Genetics of Addiction: Future Focus on Gene × Environment Interaction?
Abstract
The heritability of substance use is moderate to high. Successful efforts to find genetic variants associated with substance use (smoking, alcohol, cannabis) have been undertaken by large consortia. However, the proportion of phenotypic variance explained by the identified genetic variants is small. Interestingly, there is overlap between the genetic variants that influence different substances. Moreover, there are sets of "substance-specific" genes and sets of genes contributing to a "vulnerability for addictive behavior" in general. It is important to recognize that genes alone do not determine addiction phenotypes: Environmental factors such as parental monitoring, peer pressure, or socioeconomic status also play an important role. Despite a rich epidemiologic literature focused on the social determinants of substance use, few studies have examined the moderation of genetic influences like gene-environment (G × E) interactions. Understanding this balance may hold the key to understanding the individual differences in substance use, abuse, and addictive behavior. Recommendations for future research are described in this commentary and include increasing the power of G × E studies by using state-of-the-art methods such as polygenic risk scores instead of single genetic variants and taking genetic overlap between substances into account. Future genetic studies should also investigate environmental risk factors for addictive behavior more extensively to unravel the interaction between nature and nurture. Focusing on G × E interactions not only will give insight into the underlying biological mechanism but will also characterize subgroups (based on environmental factors) at high risk for addictive behaviors. With this information, we could bridge the gap between fundamental research and applications for society.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Multiple gene variants may interact to influence risk for addiction to different drugs, including genes involved in drug metabolism, dopaminergic and glutamatergic neurotransmission, neuroplasticity, and brain development.
“There are multiple gene variants that may interact to influence risk for addiction to different drugs, includ ing genes involved in the metabolism of drugs, in dopaminergic and glutamatergic neurotransmission, in neuroplasticity, and in brain development 92 .”
From Substance use disorders: a comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention (2023) · cites this paper as [92]
On p. 2, the paper links genetic variants to smoking, alcohol, and cannabis use and describes both substance-specific and shared genetic vulnerability. It identifies drug-metabolizing genes (p. 2) and discusses dopaminergic genes (p. 3), but our copy does not identify glutamatergic neurotransmission, neuroplasticity, or brain-development genes in this context. Its discussion of interactions concerns genes and environmental factors, not interactions among multiple gene variants. (LLM, conf=0.88)
Experimental — accuracy not yet measuredGenetic factors have been estimated to account for about 50% of overall addiction risk.
“Genetic factors have been estimated to account for about 50% of overall addiction risk.”
From Substance use disorders: a comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention (2023) · cites this paper as [92]
On p. 2, the paper says twin studies show that addictive behavior is heritable and describes substance-use heritability as moderate to high. It does not give an estimate of about 50% for overall addiction risk; it discusses heritability across different addiction-related traits rather than a single overall figure. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 37
- Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135, 885-908. doi:10.1037/a0017376 · DOI · Paper page
- Schizophrenia Working Group of the Psychiatric Genomics Consortium, . . . Neale, B. M. (2015). LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47, 291-295. doi: 10.1038/ng.3211 · DOI · Paper page
- Chen, L.-S., Baker, T. B., Jorenby, D., Piper, M., Saccone, N., Johnson, E., . . . Bierut, L. J. (2015). Genetic variation ( CHRNA5 ), medication (combination nicotine replacement therapy vs. varenicline), and smoking cessation. Drug and Alcohol Dependence, 154, 278-282. doi:10.1016/j. drugalcdep.2015.06.022 · DOI
- Dick, D. M., Viken, R., Purcell, S., Kaprio, J., Pulkkinen, L., & Rose, R. J. (2007). Parental monitoring moderates the importance of genetic and environmental influences on adolescent smoking. Journal of Abnormal Psychology, 116, 213-218. doi:10.1037/0021-843X.116.1.213 · DOI
- Distel, M. A., Vink, J. M., Bartels, M., van Beijsterveldt, C. E. M., Neale, M. C., & Boomsma, D. I. (2011). Age moderates non-genetic influences on the initiation of cannabis use: A twin-sibling study in Dutch adolescents and young adults. Addiction, 106, 1658-1666. doi:10.1111/j.1360-0443.2011.03465.x · DOI
- Dudbridge, F. (2013). Power and predictive accuracy of polygenic risk scores. PLOS Genetics, 9 (3), e1003348. doi:10.1371/journal. pgen.1003348 · DOI
- Euesden, J., Lewis, C. M., & O'Reilly, P . F. (2015). PRSice: Polygenic Risk Score software. Bioinformatics, 31, 1466-1468. doi:10.1093/ bioinformatics/btu848
- Geels, L. M., Bartels, M., van Beijsterveldt, T. C. E. M., Willemsen, G., van der Aa, N., Boomsma, D. I., & Vink, J. M. (2012). Trends in adolescent alcohol use: Effects of age, sex and cohort on prevalence and heritability. Addiction, 107, 518-527. doi:10.1111/j.1360-0443.2011.03612.x · DOI
- International Schizophrenia Consortium. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature, 460, 748-752. doi:10.1038/nature08185 · DOI · Paper page
- Johnson, E. O., Chen, L.-S., Breslau, N., Hatsukami, D., Robbins, T., Saccone, N. L., . . . Bierut, L. J. (2010). Peer smoking and the nicotinic receptor genes: An examination of genetic and environmental risks for nicotine dependence. Addiction, 105, 2014-2022. doi:10.1111/j.1360-0443.2010.03074.x · DOI
- Kirst, M., Mecredy, G., Borland, T., & Chaiton, M. (2014). Predictors of substance use among young adults transitioning away from high school: A narrative review. Substance Use & Misuse, 49, 1795-1807. doi:10.31 09/10826084.2014.933240
- Lazarus, R. S. (1993). From psychological stress to the emotions: A history of changing outlooks. Annual Review of Psychology, 44, 1-22. doi:10.1146/annurev.ps.44.020193.000245 · DOI
- Legrand, L. N., Keyes, M., McGue, M., Iacono, W . G., & Krueger, R. F. (2008). Rural environments reduce the genetic influence on adolescent substance use and rule-breaking behavior. Psychological Medicine, 38, 1341-1350. doi:10.1017/S0033291707001596 · DOI
- Lubke, G. H., Hottenga, J. J., Walters, R., Laurin, C., de Geus, E. J. C., Willemsen, G., . . . Boomsma, D. I. (2012). Estimating the genetic variance of major depressive disorder due to all single nucleotide polymorphisms. Biological Psychiatry, 72, 707-709. doi:10.1016/j.biopsych.2012.03.011 · DOI
- Mbarek, H., Milaneschi, Y ., Fedko, I. O., Hottenga, J.-J., de Moor, M. H. M., Jansen, R., . . . Vink, J. M. (2015). The genetics of alcohol dependence: Twin and SNP-based heritability, and genome-wide association study based on AUDIT scores. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 168B, 739-748. doi:10.1002/ajmg.b.32379 · DOI · Paper page
- Meyers, J. L., Cerdá, M., Galea, S., Keyes, K. M., Aiello, A. E., Uddin, M., . . . Koenen, K. C. (2013). Interaction between polygenic risk for cigarette use and environmental exposures in the Detroit Neighborhood Health Study. Translational Psychiatry, 3, e290. doi:10.1038/tp.2013.63 · DOI
- Milaniak, I., Watson, B., & Jaffee, S. R. (2015). Gene-environment interplay and substance use: A review of recent findings. Current Addiction Reports, 2, 364-371. doi:10.1007/s40429-015-0069-4 · DOI
- Nivard, M. G., Verweij, K. J. H., Minica, C. C., & Treur, J. L. International Cannabis Consortium, Vink, J. M., & Boomsma, D. I. (2016). Connecting the dots, genome-wide association studies in substance use. Molecular Psychiatry, 21, 733-735. doi:10.1038/mp.2016.14 · DOI
- Pluess, M., & Belsky, J. (2010). Children's differential susceptibility to effects of parenting. Family Science, 1, 14-25. doi:10.1080/19424620903388554 · DOI
- Salvatore, J. E., Aliev, F ., Edwards, A. C., Evans, D. M., Macleod, J., Hickman, M., . . . Dick, D. M. (2014). Polygenic scores predict alcohol problems in an independent sample and show moderation by the environment. Genes, 5, 330-346. doi:10.3390/genes5020330 · DOI
- Schepis, T. S., & Rao, U. (2005). Epidemiology and etiology of adolescent smoking. Current Opinion in Pediatrics, 17, 607-612. doi:10.1097/01. mop.0000176442.49743.31 · DOI
- Schumann, G., Coin, L. J., Lourdusamy, A., Charoen, P ., Berger, K. H., Stacey, D., . . . Elliott, P. (2011). Genome-wide association and genetic functional studies identify autism susceptibility candidate 2 gene ( AUTS2 ) in the regulation of alcohol consumption. Proceedings of the National Academy of Sciences of the United States of America, 108, 7119-7124. doi:10.1073/pnas.1017288108 · DOI · Paper page
- Stringer, S., Minic , C. C., Verweij, K. J. H., Mbarek, H., Bernard, M., Derringer, J., . . . Vink, J. M. (2016). Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium. Translational Psychiatry, 6, e769. doi:10.1038/tp.2016.36 · DOI
- Thorgeirsson, T. E., Gudbjartsson, D. F ., Surakka, I., Vink, J. M., Amin, N., Geller, F., . . . Stefansson, K. (2010). Sequence variants at CHRNB3CHRNA6 and CYP2A6 affect smoking behavior [Letter]. Nature Genetics, 42, 448-453. doi:10.1038/ng.573 · DOI · Paper page
- Tobacco and Genetics Consortium. (2010). Genome-wide meta-analyses identify multiple loci associated with smoking behavior [Letter]. Nature Genetics, 42, 441-447. doi:10.1038/ng.571 · DOI · Paper page
- Treur, J. L., Boomsma, D. I., Ligthart, L., Willemsen, G., & Vink, J. M. (in press). The heritability of high sugar consumption and the genetic overlap with substance use. American Journal of Clinical Nutrition .
- Treur, J. L., Taylor, A. E., Ware, J. J., Nivard, M. G., Neale, M. C., McMahon, G., . . . Vink, J. M. (2016). Smoking and caffeine consumption: A genetic analysis of their association. Addiction Biology. Advance online publication. doi:10.1111/adb.12391 · DOI
- van Beek, J. H., de Moor, M. H., de Geus, E. J., Lubke, G. H., Vink, J. M., Willemsen, G., & Boomsma, D. I. (2013). The genetic architecture of liver enzyme levels: GGT, ALT and AST. Behavior Genetics, 43, 329-339. doi:10.1007/s10519-013-9593-y · DOI
- Verweij, K. J. H., Zietsch, B. P., Lynskey, M. T., Medland, S. E., Neale, M. C., Martin, N. G., . . . Vink, J. M. (2010). Genetic and environmental influences on cannabis use initiation and problematic use: A meta-analysis of twin studies. Addiction, 105, 417-430. doi:10.1111/j.1360-0443.2009.02831.x · DOI
- Vink, J. M., Beem, A. L., Posthuma, D., Neale, M. C., Willemsen, G., Kendler, K. S., . . . Boomsma, D. I. (2004). Linkage analysis of smoking initiation and quantity in Dutch sibling pairs. The Pharmacogenomics Journal, 4, 274-282. doi:10.1038/sj.tpj.6500255 · DOI
- Vink, J. M., Hottenga, J. J., de Geus, E. J. C., Willemsen, G., Neale, M. C., Furberg, H., & Boomsma, D. I. (2014). Polygenic risk scores for smoking: Predictors for alcohol and cannabis use? Addiction, 109, 1141-1151. doi:10.1111/add.12491 · DOI
- Vink, J. M., Posthuma, D., Neale, M. C., Eline Slagboom, P ., & Boomsma, D. I. (2006). Genome-wide linkage scan to identify loci for age at first cigarette in Dutch sibling pairs. Behavior Genetics, 36, 100-111. doi:10.1007/s10519-005-9012-0 · DOI
- Vink, J. M., Staphorsius, A. S., & Boomsma, D. I. (2009). A genetic analysis of coffee consumption in a sample of Dutch twins. Twin Research and Human Genetics, 12, 127-131. doi:10.1375/twin.12.2.127 · DOI
- Vink, J. M., van Beijsterveldt, T. C. E. M., Huppertz, C., Bartels, M., & Boomsma, D. I. (2016). Heritability of compulsive Internet use in adolescents. Addiction Biology, 21, 460-468. doi:10.1111/adb.12218 · DOI · Paper page
- Vink, J. M., Willemsen, G., & Boomsma, D. I. (2005). Heritability of smoking initiation and nicotine dependence. Behavior Genetics, 35, 397-406. doi:10.1007/s10519-004-1327-8 · DOI
- Vink, J. M., Wolters, L. M., Neale, M. C., & Boomsma, D. I. (2010). Heritability of cannabis initiation in Dutch adult twins. Addictive Behaviors, 35, 172-174. doi:10.1016/j.addbeh.2009.09.015 · DOI
- 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. doi:10.1016/j.cpr.2011.03.005 · DOI
Cited by
Sorted by citations
The Neuroscience of Drug Reward and Addiction
Volkow, N. D., Michaelides, M., & Baler, R. (2019) · Physiological Reviews · doi:10.1152/physrev.00014.2018 · Cited by 863 · see citing works
Volkow, N. D., & Blanco, C. (2023) · World psychiatry · doi:10.1002/wps.21073 · Cited by 503 · see citing works
Adverse childhood experiences (ACEs) and substance use disorder (SUD): A scoping review
Leza, L., Siria, S., López-Goñi, J. J., & Fernández-Montalvo, J. (2021) · Drug and alcohol dependence · doi:10.1016/j.drugalcdep.2021.108563 · Cited by 305 · see citing works
Antón-Galindo, E., Cabana-Domínguez, J., Torrico, B., Corominas, R., Cormand, B., & Fernàndez-Castillo, N. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1293663 · Cited by 13 · see citing works
- Authors
- Vink, J. M.
- Affiliations
- Radboud University Nijmegen (NL)
- Corresponding author
- Vink, Jacqueline M.
- ORCID
- Vink, Jacqueline M.
- Venue
- Journal of Studies on Alcohol and Drugs
- Publisher
- Alcohol Research Documentation, Inc.
- Published
- 2016-09
- Volume
- 77
- Issue
- 5
- Pages
- 684-687
- ISSN
- 1937-1888 (ISSN-L)
- 1938-4114
- OpenAlex
- W2514782766
- Cited by
- 55
- References
- 0
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 5 · 2023: 7 · 2022: 7 · 2021: 8 · 2020: 7 · 2019: 7 · 2018: 1 · 2017: 6 · 2016: 1
- FWCI
- 4.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Studies on Alcohol and Drugs (publishedVersion)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- addiction genetics, gene-environment interaction, polygenic risk score, smoking, alcohol consumption, cannabis use, parental supervision, peer pressure, socioeconomic status
- MeSH terms
- Forecasting, Humans, Phenotype, Risk Factors, Smoking, Genetic Variation, Behavior, Addictive, Substance-Related Disorders, Polymorphism, Single Nucleotide, Gene-Environment Interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review