Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2018 Aug 24.
Published in final edited form as: Policy Insights Behav Brain Sci. 2017 Aug 24;4(2):186–193. doi: 10.1177/2372732217720222

Alcohol Misuse Across the Lifespan: Insights from Developmental Studies in Behavior Genetics

Jeanne E Savage 1, Elizabeth C Long 1, Sally I-Chun Kuo 2, Megan E Cooke 1, Jinni Su 2, Peter B Barr 2, Jessica E Salvatore 1,2
PMCID: PMC5802413  NIHMSID: NIHMS916794  PMID: 29430518

Abstract

Alcohol misuse, one of today’s greatest public health challenges, is a developmentally dynamic, complex behavior at the intersection of genetic and environmental influences. This review examines such influences from a behavior genetics perspective and discusses implications for public policy. Alcohol misuse is moderately heritable with genetic influences accounting for around 50% of its variance, but to date few specific genes have been identified. However, numerous environmental and social factors moderate genetic risk, including parents, peers, romantic partners, family dynamics, employment, laws, and cultural influences. These moderating factors change in salience across development, and, accordingly, no one-size-fits-all approach is suitable for reducing alcohol misuse at a large scale. We provide examples of some effective prevention and intervention programs and discuss a framework for using the behavior genetics evidence to inform future public policy efforts.

Keywords: alcohol misuse, alcohol use disorder, public policy, behavior genetics, development

Overview

Alcohol misuse (AM) is one of today’s greatest public health challenges (Office of the Surgeon General, 2016). In the United States alone, alcohol-related diseases are the third leading cause of preventable death (Mokdad, Marks, Stroup, & Gerberding, 2005), and AM costs the U.S. nearly $250 billion annually (Rehm et al., 2009; Sacks, Gonzales, Bouchery, Tomedi, & Brewer, 2015). As a public health concern, the consequences of an individual’s AM are not self-contained but impact multiple aspects of society: from family and peer networks (e.g. damaged relationships, physical and sexual assault) to regional and national communities (e.g. drunk driving, burdens on the healthcare system). Social influences, in turn, reciprocally shape individuals’ alcohol use (Galea, Nandi, & Vlahov, 2004).

The intersection of individual predispositions with environmental and social facilitation (or deterrence) has made AM a key outcome for investigation in the behavior genetics (BG) field. This science seeks to unravel the contributions of genetic and environmental influences to complex behaviors. The BG perspective has conceptualized AM as a dynamic phenomenon in which one’s innate predispositions are shaped towards or away from alcohol problems by the risk and protective factors encountered across development (Tarter & Vanyukov, 1994). In recent decades, BG research has begun to identify the genetic influences on those predispositions and discover what shapes their expression.

AM encompasses multiple behaviors involving the excessive, inappropriate, or harmful consumption of alcohol that causes negative consequences in health, work, legal, or social functioning. It is often defined based on alcohol use disorder (AUD) criteria set forth by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), or by measures of acutely harmful excessive consumption such as binge drinking, defined by the National Institute on Alcohol Abuse and Alcoholism as 4 or more drinks in one occasion for women and 5 or more for men (Wechsler & Nelson, 2008). Although binge drinking is episodic, AUDs reflect a progressive pattern of excessive use despite repeated negative consequences.

In the U.S. population, the lifetime prevalence of AUDs is estimated at 14% (Grant, Goldstein, Saha, & et al., 2015) and past-month binge-drinking prevalence at 17–25% (Center for Behavioral Health Statistics and Quality, 2016). However, the prevalence of alcohol use and misuse differs across demographic factors. Age is one: AUD prevalence increases sharply between adolescence and adulthood (Substance Abuse and Mental Health Services Administration, 2014). This increase is more pronounced for males than females (Chen, Yi, & Faden, 2015). After mid-adolescence, males consistently drink and experience problems at higher rates than females, although gender differences have attenuated recently due to secular trends of increasing AM among females (Grucza, Bucholz, Rice, & Bierut, 2008). AM also varies significantly across racial/ethnic groups (Chartier & Caetano, 2010), with higher rates in European Americans than minority populations in the U.S. (Grant et al., 2015). Education, religious affiliation, and geographic region are also linked to rates of AM (Meyers, Brown, Grant, & Hasin, 2016; Naimi et al., 2003).

These many contributing factors demonstrate the malleability of AM and highlight why policy interventions can shape outcomes. Although consumption and metabolism of alcohol is a basic biological process, individuals’ alcohol use is complex and modified by higher-order factors (Kendler, 2012). Alcohol use is also developmentally dynamic, with biological and psychosocial influences both changing constantly throughout the lifespan, as we review in the following section.

Etiology of alcohol misuse

Genetic influences

The heritability of adult alcohol use and AUD is approximately 50% (Dick, Meyers, Rose, Kaprio, & Kendler, 2011; Verhulst, Neale, & Kendler, 2015), indicating that genetic factors account for roughly half of the inter-individual differences in both alcohol consumption and AUD in the population. Shared genetic influences are found between heavy alcohol consumption and AUD symptoms (Dick et al., 2011), and between these alcohol use behaviors and disorders such as other drug dependence, conduct disorder, and antisocial personality disorder (Dick, Prescott, & McGue, 2009). This suggests a broad genetic predisposition toward “externalizing” behaviors, which may reflect a common intermediate mechanism such as impulsivity or reward-seeking (Dick et al., 2010).

Although well-replicated research demonstrates genetic influences on alcohol use/misuse, identifying the specific genes that underlie this heritability has been challenging. AM is polygenic: many genetic variants of small effects across the genome contribute to its heritability (Sullivan, Daly, & O’Donovan, 2012). The large sample sizes needed to detect such small effects have made it difficult to identify the genes involved. Notable exceptions include variants in genes that code for alcohol dehydrogenase and aldehyde dehydrogenase, which help metabolize alcohol (Hart & Kranzler, 2015). Some variants of these genes result in unpleasant side effects when drinking (nausea, facial flushing), leading to decreased alcohol consumption. Genetic variants in neurotransmitter systems show mixed evidence (Buhler et al., 2015), but meta-analyses implicate some receptors for dopamine (Wang, Simen, Arias, Lu, & Zhang, 2013), GABA (Zintzaras, 2012), and opioids (Schwantes-An et al., 2016). However, few genes have been implicated by the stringent, genome-wide association tests considered standard today.

The lack of gene identification despite considerable efforts suggests that AM is not a uniform outcome with a single set of genetic risk factors. Indeed, research supports numerous potential genetic predisposition pathways to AM, such as sensitivity to its rewarding or withdrawal-induced effects (Koob & Le Moal, 2008), impaired executive functioning and impulse control (Everitt & Robbins, 2005), and intoxication susceptibility (Schuckit et al., 2009).

Environmental and social influences

The environmental influences on AM expand outward from the individual, who is nested in the immediate social microsystem, which is nested in the community exosystem, all of which is encompassed by the broader geographical, cultural, and temporal macrosystem (e.g. Bronfenbrenner, 1979). At the microsystem level, family characteristics (such as cohesion, support, acceptance, and affection) are associated with lower rates of AM among adolescents (Reeb et al., 2015), whereas family conflict, aggression, and neglect are associated with higher risk (Repetti, Taylor, & Seeman, 2002). Parenting behaviors, such as monitoring and involvement in children’s lives, influence adolescent AM (Barnes, Reifman, Farrell, & Dintcheff, 2000; Profe & Wild, 2015), while peers shape AM in adolescents and young adults (Samek, Keyes, Iacono, & McGue, 2013), and romantic partners begin to impact AM in young adulthood (Fischer & Wiersma, 2012).

Outside of the immediate social circles, local communities also play important roles. School-level norms and attitudes about alcohol use predict students’ individual alcohol use (Su & Supple, 2016). Perceived social norms regarding drinking predict AM in college campuses (Perkins, 2002). Adverse neighborhood conditions, including economic and social disadvantage, are associated with increased AM (Cerdá, Diez-Roux, Tchetgen, Gordon-Larsen, & Kiefe, 2010; Mulia & Karriker-Jaffe, 2012).

Broader social conditions such as national or local legal restrictions can directly affect one’s ability to obtain alcohol, and therefore one’s likelihood to develop problems. Increases in the minimum legal drinking age reduce alcohol consumption (Wagenaar & Toomey, 2002), as do policies like higher taxes on alcohol sales (Elder et al., 2010; Wagenaar, Salois, & Komro, 2009). Economic conditions affect drinking rates: alcohol consumption is greater in wealthier countries (Rehm et al., 2009), but economic instability and income inequality also increase alcohol problems (de Goeij et al., 2015; Elgar, Roberts, Parry-Langdon, & Boyce, 2005). Cross-cultural differences in alcohol use/misuse patterns show higher rates for nations (and ethnic groups) with more individualistic than collectivist cultural orientations (Johnson, 2007). For racial/ethnic minority populations, culturally relevant experiences such as discrimination (Martin, Tuch, & Roman, 2003) and racial socialization (Hughes et al., 2006) predict risk for alcohol problems. In the U.S., immigrants and ethnic minorities have lower rates of AM than European Americans (Hasin, Stinson, Ogburn, & Grant, 2007), but rates increase as a function of integration into the national culture (Savage & Mezuk, 2014).

Developmental influences

On average, alcohol use and AM increase sharply from age 12–17, peak around age 18–25, and level off into adulthood (Substance Abuse and Mental Health Services Administration, 2014). However, such averages mask individual differences in alcohol use trajectories. Although most people exhibit a stable pattern of low or moderate use after initiation, more problematic trajectories also occur: a) early initiation and persistent heavy use throughout life (heavy chronic users), b) initiation in mid/late high school and a rapid, steep increase in use shortly after (late-onset users), and c) time-limited brief periods of heavy use, typically in adolescence (“fling” drinkers) (Brown et al., 2008; Schulenberg, O’Malley, Bachman, Wadsworth, & Johnston, 1996).

Interaction among genetic, environmental, and developmental factors

Different aspects of the environment vary in salience across development (Masten, Burt, & Coatsworth, 2006), so genetic and environmental factors influencing AM change across the lifespan. Gene by development (GxD) and environment by development (ExD) effects shaping AM can occur as certain factors “come online” (or offline) at each life stage. However, genetic and environmental factors can also moderate each other (gene-by-environment interaction; GxE), and these interaction effects may also change across development (GxExD). This makes for an extraordinarily complex picture.

GxD

Genetic influences on alcohol use change across life, particularly during the transition from childhood to adulthood. Estimates of heritability for alcohol use start at zero for adolescents aged 13–15 and increase throughout late adolescence and early adulthood, with a plateau near 50% around age 24 (Dick et al., 2007; Dick et al., 2009). Environmental factors therefore largely influence initiation of use, while genetic influences play an increasing role on developing alcohol use patterns later in life. Genetic influences on AM also change qualitatively. In adolescence, genetic predispositions impacting alcohol use are mostly shared with antisocial behavior, while in young adulthood genetic influences become more specific to AM (Meyers et al., 2014). Some studies have shown that the same genes influence AM across development (van Beek et al., (2012) while others show evidence of new genes coming online later in life (Edwards & Kendler, 2013; Long, Lonn, Sundquist, Sundquist, & Kendler, 2017).

ExD

As reviewed above, family, peers, and schools have their greatest influences on alcohol use/misuse during childhood and adolescence when home and school environments dominate an individual’s life. Adolescence represents a period linking childhood and adulthood, with tremendous transitions in social/occupational roles, family, and self-identity (Windle et al., 2008). The increased autonomy, opportunities, and responsibilities experienced during the transition mean that experiences in these new domains (e.g., leaving home, changing occupational roles, romantic relationships, parenthood/family) become more salient predictors of adult alcohol use/misuse (for a review, see Stone, Becker, Huber, & Catalano, 2012).

GxE

The most consistent GxE effects are for environments that differ in the opportunity to express genetic predispositions toward alcohol problems. Generally, genetic influences on alcohol outcomes are greater in more permissive environments (Shanahan & Hofer, 2005). In adolescence, greater genetic influences emerge in the context of lower parental monitoring/knowledge (Miles, Silberg, Pickens, & Eaves, 2005) and higher peer deviance (Dick et al., 2007; Harden, Hill, Turkheimer, & Emery, 2008). Urban residency, a higher percentage of young adults in one’s neighborhood, and higher neighborhood transiency (frequent migration in and out of the community) are associated with greater genetic influences on adult alcohol outcomes (Dick, Rose, Viken, Kaprio, & Koskenvuo, 2001; Rose, Dick, Viken, & Kaprio, 2001).

Also related is the process of gene-environment correlation (rGE), whereby individuals create or self-select into particular environments that are associated with their genotype (Scarr & McCartney, 1983). For example, genetic factors that contribute to AUD are also associated with divorce (Salvatore et al., 2017). Thus, individuals who have a genetic predisposition for AUD may also be more likely to find themselves in environments that increase their risk.

GxExD

The importance of different environmental moderators of genetic risk matter varies across developmental stages. For example, parental monitoring shows a greater moderating role on alcohol use starting earlier in adolescence, while the moderating role of peer substance use does not emerge until later in adolescence (Dick et al., 2007). This suggests that genetic influences on alcohol use require understanding the environment, while also keeping mind that the relevance of many environmental factors shifts across development.

Implications for policy

BG methodologies are informative for evidence-based policy because they can tease apart which outcomes are genetically or environmentally influenced and refute the idea that behaviors are biologically determined, or static across environments. They also can achieve a greater traction on causality than epidemiological studies, and thus aid in finding the most efficacious targets for prevention and intervention efforts (McGue, Osler, & Christensen, 2010). However, policy efforts have largely not yet integrated this kind of information. In the interim, we provide some examples of policy interventions that have reduced AM, and we discuss the implications of the existing scientific evidence for developing future policy approaches.

Policy examples

The level-of-response-to-alcohol paradigm (Schuckit, Kalmijn, Smith, Saunders, & Fromme, 2012) tailors prevention toward college students with a low level of response to alcohol (LR), which is a genetically influenced risk factor (Schuckit et al., 2009). Students decreased their heavy drinking when enrolled in a prevention program matched to their known risk factor of low LR. A subsequent replication found that the low LR participants were the most likely to respond to a prevention program, whether it was tailored or not (Savage et al., 2015), indicating that those at highest risk for AM are also most likely to benefit from programming efforts.

Preventure (Conrod, Stewart, Comeau, & Maclean, 2006) is a school-based brief intervention that draws on evidence showing specific personality profiles to be more at risk for developing alcohol problems (i.e., sensation seeking, impulsivity, anxiety sensitivity, and hopelessness/negative thinking). The focus is to teach adolescents more effective coping strategies for their given personality. Preventure reduced drinking quantity, binge drinking rates, and alcohol problems in a pilot study in Canada (Conrod et al., 2006) and demonstrated similar efficacy in the UK (O’Leary-Barrett et al., 2013), Australia (Teesson et al., 2017), and the Netherlands (Lammers et al., 2017).

These examples of personalized prevention/intervention suggest that individually tailored efforts can be effective, illustrating that selective rather than universal prevention might be most beneficial. However, as discussed above, individual-level risk factors are only one piece of the puzzle. In a larger scale program in Iceland, for example, a broad range of individual, family, community, and societal level factors were targeted through collaborations between scientists, practitioners, policy-makers, and the community (Sigfúsdóttir, Thorlindsson, Kristjánsson, Roe, & Allegrante, 2009). Some initiatives supported parents in enforcing curfews, discouraging unsupervised parties, and monitoring youth in the community. Adolescents’ recreational and extracurricular activities opportunities were increased, and support networks between parents, youth, and the community were encouraged. After implementation, the percentage of 10th graders who reported drinking to intoxication in the last 30 days was cut in half over a decade (Sigfúsdóttir et al., 2009). The Icelandic findings suggest that prevention efforts are most effective when they simultaneously strengthen protective factors and reduce risk factors at all levels - individual, family, community, and societal.

The U.S. has relied primarily on tax policy interventions and minimum drinking age laws to reduce AM, with some demonstrated effectiveness. Two systematic reviews showed that higher alcohol costs or taxes consistently decreased excessive drinking, alcohol-related health outcomes, and alcohol-related harms (Elder et al., 2010; Wagenaar et al., 2009). Likewise, a large critical review examining the effect of minimum drinking age law showed an inverse relationship between the minimum drinking age and alcohol consumption as well as traffic accidents (Wagenaar & Toomey, 2002). Thus, large-scale policy efforts that reduce access to alcohol are one effective means of reducing alcohol consumption and related consequences.

Inferences from research

The epidemiological, neurobiological, and behavior genetics research indicate possible directions to improve existing policy. First, although the difficulties in identifying the specific genes or genetic variants that confer risk for AM has made the move towards personalized medicine challenging (Collins & Varmus, 2015), we have gained some knowledge about how genes (in aggregate) influence AM. Research has delineated pathways of genetic risk for alcohol use disorder, and how environmental factors may shape the ultimate expression of that genetic predisposition. For example, knowing that environmental influences matter more than genetic influences on adolescent alcohol use suggests reducing adolescents’ alcohol misuse will be dependent on parental, peer, and school interventions. Likewise, knowing that genetic predispositions for alcohol use disorder manifest earlier in development as higher levels of sensation seeking and impulsivity can help identify children who may benefit most from preventive strategies aimed at channeling those tendencies into more pro-social outlets (e.g., rock climbing, rather than binge drinking) later in life. The gene-environment interplay for alcohol outcomes highlights the roles for parents, peers, romantic partners, family dynamics, employment opportunities, legal restrictions, and cultural norms to shape whether and how a genetic predisposition for alcohol misuse may manifest.

Second, a BG perspective recognizes that individuals, and their inherent biology, exist within multi-leveled systems. Influences at each level shape AM, so the range of targets for policy implementation is broad. An integrative policy approach might include programs that strengthen parenting behaviors and reduce misperceptions of alcohol use norms among peers (at the micro-level), improve neighborhood cohesion and provide school/community activities as an alternative to drinking (at the exo-level), and affect legal availability and reshape cultural practices that normalize binge drinking and other harmful alcohol use (at the macro-level). Such programs could take shape in countless ways.

Third, developmental research provides an overarching structure to interpret and actualize other findings. Because AM is developmentally dynamic and its genetic and environmental determinants change (quantitatively and qualitatively) across time, effective policies for reducing AM will differ across the lifespan. What is considered problematic alcohol use changes from childhood to adulthood; thus the target behavior to be modified and effective policies with which to do so also change rapidly as different genetic, environmental, and social risk/resilience factors become more salient. Even at a within-person level of development, different programming (prevention vs. intervention vs. treatment) will be most applicable at different stages of an individual’s alcohol use trajectory. No one-size-fits all program can effectively reduce AM on a large scale.

Conclusions

Although much remains to be learned, a few conclusions follow from existing research. We can reliably say that any factor that facilitates easier alcohol access increases AM, while socially or legally restrictive factors reduce AM. This well-replicated pattern points to a social/environmental suppression of genetic predispositions. Such mechanisms work indirectly because they reduce overall alcohol use, not specifically problematic use. However, the costs to society are not driven solely by binge drinking and AUDs, as even normative levels of alcohol use can contribute to health consequences, productivity loss, accidents, and crime (Office of the Surgeon General, 2016). On the other hand, individual-level risk factors can be effectively incorporated into targeted programs to prevent or reduce AM among a high-risk subset of the population, who are also the most likely to benefit from programming. The goal of a policy intervention determines which approach is most desirable, although the complexity of alcohol misuse calls for an integrative, multifaceted approach for any effective policy.

A BG perspective provides two takeaways for planning such approaches. First, the heritability of AM means that some people are more at risk of developing problems than others. Knowing the specific genes involved and how genetic risk is shared with other traits and disorders identifies the etiological pathways essential to understanding the nature of AM. Second, the fact that genetic influences on AM change across environmental contexts and across development tells us that policies are worthwhile, because AM is not static, unchangeable behavior. This approach also informs when certain policies would be most effective, and which environmental moderators could be used to enact them. Although we are still far from predicting individual risk of alcohol problems from DNA, gene identification for other complex behavioral traits are rapidly gaining success (CONVERGE Consortium, 2015; Otowa et al., 2016; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). As BG research evolves, identification of specific genes involved in AM may yield richer insight into the biological mechanisms behind heritability and gene-environment interaction. Changing social environments, such as norms about marriage and other social relationships, may provide new avenues for environmental and developmental moderation. Despite these changes, alcohol misuse will always be a complex behavior at the intersection of individual predispositions and nuanced social influences, unfolding across development. Any efforts to fully understand this behavior, and especially to change it, require recognition of its complexities.

Highlights.

  • Alcohol misuse prevalence and characteristics differ across development and numerous demographic factors.

  • Alcohol misuse is heritable, but many genes have small individual effects, most genes have not yet been identified, and their importance changes at different ages.

  • Environmental and social factors can deter access to alcohol, reduce misuse, and diminish the expression of genetic predispositions toward developing problems.

  • Both individually tailored programs and universal policy changes can reduce alcohol misuse, depending on context and goals.

  • The complexity of alcohol misuse requires an integrative, multifaceted policy approach that recognizes individuals as nested within sociocultural influences that change across development.

Acknowledgments

The authors of this work were supported by grants F31AA024378 (Savage), F31AA024380 (Cooke), K01AA024152 (Salvatore) from the National Institute on Alcohol Abuse and Alcoholism. The funding agencies had no further role in the preparation of this work or the decision to submit the manuscript for publication, and the content does not necessarily reflect the official views of these organizations.

Footnotes

Conflicts of interest: None

References

  1. Barnes GM, Reifman AS, Farrell MP, Dintcheff BA. The effects of parenting on the development of adolescent alcohol misuse: a Six-Wave latent growth model. Journal of Marriage and Family. 2000;62(1):175–186. [Google Scholar]
  2. Bronfenbrenner U. The ecology of human development. Cambridge, Massachusetts: Harvard University Press; 1979. [Google Scholar]
  3. Brown SA, McGue M, Maggs J, Schulenberg J, Hingson R, Swartzwelder S, … Murphy S. A developmental perspective on alcohol and youths 16 to 20 years of age. Pediatrics. 2008;121(Suppl 4):S290–310. doi: 10.1542/peds.2007-2243D. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Buhler KM, Gine E, Echeverry-Alzate V, Calleja-Conde J, de Fonseca FR, Lopez-Moreno JA. Common single nucleotide variants underlying drug addiction: more than a decade of research. Addiction Biolology. 2015;20(5):845–871. doi: 10.1111/adb.12204. [DOI] [PubMed] [Google Scholar]
  5. Center for Behavioral Health Statistics and Quality. Key substance use and mental health indicators in the United States: Results from the 2015 National Survey on Drug Use and Health. 2016 (HHS Publication No. SMA 16-4984, NSDUH Series H-51). Retrieved from http://www.samhsa.gov/data/
  6. Cerdá M, Diez-Roux AV, Tchetgen ET, Gordon-Larsen P, Kiefe C. The Relationship Between Neighborhood Poverty and Alcohol Use: Estimation by Marginal Structural Models. Epidemiology. 2010;21(4):482–489. doi: 10.1097/EDE.0b013e3181e13539. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chartier K, Caetano R. Ethnicity and Health Disparities in Alcohol Research. Alcohol Research and Health. 2010;33(1&2):152–160. [PMC free article] [PubMed] [Google Scholar]
  8. Chen CM, Yi H-y, Faden VB. Trends in underage drinking in the United States 1991–2013 (Surveillance Report #101) 2015 Retrieved from http://pubs.niaaa.nih.gov/publications/surveillance101/Underage13.htm.
  9. Collins FS, Varmus H. A new initiative on precision medicine. New England Journal of Medicine. 2015;372(9):793–795. doi: 10.1056/NEJMp1500523. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Conrod PJ, Stewart SH, Comeau N, Maclean AM. Efficacy of cognitive-behavioral interventions targeting personality risk factors for youth alcohol misuse. Journal of Clinical Child and Adolescent Psychology. 2006;35(4):550–563. doi: 10.1207/s15374424jccp3504_6. [DOI] [PubMed] [Google Scholar]
  11. CONVERGE Consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature. 2015 doi: 10.1038/nature14659. Epub ahead of print. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. de Goeij MCM, Suhrcke M, Toffolutti V, van de Mheen D, Schoenmakers TM, Kunst AE. How economic crises affect alcohol consumption and alcohol-related health problems: A realist systematic review. Social Science and Medicine. 2015;131:131–146. doi: 10.1016/j.socscimed.2015.02.025. [DOI] [PubMed] [Google Scholar]
  13. Dick DM, Meyers JL, Rose RJ, Kaprio J, Kendler KS. Measures of current alcohol consumption and problems: two independent twin studies suggest a complex genetic architecture. Alcoholism: Clinical & Experimental Research. 2011;35(12):2152–2161. doi: 10.1111/j.1530-0277.2011.01564.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dick DM, Pagan JL, Viken R, Purcell S, Kaprio J, Pulkkinen L, Rose RJ. Changing environmental influences on substance use across development. Twin Research and Human Genetics. 2007;10(2):315–326. doi: 10.1375/twin.10.2.315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dick DM, Prescott CA, McGue M. The genetics of substance use and substance use disorders. In: Kim YK, editor. Handbook of behavior genetics. New York: Springer; 2009. pp. 433–453. [Google Scholar]
  16. Dick DM, Rose RJ, Viken RJ, Kaprio J, Koskenvuo M. Exploring gene-environment interactions: socioregional moderation of alcohol use. J Abnorm Psychol. 2001;110(4):625–632. doi: 10.1037//0021-843x.110.4.625. [DOI] [PubMed] [Google Scholar]
  17. Dick DM, Smith G, Olausson P, Mitchell SH, Leeman RF, O’Malley SS, Sher K. Understanding the construct of impulsivity and its relationship to alcohol use disorders. Addiction Biolology. 2010;15(2):217–226. doi: 10.1111/j.1369-1600.2009.00190.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Edwards AC, Kendler KS. Alcohol consumption in men is influenced by qualitatively different genetic factors in adolescence and adulthood. Psychological Medicine. 2013;43(9):1857–1868. doi: 10.1017/S0033291712002917. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Elder RW, Lawrence B, Ferguson A, Naimi TS, Brewer RD, Chattopadhyay SK … Task Force on Community Preventive, S. The effectiveness of tax policy interventions for reducing excessive alcohol consumption and related harms. American Journal of Preventive Medicine. 2010;38(2):217–229. doi: 10.1016/j.amepre.2009.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Elgar FJ, Roberts C, Parry-Langdon N, Boyce W. Income inequality and alcohol use: a multilevel analysis of drinking and drunkenness in adolescents in 34 countries. European Journal of Public Health. 2005;15(3):245–250. doi: 10.1093/eurpub/cki093. [DOI] [PubMed] [Google Scholar]
  21. Everitt BJ, Robbins TW. Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nature Neuroscience. 2005;8(11):1481–1489. doi: 10.1038/nn1579. [DOI] [PubMed] [Google Scholar]
  22. Fischer JL, Wiersma JD. Romantic relationships and alcohol use. Current Drug Abuse Reviews. 2012;5(2):98–116. doi: 10.2174/1874473711205020098. [DOI] [PubMed] [Google Scholar]
  23. Galea S, Nandi A, Vlahov D. The Social Epidemiology of Substance Use. Epidemiologic Reviews. 2004;26(1):36–52. doi: 10.1093/epirev/mxh007. [DOI] [PubMed] [Google Scholar]
  24. Grant BF, Goldstein RB, Saha TD, et al. Epidemiology of dsm-5 alcohol use disorder: Results from the national epidemiologic survey on alcohol and related conditions iii. JAMA Psychiatry. 2015;72(8):757–766. doi: 10.1001/jamapsychiatry.2015.0584. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Grucza RA, Bucholz KK, Rice JP, Bierut LJ. Secular trends in the lifetime prevalence of alcohol dependence in the United States: a re-evaluation. Alcoholism: Clinical & Experimental Research. 2008;32(5):763–770. doi: 10.1111/j.1530-0277.2008.00635.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Harden KP, Hill JE, Turkheimer E, Emery RE. Gene-environment correlation and interaction in peer effects on adolescent alcohol and tobacco use. Behavior Genetics. 2008;38(4):339–347. doi: 10.1007/s10519-008-9202-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Hart AB, Kranzler HR. Alcohol Dependence Genetics: Lessons Learned From Genome-Wide Association Studies (GWAS) and Post-GWAS Analyses. Alcoholism: Clinical & Experimental Research. 2015;39(8):1312–1327. doi: 10.1111/acer.12792. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hasin DS, Stinson FS, Ogburn E, Grant BF. Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Archives of General Psychiatry. 2007;64(7):830–842. doi: 10.1001/archpsyc.64.7.830. [DOI] [PubMed] [Google Scholar]
  29. Hughes D, Rodriguez J, Smith EP, Johnson DJ, Stevenson HC, Spicer P. Parents’ ethnic-racial socialization practices: a review of research and directions for future study. Developmental Psychology. 2006;42(5):747–770. doi: 10.1037/0012-1649.42.5.747. [DOI] [PubMed] [Google Scholar]
  30. Johnson TP. Cultural-level influences on substance use & misuse. Substance Use and Misuse. 2007;42(2–3):305–316. doi: 10.1080/10826080601142022. [DOI] [PubMed] [Google Scholar]
  31. Kendler KS. Levels of explanation in psychiatric and substance use disorders: implications for the development of an etiologically based nosology. Molecular Psychiatry. 2012;17(1):11–21. doi: 10.1038/mp.2011.70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Koob GF, Le Moal M. Addiction and the brain antireward system. Annual Review of Psychology. 2008;59:29–53. doi: 10.1146/annurev.psych.59.103006.093548. [DOI] [PubMed] [Google Scholar]
  33. Lammers J, Goossens F, Conrod P, Engels R, Wiers RW, Kleinjan M. Effectiveness of a selective alcohol prevention program targeting personality risk factors: Results of interaction analyses. Addictive Behaviors. 2017;71:82–88. doi: 10.1016/j.addbeh.2017.02.030. [DOI] [PubMed] [Google Scholar]
  34. Long EC, Lonn SL, Sundquist J, Sundquist K, Kendler KS. A National Swedish Longitudinal Twin-Sibling Study of alcohol use disorders among males. Addiction. 2017 doi: 10.1111/add.13833. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Martin JK, Tuch SA, Roman PM. Problem drinking patterns among African Americans: the impacts of reports of discrimination, perceptions of prejudice, and” risky” coping strategies. Journal of Health and Social Behavior. 2003:408–425. [PubMed] [Google Scholar]
  36. Masten AS, Burt KB, Coatsworth JD. Competence and psychopathology in development. In: Cicchetti D, Cohen DJ, editors. Developmental psychopathology: Risk, disorder, and adaptation. 2. Vol. 3. Hoboken, NJ: Wiley; 2006. pp. 696–738. [Google Scholar]
  37. McGue M, Osler M, Christensen K. Causal Inference and Observational Research: The Utility of Twins. Perspect Psychol Sci. 2010;5(5):546–556. doi: 10.1177/1745691610383511. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Meyers JL, Brown Q, Grant BF, Hasin D. Religiosity, race/ethnicity, and alcohol use behaviors in the United States. Psychological Medicine. 2016;47(1):103–114. doi: 10.1017/S0033291716001975. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Meyers JL, Salvatore JE, Vuoksimaa E, Korhonen T, Pulkkinen L, Rose RJ, … Dick DM. Genetic influences on alcohol use behaviors have diverging developmental trajectories: a prospective study among male and female twins. Alcoholism: Clinical & Experimental Research. 2014;38(11):2869–2877. doi: 10.1111/acer.12560. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Miles DR, Silberg JL, Pickens RW, Eaves LJ. Familial influences on alcohol use in adolescent female twins: testing for genetic and environmental interactions. Journal of Studies on Alcohol. 2005;66(4):445–451. doi: 10.15288/jsa.2005.66.445. [DOI] [PubMed] [Google Scholar]
  41. Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Correction: actual causes of death in the United States, 2000. JAMA. 2005;293(3):293–294. doi: 10.1001/jama.293.3.293. [DOI] [PubMed] [Google Scholar]
  42. Mulia N, Karriker-Jaffe KJ. Interactive Influences of Neighborhood and Individual Socioeconomic Status on Alcohol Consumption and Problems. Alcohol and Alcoholism. 2012;47(2):178–186. doi: 10.1093/alcalc/agr168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Naimi TS, Brewer RD, Mokdad A, Denny C, Serdula MK, Marks JS. Binge drinking among us adults. JAMA. 2003;289(1):70–75. doi: 10.1001/jama.289.1.70. [DOI] [PubMed] [Google Scholar]
  44. O’Leary-Barrett M, Topper L, Al-Khudhairy N, Pihl RO, Castellanos-Ryan N, Mackie CJ, Conrod PJ. Two-year impact of personality-targeted, teacher-delivered interventions on youth internalizing and externalizing problems: a cluster-randomized trial. Journal of the American Academy of Child & Adolescent Psychiatry. 2013;52(9):911–920. doi: 10.1016/j.jaac.2013.05.020. [DOI] [PubMed] [Google Scholar]
  45. Office of the Surgeon General. Facing Addiction in America: The Surgeon General’s Report on Alcohol, Drugs, and Health. Washington, DC: US Department of Health and Human Services; 2016. [PubMed] [Google Scholar]
  46. Otowa T, Hek K, Lee M, Byrne EM, Mirza SS, Nivard MG, … Hettema JM. Meta-analysis of genome-wide association studies of anxiety disorders. Molecular Psychiatry. 2016;21(10):1391–1399. doi: 10.1038/mp.2015.197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Perkins HW. Social norms and the prevention of alcohol misuse in collegiate contexts. Journal of Studies on Alcohol Supplement. 2002;(14):164–172. doi: 10.15288/jsas.2002.s14.164. [DOI] [PubMed] [Google Scholar]
  48. Profe W, Wild LG. Mother, father, and grandparent involvement associations with adolescent mental health and substance use. Journal of Family Issues. 2015 0192513X15583069.
  49. Reeb BT, Chan SY, Conger KJ, Martin MJ, Hollis ND, Serido J, Russell ST. Prospective Effects of Family Cohesion on Alcohol-Related Problems in Adolescence: Similarities and Differences by Race/Ethnicity. Journal of Youth and Adolescence. 2015;44(10):1941–1953. doi: 10.1007/s10964-014-0250-4. [DOI] [PubMed] [Google Scholar]
  50. Rehm J, Mathers C, Popova S, Thavorncharoensap M, Teerawattananon Y, Patra J. Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. The Lancet. 2009;373(9682):2223–2233. doi: 10.1016/S0140-6736(09)60746-7. [DOI] [PubMed] [Google Scholar]
  51. Repetti RL, Taylor SE, Seeman TE. Risky families: family social environments and the mental and physical health of offspring. Psychological Bulletin. 2002;128(2):330–366. [PubMed] [Google Scholar]
  52. Rose RJ, Dick DM, Viken RJ, Kaprio J. Gene-environment interaction in patterns of adolescent drinking: regional residency moderates longitudinal influences on alcohol use. Alcoholism: Clinical & Experimental Research. 2001;25(5):637–643. [PubMed] [Google Scholar]
  53. Sacks JJ, Gonzales KR, Bouchery EE, Tomedi LE, Brewer RD. 2010 National and State Costs of Excessive Alcohol Consumption. American Journal of Preventive Medicine. 2015;49(5):e73–e79. doi: 10.1016/j.amepre.2015.05.031. [DOI] [PubMed] [Google Scholar]
  54. Salvatore JE, Larsson Lonn S, Sundquist J, Lichtenstein P, Sundquist K, Kendler KS. Alcohol use disorder and divorce: evidence for a genetic correlation in a population-based Swedish sample. Addiction. 2017;112(4):586–593. doi: 10.1111/add.13719. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Samek DR, Keyes MA, Iacono WG, McGue M. Peer deviance, alcohol expectancies, and adolescent alcohol use: explaining shared and nonshared environmental effects using an adoptive sibling pair design. Behavior Genetics. 2013;43(4):286–296. doi: 10.1007/s10519-013-9595-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Savage JE, Mezuk B. Psychosocial and contextual determinants of alcohol and drug use disorders in the National Latino and Asian American Study. Drug and Alcohol Dependence. 2014;139:71–78. doi: 10.1016/j.drugalcdep.2014.03.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Scarr S, McCartney K. How people make their own environments: a theory of genotype greater than environment effects. Child Development. 1983;54(2):424–435. doi: 10.1111/j.1467-8624.1983.tb03884.x. [DOI] [PubMed] [Google Scholar]
  58. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511(7510):421–427. doi: 10.1038/nature13595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Schuckit MA, Kalmijn JA, Smith TL, Saunders G, Fromme K. Structuring a college alcohol prevention program on the low level of response to alcohol model: a pilot study. Alcohol Clin Exp Res. 2012;36(7):1244–1252. doi: 10.1111/j.1530-0277.2011.01723.x. [DOI] [PubMed] [Google Scholar]
  60. Schuckit MA, Smith TL, Danko GP, Trim R, Bucholz KK, Edenberg HJ, … Dick DM. An evaluation of the full level of response to alcohol model of heavy drinking and problems in COGA offspring. Journal of Studies on Alcohol and Drugs. 2009;70(3):436–445. doi: 10.15288/jsad.2009.70.436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Schulenberg J, O’Malley PM, Bachman JG, Wadsworth KN, Johnston LD. Getting drunk and growing up: trajectories of frequent binge drinking during the transition to young adulthood. Journal of Studies on Alcohol. 1996;57(3):289–304. doi: 10.15288/jsa.1996.57.289. [DOI] [PubMed] [Google Scholar]
  62. Schwantes-An TH, Zhang J, Chen LS, Hartz SM, Culverhouse RC, Chen X, … Saccone NL. Association of the OPRM1 Variant rs1799971 (A118G) with Non-Specific Liability to Substance Dependence in a Collaborative de novo Meta-Analysis of European-Ancestry Cohorts. Behavior Genetics. 2016;46(2):151–169. doi: 10.1007/s10519-015-9737-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Shanahan MJ, Hofer SM. Social context in gene-environment interactions: retrospect and prospect. Journals of Gerontology Series B, Psychological Sciences and Social Sciences. 2005;60(Spec No 1):65–76. doi: 10.1093/geronb/60.special_issue_1.65. [DOI] [PubMed] [Google Scholar]
  64. Sigfúsdóttir ID, Thorlindsson T, Kristjánsson ÁL, Roe KM, Allegrante JP. Substance use prevention for adolescents: the Icelandic model. Health Promotion International. 2009;24(1):16–25. doi: 10.1093/heapro/dan038. [DOI] [PubMed] [Google Scholar]
  65. Stone AL, Becker LG, Huber AM, Catalano RF. Review of risk and protective factors of substance use and problem use in emerging adulthood. Addictive Behaviors. 2012;37(7):747–775. doi: 10.1016/j.addbeh.2012.02.014. [DOI] [PubMed] [Google Scholar]
  66. Su J, Supple AJ. School Substance Use Norms and Racial Composition Moderate Parental and Peer Influences on Adolescent Substance Use. American Journal of Community Psychology. 2016;57(3–4):280–290. doi: 10.1002/ajcp.12043. [DOI] [PubMed] [Google Scholar]
  67. Substance Abuse and Mental Health Services Administration. Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings. 2014. Retrieved from. [PubMed] [Google Scholar]
  68. Sullivan PF, Daly MJ, O’Donovan M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nature Reviews Genetics. 2012;13(8):537–551. doi: 10.1038/nrg3240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Tarter RE, Vanyukov M. Alcoholism: a developmental disorder. Journal of Consulting and Clinical Psychology. 1994;62(6):1096–1107. doi: 10.1037//0022-006x.62.6.1096. [DOI] [PubMed] [Google Scholar]
  70. Teesson M, Newton N, Slade T, Carragher N, Barrett E, Champion K, … Conrod P. Combined universal and selective prevention for adolescent alcohol use: a cluster randomized controlled trial. Psychological Medicine. 2017:1–10. doi: 10.1017/S0033291717000198. [DOI] [PubMed] [Google Scholar]
  71. van Beek JH, Kendler KS, de Moor MH, Geels LM, Bartels M, Vink JM, … Boomsma DI. Stable genetic effects on symptoms of alcohol abuse and dependence from adolescence into early adulthood. Behavior Genetics. 2012;42(1):40–56. doi: 10.1007/s10519-011-9488-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Verhulst B, Neale MC, Kendler KS. The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies. Psychological Medicine. 2015;45(5):1061–1072. doi: 10.1017/S0033291714002165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Wagenaar AC, Salois MJ, Komro KA. Effects of beverage alcohol price and tax levels on drinking: a meta-analysis of 1003 estimates from 112 studies. Addiction. 2009;104(2):179–190. doi: 10.1111/j.1360-0443.2008.02438.x. [DOI] [PubMed] [Google Scholar]
  74. Wagenaar AC, Toomey TL. Effects of minimum drinking age laws: review and analyses of the literature from 1960 to 2000. Journal of Studies on Alcohol, Supplement. 2002;(s14):206–225. doi: 10.15288/jsas.2002.s14.206. [DOI] [PubMed] [Google Scholar]
  75. Wang F, Simen A, Arias A, Lu QW, Zhang H. A large-scale meta-analysis of the association between the ANKK1/DRD2 Taq1A polymorphism and alcohol dependence. Human Genetics. 2013;132(3):347–358. doi: 10.1007/s00439-012-1251-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Wechsler H, Nelson TF. What we have learned from the Harvard School Of Public Health College Alcohol Study: focusing attention on college student alcohol consumption and the environmental conditions that promote it. Journal of Studies on Alcohol and Drugs. 2008;69(4):481–490. doi: 10.15288/jsad.2008.69.481. [DOI] [PubMed] [Google Scholar]
  77. Windle M, Spear LP, Fuligni AJ, Angold A, Brown JD, Pine D, … Dahl RE. Transitions into underage and problem drinking: developmental processes and mechanisms between 10 and 15 years of age. Pediatrics. 2008;121(Suppl 4):S273–289. doi: 10.1542/peds.2007-2243C. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Zintzaras E. Gamma-aminobutyric acid A receptor, alpha-2 (GABRA2) variants as individual markers for alcoholism: a meta-analysis. Psychiatric Genetics. 2012;22(4):189–196. doi: 10.1097/YPG.0b013e328353ae53. [DOI] [PubMed] [Google Scholar]

RESOURCES