Hostname: page-component-8448b6f56d-cfpbc Total loading time: 0 Render date: 2024-04-18T13:47:03.766Z Has data issue: false hasContentIssue false
  • English
  • Français

The moderation of the genetic risk for alcohol and drug use disorders in a Swedish national sample by the genetic aptitude for educational attainment

Published online by Cambridge University Press:  23 December 2021

Kenneth S. Kendler Open the ORCID record for Kenneth S. Kendler [Opens in a new window] ,
Henrik Ohlsson ,
Jan Sundquist  and
Kristina Sundquist
Kenneth S. Kendler*
Affiliation:
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
Henrik Ohlsson
Affiliation:
Center for Primary Health Care Research, Lund University, Malmö, Sweden
Jan Sundquist
Affiliation:
Center for Primary Health Care Research, Lund University, Malmö, Sweden Department of Family Medicine and Community Health, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA
Kristina Sundquist
Affiliation:
Center for Primary Health Care Research, Lund University, Malmö, Sweden Department of Family Medicine and Community Health, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA
*
Author for correspondence: Kenneth S. Kendler, E-mail: Kenneth.Kendler@vcuhealth.org
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Does the genetic aptitude for educational attainment (GAEA) moderate the genetic risk for alcohol use disorder (AUD) and drug use disorder (DUD)?

Methods

In the native Swedish population, born 1960–1980 and followed through 2017 (n = 1 862 435), the family genetic risk score (FGRS) for AUD and DUD and GAEA were calculated from, respectively, the educational attainment and risk for AUD and DUD, of 1st through 5th degree relatives from Swedish national registers. Analyses utilized Aalen's linear hazards models.

Results

Risk for AUD was robustly predicted by the main effects of FGRSAUD [b = 6.32 (95% CI 6.21–6.43), z = 64.9, p < 0.001) and GAEA [b = −2.90 (2.83–2.97), z = 44.1, p < 0.001] and their interaction [b = −1.93 (1.83–2.03), z = 32.9, p < 0.001]. Results were similar for the prediction of DUD by the main effects of FGRSDUD [b = 4.65 (CI 4.56–4.74), z = 59.4, p < 0.001] and GAEA [−2.08 (2.03–2.13), z = 46.4, p < 0.001] and their interaction [b = −1.58 (1.50–1.66)), z = 30.2, p < 0.001]. The magnitude of the interactions between GAEA and FGRSAUD and FGRSDUD in the prediction of, respectively, AUD and DUD was attenuated only slightly by the addition of educational attainment to the model.

Conclusions and relevance

The genetic propensity to high educational attainment robustly moderates the genetic risk for both AUD and DUD such that the impact of the genetic liability to AUD and DUD on the risk of illness is substantially attenuated in those with high v. low GAEA. This effect is not appreciably mediated by the actual level of educational attainment. These naturalistic findings could form the basis of prevention efforts in high-risk youth.

Keywords

Alcohol use disorderdrug use disordereducational attainmentgenetics
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 7 , May 2023 , pp. 3077 - 3084
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

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

References

Aalen, O. O. (1989). A linear regression model for the analysis of life times. Statistics in Medicine, 8(8), 907–925. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/2678347.CrossRefGoogle ScholarPubMed
American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders: Fifth edition, DSM-5. Washington, DC: American Psychiatric Association.Google Scholar
Baker, L. A., Treloar, S. A., Reynolds, C. A., Heath, A. C., & Martin, N. G. (1996). Genetics of educational attainment in Australian twins: Sex differences and secular changes. Behavior Genetics, 26(2), 89–102. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8639155.CrossRefGoogle ScholarPubMed
Barr, P. B., Silberg, J., Dick, D. M., & Maes, H. H. (2018). Childhood socioeconomic status and longitudinal patterns of alcohol problems: Variation across etiological pathways in genetic risk. Social Science & Medicine, 209, 51–58.CrossRefGoogle ScholarPubMed
Belcher, A. M., Volkow, N. D., Moeller, F. G., & Ferre, S. (2014). Personality traits and vulnerability or resilience to substance use disorders. Trends in Cognitive Sciences, 18(4), 211–217. doi: S1364-6613(14)00027-8 [pii];10.1016/j.tics.2014.01.010CrossRefGoogle ScholarPubMed
Belsky, D. W., Moffitt, T. E., Corcoran, D. L., Domingue, B., Harrington, H., Hogan, S., … Williams, B. S. (2016). The genetics of success: How single-nucleotide polymorphisms associated with educational attainment relate to life-course development. Psychological Science, 27(7), 957–972.CrossRefGoogle ScholarPubMed
Bogg, T., & Roberts, B. W. (2004). Conscientiousness and health-related behaviors: A meta-analysis of the leading behavioral contributors to mortality. Psychological Bulletin, 130(6), 887.CrossRefGoogle ScholarPubMed
Branigan, A. R., McCallum, K. J., & Freese, J. (2013). Variation in the heritability of educational attainment: An international meta-analysis. Social Forces, 92(1), 109–140. https://doi.org/10.1093/sf/sot076.CrossRefGoogle Scholar
Bulik-Sullivan, B., Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P. R., … Neale, B. M. (2015). An atlas of genetic correlations across human diseases and traits. Nature Genetics, 47(11), 1236–1241. doi: ng.3406 [pii];10.1038/ng.3406CrossRefGoogle ScholarPubMed
Compton, W. M., Thomas, Y. F., Stinson, F. S., & Grant, B. F. (2007). Prevalence, correlates, disability, and comorbidity of DSM-IV drug abuse and dependence in the United States: Results from the national epidemiologic survey on alcohol and related conditions. Archives of General Psychiatry, 64(5), 566–576. doi: 64/5/566 [pii];10.1001/archpsyc.64.5.566CrossRefGoogle ScholarPubMed
Cross-Disorder Group of the Psychiatric Genomics Consortium (2013). Genome-wide analysis identifies loci with shared effects on five major psychiatric disorders. Lancet (London, England), 381(9875), 1371–1379.CrossRefGoogle Scholar
Cross-Disorder Group of the Psychiatric Genomics Consortium (2019). Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell, 179(7), 1469–1482.e1411. doi: 10.1016/j.cell.2019.11.020CrossRefGoogle Scholar
Davis, C. N., & Slutske, W. S. (2018). Socioeconomic status and adolescent alcohol involvement: Evidence for a gene–environment interaction. Journal of Studies on Alcohol and Drugs, 79(5), 725–732.CrossRefGoogle ScholarPubMed
Eggert, L. L., Thompson, E. A., Herting, J. R., Nicholas, L. J., & Dicker, B. G. (1994). Preventing adolescent drug abuse and high school dropout through an intensive school-based social network development program. American Journal of Health Promotion, 8(3), 202–215. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10172017.CrossRefGoogle ScholarPubMed
Ellingson, J. M., Fleming, K. A., Vergés, A., Bartholow, B. D., & Sher, K. J. (2014). Working memory as a moderator of impulsivity and alcohol involvement: Testing the cognitive-motivational theory of alcohol use with prospective and working memory updating data. Addictive Behaviors, 39(11), 1622–1631.CrossRefGoogle ScholarPubMed
Fletcher, A., Bonell, C., Sorhaindo, A., & Strange, V. (2009). How might schools influence young people's drug use? Development of theory from qualitative case-study research. Journal of Adolescent Health, 45(2), 126–132. doi: S1054-139X(09)00051-2 [pii];10.1016/j.jadohealth.2008.12.021CrossRefGoogle ScholarPubMed
Fothergill, K. E., Ensminger, M. E., Green, K. M., Crum, R. M., Robertson, J., & Juon, H. S. (2008). The impact of early school behavior and educational achievement on adult drug use disorders: A prospective study. Drug and Alcohol Dependence, 92(1–3), 191–199. doi: S0376-8716(07)00291-8 [pii];10.1016/j.drugalcdep.2007.08.001CrossRefGoogle ScholarPubMed
Gauffin, K., Vinnerljung, B., Fridell, M., Hesse, M., & Hjern, A. (2013). Childhood socio-economic status, school failure and drug abuse: A Swedish national cohort study. Addiction, 108(8), 1441–1449. doi: 10.1111/add.12169CrossRefGoogle ScholarPubMed
Hamdi, N. R., Krueger, R. F., & South, S. C. (2015). Socioeconomic status moderates genetic and environmental effects on the amount of alcohol use. Alcoholism: Clinical and Experimental Research, 39(4), 603–610.CrossRefGoogle ScholarPubMed
Hicks, B. M., Iacono, W. G., & McGue, M. (2012). Index of the transmissible common liability to addiction: Heritability and prospective associations with substance abuse and related outcomes. Drug and Alcohol Dependence, 123(Suppl. 1), S18–S23. doi: S0376-8716(11)00554-0 [pii];10.1016/j.drugalcdep.2011.12.017CrossRefGoogle ScholarPubMed
Hirschi, T. (1969). Causes of delinquency. Berkeley, CA: University of California Press.Google Scholar
Hujoel, M. L., Gazal, S., Loh, P.-R., Patterson, N., & Price, A. L. (2020). Liability threshold modeling of case–control status and family history of disease increases association power. Nature Genetics, 52(5), 541–547.CrossRefGoogle ScholarPubMed
Johnson, W., Deary, I. J., & Iacono, W. G. (2009). Genetic and environmental transactions underlying educational attainment. Intelligence, 37(5), 466–478.CrossRefGoogle ScholarPubMed
Kellam, S. G., Brown, C. H., Poduska, J. M., Ialongo, N. S., Wang, W., Toyinbo, P., … Wilcox, H. C. (2008). Effects of a universal classroom behavior management program in first and second grades on young adult behavioral, psychiatric, and social outcomes. Drug and Alcohol Dependence, 95(Suppl 1), S5–S28. doi: S0376-8716(08)00019-7 [pii];10.1016/j.drugalcdep.2008.01.004CrossRefGoogle Scholar
Kendler, K. S., Aggen, S. H., Knudsen, G. P., Roysamb, E., Neale, M. C., & Reichborn-Kjennerud, T. (2011). The structure of genetic and environmental risk factors for syndromal and subsyndromal common DSM-IV axis I and all axis II disorders. American Journal of Psychiatry, 168(1), 29–39. doi: appi.ajp.2010.10030340 [pii];10.1176/appi.ajp.2010.10030340CrossRefGoogle ScholarPubMed
Kendler, K. S., & Gardner, C. O. (2010). Interpretation of interactions: Guide for the perplexed. British Journal of Psychiatry, 197(3), 170–171. doi: 10.1192/bjp.bp.110.081331CrossRefGoogle ScholarPubMed
Kendler, K. S., Ji, J., Edwards, A. C., Ohlsson, H., Sundquist, J., & Sundquist, K. (2015a). An extended Swedish national adoption study of alcohol use disorder. JAMA Psychiatry, 72(3), 211–218. doi: 2088151 [pii];10.1001/jamapsychiatry.2014.2138CrossRefGoogle ScholarPubMed
Kendler, K. S., Lönn, S. L., Salvatore, J., Sundquist, J., & Sundquist, K. (2018a). The origin of spousal resemblance for alcohol use disorder. JAMA Psychiatry, 75(3), 280–286. doi: doi:10.1001/jamapsychiatry.2017.4457CrossRefGoogle ScholarPubMed
Kendler, K. S., & Myers, J. (2014). The boundaries of the internalizing and externalizing genetic spectra in men and women. Psychological Medicine, 44(3), 647–655. doi: 10.1017/S0033291713000585CrossRefGoogle ScholarPubMed
Kendler, K. S., Neale, M. C., Sullivan, P., Corey, L. A., Gardner, C. O., & Prescott, C. A. (1999). A population-based twin study in women of smoking initiation and nicotine dependence. Psychological Medicine, 29(2), 299–308. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10218922.CrossRefGoogle ScholarPubMed
Kendler, K. S., Ohlsson, H., Fagan, A. A., Lichtenstein, P., Sundquist, J., & Sundquist, K. (2018b). Academic achievement and drug abuse risk assessed using instrumental variable analysis and co-relative designs. JAMA Psychiatry, 75(11), 1182–1188. doi: 2697855 [pii];10.1001/jamapsychiatry.2018.2337CrossRefGoogle ScholarPubMed
Kendler, K. S., Ohlsson, H., Fagan, A. A., Lichtenstein, P., Sundquist, J., & Sundquist, K. (2020). Nature of the causal relationship between academic achievement and the risk for alcohol use disorder. Journal of Studies on Alcohol and Drugs, 81(4), 446–453. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/32800080.CrossRefGoogle ScholarPubMed
Kendler, K. S., Ohlsson, H., Maes, H. H., Sundquist, K., Lichtenstein, P., & Sundquist, J. (2015b). A population-based Swedish twin and sibling study of cannabis, stimulant and sedative abuse in men. Drug and Alcohol Dependence, 149, 49–54. doi: 10.1016/j.drugalcdep.2015.01.016CrossRefGoogle ScholarPubMed
Kendler, K. S., Ohlsson, H., Mościcki, E. K., Sundquist, J., Edwards, A. C., & Sundquist, K. (In press). Genetic liability to suicide attempt, suicide death and psychiatric and substance use disorders on the risk for suicide attempt and suicide death: A Swedish national study. Psychological Medicine.Google Scholar
Kendler, K. S., Ohlsson, H., Sundquist, J., & Sundquist, K. (2021a). Family genetic risk scores and the genetic architecture of major affective and psychotic disorders in a Swedish national sample. JAMA Psychiatry, 78, 735–743. doi: 10.1001/jamapsychiatry.2021.0336CrossRefGoogle Scholar
Kendler, K. S., Ohlsson, H., Sundquist, J., & Sundquist, K. (2021b). The patterns of family genetic risk scores for eleven major psychiatric and substance use disorders in a Swedish national sample. Translational Psychiatry, 11(1), 1–8.CrossRefGoogle Scholar
Kessler, R. C., Crum, R. M., Warner, L. A., Nelson, C. B., Schulenberg, J., & Anthony, J. C. (1997). Lifetime co-occurrence of DSM-III-R alcohol abuse and dependence with other psychiatric disorders in the National Comorbidity Survey. Archives of General Psychiatry, 54(4), 313–321. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9107147.CrossRefGoogle ScholarPubMed
Kranzler, H. R., Zhou, H., Kember, R. L., Smith, R. V., Justice, A. C., Damrauer, S., … Reid, J. (2019). Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nature Communications, 10(1), 1499.CrossRefGoogle ScholarPubMed
Krapohl, E., Rimfeld, K., Shakeshaft, N. G., Trzaskowski, M., McMillan, A., Pingault, J.-B., … Dale, P. S. (2014). The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence. Proceedings of the National Academy of Sciences, 111(42), 15273–15278.CrossRefGoogle Scholar
Kringlen, E., Torgersen, S., & Cramer, V. (2001). A Norwegian psychiatric epidemiological study. American Journal of Psychiatry, 158(7), 1091–1098. doi: 10.1176/appi.ajp.158.7.1091CrossRefGoogle ScholarPubMed
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., … Cesarini, D. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112–1121. doi: 10.1038/s41588-018-0147-3CrossRefGoogle ScholarPubMed
Lewis, K. M., Bavarian, N., Snyder, F. J., Acock, A., Day, J., DuBois, D. L., … Flay, B. R. (2012). Direct and mediated effects of a social-emotional and character development program on adolescent substance use. International Journal of Emotional Education 4(1), 56–78. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/24308013.Google ScholarPubMed
Martinussen, T., & Scheike, T. H. (2006). Dynamic regression models for survival data (2006 edition ed.). New York, NY: Springer.Google Scholar
Müller, M., Kowalewski, R., Metzler, S., Stettbacher, A., Rössler, W., & Vetter, S. (2013). Associations between IQ and alcohol consumption in a population of young males: A large database analysis. Social Psychiatry and Psychiatric Epidemiology, 48(12), 1993–2005.CrossRefGoogle Scholar
Prescott, C. A., & Kendler, K. S. (1999). Genetic and environmental contributions to alcohol abuse and dependence in a population-based sample of male twins. American Journal of Psychiatry, 156(1), 34–40. doi: 10.1176/ajp.156.1.34CrossRefGoogle Scholar
R Core Team (2020). R: A language and environment for statistical computing. Retrieved from https://www.R-project.org/.Google Scholar
Rogne, A. F., Pedersen, W., & Von Soest, T. (2021). Intelligence, alcohol consumption, and adverse consequences. A study of young Norwegian men. Scandinavian Journal of Public Health, 49(4), 411–418.CrossRefGoogle ScholarPubMed
Rosoff, D. B., Kaminsky, Z. A., McIntosh, A. M., Smith, G. D., & Lohoff, F. W. (2020). Educational attainment reduces the risk of suicide attempt among individuals with and without psychiatric disorders independent of cognition: A bidirectional and multivariable Mendelian randomization study with more than 815000 participants. Translational Psychiatry, 10(1), 1–15.CrossRefGoogle Scholar
SAS Institute, Inc. (2012). SAS/STAT® Online Documentation, Version 9.4. Cary, N.C.: SAS Institute, Inc. In. (Reprinted from: Not in File).Google Scholar
Scheike, T. H., & Zhang, M. J. (2011). Analyzing competing risk data using the R timereg package. Journal of Statistical Software, 38(2). Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/22707920.CrossRefGoogle Scholar
Schulenberg, J., Bachman, J. G., O'Malley, P. M., & Johnston, L. D. (1994). High school educational success and subsequent substance use: A panel analysis following adolescents into young adulthood. Journal of Health and Social Behavior, 35(1), 45–62. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8014429.CrossRefGoogle ScholarPubMed
Settles, R. E., Fischer, S., Cyders, M. A., Combs, J. L., Gunn, R. L., & Smith, G. T. (2012). Negative urgency: A personality predictor of externalizing behavior characterized by neuroticism, low conscientiousness, and disagreeableness. Journal of Abnormal Psychology, 121(1), 160.CrossRefGoogle ScholarPubMed
Sjolund, S., Allebeck, P., & Hemmingsson, T. (2012). Intelligence quotient (IQ) in adolescence and later risk of alcohol-related hospital admissions and deaths – 37-year follow-up of Swedish conscripts. Addiction, 107(1), 89–97. doi: 10.1111/j.1360-0443.2011.03544.xCrossRefGoogle ScholarPubMed
Sjölund, S., Hemmingsson, T., & Allebeck, P. (2015a). IQ and level of alcohol consumption – findings from a national survey of Swedish conscripts. Alcoholism: Clinical and Experimental Research, 39(3), 548–555.CrossRefGoogle ScholarPubMed
Sjölund, S., Hemmingsson, T., Gustafsson, J.-E., & Allebeck, P. (2015b). IQ and alcohol-related morbidity and mortality among Swedish men and women: The importance of socioeconomic position. Journal of Epidemiology and Community Health, 69(9), 858–864.CrossRefGoogle ScholarPubMed
Smith-Woolley, E., Selzam, S., & Plomin, R. (2019). Polygenic score for educational attainment captures DNA variants shared between personality traits and educational achievement. Journal of Personality and Social Psychology, 117(6), 1145.CrossRefGoogle ScholarPubMed
Tambs, K., Sundet, J. M., Magnus, P., & Berg, K. (1989). Genetic and environmental contributions to the covariance between occupational status, educational attainment, and IQ: A study of twins. Behavior Genetics, 19(2), 209–222.CrossRefGoogle Scholar
Tang, Y.-Y., Posner, M. I., Rothbart, M. K., & Volkow, N. D. (2015). Circuitry of self-control and its role in reducing addiction. Trends in Cognitive Sciences, 19(8), 439–444.CrossRefGoogle ScholarPubMed
Tsuang, M. T., Lyons, M. J., Eisen, S. A., Goldberg, J., True, W., Lin, N., … Eaves, L. (1996). Genetic influences on DSM-III-R drug abuse and dependence: A study of 3,372 twin pairs. American Journal of Medical Genetics, 67(5), 473–477. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8886164.3.0.CO;2-L>CrossRefGoogle ScholarPubMed
Verhulst, B., Neale, M. C., & Kendler, K. S. (2015). The heritability of alcohol use disorders: A meta-analysis of twin and adoption studies. Psychological Medicine, 45(5), 1061–1072. doi: S0033291714002165 [pii];10.1017/S0033291714002165CrossRefGoogle ScholarPubMed
Wennberg, P., Andersson, T., & Bohman, M. (2002). Psychosocial characteristics at age 10; differentiating between adult alcohol use pathways: A prospective longitudinal study. Addictive Behaviors, 27(1), 115–130.CrossRefGoogle Scholar
Zettergren, P., & Bergman, L. R. (2014). Adolescents with high IQ and their adjustment in adolescence and midlife. Research in Human Development, 11(3), 186–203.CrossRefGoogle Scholar
Supplementary material: File

Kendler et al. supplementary material

Kendler et al. supplementary material

Download Kendler et al. supplementary material(File)
File 243.9 KB