-GWAS in psychiatric genetics: A developmental perspective on the “other” next steps. Genes, Brain & Behavior, 17(3), e12447. https://doi.org/10.1111/gbb.12447.CrossRefGoogle ScholarPubMed
Dick, D. M., Barr, P., Guy, M., Nasim, A., & Scott, D. (2017). Review: Genetic research on alcohol use outcomes in African American populations: A review of the literature, associated challenges, and implications. The American Journal on Addictions, 26, 486–493. https://doi.org/10.1111/ajad.12495CrossRefGoogle ScholarPubMed
Dick, D. M., Cho, S. B., Latendresse, S. J., Aliev, F., Nurnberger, J. I., Edenberg, H. J., … Kuperman, S. (2014). Genetic influences on alcohol use across stages of development: GABRA2 and longitudinal trajectories of drunkenness from adolescence to young adulthood. Addiction Biology, 19, 1055–1064. https://doi.org/10.1111/adb.12066CrossRefGoogle ScholarPubMed
Dick, D. M., & Kendler, K. S. (2012). The impact of gene–environment interaction on alcohol use disorders. Alcohol Research: Current Reviews, 34, 318–324.Google ScholarPubMed
Dick, D. M., Latendresse, S. J., & Riley, B. (2011). Incorporating genetics into your studies: a guide for social scientists. Frontiers in Psychiatry, 2, 17. https://doi.org/10.3389/fpsyt.2011.00017CrossRefGoogle ScholarPubMed
Dick, D. M., Pagan, J. L., Viken, R., Purcell, S., Kaprio, J., Pulkkinen, L., & Rose, R. J. (2007). Changing environmental influences on substance use across development. Twin Research and Human Genetics: The Official Journal of the International Society for Twin Studies, 10, 315–326. https://doi.org/10.1375/twin.10.2.315CrossRefGoogle ScholarPubMed
Dodge, K. A., Dishion, T. J., & Lansford, J. E. (2006). Deviant peer influences in intervention and public policy for youth. Social Policy Report, 20, 1–20. https://doi.org/10.1002/j.2379-3988.2006.tb00046.xCrossRefGoogle Scholar
Dudbridge, F. (2013). Power and predictive accuracy of polygenic risk scores. PLoS Genetics, 9(3), e1003348. https://doi.org/10.1371/journal.pgen.1003348CrossRefGoogle ScholarPubMed
Dudbridge, F. (2016). Polygenic epidemiology. Genetic Epidemiology, 40, 268–272. https://doi.org/10.1002/gepi.21966CrossRefGoogle ScholarPubMed
Dudbridge, F., Pashayan, N., & Yang, J. (2018). Predictive accuracy of combined genetic and environmental risk scores. Genetic Epidemiology, 42, 4–19. https://doi.org/10.1002/gepi.22092CrossRefGoogle ScholarPubMed
Dugosh, K., Abraham, A., Seymour, B., McLoyd, K., Chalk, M., & Festinger, D. (2016). A systematic review on the use of psychosocial interventions in conjunction with medications for the treatment of opioid addiction. Journal of Addiction Medicine, 10, 91–101. https://doi.org/10.1097/ADM.0000000000000193CrossRefGoogle ScholarPubMed
Duncan, L. E., & Keller, M. C. (2011). A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. The American Journal of Psychiatry, 168, 1041–1049. https://doi.org/10.1176/appi.ajp.2011.11020191CrossRefGoogle Scholar
Dutra, L., Stathopoulou, G., Basden, S. L., Leyro, T. M., Powers, M. B., & Otto, M. W. (2008). A meta-analytic review of psychosocial interventions for substance use disorders. American Journal of Psychiatry, 165, 179–187. https://doi.org/10.1176/appi.ajp.2007.06111851CrossRefGoogle ScholarPubMed
Edenberg, H. J. (2007). The genetics of alcohol metabolism: Role of alcohol dehydrogenase and aldehyde dehydrogenase variants. Alcohol Research & Health, 30, 5–13.Google ScholarPubMed
Edenberg, H. J., Dick, D. M., Xuei, X., Tian, H., Almasy, L., Bauer, L. O., … Begleiter, H. (2004). Variations in GABRA2, encoding the α2 subunit of the GABAA receptor, are associated with alcohol dependence and with brain oscillations. American Journal of Human Genetics, 74, 705–714.CrossRefGoogle Scholar
Edwards, A. C., & Kendler, K. S. (2013). Alcohol consumption in men is influenced by qualitatively different genetic factors in adolescence and adulthood. Psychological Medicine, 43, 1857–1868. https://doi.org/10.1017/S0033291712002917CrossRefGoogle ScholarPubMed
Evangelou, E., Warren, H. R., Mosen-Ansorena, D., Mifsud, B., Pazoki, R., Gao, H., … Caulfield, M. J. (2018). Genetic analysis of over one million people identifies 535 new loci associated with blood pressure traits. Nature Genetics, 50, 1412–1425. https://doi.org/10.1038/s41588-018-0205-xCrossRefGoogle Scholar
Ewing, S. W. F., LaChance, H. A., Bryan, A., & Hutchison, K. E. (2009). Do genetic and individual risk factors moderate the efficacy of motivational enhancement therapy? Drinking outcomes with an emerging adult sample. Addiction Biology, 14, 356–365. https://doi.org/10.1111/j.1369-1600.2009.00149.xCrossRefGoogle Scholar
Folkersen, L., Pain, O., Ingasson, A., Werge, T., Lewis, C. M., & Austin, J. (2019). Impute.me: an open source, non-profit tool for using data from DTC genetic testing to calculate and interpret polygenic risk scores. BioRxiv, 861831. https://doi.org/10.1101/861831Google Scholar
Furr, L. A. (2002). Perceptions of genetics research as harmful to society: Differences among samples of African-Americans and European-Americans. Genetic Testing, 6, 25–30. https://doi.org/10.1089/109065702760093889CrossRefGoogle ScholarPubMed
Gelernter, J., Kranzler, H. R., Sherva, R., Almasy, L., Koesterer, R., Smith, A. H., & Anton, R. (2014). Genome-wide association study of alcohol dependence:significant findings in African- and European-Americans including novel risk loci. Molecular Psychiatry, 19(1), 41–49. http://dx.doi.org/10.1038/mp.2013.145CrossRefGoogle ScholarPubMed
Glantz, M. D., & Leshner, A. I. (2000). Drug abuse and developmental psychopathology. Development and Psychopathology, 12, 795–814. https://doi.org/10.1017/s0954579400004120CrossRefGoogle ScholarPubMed
Griffiths, S.-E. (2019). Genetic study aims to help poor, bright children succeed. The Sunday Times. https://www.thetimes.co.uk/article/genetic-study-aims-to-help-poor-bright-children-succeed-bjfmq58v3Google Scholar
Gryczynski, J., Schwartz, R. P., O'Grady, K. E., Restivo, L., Mitchell, S. G., & Jaffe, J. H. (2016). Understanding patterns of high-cost health care use across different substance user groups. Health Affairs (Project Hope), 35, 12–19. https://doi.org/10.1377/hlthaff.2015.0618CrossRefGoogle ScholarPubMed
Haga, S. B. (2010). Impact of limited population diversity of genome-wide association studies. Genetics in Medicine, 12, 81–84. https://doi.org/10.1097/GIM.0b013e3181ca2bbfCrossRefGoogle ScholarPubMed
Halder, I., Shriver, M., Thomas, M., Fernandez, J. R., & Frudakis, T. (2008). A panel of ancestry informative markers for estimating individual biogeographical ancestry and admixture from four continents: Utility and applications. Human Mutation, 29, 648–658. https://doi.org/10.1002/humu.20695CrossRefGoogle ScholarPubMed
Hansen, W. B., & Graham, J. W. (1991). Preventing alcohol, marijuana, and cigarette use among adolescents: Peer pressure resistance training versus establishing conservative norms. Preventive Medicine, 20, 414–430. https://doi.org/10.1016/0091-7435(91)90039-7CrossRefGoogle ScholarPubMed
Harden, K. P., Hill, J. E., Turkheimer, E., & Emery, R. E. (2008). Gene-environment correlation and interaction in peer effects on adolescent alcohol and tobacco use. Behavior Genetics, 38, 339–347. https://doi.org/10.1007/s10519-008-9202-7CrossRefGoogle ScholarPubMed
Hellwege, J., Keaton, J., Giri, A., Gao, X., Velez Edwards, D. R., & Edwards, T. L. (2017). Population stratification in genetic association studies. Current Protocols in Human Genetics, 95, 1.22.1–1.22.23. https://doi.org/10.1002/cphg.48CrossRefGoogle ScholarPubMed
Henkel, D. (2011, March). Unemployment and Substance Use: A Review of the Literature (1990-2010) [Text]. https://www.ingentaconnect.com/content/ben/cdar/2011/00000004/00000001/art00002CrossRefGoogle Scholar
Hien, D. A., Levin, F. R., Ruglass, L. M., López-Castro, T., Papini, S., Hu, M.-C., … Herron, A. (2015). Combining seeking safety with sertraline for PTSD and alcohol use disorders: A randomized controlled trial. Journal of Consulting and Clinical Psychology, 83, 359–369. https://doi.org/10.1037/a0038719CrossRefGoogle ScholarPubMed
Hingson, R. W., Zha, W., & Weitzman, E. R. (2009). Magnitude of and trends in alcohol-related mortality and morbidity among U.S. college students ages 18-24, 1998-2005. Journal of Studies on Alcohol and Drugs. Supplement, 16, 12–20.CrossRefGoogle Scholar
Hippman, C., Ringrose, A., Inglis, A., Cheek, J., Albert, A. Y. K., Remick, R., & Honer, W. G. (2016). A pilot randomized clinical trial evaluating the impact of genetic counseling for serious mental illnesses. The Journal of Clinical Psychiatry, 77(02), e190–e198. http://dx.doi.org/10.4088/JCP.14m09710CrossRefGoogle ScholarPubMed
Hong, E. P., & Park, J. W. (2012). Sample size and statistical power calculation in genetic association studies. Genomics & Informatics, 10, 117–122. https://doi.org/10.5808/GI.2012.10.2.117CrossRefGoogle ScholarPubMed
Huh, D., Mun, E.-Y., Larimer, M. E., White, H. R., Ray, A. E., Rhew, I. C., … Atkins, D. C. (2015). Brief motivational interventions for college student drinking may not be as powerful as we think: An individual participant-level data meta-analysis. Alcoholism, Clinical and Experimental Research, 39, 919–931. https://doi.org/10.1111/acer.12714CrossRefGoogle Scholar
Jaffee, S. R., & Price, T. S. (2007). Gene-environment correlations: a review of the evidence and implications for prevention of mental illness. Molecular Psychiatry, 12, 432–442. https://doi.org/10.1038/sj.mp.4001950CrossRefGoogle ScholarPubMed
Johnson, E. C., Border, R., Melroy-Greif, W. E., de Leeuw, C. A., Ehringer, M. A., & Keller, M. C. (2017). No evidence that schizophrenia candidate genes are more associated with schizophrenia than noncandidate genes. Biological Psychiatry, 82, 702–708. https://doi.org/10.1016/j.biopsych.2017.06.033CrossRefGoogle ScholarPubMed
Juibari, T. A., Behrouz, B., Attaie, M., Farnia, V., Golshani, S., Moradi, M., & Alikhani, M. (2018). Characteristics and correlates of psychiatric problems in wives of men with substance-related disorders, Kermanshah, Iran. Oman Medical Journal, 33, 512–519. https://doi.org/10.5001/omj.2018.93CrossRefGoogle ScholarPubMed
Kalb, F. M., Vincent, V., Herzog, T., & Austin, J. (2017). Genetic counseling for alcohol addiction: assessing perceptions and potential utility in individuals with lived experience and their family members. Journal of Genetic Counseling, 26(5), 963–970. http://dx.doi.org/10.1007/s10897-017-0075-xCrossRefGoogle ScholarPubMed
Keller, M. C. (2014). Gene × environment interaction studies have not properly controlled for potential confounders: The problem and the (simple) solution. Biological Psychiatry, 75, 18–24. https://doi.org/10.1016/j.biopsych.2013.09.006CrossRefGoogle Scholar
Kendler, K. S., & Baker, J. H. (2007). Genetic influences on measures of the environment: A systematic review. Psychological Medicine, 37, 615–626. https://doi.org/10.1017/S0033291706009524CrossRefGoogle ScholarPubMed
Kendler, K. S., Jacobson, K. C., Gardner, C. O., Gillespie, N., Aggen, S. A., & Prescott, C. A. (2007). Creating a social world. Archives of General Psychiatry, 64, 958–965. https://doi.org/10.1001/archpsyc.64.8.958CrossRefGoogle ScholarPubMed
Kendler, K. S., Jacobson, K., Myers, J. M., & Eaves, L. J. (2008). A genetically informative developmental study of the relationship between conduct disorder and peer deviance in males. Psychological Medicine, 38, 1001–1011. https://doi.org/10.1017/S0033291707001821CrossRefGoogle ScholarPubMed
Kendler, K. S., Jaffee, S., & Romer, D. (2011). The dynamic genome and mental health: The Role of Genes and Environments in Youth Development. New York: Oxford University Press.Google Scholar
Kendler, K. S., Prescott, C. A., Myers, J., & Neale, M. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry, 60, 929–937. https://doi.org/10.1001/archpsyc.60.9.929CrossRefGoogle ScholarPubMed
King, S. M., Iacono, W. G., & McGue, M. (2004). Childhood externalizing and internalizing psychopathology in the prediction of early substance use. Addiction (Abingdon, England), 99, 1548–1559. https://doi.org/10.1111/j.1360-0443.2004.00893.xCrossRefGoogle ScholarPubMed
Kranzler, H. R., Zhou, H., Kember, R. L., Smith, R. V., Justice, A. C., Damrauer, S., … Gelernter, J. (2019). Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nature Communications, 10, 1499. https://doi.org/10.1038/s41467-019-09480-8CrossRefGoogle ScholarPubMed
Krueger, R. F., Hicks, B. M., Patrick, C. J., Carlson, S. R., Iacono, W. G., & McGue, M. (2002). Etiologic connections among substance dependence, antisocial behavior, and personality: Modeling the externalizing spectrum. Journal of Abnormal Psychology, 111, 411–424.CrossRefGoogle ScholarPubMed
Kuo, S. I.-C., Salvatore, J. E., Aliev, F., Ha, T., Dishion, T. J., & Dick, D. M. (2019). The family check-up intervention moderates polygenic influences on long-term alcohol outcomes: Results from a randomized intervention trial. Prevention Science, 20(7), 975–985. http://dx.doi.org/10.1007/s11121-019-01024-2CrossRefGoogle ScholarPubMed
Larimer, M. E., & Cronce, J. M. (2007). Identification, prevention, and treatment revisited: Individual-focused college drinking prevention strategies 1999–2006. Addictive Behaviors, 32, 2439–2468.CrossRefGoogle ScholarPubMed
Latendresse, S. J., Musci, R., & Maher, B. S. (2018). Critical issues in the inclusion of genetic and epigenetic information in prevention and intervention trials. Prevention Science, 19, 58–67.CrossRefGoogle ScholarPubMed
Lebowitz, M. S. (2019). The implications of genetic and other biological explanations for thinking about mental disorder. Hastings Center Report, 49, S82–S87.CrossRefGoogle Scholar
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., … Cesarini, D. (2018). Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment. Nature Genetics, 50, 1112–1121. https://doi.org/10.1038/s41588-018-0147-3CrossRefGoogle ScholarPubMed
Linnér, R. K., Biroli, P., Kong, E., Meddens, S. F. W., Wedow, R., Fontana, M. A., … Beauchamp, J. P. (2019). Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nature Genetics, 51, 245. https://doi.org/10.1038/s41588-018-0309-3CrossRefGoogle Scholar
Liu, M., Jiang, Y., Wedow, R., Li, Y., Brazel, D. M., Chen, F., … Vrieze, S. (2019). Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nature Genetics, 51, 237. https://doi.org/10.1038/s41588-018-0307-5CrossRefGoogle ScholarPubMed
Maher, B. S. (2015). Polygenic scores in epidemiology: Risk prediction, etiology, and clinical utility. Current Epidemiology Reports, 2, 239–244. https://doi.org/10.1007/s40471-015-0055-3CrossRefGoogle ScholarPubMed
Maier, R. M., Visscher, P. M., Robinson, M. R., & Wray, N. R. (2018). Embracing polygenicity: A review of methods and tools for psychiatric genetics research. Psychological Medicine, 48, 1055–1067. https://doi.org/10.1017/S0033291717002318CrossRefGoogle ScholarPubMed
Maisto, S. A., Pollock, N. K., Cornelius, J. R., Lynch, K. G., & Martin, C. S. (2003). Alcohol relapse as a function of relapse definition in a clinical sample of adolescents. Addictive Behaviors, 28, 449–459.CrossRefGoogle Scholar
Márquez-Luna, C., & Loh, P.-R. South Asian Type 2 Diabetes (SAT2D) Consortium, SIGMA Type 2 Diabetes Consortium, & Price, A. L. (2017). Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genetic Epidemiology, 41, 811–823. https://doi.org/10.1002/gepi.22083CrossRefGoogle ScholarPubMed
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 51, 584. https://doi.org/10.1038/s41588-019-0379-xCrossRefGoogle ScholarPubMed
Masten, A. S., Faden, V. B., Zucker, R. A., & Spear, L. P. (2009). A developmental perspective on underage alcohol use. Alcohol Research & Health: The Journal of the National Institute on Alcohol Abuse and Alcoholism, 32, 3–15.Google ScholarPubMed
Mayes, L. C., & Suchman, N. E. (2015). Developmental pathways to substance abuse. In D. Cicchetti & D. J. Cohen (Eds.), Developmental Psychopathology (pp. 599–619). Hoboken, NJ: John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470939406.ch16Google Scholar
McGeary, J. (2009). The DRD4 exon 3 VNTR polymorphism and addiction-related phenotypes: A review. Pharmacology Biochemistry and Behavior, 93, 222–229. https://doi.org/10.1016/j.pbb.2009.03.010CrossRefGoogle ScholarPubMed
McHugh, R. K., Hofmann, S. G., Asnaani, A., Sawyer, A. T., & Otto, M. W. (2010). The serotonin transporter gene and risk for alcohol dependence: A meta-analytic review. Drug and Alcohol Dependence, 108, 1–6. https://doi.org/10.1016/j.drugalcdep.2009.11.017CrossRefGoogle ScholarPubMed
Meyers, J. L., Salvatore, J. E., Vuoksimaa, E., Korhonen, T., Pulkkinen, L., Rose, R. J., … Dick, D. M. (2014). Genetic influences on alcohol use behaviors have diverging developmental trajectories: A prospective study among male and female twins. Alcoholism, Clinical and Experimental Research, 38, 2869–2877. https://doi.org/10.1111/acer.12560CrossRefGoogle ScholarPubMed
Moser, G., Lee, S. H., Hayes, B. J., Goddard, M. E., Wray, N. R., & Visscher, P. M. (2015). Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genetics, 11, e1004969. https://doi.org/10.1371/journal.pgen.1004969CrossRefGoogle ScholarPubMed
Musci, R. J., Fairman, B., Masyn, K. E., Uhl, G., Maher, B., Sisto, D. Y., … Ialongo, N. S. (2018). Polygenic score × intervention moderation: An application of discrete-time survival analysis to model the timing of first marijuana use among Urban youth. Prevention Science, 19, 6–14. https://doi.org/10.1007/s11121-016-0729-1CrossRefGoogle ScholarPubMed
Musci, R. J., Masyn, K. E., Uhl, G., Maher, B., Kellam, S. G., & Ialongo, N. S. (2015). Polygenic score x intervention moderation: An application of discrete-time survival analysis to modeling the timing of first tobacco use among urban youth. Development and Psychopathology, 27, 111–122. https://doi.org/10.1017/S0954579414001333CrossRefGoogle ScholarPubMed
Musci, R. J., & Schlomer, G. (2018). The implications of genetics for prevention and intervention programming. Prevention Science, 19(1), 1–5.CrossRefGoogle ScholarPubMed
National Drug Intelligence Center. (2011). The economic impact of illicit drug use on American society. Washington, DC: United States Department of Justice.Google Scholar
Pardiñas, A. F., Holmans, P., Pocklington, A. J., Escott-Price, V., Ripke, S., Carrera, N., … Walters, J. T. R. (2018). Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nature Genetics, 50, 381–389. https://doi.org/10.1038/s41588-018-0059-2CrossRefGoogle ScholarPubMed
Parens, E., Appelbaum, P. S., & Lebowitz, M. S. (2019). The implications of genetic and other biological explanations for thinking about mental disorders. Hastings Center Report, 49, S82–S87. http://dx.doi.org/10.1002/hast.1020Google Scholar
Pasman, J. A., Verweij, K. J. H., Abdellaoui, A., Hottenga, J. J., Fedko, I. O., Willemsen, G., … Vink, J. M. (2020). Substance use: Interplay between polygenic risk and neighborhood environment. Drug and Alcohol Dependence, 209, 107948. https://doi.org/10.1016/j.drugalcdep.2020.107948CrossRefGoogle ScholarPubMed
Pasman, J. A., Verweij, K. J. H., Gerring, Z., Stringer, S., Sanchez-Roige, S., Treur, J. L., … Vink, J. M. (2018). GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability. Nature Neuroscience, 21, 1161. https://doi.org/10.1038/s41593-018-0206-1CrossRefGoogle Scholar
Pearson, Y. E., & Liu-Thompkins, Y. (2012). Consuming direct-to-consumer genetic tests: The role of genetic literacy and knowledge calibration. Journal of Public Policy & Marketing, 31(1), 42–57. http://dx.doi.org/10.1509/jppm.10.066CrossRefGoogle Scholar
Peterson, R. E., Edwards, A. C., Bacanu, S.-A., Dick, D. M., Kendler, K. S., & Webb, B. T. (2017). The utility of empirically assigning ancestry groups in cross-population genetic studies of addiction. The American Journal on Addictions, 26, 494–501. https://doi.org/10.1111/ajad.12586CrossRefGoogle Scholar
Polimanti, R., Kaufman, J., Zhao, H., Kranzler, H. R., Ursano, R. J., Kessler, R. C., … Stein, M. B. (2018). A genome-wide gene-by-trauma interaction study of alcohol misuse in two independent cohorts identifies PRKG1 as a risk locus. Molecular Psychiatry, 23, 154–160. https://doi.org/10.1038/mp.2017.24CrossRefGoogle ScholarPubMed
Polimanti, R., Peterson, R. E., Ong, J.-S., MacGregor, S., Edwards, A. C., Clarke, T.-K., … Derks, E. M. (2019). Evidence of causal effect of major depression on alcohol dependence: Findings from the psychiatric genomics consortium. Psychological Medicine, 49, 1218–1226. https://doi.org/10.1017/S0033291719000667CrossRefGoogle ScholarPubMed
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 38, 904–909. https://doi.org/10.1038/ng1847CrossRefGoogle ScholarPubMed
Purcell, S. M., Wray, N. R., Stone, J. L., Visscher, P. M., O'Donovan, M. C., Sullivan, P. F., & Sklar, P. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature, 460, 748–752. https://doi.org/10.1038/nature08185Google ScholarPubMed
Regalado, A. (2019, February 11). More than 26 million people have taken an at-home ancestry test. MIT Technology Review. https://www.technologyreview.com/s/612880/more-than-26-million-people-have-taken-an-at-home-ancestry-test/Google Scholar
Reich, D., Price, A. L., & Patterson, N. (2008). Principal component analysis of genetic data. Nature Genetics, 40, 491–492. https://doi.org/10.1038/ng0508-491CrossRefGoogle ScholarPubMed
Rogosch, F. A., Oshri, A., & Cicchetti, D. (2010). From child maltreatment to adolescent cannabis abuse and dependence: A developmental cascade model. Development and Psychopathology, 22, 883–897. https://doi.org/10.1017/S0954579410000520CrossRefGoogle ScholarPubMed
Rose, R. J., Dick, D. M., Viken, R. J., & Kaprio, J. (2001). Gene-environment interaction in patterns of adolescent drinking: Regional residency moderates longitudinal influences on alcohol use. Alcoholism: Clinical and Experimental Research, 25, 637–643. https://doi.org/10.1111/j.1530-0277.2001.tb02261.xCrossRefGoogle ScholarPubMed
Russell, M. A., Schlomer, G. L., Cleveland, H. H., Feinberg, M. E., Greenberg, M. T., Spoth, R. L., … Vandenbergh, D. J. (2018). PROSPER intervention effects on adolescents’ alcohol misuse vary by GABRA2 genotype and age. Prevention Science: The Official Journal of the Society for Prevention Research, 19, 27–37. https://doi.org/10.1007/s11121-017-0751-yCrossRefGoogle ScholarPubMed
Sacks, J. J., Gonzales, K. R., Bouchery, E. E., Tomedi, L. E., & Brewer, R. D. (2015). 2010 national and state costs of excessive alcohol consumption. American Journal of Preventive Medicine, 49, e73–e79. https://doi.org/10.1016/j.amepre.2015.05.031CrossRefGoogle ScholarPubMed
Salvatore, J. E., Aliev, F., Edwards, A. C., Evans, D. M., Macleod, J., Hickman, M., … Dick, D. M. (2014). Polygenic scores predict alcohol problems in an independent sample and show moderation by the environment. Genes, 5, 330–346. https://doi.org/10.3390/genes5020330CrossRefGoogle Scholar
Sanchez-Roige, S., Palmer, A. A., Fontanillas, P., Elson, S. L., 23andMe Research Team, the Substance Use Disorder Working Group of the Psychiatric Genomics Consortium, Adams, M. J., … Clarke, T.-K. (2019). Genome-wide association study meta-analysis of the Alcohol Use Disorders Identification Test (AUDIT) in two population-based cohorts. The American Journal of Psychiatry, 176, 107–118. https://doi.org/10.1176/appi.ajp.2018.18040369CrossRefGoogle Scholar
Sandler, I., Wolchik, S. A., Cruden, G., Mahrer, N. E., Ahn, S., Brincks, A., & Brown, C. H. (2014). Overview of meta-analyses of the prevention of mental health, substance use and conduct problems. Annual Review of Clinical Psychology, 10, 243–273. https://doi.org/10.1146/annurev-clinpsy-050212-185524CrossRefGoogle ScholarPubMed
Schlomer, G. L., Cleveland, H. H., Feinberg, M. E., Wolf, P. S. A., Greenberg, M. T., Spoth, R. L., … Vandenbergh, D. J. (2017). Extending previous cG×I findings on 5-HTTLPR's moderation of intervention effects on adolescent substance misuse initiation. Child Development, 88, 2001–2012. https://doi.org/10.1111/cdev.12666CrossRefGoogle ScholarPubMed
Schulenberg, J. E., Johnston, L. D., O'Malley, P. M., Bachman, J. G., Miech, R. A., & Patrick, M. E. (2018). Monitoring the future national survey results on drug use, 1975-2017. Volume II, college students & adults ages 19-55. Ann Arbor: MI: Institute for Social Research, The University of Michigan.CrossRefGoogle Scholar
Schulz, A., Caldwell, C., & Foster, S. (2003). What are they going to do with the information?” Latino/Latina and African American perspectives on the Human Genome Project. Health Education & Behavior, 30, 151–169. https://doi.org/10.1177/1090198102251026CrossRefGoogle Scholar
Scott, M. S. (2017). Commentary: Perspectives on alcohol-related gene and environment interplay in diverse populations. The American Journal on Addictions, 26, 526–531. https://doi.org/10.1111/ajad.12584CrossRefGoogle ScholarPubMed
Skidmore, C. R., Kaufman, E. A., & Crowell, S. E. (2016). Substance use among college students. Child and Adolescent Psychiatric Clinics of North America, 25, 735–753. https://doi.org/10.1016/j.chc.2016.06.004CrossRefGoogle ScholarPubMed
Slutske, W. S., Deutsch, A. R., & Piasecki, T. M. (2019). Neighborhood alcohol outlet density and genetic influences on alcohol use: Evidence for gene–environment interaction. Psychological Medicine, 49, 474–482. https://doi.org/10.1017/S0033291718001095CrossRefGoogle ScholarPubMed
Smit, E., Verdurmen, J., Monshouwer, K., & Smit, F. (2008). Family interventions and their effect on adolescent alcohol use in general populations; a meta-analysis of randomized controlled trials. Drug and Alcohol Dependence, 97, 195–206. https://doi.org/10.1016/j.drugalcdep.2008.03.032CrossRefGoogle ScholarPubMed
Spoth, R., Greenberg, M., & Turrisi, R. (2008). Preventive interventions addressing underage drinking: State of the evidence and steps toward public health impact. Pediatrics, 121(Suppl 4), S311–336. https://doi.org/10.1542/peds.2007-2243ECrossRefGoogle ScholarPubMed
Stuart, G. L., McGeary, J., Shorey, R. C., & Knopik, V. S. (2016). Genetics moderate alcohol and intimate partner violence treatment outcomes in a randomized controlled trial of hazardous drinking men in batterer intervention programs: A preliminary investigation. Journal of Consulting and Clinical Psychology, 84, 592–598. https://doi.org/10.1037/a0040219CrossRefGoogle Scholar
Sussman, S., & Arnett, J. J. (2014). Emerging adulthood: Developmental period facilitative of the addictions. Evaluation & the Health Professions, 37, 147–155. https://doi.org/10.1177/0163278714521812CrossRefGoogle ScholarPubMed
Tanner-Smith, E. E., & Risser, M. D. (2016). A meta-analysis of brief alcohol interventions for adolescents and young adults: Variability in effects across alcohol measures. The American Journal of Drug and Alcohol Abuse, 42, 140–151. https://doi.org/10.3109/00952990.2015.1136638CrossRefGoogle Scholar
Tarantino, N., Tully, E. C., Garcia, S. E., South, S., Iacono, W. G., & McGue, M. (2014). Genetic and environmental influences on affiliation with deviant peers during adolescence and early adulthood. Developmental Psychology, 50, 663–673. https://doi.org/10.1037/a0034345CrossRefGoogle ScholarPubMed
Tarter, R. E. (2002). Etiology of adolescent substance abuse: A developmental perspective. The American Journal on Addictions, 11, 171–191. https://doi.org/10.1080/10550490290087965CrossRefGoogle ScholarPubMed
Tawa, E. A., Hall, S. D., & Lohoff, F. W. (2016). Overview of the genetics of alcohol use disorder. Alcohol and Alcoholism (Oxford, Oxfordshire), 51, 507–514. https://doi.org/10.1093/alcalc/agw046CrossRefGoogle ScholarPubMed
Timpson, N. J., Greenwood, C. M. T., Soranzo, N., Lawson, D. J., & Richards, J. B. (2018). Genetic architecture: The shape of the genetic contribution to human traits and disease. Nature Reviews Genetics, 19, 110–124. https://doi.org/10.1038/nrg.2017.101CrossRefGoogle ScholarPubMed
Uhl, G. R., Liu, Q.-R., Drgon, T., Johnson, C., Walther, D., Rose, J. E., … Lerman, C. (2008). Molecular genetics of successful smoking cessation: Convergent genome-wide association study results. Archives of General Psychiatry, 65, 683–693. https://doi.org/10.1001/archpsyc.65.6.683CrossRefGoogle ScholarPubMed
Uhl, G. R., Walther, D., Musci, R., Fisher, C., Anthony, J. C., Storr, C. L., … Rose, J. E. (2014). Smoking quit success genotype score predicts quit success and distinct patterns of developmental involvement with common addictive substances. Molecular Psychiatry, 19, 50–54. https://doi.org/10.1038/mp.2012.155CrossRefGoogle ScholarPubMed
Vandenbergh, D. J., Schlomer, G. L., Cleveland, H. H., Schink, A. E., Hair, K. L., Feinberg, M. E., … Redmond, C. (2015). An adolescent substance prevention model blocks the effect of CHRNA5 genotype on smoking during high school. Nicotine & Tobacco Research, 18, 212–220.CrossRefGoogle ScholarPubMed
Van der Auwera, S., Peyrot, W. J., Milaneschi, Y., Hertel, J., Baune, B., Breen, G., … Grabe, H. (2018, January 1). Genome-wide gene-environment interaction in depression: A systematic evaluation of candidate genes. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 177(1), 40–49. https://doi.org/10.1002/ajmg.b.32593CrossRefGoogle ScholarPubMed
Vanyukov, M. M., Tarter, R. E., Kirillova, G. P., Kirisci, L., Reynolds, M. D., Kreek, M. J., … Ridenour, T. A. (2012). Common liability to addiction and “gateway hypothesis”: Theoretical, empirical and evolutionary perspective. Drug and Alcohol Dependence, 123(Suppl 1), S3–17. https://doi.org/10.1016/j.drugalcdep.2011.12.018CrossRefGoogle ScholarPubMed
Vanyukov, M. M., Tarter, R. E., Kirisci, L., Kirillova, G. P., Maher, B. S., & Clark, D. B. (2003). Liability to substance use disorders: 1. Common mechanisms and manifestations. Neuroscience & Biobehavioral Reviews, 27, 507–515. https://doi.org/10.1016/j.neubiorev.2003.08.002CrossRefGoogle ScholarPubMed
Walters, R. K., Adams, M. J., Adkins, A. E., Aliev, F., Bacanu, S.-A., Batzler, A., … Chen, L.-S. (2018). Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nature neuroscience, 21(12), 1656–1669. https://doi.org/10.1038/s41593-018-0275-1CrossRefGoogle ScholarPubMed
Witkiewitz, K., & Masyn, K. E. (2008). Drinking trajectories following an initial lapse. Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors, 22, 157–167. https://doi.org/10.1037/0893-164X.22.2.157CrossRefGoogle ScholarPubMed
Wojnar, M., Brower, K. J., Strobbe, S., Ilgen, M., Matsumoto, H., Nowosad, I., … Burmeister, M. (2009). Association between Val66Met Brain-Derived Neurotrophic Factor (BDNF) gene polymorphism and post-treatment relapse in alcohol dependence. Alcoholism: Clinical and Experimental Research, 33, 693–702. https://doi.org/10.1111/j.1530-0277.2008.00886.xCrossRefGoogle ScholarPubMed
Wray, N. R., Goddard, M. E., & Visscher, P. M. (2007). Prediction of individual genetic risk to disease from genome-wide association studies. Genome Research, 17, 1520–1528. https://doi.org/10.1101/gr.6665407CrossRefGoogle ScholarPubMed
Wray, N. R., Lee, S. H., Mehta, D., Vinkhuyzen, A. A. E., Dudbridge, F., & Middeldorp, C. M. (2014). Research review: Polygenic methods and their application to psychiatric traits. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 55, 1068–1087. https://doi.org/10.1111/jcpp.12295CrossRefGoogle ScholarPubMed
Young-Wolff, K. C., Enoch, M.-A., & Prescott, C. A. (2011). The influence of gene-environment interactions on alcohol consumption and alcohol use disorders: a comprehensive review. Clinical Psychology Review, 31, 800–816. https://doi.org/10.1016/j.cpr.2011.03.005CrossRefGoogle ScholarPubMed
Zheng, Y., Albert, D., McMahon, R. J., Dodge, K., Dick, D., & Conduct Problems Prevention Research Group. (2018). Glucocorticoid receptor (NR3C1) gene polymorphism moderate intervention effects on the developmental trajectory of African-American adolescent alcohol abuse. Prevention Science, 19, 79–89. https://doi.org/10.1007/s11121-016-0726-4CrossRefGoogle ScholarPubMed