Advertisement

Challenges and Strategies for Integrating Molecular Genetics into Behavioral Science

  • Amanda M. Griffin
  • Gabriel L. Schlomer
  • David J. Vandenbergh
  • H. Harrington ClevelandEmail author
Chapter
Part of the Emerging Issues in Family and Individual Resilience book series (EIIFR)

Abstract

In the past 20 years, technological advances in genetics have made it relatively easy to add molecular genetic data collection to conventional datasets. This ease creates valuable opportunities for behavioral scientists to consider how genetic factors might influence individuals’ sensitivity to environmental experiences, including those that put individuals at developmental risk. Successfully incorporating molecular genetic approaches into conventional behavioral science designs is not without challenges. To help behavioral scientists understand and address these challenges, this chapter reviews critical issues involved in the data collection and analyses of molecular genetic data in a GxE context. More specifically, this chapter provides a brief summary of basic principles of molecular genetics, an overview of how to check the quality of genetic data prior to analysis, a brief review of the history of candidate gene-by-environment (cGxE) research and its associated criticisms, a framework for evaluating the quality of individual cGxE studies, and an explanation of current approaches used to combine multiple genetic variants to advance GxE research. We aim to provide information that will function as a starting point for behavioral scientists considering adding GxE approaches to their own research area.

Keywords

Gene-by-environment interaction GxE Candidate gene Polygenic risk Gene score 

References

  1. 1000 Genomes Project Consortium. (2012). An integrated map of genetic variation from 1,092 human genomes. Nature, 491(7422), 56–65.  https://doi.org/10.1038/nature11632CrossRefGoogle Scholar
  2. Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage.Google Scholar
  3. Aliev, F., Latendresse, S. J., Bacanu, S. A., Neale, M. C., & Dick, D. M. (2014). Testing for measured gene-environment interaction: Problems with the use of cross-product terms and a regression model reparameterization solution. Behavior Genetics, 44(2), 165–181.  https://doi.org/10.1007/s10519-014-9642-1CrossRefGoogle Scholar
  4. Anderson, C. A., Pettersson, F. H., Clarke, G. M., Cardon, L. R., Morris, A. P., & Zondervan, K. T. (2010). Data quality control in genetic case-control association studies. Nature Protocols, 5(9), 1564–1573.  https://doi.org/10.1038/nprot.2010.116CrossRefGoogle Scholar
  5. Asghari, V., Sanyal, S., Buchwaldt, S., Paterson, A., Jovanovic, V., & Van Tol, H. H. (1995). Modulation of intracellular cyclic AMP levels by different human dopamine D4 receptor variants. Journal of Neurochemistry, 65(3), 1157–1165.  https://doi.org/10.1046/j.1471-4159.1995.65031157.xCrossRefGoogle Scholar
  6. Ayorech, Z., Selzam, S., Smith-Woolley, E., Knopik, V. S., Neiderhiser, J. M., DeFries, J. C., & Plomin, R. (2016). Publication trends over 55 years of behavioral genetic research. Behavior Genetics, 46(5), 603–607.  https://doi.org/10.1007/s10519-016-9786-2CrossRefGoogle Scholar
  7. Bakermans-Kranenburg, M. J., & van Ijzendoorn, M. H. (2015). The hidden efficacy of interventions: Gene×environment experiments from a differential susceptibility perspective. Annual Review of Psychology, 66(1), 381–409.  https://doi.org/10.1146/annurev-psych-010814-015407CrossRefGoogle Scholar
  8. 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(5), 513–521.  https://doi.org/10.1037/a0020835CrossRefGoogle Scholar
  9. Belsky, D. W., Moffitt, T. E., Baker, T. B., Biddle, A. K., Evans, J. P., Harrington, H., … Caspi, A. (2013). Polygenic risk and the developmental progression to heavy, persistent smoking and nicotine dependence: Evidence from a 4-decade longitudinal study. JAMA Psychiatry, 70(5), 534–542.  https://doi.org/10.1001/jamapsychiatry.2013.736CrossRefGoogle Scholar
  10. Belsky, J. (2014, November 30). The downside of resilience. New York Times, p. 34.Google Scholar
  11. Belsky, J., Bakermans-kranenburg, M. J., Van Ijzendoorn, M. H., Issues, S., Kingdom, U., & Studies, F. (2007). For better and for worse: Differential susceptibility to environmental influences. Current Directions in Psychological Science, 16(6), 300–304.  https://doi.org/10.1111/j.1467-8721.2007.00525.xCrossRefGoogle Scholar
  12. Belsky, J., & Beaver, K. M. (2011). Cumulative-genetic plasticity, parenting and adolescent self-regulation. Journal of Child Psychology and Psychiatry, 52(5), 619–626.  https://doi.org/10.1111/j.1469-7610.2010.02327.xCrossRefGoogle Scholar
  13. Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135(6), 885–908.  https://doi.org/10.1037/a0017376CrossRefGoogle Scholar
  14. Belsky, J., & van Ijzendoorn, M. H. (2015). What works for whom? Genetic moderation of intervention efficacy. Development and Psychopathology, 27(1), 1–6.  https://doi.org/10.1017/S0954579414001254CrossRefGoogle Scholar
  15. Black, K., & Lobo, M. (2008). A conceptual review of family resilience factors. Journal of Family Nursing, 14(1), 33–55.  https://doi.org/10.1177/1074840707312237CrossRefGoogle Scholar
  16. Bradley, R. G., Binder, E. B., Epstein, M. P., Tang, Y., Nair, H. P., Liu, W., … Ressler, K. J. (2008). Influence of child abuse on adult depression: Moderation by the corticotropin-releasing hormone receptor gene. Archives of General Psychiatry, 65(2), 190.  https://doi.org/10.1001/archgenpsychiatry.2007.26CrossRefGoogle Scholar
  17. Brody, G. H., Beach, S. R. H., Philibert, R. A., Chen, Y., Lei, M.-K., Murry, V. M., & Brown, A. C. (2009). Parenting moderates a genetic vulnerability factor in longitudinal increases in youths’ substance use. Journal of Consulting and Clinical Psychology, 77(1), 1–11.  https://doi.org/10.1037/a0012996CrossRefGoogle Scholar
  18. Brody, G. H., Beach, S. R. H., Philibert, R. a., Chen, Y., & Murry, V. M. (2009). Prevention effects moderate the association of 5-HTTLPR and youth risk behavior initiation: Gene × environment hypotheses tested via a randomized prevention design. Child Development, 80(3), 645–661.  https://doi.org/10.1111/j.1467-8624.2009.01288.xCrossRefGoogle Scholar
  19. Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., Craig, I. W., … Poulton, R. (2002). Role of genotype in the cycle of violence in maltreated children. Science, 297(5582), 851–854.  https://doi.org/10.1126/science.1072290CrossRefGoogle Scholar
  20. Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., … Poulton, R. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301(5631), 386–389.  https://doi.org/10.1126/science.1083968CrossRefGoogle Scholar
  21. Chen, J., Lipska, B. K., Halim, N., Ma, Q. D., Matsumoto, M., Melhem, S., … Weinberger, D. R. (2004). Functional analysis of genetic variation in Catechol-O-Methyltransferase (COMT): Effects on mRNA, protein, and enzyme activity in postmortem human brain. The American Journal of Human Genetics, 75(5), 807–821.  https://doi.org/10.1086/425589CrossRefGoogle Scholar
  22. Chen, L.-S., Baker, T. B., Piper, M. E., Breslau, N., Cannon, D. S., Doheny, K. F., … Bierut, L. J. (2012). Interplay of genetic risk factors (CHRNA5 - CHRNA3 - CHRNB4) and cessation treatments in smoking cessation success. American Journal of Psychiatry, 169(7), 735–742.  https://doi.org/10.1176/appi.ajp.2012.11101545CrossRefGoogle Scholar
  23. Cicchetti, D. (2010). Resilience under conditions of extreme stress: A multilevel perspective. World Psychiatry, 9(3), 145–154.  https://doi.org/10.1002/j.2051-5545.2010.tb00297.xCrossRefGoogle Scholar
  24. Cleveland, H. H. (2003). Disadvantaged neighborhoods and adolescent aggression: Behavioral genetic evidence of contextual effects. Journal of Research on Adolescence, 13(2), 211–238.  https://doi.org/10.1111/1532-7795.1302004CrossRefGoogle Scholar
  25. 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(1), 51–67.  https://doi.org/10.1017/S0954579414001291CrossRefGoogle Scholar
  26. Cleveland, H. H., Schlomer, G. L., Vandenbergh, D. J., & Wiebe, R. P. (2016). Gene × Intervention designs: A promising step toward understanding eitiology and building better preventive interventions. Criminology and Public Policy, 15(3), 711–720.  https://doi.org/10.1111/1745-9133.12221CrossRefGoogle Scholar
  27. Cleveland, H. H., Griffin, A. M., Wolf, P. S. A., Wiebe, R. P., Schlomer, G. L., Feinberg, M. E., … Vandenbergh, D. J. (2018). Transactions between substance use intervention, the oxytocin receptor (OXTR) gene, and peer substance use predicting youth alcohol use. Prevention Science, 19(1), 15–26.  https://doi.org/10.1007/s11121-017-0749-5
  28. Cleveland, H. H., Schlomer, G. L., Vandenbergh, D. J., Wolf, P. S. A., Feinberg, M. E., Greenberg, M. T., … Redmond, C. (2018). Associations between alcohol dehydrogenase genes and alcohol use across early and middle adolescence: Moderation × Preventive intervention. Development and Psychopathology, 30(1), 297–313.  https://doi.org/10.1017/S0954579417000633CrossRefGoogle Scholar
  29. Cleveland, H. H., & Wiebe, R. P. (2003). The moderation of genetic and shared-environmental influences on adolescent drinking by levels of parental drinking. Journal of Studies on Alcohol, 64(2), 182–194.CrossRefGoogle Scholar
  30. Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART). American Journal of Preventive Medicine, 32(5), S112–S118.  https://doi.org/10.1016/j.amepre.2007.01.022CrossRefGoogle Scholar
  31. Creswell, K. G., Sayette, M. A., Manuck, S. B., Ferrell, R. E., Hill, S. Y., & Dimoff, J. D. (2012). DRD4 polymorphism moderates the effect of alcohol consumption on social bonding. PLoS One, 7(2), e28914.  https://doi.org/10.1371/journal.pone.0028914CrossRefGoogle Scholar
  32. Crick, F. H. (1958). On protein synthesis. Symposia of the Society for Experimental Biology, 12, 138–163.  https://doi.org/10.1038/227561a0CrossRefGoogle Scholar
  33. Culverhouse, R. C., Saccone, N. L., Horton, A. C., Ma, Y., Anstey, K. J., Banaschewski, T., … Bierut, L. J. (2017). Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression. Molecular Psychiatry, 1–10.  https://doi.org/10.1038/mp.2017.44
  34. Daw, N. D., Kakade, S., & Dayan, P. (2002). Opponent interactions between serotonin and dopamine. Neural Networks, 15, 603–616.  https://doi.org/10.1016/S0893-6080(02)00052-7CrossRefGoogle Scholar
  35. 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, 10(1), 37–59.  https://doi.org/10.1177/1745691614556682CrossRefGoogle Scholar
  36. 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(10), 1041–1049.  https://doi.org/10.1176/appi.ajp.2011.11020191CrossRefGoogle Scholar
  37. Edenberg, H. J., & Foroud, T. (2014). Genetics of alcoholism. In Handbook of clinical neurology (Vol. 125, pp. 561–571). Waltham, MA: Elsevier.  https://doi.org/10.1016/B978-0-444-62619-6.00032-X
  38. Egeland, B., Carlson, E., & Sroufe, L. A. (1993). Resilience as process. Development and Psychopathology, 5(4), 517–528.  https://doi.org/10.1017/S0954579400006131CrossRefGoogle Scholar
  39. Elam, K. K., Wang, F., Bountress, K., Chassin, L., Lemery-Chalfant, K., Dick, D., ... Sher, K. (2014, November). Using a literature-based polygenic risk score for behavioral undercontrol to examine evocative genotypeenvironment correlation. In Behavior Genetics (Vol. 44, No. 6, pp. 657–657). New York, NY: Springer.Google Scholar
  40. Elam, K. I. T. K., Wang, F. L., Bountress, K., Chassin, L., Pandika, D., & Lemery-chalfant, K. (2016). Predicting substance use in emerging adulthood: A genetically informed study of developmental transactions between impulsivity and family conflict. Development and Psychopathology, 28, 673–688.  https://doi.org/10.1017/S0954579416000249CrossRefGoogle Scholar
  41. Elam, K. K., Chassin, L., Lemery-Chalfant, K., Pandika, D., Wang, F. L., Bountress, K., ... & Agrawal, A. (2017). Affiliation with substance-using peers: Examining gene-environment correlations among parent monitoring, polygenic risk, and children’s impulsivity. Developmental psychobiology, 59(5), 561–573.Google Scholar
  42. Ellis, B. J., Boyce, W. T., Belsky, J., Bakermans-Kranenburg, M. J., & van Ijzendoorn, M. H. (2011). Differential susceptibility to the environment: An evolutionary–neurodevelopmental theory. Development and Psychopathology, 23(1), 7–28.  https://doi.org/10.1017/S0954579410000611CrossRefGoogle Scholar
  43. Fosco, G. M., DeBoard, R. L., & Grych, J. H. (2007). Making sense of family violence: Implications of children’s appraisals of interparental aggression for their short- and long-term functioning. European Psychologist, 12(1), 6–16.  https://doi.org/10.1027/1016-9040.12.1.6CrossRefGoogle Scholar
  44. Freedman, M. L., Reich, D., Penney, K. L., McDonald, G. J., Mignault, A. A., Patterson, N., … Altshuler, D. (2004). Assessing the impact of population stratification on genetic association studies. Nature Genetics, 36(4), 388–393.  https://doi.org/10.1038/ng1333CrossRefGoogle Scholar
  45. Fullerton, S. M., Yu, J. H., Crouch, J., Fryer-Edwards, K., & Burke, W. (2010). Population description and its role in the interpretation of genetic association. Human Genetics, 127(5), 563–572.  https://doi.org/10.1007/s00439-010-0800-0CrossRefGoogle Scholar
  46. Gajos, J. M., Fagan, A. A., & Beaver, K. M. (2016). Use of genetically informed evidence-based prevention science to understand and prevent crime and related behavioral disorders. Criminology & Public Policy, 15(3), 683–701.  https://doi.org/10.1111/1745-9133.12214CrossRefGoogle Scholar
  47. 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(5), 648–658.  https://doi.org/10.1002/humu.20695CrossRefGoogle Scholar
  48. Howe, G. W., Beach, S. R. H., & Brody, G. H. (2010). Microtrial methods for translating gene-environment dynamics into preventive interventions. Prevention Science, 11(4), 343–354.  https://doi.org/10.1007/s11121-010-0177-2CrossRefGoogle Scholar
  49. Iacono, W. G., Malone, S. M., & McGue, M. (2008). Behavioral disinhibition and the development of early-onset addiction: Common and specific Influences. Annual Review of Clinical Psychology, 4(1), 325–348.  https://doi.org/10.1146/annurev.clinpsy.4.022007.141157CrossRefGoogle Scholar
  50. Janssens, A., Van Den Noortgate, W., Goossens, L., Verschueren, K., Colpin, H., De Laet, S., … Van Leeuwen, K. (2015). Externalizing problem behavior in adolescence: Dopaminergic genes in interaction with peer acceptance and rejection. Journal of Youth and Adolescence, 44(7), 1441–1456.  https://doi.org/10.1007/s10964-015-0304-2CrossRefGoogle Scholar
  51. Kapur, S., & Remington, G. (1996). Serotonin-dopamine interactions and Its relevance to schizophrenia. American Journal of Psychiatry, 153(4), 466–476.  https://doi.org/10.1176/ajp.153.4.466CrossRefGoogle Scholar
  52. Karg, K., Burmeister, M., Shedden, K., & Sen, S. (2011). The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: Evidence of genetic moderation. Archives of General Psychiatry, 68(5), 444–454.  https://doi.org/10.1001/archgenpsychiatry.2010.189CrossRefGoogle Scholar
  53. Knerr, S., Wayman, D., & Bonham, V. L. (2011). Inclusion of racial and ethnic minorities in genetic research: Advance the spirit by changing the rules? Journal of Law, Medicine and Ethics, 39(3), 502–512.  https://doi.org/10.1111/j.1748-720X.2011.00617.xCrossRefGoogle Scholar
  54. Larsen, H., van der Zwaluw, C. S., Overbeek, G., Granic, I., Franke, B., & Engels, R. C. M. E. (2010). A variable-number-of-tandem-repeats polymorphism in the dopamine D4 receptor gene affects social adaptation of alcohol use: Investigation of a gene-environment interaction. Psychological Science, 21(8), 1064–1068.  https://doi.org/10.1177/0956797610376654CrossRefGoogle Scholar
  55. Leve, L. D., Harold, G. T., Ge, X., Neiderhiser, J. M., & Patterson, G. (2010). Refining intervention targets in family-based research: Lessons from quantitative behavioral genetics. Perspectives on Psychological Science, 5(5), 516–526.  https://doi.org/10.1177/1745691610383506CrossRefGoogle Scholar
  56. Luan, J. a., Wong, M. Y., Day, N. E., & Wareham, N. J. (2001). Sample size determination for studies of gene-environment interaction. International Journal of Epidemiology, 30(5), 1035–1040.  https://doi.org/10.1093/ije/30.5.1035CrossRefGoogle Scholar
  57. Luthar, S. S., Cicchetti, D., & Becker, B. (2000). The construct of resilience: A critical evaluation and guidelines for future work. Child Development, 71(3), 543–562.  https://doi.org/10.1111/1467-8624.00164CrossRefGoogle Scholar
  58. Luthar, S. S., & Zigler, E. (1991). Vulnerability and competence: A review of research on resilience in childhood. The American Journal of Orthopsychiatry, 61(1), 6–22.  https://doi.org/10.1037/h0079218CrossRefGoogle Scholar
  59. Malik, A. I., Zai, C. C., Abu, Z., Nowrouzi, B., & Beitchman, J. H. (2012). The role of oxytocin and oxytocin receptor gene variants in childhood‐onset aggression. Genes, brain and behavior, 11(5), 545–551Google Scholar
  60. Mannucci, A., Sullivan, K. M., Ivanov, P. L., & Gill, P. (1994). Forensic application of a rapid and quantitative DNA sex test by amplification of the X-Y homologous gene amelogenin. International Journal of Legal Medicine, 106(4), 190–193.  https://doi.org/10.1007/BF01371335CrossRefGoogle Scholar
  61. Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., … Visscher, P. M. (2009). Finding the missing heritability of complex diseases. Nature, 461(7265), 747–753.  https://doi.org/10.1038/nature08494CrossRefGoogle Scholar
  62. Manuck, S. B., & McCaffery, J. M. (2014). Gene-environment interaction. Annual Review of Psychology, 65(1), 41–70.  https://doi.org/10.1146/annurev-psych-010213-115100CrossRefGoogle Scholar
  63. Masten, A., Hubbard, J. J., Gest, S. D., Tellegen, A., Garmezy, N., & Ramirez, M. (1999). Competence in the context of adversity: Pathways to resilience and maladaptation from childhood to late adolescence. Development and Psychopathology, 11(1), 143–169.  https://doi.org/10.1017/S0954579499001996CrossRefGoogle Scholar
  64. Masten, A. S. (2001). Ordinary magic: Resilience processes in development. American Psychologist, 56(3), 227–238.  https://doi.org/10.1037//0003-066X.56.3.227CrossRefGoogle Scholar
  65. Masten, A. S., Best, K. M., & Garmezy, N. (1990). Resilience and development: Contributions from the study of children who overcome adversity. Development and Psychopathology, 2(4), 425–444.  https://doi.org/10.1017/S0954579400005812CrossRefGoogle Scholar
  66. Masten, A. S., & Obradović, J. (2006). Competence and resilience in development. Annals of the New York Academy of Sciences, 1094(1), 13–27.  https://doi.org/10.1196/annals.1376.003CrossRefGoogle Scholar
  67. Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., … Stamatoyannopoulos, J. A. (2012). Systematic localization of common disease-associated variation in regulatory DNA. Science, 337(6099), 1190–1195.  https://doi.org/10.1126/science.1222794CrossRefGoogle Scholar
  68. McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin, 114(2), 376–390.  https://doi.org/10.1037/0033-2909.114.2.376CrossRefGoogle Scholar
  69. Monroe, S., & Simons, A. (1991). Diathesis-stress theories in the context of life stress research: Implications for the depressive disorders. Psychological Bulletin, 110(3), 406–425.CrossRefGoogle Scholar
  70. Monroe, S. M., & Reid, M. (2008). Gene-environment interactions in depression research genetic polymorphisms and life-stress polyprocedures. Psychological Science, 19(10), 947–957.  https://doi.org/10.1111/j.1467-9280.2008.02181.xCrossRefGoogle Scholar
  71. Munafò, M. R. (2006). Candidate gene studies in the 21st century: Meta-analysis, mediation, moderation. Genes, Brain, and Behavior, 5(Suppl 1), 3–8.  https://doi.org/10.1111/j.1601-183X.2006.00188.xCrossRefGoogle Scholar
  72. Munafò, M. R., Durrant, C., Lewis, G., & Flint, J. (2009). Gene × environment interactions at the serotonin transporter locus. Biological Psychiatry, 65(3), 211–219.  https://doi.org/10.1016/j.biopsych.2008.06.009CrossRefGoogle Scholar
  73. 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(1), 111–122.  https://doi.org/10.1038/leu.2015.15\r10.1017/s0954579414001333CrossRefGoogle Scholar
  74. Musci, R. J., & Schlomer, G. L. (2017). The implications of genetics for prevention and intervention programming. Prevention Science, 19, 1–5.  https://doi.org/10.1007/s11121-017-0837-6CrossRefGoogle Scholar
  75. Nicolae, D. L., Gamazon, E., Zhang, W., Duan, S., Eileen Dolan, M., & Cox, N. J. (2010). Trait-associated SNPs are more likely to be eQTLs: Annotation to enhance discovery from GWAS. PLoS Genetics, 6(4), e1000888.  https://doi.org/10.1371/journal.pgen.1000888CrossRefGoogle Scholar
  76. Nikolova, Y. S., Ferrell, R. E., Manuck, S. B., & Hariri, A. R. (2011). Multilocus genetic profile for dopamine signaling predicts ventral striatum reactivity. Neuropsychopharmacology, 36(9), 1940–1947.  https://doi.org/10.1038/npp.2011.82CrossRefGoogle Scholar
  77. Perkel, J. (2008). SNP genotyping: Six technologies that keyed a revolution. Nature Methods, 5(5), 575–575.  https://doi.org/10.1038/nmeth0608-575bCrossRefGoogle Scholar
  78. Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31(4), 437–448.  https://doi.org/10.3102/10769986031004437CrossRefGoogle Scholar
  79. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., … Sham, P. C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3), 559–575.  https://doi.org/10.1086/519795CrossRefGoogle Scholar
  80. Reiss, D., & Leve, L. D. (2007). Genetic expression outside the skin: Clues to mechanisms of genotype × environment interaction. Development and Psychopathology, 19(4), 181–204.  https://doi.org/10.1017/S0954579407000508CrossRefGoogle Scholar
  81. Reiss, D., Leve, L. D., & Neiderhiser, J. M. (2013). How genes and the social environment moderate each other. American Journal of Public Health, 103(Suppl.1), 1–10.  https://doi.org/10.2105/AJPH.2013.301408CrossRefGoogle Scholar
  82. Risch, N., Herrell, R., Lehner, T., Liang, K. Y., Eaves, L., Hoh, J., … Merikangas, K. R. (2009). Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: A meta-analysis. Jama, 301(23), 2462–2472.  https://doi.org/10.1001/jama.2009.878CrossRefGoogle Scholar
  83. Roisman, G. I., Newman, D. A., Fraley, R. 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(2), 389–409.  https://doi.org/10.1017/S0954579412000065CrossRefGoogle Scholar
  84. 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, 19(1), 27–37.  https://doi.org/10.1007/s11121-017-0751-yCrossRefGoogle Scholar
  85. Rutter, M. (1987). Psychosocial resilience and protective mechanisms. American Journal of Orthopsychiatry, 57(3), 316–331.  https://doi.org/10.1111/j.1939-0025.1987.tb03541.xCrossRefGoogle Scholar
  86. Rutter, M. (1993). Resilience: Some conceptual considerations. Journal of Adolescent Health, 14(8), 626–631.  https://doi.org/10.1016/1054-139X(93)90196-VCrossRefGoogle Scholar
  87. Rutter, M. (2012). Resilience as a dynamic concept. Development and Psychopathology, 24, 335–344.  https://doi.org/10.1017/S0954579412000028CrossRefGoogle Scholar
  88. Rutter, M., Thapar, A., & Pickles, A. (2009). Gene-environment interactions. Archives of General Psychiatry, 66(12), 1287–1289.  https://doi.org/10.1001/archgenpsychiatry.2009.167CrossRefGoogle Scholar
  89. Salvatore, J. E., & Dick, D. M. (2015). Gene-environment interplay: Where we are, where we are going. Journal of Marriage and Family, 77(2), 344–350.  https://doi.org/10.1111/jomf.12164CrossRefGoogle Scholar
  90. Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype environment effects. Child Development, 54(2), 424–435.  https://doi.org/10.2307/1129703CrossRefGoogle Scholar
  91. Schlomer, G. L., Cleveland, H. H., Feinberg, M. E., Wolf, P. S., 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(6), 2001–2012.  https://doi.org/10.1111/cdev.12666
  92. Schlomer, G. L., Cleveland, H. H., Vandenbergh, D. J., Fosco, G. M., & Feinberg, M. E. (2015). Looking forward in candidate gene research: Concerns and suggestions. Journal of Marriage and Family, 77(2), 351–354.  https://doi.org/10.1111/jomf.12165CrossRefGoogle Scholar
  93. Schlomer, G. L., Cleveland, H. H., Vandenbergh, D. J., Wolf, P. S. A., Feinberg, M. E., & Greenberg, M. T. (n.d.). 5-HTTLPR moderates intervention effects on early adolescent substance abuse initiation. Child Development.Google Scholar
  94. Schlomer, G. L., Fosco, G. M., Cleveland, H. H., Vandenbergh, D. J., & Feinberg, M. E. (2015). Interparental relationship sensitivity leads to adolescent internalizing problems: Different genotypes, different pathways. Journal of Marriage and Family, 77(2), 329–343.  https://doi.org/10.1111/jomf.12168CrossRefGoogle Scholar
  95. Schoots, O., & Van Tol, H. H. M. (2003). The human dopamine D4 receptor repeat sequences modulate expression. The Pharmacogenomics Journal, 3(6), 343–348.  https://doi.org/10.1038/sj.tpj.6500208CrossRefGoogle Scholar
  96. Stephens, S. H., Hartz, S. M., Hoft, N. R., Saccone, N. L., Corley, R. C., Hewitt, J. K., … Ehringer, M. A. (2013). Distinct loci in the CHRNA5/CHRNA3/CHRNB4 gene cluster are associated with onset of regular smoking. Genetic Epidemiology, 37(8), 846–859.  https://doi.org/10.1002/gepi.21760CrossRefGoogle Scholar
  97. Turkheimer, E. (2000). Three laws of behavior genetics and what they mean. Current Directions in Psychological Science, 9(5), 160–164.  https://doi.org/10.1111/1467-8721.00084CrossRefGoogle Scholar
  98. Uher, R., & McGuffin, P. (2008). The moderation by the serotonin transporter gene of environmental adversity in the aetiology of mental illness: Review and methodological analysis. Molecular Psychiatry, 13(2), 131–146.  https://doi.org/10.1038/sj.mp.4002067CrossRefGoogle Scholar
  99. Uher, R., & McGuffin, P. (2010). The moderation by the serotonin transporter gene of environmental adversity in the etiology of depression: 2009 update. Molecular Psychiatry, 15(1), 18–22.  https://doi.org/10.1038/mp.2009.123CrossRefGoogle Scholar
  100. Uhl, G. R., Drgon, T., Johnson, C., Ramoni, M. F., Behm, F. M., & Rose, J. E. (2010). Genome-wide association for smoking cessation success in a trial of precessation nicotine replacement. Molecular Medicine, 16(11), 513–526.  https://doi.org/10.2119/molmed.2010.00052CrossRefGoogle Scholar
  101. 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(1), 50–54.  https://doi.org/10.1038/mp.2012.155CrossRefGoogle Scholar
  102. Van Ijzendoorn, M. H., Bakermans-Kranenburg, M. J., Belsky, J., Beach, S., Brody, G., Dodge, K. A., … Scott, S. (2011). Gene-by-environment experiments: A new approach to finding the missing heritability. Nature Reviews Genetics, 12(12), 881–881.  https://doi.org/10.1038/nrg2764-c1CrossRefGoogle Scholar
  103. Vandenbergh, D. J., & Schlomer, G. L. (2014). Finding genomic function for genetic associations in nicotine addiction research: The ENCODE project’s role in future pharmacogenomic analysis. Pharmacology Biochemistry and Behavior, 123(5), 34–44.  https://doi.org/10.1016/j.pbb.2014.01.009CrossRefGoogle Scholar
  104. 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(2), 212–220.Google Scholar
  105. Villafuerte, S., Trucco, E. M., Heitzeg, M. M., Burmeister, M., & Zucker, R. A. (2014). Genetic variation in GABRA2 moderates peer influence on externalizing behavior in adolescents. Brain and Behavior, 4(6), 833–840.  https://doi.org/10.1002/brb3.291CrossRefGoogle Scholar
  106. Wang, T., & Elston, R. C. (2005). Two-level Haseman-Elston regression for general pedigree data analysis. Genetic Epidemiology, 29(1), 12–22.  https://doi.org/10.1002/gepi.20075CrossRefGoogle Scholar
  107. Widaman, K. F., Helm, J. L., Castro-Schilo, L., Pluess, M., Stallings, M. C., & Belsky, J. (2012). Distinguishing ordinal and disordinal interactions. Psychological Methods, 17(4), 615–622.  https://doi.org/10.1037/a0030003CrossRefGoogle Scholar
  108. Wong, P. T.-H., Feng, H., & Teo, W. L. (1995). Interaction of the dopaminergic and serotonergic systems in the rat striatum: Effects of selective antagonists and uptake inhibitors. Neuroscience Research, 23(1), 115–119.  https://doi.org/10.1016/0168-0102(95)90023-3CrossRefGoogle Scholar
  109. Yacubian, J., Sommer, T., Schroeder, K., Gläscher, J., Kalisch, R., Leuenberger, B., … Büchel, C. (2007). Gene-gene interaction associated with neural reward sensitivity. Proceedings of the National Academy of Sciences of the United States of America, 104(19), 8125–8130.  https://doi.org/10.1073/pnas.0702029104CrossRefGoogle Scholar
  110. Zakhari, S. (2006). Overview: How is alcohol metabolized by the body? Alcohol Research & Health, 29(4), 245–254.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Amanda M. Griffin
    • 1
  • Gabriel L. Schlomer
    • 2
  • David J. Vandenbergh
    • 3
    • 4
  • H. Harrington Cleveland
    • 5
    Email author
  1. 1.University of OregonEugeneUSA
  2. 2.University at Albany, State University of New YorkAlbanyUSA
  3. 3.Department of Biobehavioral HealthThe Pennsylvania State UniversityUniversity ParkUSA
  4. 4.Institute of the NeurosciencesThe Pennsylvania State UniversityUniversity ParkUSA
  5. 5.Department of Human Development and Family ScienceThe Pennsylvania State UniversityUniversity ParkUSA

Personalised recommendations