Hostname: page-component-8448b6f56d-t5pn6 Total loading time: 0 Render date: 2024-04-18T00:16:05.368Z Has data issue: false hasContentIssue false
  • English
  • Français

Neighborhood × Serotonin Transporter Linked Polymorphic Region (5-HTTLPR) interactions for substance use from ages 10 to 24 years using a harmonized data set of African American children

Published online by Cambridge University Press:  15 June 2015

Michael Windle ,
Steven M. Kogan ,
Sunbok Lee ,
Yi-Fu Chen ,
Karlo Mankit Lei ,
Gene H. Brody ,
Steven R. H. Beach  and
Tianyi Yu
Michael Windle*
Affiliation:
Emory University
Steven M. Kogan
Affiliation:
University of Georgia
Sunbok Lee
Affiliation:
University of Georgia
Yi-Fu Chen
Affiliation:
University of Georgia
Karlo Mankit Lei
Affiliation:
University of Georgia
Gene H. Brody
Affiliation:
University of Georgia
Steven R. H. Beach
Affiliation:
University of Georgia
Tianyi Yu
Affiliation:
University of Georgia
*
Address correspondence and reprint requests to: Michael Windle, Department of Behavioral Sciences and Health Education, Emory University, 1518 Clifton Road NE, Room 514, Atlanta, GA 30322; E-mail: mwindle@emory.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

This study investigated the influences of neighborhood factors (residential stability and neighborhood disadvantage) and variants of the serotonin transporter linked polymorphic region (5-HTTLPR) genotype on the development of substance use among African American children aged 10–24 years. To accomplish this, a harmonized data set of five longitudinal studies was created via pooling overlapping age cohorts to establish a database with 2,689 children and 12,474 data points to span ages 10–24 years. A description of steps used in the development of the harmonized data set is provided, including how issues such as the measurement equivalence of constructs were addressed. A sequence of multilevel models was specified to evaluate Gene × Environment effects on growth of substance use across time. Findings indicated that residential instability was associated with higher levels and a steeper gradient of growth in substance use across time. The inclusion of the 5-HTTLPR genotype provided greater precision to the relationships in that higher residential instability, in conjunction with the risk variant of 5-HTTLPR (i.e., the short allele), was associated with the highest level and steepest gradient of growth in substance use across ages 10–24 years. The findings demonstrated how the creation of a harmonized data set increased statistical power to test Gene × Environment interactions for an under studied sample.

Type
Regular Articles
Information
Development and Psychopathology , Volume 28 , Issue 2 , May 2016 , pp. 415 - 431
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aiken, L. S., & West, S. W. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.Google Scholar
Auerbach, J. G., Faroy, M., Ebstein, R., Kahana, M., & Levine, J. (2001). The association of the dopamine D4 receptor gene (DRD4) and the serotonin transporter promoter gene (5-HTTLPR) with temperament in 12-month-old infants. Journal of Child Psychology and Psychiatry, 42, 777–783.CrossRefGoogle ScholarPubMed
Bauer, D. J., & Hussong, A. M. (2009). Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models. Psychological Methods, 14, 101–125.Google Scholar
Beach, S. R. H., 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, 513–521.CrossRefGoogle ScholarPubMed
Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135, 885–908.Google Scholar
Bennett, S. N., Caporaso, N., Fitzpatrick, A. L., Agrawal, A., Barnes, K., Boyd, H. A., et al. (2011). Phenotype harmonization and cross-study collaboration in GWAS Consortia: The GENEVA experience. Genetic Epidemiology, 35, 159–173.Google Scholar
Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Hoboken, NJ: Wiley.Google Scholar
Bradley, S. L., Dodelzon, K., Sandhu, H. K., & Philibert, R. A. (2005). Relationship of serotonin transporter gene polymorphisms and haplotypes to mRNA transcription. American Journal of Medical Genetics, 136B, 58–61.Google Scholar
Brody, G. H., Beach, S. R. H., Hill, K. G., Howe, G. W., Prado, G., & Fullerton, S. M. (2013). Using genetically informed, randomized prevention trials to test etiological hypotheses about child and adolescent drug use and psychopathology. American Journal of Public Health, 103(Suppl. 1), S19–S24.Google Scholar
Brody, G. H., Chen, Y. F., & Beach, S. R. H. (2013). Differential susceptibility to prevention: GABAergic, dopaminergic, and multilocus effects. Journal of Child Psychology and Psychiatry 54, 863–871.Google Scholar
Brody, G. H., Chen, Y. F., & Kogan, S. M. (2010). A cascade model connecting life stress to risk behavior among rural African American emerging adults. Development and Psychopathology, 22, 667–678.Google Scholar
Brody, G. H., Ge, X., Conger, R., Gibbons, F. X., McBride, M. V., Gerrard, M., et al. (2001). The influence of neighborhood disadvantage, collective socialization, and parenting on African American children's affiliation with deviant peers. Child Development, 72, 1231–1246.CrossRefGoogle ScholarPubMed
Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage.Google Scholar
Buchmann, A. F., Schmid, B., Blomeyer, D., Becker, K., Treutlein, J., Zimmermann, U. S., et al. (2009). Impact of age at first drink on vulnerability to alcohol-related problems: Testing the marker hypothesis in a prospective study of young adults. Journal of Psychiatric Research, 43, 1205–1212.Google Scholar
Canli, T., Qiu, M., Omura, K., Congdon, E., Haas, B. W., & Amin, Z. (2006). Neural correlates of epigenesis. Proceedings of the National Academy of Sciences, 103, 16033–16038.CrossRefGoogle ScholarPubMed
Caspi, A., Hariri, A. R., Holmes, A., Uher, R., & Moffitt, T. E. (2010). Genetic sensitivity to the environment: The case of the serotonin transporter gene and its implications for studying complex diseases and traits. American Journal of Psychiatry, 167, 509–527.Google Scholar
Chassin, L., Sher, K. J., Hussong, A., & Curran, P. (2013). The developmental psychopathology of alcohol use and alcohol disorders: Research achievements and future directions Development and Psychopathology, 25, 1567–1584.Google Scholar
Chen, P., & Jacobson, K. C. (2012). Developmental trajectories of substance use from early adolescence to young adulthood: Gender and racial/ethnic differences. Journal of Adolescent Health, 50, 154–163.Google Scholar
Curran, P. J., & Hussong, A. M. (2009). Integrative data analysis: The simultaneous analysis of multiple data sets. Psychological Methods, 14, 81–100.Google Scholar
Curran, P. J., Hussong, A. M., Cai, L., Huang, W., Chassin, L., Sher, K. J., et al. (2008). Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis. Developmental Psychology, 44, 365–380.Google Scholar
Cutrona, C. E., Russell, D. W., Brown, P. A., Hessling, R. M., Clark, L. A., & Gardner, K. A. (2005). Neighborhood context, personality, and stressful life events as predictors of depression among African American women. Journal of Abnormal Psychology, 114, 3–15.CrossRefGoogle ScholarPubMed
Dalaker, J. (2001). Poverty in the United States, 2000. Washington, DC: US Census Bureau.Google Scholar
Donovan, J. E. (2009). Estimated blood alcohol concentrations for child and adolescent drinking and their implications for screening instruments. Pediatrics, 123, e975–e981.Google Scholar
Duncan, L. E., & Keller, M. C. (2011). A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. American Journal of Psychiatry, 168, 1041–1049.Google Scholar
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 (GxE) research. American Psychologists, 69, 249–268.Google Scholar
Duncan, S. C., & Duncan, T. E. (1996). A multivariate latent growth curve analysis of adolescent substance use. Structural Equation Modeling, 3, 323–347.Google Scholar
El Hage, W., Powell, J. F., & Surguladze, S. A. (2009). Vulnerability to depression: What is the role of stress genes in gene x environment interaction? Psychological Medicine, 39, 1407–1411.Google Scholar
Ellickson, P. L., Bell, R. M., & McGuigan, K. (1993). Preventing adolescent drug use: Long-term results of a junior high program. American Journal of Public Health, 83, 856–861.Google Scholar
Espeland, M. A., Dotson, K., Jaramillo, S. A., Kahn, S. E., Harrison, B., Montez, M., et al. (2006). Consent for genetics studies among clinical trial participants: Findings from Action for Health in Diabetes (Look AHEAD). Clinical Trials, 3, 443–456.Google Scholar
Feinn, R., Nellissery, M., & Kranzler, H. R. (2005). Meta-analysis of the association of a functional serotonin transporter promoter polymorphism with alcohol dependence. American Journal of Medical Genetics, 133B, 79–84.Google Scholar
Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society (London), 222A, 309–368.Google Scholar
Fortier, I., Burton, P. R., Robson, P. J., Ferretti, V., Little, J., L'Heureux, F., et al. (2010). Quality, quantity and harmony: The DataSHaPER approach to integrating data across bioclinical studies. International Journal of Epidemiology, 39, 1383–1393.Google Scholar
French, K., Finkbiner, R., & Duhamel, L. (2002). Patterns of substance use among minority youth and adults in the United States: An overview and synthesis of national survey findings. Fairfax, VA: US Department of Health and Human Services.Google Scholar
Galea, S., & Rudenstine, S. (2005). Challenges in understanding disparities in drug use and its consequences. Journal of Urban Health, 82(2, Suppl. 3), iii, 5–12.Google Scholar
Goldstein, D. B. (2009). Common genetic variation and human traits. New England Journal of Medicine, 360, 1696–1698.Google Scholar
Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576.Google Scholar
Granda, P., & Blasczyk, E. (2010). Data harmonization: Guidelines for best practice in cross-cultural surveys. Ann Arbor, MI: University of Michigan, Institute for Social Research, Survey Research Centre.Google Scholar
Green, D., Cushman, M., Dermond, N., Johnson, E. A., Castro, C., Arnett, D., et al. (2006). Obtaining informed consent for genetic studies: The multiethnic study of atherosclerosis. American Journal of Epidemiology, 164, 845–851.Google Scholar
Guo, G., Wilhelmsen, K., & Hamilton, N. (2007). Gene-life course interaction for alcohol consumption in adolescence and young adulthood: Five monoamine genes. American Journal of Medical Genetics, 144B, 417–423.Google Scholar
Hariri, A. R. (2011). Neurobiological mechanisms supporting gene–environment interaction effects. In Dodge, K. & Rutter, M. (Eds.) Gene–environment interactions in developmental psychopathology (pp. 59–69). New York: Guilford Press.Google Scholar
Hariri, A. R., Drabant, E. M., Munoz, K. E., Kolachana, B. S., Mattay, V. S., Egan, M. F., et al. (2005). A susceptibility gene for affective disorders and the response of the human amygdala. Archives of General Psychiatry, 62, 146–152.Google Scholar
Hawkins, J. D., Catalano, R. F., & Arthur, M. W. (2002). Promoting science-based prevention in communities. Addictive Behaviors, 27, 951–976.Google Scholar
Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance use prevention. Psychological Bulletin, 112, 64–105.Google Scholar
Heils, A., Teufel, A., Petri, S., Stober, G., Riederer, P., Bengel, D., et al. (1996). Allelic variation of human serotonin transporter gene expression. Journal of Neurochemistry, 66, 2621–2624.Google Scholar
Herman, A. I., Kaiss, K. M., Ma, R., Philbeck, J. W., Hasan, A., Dasti, H., et al. (2005). Serotonin transporter promoter polymorphism and monoamine oxidase Type A VNTR allelic variants together influence alcohol binge drinking risk in young women. American Journal of Medical Genetics, 133B, 74–78.Google Scholar
Hofer, S. M., & Piccinin, A. M. (2009). Integrative data analysis through coordination of measurement and analysis protocol across independent longitudinal studies. Psychological Methods, 14, 150–164.Google Scholar
Honaker, J., King, G., & Blackwell, M. (2012). AMELIA II: A program for missing data [Computer software]. Retrieved from http://gking.harvard.edu/amelia Google Scholar
Hopfer, C. J., Timberlake, D., Haberstick, B., Lessem, J. M., Ehringer, M. A., Smolen, A., et al. (2005). Genetic influence on quantity of alcohol consumed by adolescents and young adults. Drug and Alcohol Dependence, 78, 187–193.Google Scholar
Hurd, N. M., Stoddard, S. A., & Zimmerman, M. A. (2013). Neighborhoods, social support, and African American adolescents’ mental health outcomes: A multilevel path analysis. Child Development, 84, 858–874.Google Scholar
Hussong, A. M., Curran, P. J., & Bauer, D. J. (2013). Integrative data analysis in clinical psychology research. Annual Review of Clinical Psychology, 9, 61–89.Google Scholar
Hussong, A. M., Flora, D. B., Curran, P. J., Chassin, L. A., & Zucker, R. A. (2008). Defining risk heterogeneity for internalizing symptoms among children of alcoholic parents. Development and Psychopathology, 20, 165–193.Google Scholar
Hussong, A. M., Huang, W., Curran, P. J., Chassin, L., & Zucker, R. A. (2010). Parent alcoholism impacts the severity and timing of children’s externalizing symptoms. Journal of Abnormal Child Psychology, 38, 367–380.Google Scholar
Johnston, L. D., O'Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2013). Demographic subgroup trends among adolescents for fifty-one classes of licit and illicit drugs, 1975–2012 (Monitoring the Future Occasional Paper No. 79). Ann Arbor, MI: University of Michigan, Institute for Social Research. Retrieved from http://www.monitoringthefuture.org/pubs/occpapers/mtf-occ79.pdf Google Scholar
Kandel, D. B. (1995). Ethnic differences in drug use: Patterns and paradoxes. In Botvin, G. J., Schinke, S., & Orlandi, M. A. (Eds.), Drug abuse prevention with multiethnic youth (pp. 81–104). Thousand Oaks, CA: Sage.Google Scholar
Kandel, D., Schaffran, C., Hu, M. C., & Thomas, Y. (2011) Age-related differences in cigarette smoking among Whites and African-Americans: Evidence for the crossover hypothesis. Drug and Alcohol Dependence, 118, 280–287.Google Scholar
Kann, L., Kinchen, S., Shanklin, S., Flint, K. H., Hawkins, J., Harris, W. A., et al. (2014). Youth risk behavior surveillance—United States, 2013. Mortality and Morbidity Weekly Report, 63, 1–168.Google Scholar
Kaufman, J., Yang, B., Douglas-Palumber, H., Crouse-Artus, M., Lipschitz, D., Krystal, J., et al. (2007). Genetic and environmental predictors of early alcohol use. Biological Psychiatry, 61, 1228–1234.Google Scholar
Kawachi, I., & Berkman, L. F. (Eds.). (2003). Neighborhoods and health. Oxford: Oxford University Press.Google Scholar
Kendler, K. S., & Eaves, L. (1986). Models for the joint effect of genotype and environment on liability to psychiatric illness. American Journal of Psychiatry, 143, 279–289.Google Scholar
Kogan, S. M., Berkel, C., Chen, Y.-F., Brody, G. H., & Murry, V. M. (2006). Metro status and African-American adolescents’ risk for substance use. Journal of Adolescent Health, 38, 454–457.Google Scholar
Kreek, M. J., Nielsen, D. A., Butelman, E., & LaForge, S. (2005). Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nature Neuroscience, 8, 1450–1457.Google Scholar
Lesch, K. P. (2005). Alcohol dependence and Gene x Environment interaction in emotion regulation: Is serotonin the link? European Journal of Pharmacology, 526, 113–124.Google Scholar
Leventhal, T., & Brooks-Gunn, J. (2000). The neighborhoods they live in: The effects of neighborhood residence on child and adolescent outcomes. Psychological Bulletin, 126, 309–337.Google Scholar
Levine, S. B., & Coupey, S. M. (2003). Adolescent substance use, sexual behavior, and metropolitan status: Is “urban” a risk factor? Journal of Adolescent Health, 32, 350–355.Google Scholar
McQuillan, G. M., Pan, Q., & Porter, K. S. (2006). Consent for genetic research in a general population: An update on the National Health and Nutrition Examination Survey experience. Genetics in Medicine, 8, 354–360.Google Scholar
McQuillan, G. M., Porter, K. S., Agelli, M., & Kington, R. (2003). Consent for genetic research in a general population: The NHANES experience. Genetics in Medicine, 5, 35–42.Google Scholar
Mennis, J., & Mason, M. J. (2012). Social and geographic contexts of adolescent substance use: The moderating effects of age and gender. Social Networks, 34, 150–157.Google Scholar
Moffitt, T. E., Caspi, A., & Rutter, M. L. (2006). Measured gene–environment interactions in psychopathology: Concepts, research strategies, and implications for research, intervention, and public understanding of genetics. Perspectives on Psychological Science, 1, 5–27.Google Scholar
Monahan, K., Egan, E. A., Van Horn, M. L., Arthur, M., & Hawkins, D. (2011). Community-level effects of individual and peer risk and protective factors on adolescent substance use. Journal of Community Psychology, 39, 478–498.Google Scholar
Neyman, J. (1934). On the two different aspects of the representative method: The method of stratified sampling and the method of purposive selection. Journal of the Royal Statistical Society, 97, 558–606.Google Scholar
Odgerel, Z., Talati, A., Hamilton, S. P., Levinson, D. F., & Weissman, M. M. (2013). Genotyping serotonin transporter polymorphisms 5-HTTLPR and rs25531 in European- and African-American subjects from the National Institute of Mental Health's Collaborative Center for Genomic Studies. Translational Psychiatry, 3, e307.Google Scholar
Patkar, A., Berrettini, W. H., Hoehe, M., Hill, K. P., Sterling, R.C, Gottheil, E., et al. (2001). Serotonin transporter (5-HTT) gene polymorphisms and susceptibility to cocaine dependence among African-American individuals. Addiction Biology, 6, 337–345.Google Scholar
Pentz, M. A., Dwyer, J. H., MacKinnon, D. P., Flay, B. R., Hansen, W. B., Wang, E. Y., et al. (1989). A multicommunity trial for primary prevention of adolescent drug abuse: Effects on drug use prevalence. Journal of the American Medical Association, 261, 3259–3266.Google Scholar
Purcell, S. M., Wray, N. R., Stone, J. L., Visscher, P. M., O'Donovan, M. C., Sullivan, P. F., et al. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature, 460, 748–752.Google Scholar
Reardon, S. F., & Buka, S. L. (2002). Differences in onset and persistence of substance abuse and dependence among Whites, Blacks, and Hispanics. Public Health Reports, 117(Suppl. 1), S51–S59.Google Scholar
Robinson, O. J., Overstreet, C., Allen, P. S., Letkiewicz, A., Vytal, K., Pine, D. S., et al. (2013). The role of serotonin in the neurocircuitry of negative affective bias: Serotonergic modulation of the dorsal medial prefrontal-amygdala “aversive amplification” circuit. NeuroImage 78, 217–223.Google Scholar
Roisman, G. I., Newman, D. A., Fraley, C., Haltigan, J. D., Groh, A. M., & Haydon, K. C. (2012). Distinguishing differential susceptibility from diathesis stress: Recommendations for evaluating interaction effects. Development and Psychopathology, 24, 389–409.Google Scholar
Rowe, D. (1994). The limits of family influence. New York: Guilford Press.Google Scholar
Samejima, F. (1997). Graded response model. In van der Linden, W. J. & Hambleton, R. K. (Eds.), Handbook of modern item response theory (pp. 85–100). New York: Springer–Verlag.Google Scholar
Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 5328, 918–924.Google Scholar
Scarr, S. (1992). Developmental theories for the 1990s: Development and individual differences. Child Development, 63, 1–19.Google Scholar
Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype → environment effects. Child Development, 54, 424–435.Google Scholar
Schafer, J. L. (1997). Analysis of incomplete multivariate data. London: Chapman & Hall.Google Scholar
Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177.Google Scholar
Schafer, J. L., & Olsen, M. K. (1998). Multiple imputation for multivariate missing-data problems: A data analyst's perspective. Multivariate Behavioral Research, 33, 545–571.Google Scholar
Schulenberg, J. E., & Maggs, J. L. (2002). A developmental perspective on alcohol use and heavy drinking during adolescence and the transition to young adulthood. Journal of Studies on Alcohol, 14(Suppl.), 54–70.Google Scholar
Schulenberg, J. E., Maggs, J. M., & O'Malley, P. M. (2003). How and why the understanding of developmental continuity and discontinuity is important: The sample case of long-term consequences of adolescent substance use. In Mortimer, J. T. & Shanahan, M. J. (Eds.), Handbook of the life course (pp. 413–436). New York: Plenum Press.Google Scholar
Schuster, M. A., Elliott, M. N., Kanouse, D. E., Wallander, J. L., Tortolero, S. R., Ratner, J. A., et al. (2012). Racial and ethnic health disparities among fifth-graders in three cities. New England Journal of Medicine, 367, 735–745.Google Scholar
Shanahan, M. J., & Hofer, S. M. (2005). Social context in gene–environment interactions: Retrospect and prospect. Journals of Gerontology, 60B, 65–76.Google Scholar
Shaw, C. R., & McKay, H., 1942. Juvenile delinquency in urban areas. Chicago: University of Chicago Press.Google Scholar
Sher, K. J., Jackson, K. M., & Steinley, D. (2011). Alcohol use trajectories and the ubiquitous cat's cradle: Cause for concern? Journal of Abnormal Psychology, 120, 322–335.Google Scholar
Simons, R. L., Simons, L. G., Burt, C. H., Brody, G. H., & Cutrona, C. (2005). Collective efficacy, authoritative parenting and delinquency: A longitudinal test of a model integrating community- and family level processes. Criminology, 43, 989–1029.Google Scholar
Tarter, R. E., & Vanyukov, M. (1994). Alcoholism: A developmental disorder. Journal of Consulting and Clinical Psychology, 62, 1096–1107.Google Scholar
Tucker, J. S., Pollard, M. S., de la Haye, K., Kennedy, D. P., & Green, H. D. Jr. (2013). Neighborhood characteristics and the initiation of marijuana use and binge drinking. Drug and Alcohol Dependence 128, 83–89.Google Scholar
van der Zwaluw, C. S., Engels, R. C. M. E., Vermulst, A. A., Rose, R. J.,Verkes, R. J., Buitelaar, J., et al. (2010). A serotonin transporter polymorphism (5-HTTLPR) predicts the development of adolescent alcohol use. Drug and Alcohol Dependence, 112, 134–139.Google Scholar
Wallace, J. M. Jr., Bachman, J. G., O'Malley, P. M., Johnston, L. D., Schulenberg, J. E., & Cooper, S. M. (2002). Tobacco, alcohol, and illicit drug use: Racial and ethnic differences among U.S. high school seniors, 1976–2000. Public Health Reports, 117(Suppl. 1), S67–S75.Google Scholar
Wills, T. A., Murry, V. M., Brody, G. H., Gibbons, F. X., Gerrard, M., Walker, C., et al. (2007). Ethnic pride and self-control related to protective and risk factors: Test of the theoretical model for the Strong African American Families program. Health Psychology, 26, 50–59.CrossRefGoogle ScholarPubMed
Windle, M. (2010). A multilevel developmental contextual approach to substance use and addiction. BioSocieties, 5, 124–136.Google Scholar
Windle, M., & Davies, P. (1999). Developmental theory and research. In Leonard, K. E. & Blane, H. T. (Eds.), Psychological theories of drinking and alcoholism, 2nd ed. (pp. 164–202). New York: Guilford Press.Google Scholar
Windle, M., Spear, L. P., Fuligni, A. J., Angold, A., Brown, J. D., Pine, D., et al. (2008). Transitions into underage and problem drinking: Developmental processes and mechanisms between ages 10 and 15. Pediatrics, 121(Suppl. 4), S273–S289.Google Scholar
Xie, P., Kranzler, H. R., Poling, J., Stein, M. B., Anton, R. F., Brady, K., et al. (2009). Interactive effect of stressful life events and the serotonin transporter 5-HTTLPR genotype on posttraumatic stress disorder diagnosis in 2 independent populations. Archives of General Psychiatry, 66, 1201–1209.Google Scholar
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.Google Scholar
Zucker, R. A. (2006). Alcohol use and the alcohol use disorders: A developmental–biopsychosocial systems formulation covering the life course. In Cicchetti, D. & Cohen, D. J. (Eds.), Developmental psychopathology: Vol. 3. Risk, disorder, and adaptation (2nd ed., pp. 620–656). Hoboken, NJ: Wiley.Google Scholar