Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-15T07:26:46.282Z Has data issue: false hasContentIssue false
  • English
  • Français

Genetic risk for schizophrenia is associated with substance use in emerging adulthood: an event-level polygenic prediction model

Published online by Cambridge University Press:  12 October 2018

Travis T. Mallard Open the ORCID record for Travis T. Mallard [Opens in a new window] ,
K. Paige Harden  and
Kim Fromme
Travis T. Mallard*
Affiliation:
Department of Psychology, University of Texas at Austin, 108 E. Dean Keeton Stop A8000, Austin, TX 78712, USA
K. Paige Harden
Affiliation:
Department of Psychology, University of Texas at Austin, 108 E. Dean Keeton Stop A8000, Austin, TX 78712, USA
Kim Fromme
Affiliation:
Department of Psychology, University of Texas at Austin, 108 E. Dean Keeton Stop A8000, Austin, TX 78712, USA
*
Author for correspondence: Travis T. Mallard, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Emerging adulthood is a peak period of risk for alcohol and illicit drug use. Recent advances in psychiatric genetics suggest that the co-occurrence of substance use and psychopathology arises, in part, from a shared genetic etiology. We sought to extend this research by investigating the influence of genetic risk for schizophrenia on trajectories of four substance use behaviors as they occurred across emerging adulthood.

Method

Young adult participants of non-Hispanic European descent provided DNA samples and completed daily reports of substance use for 1 month per year across 4 years (N = 30 085 observations of N = 342 participants). A schizophrenia polygenic score was included in two-level hierarchical linear models designed to test associations between genetic risk for schizophrenia, participant age, and four substance use phenotypes.

Results

Participants with a greater schizophrenia polygenic score experienced greater age-related increases in the likelihood of using substances across emerging adulthood (p < 0.005). Additionally, our results suggest that the polygenic score was positively associated with participants’ overall likelihood to engage in illicit drug use but not alcohol-related substance use.

Conclusions

This study used a novel combination of polygenic prediction and intensive longitudinal methods to characterize the influence of genetic risk for schizophrenia on patterns of age-related change in substance use across emerging adulthood. Results suggest that genetic risk for schizophrenia has developmentally specific effects on substance use behaviors in a non-clinical population of young adults.

Keywords

Emerging adulthoodlongitudinalmultilevel modelingpolygenic predictionschizophreniasubstance use
Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 12 , September 2019 , pp. 2027 - 2035
Copyright
Copyright © Cambridge University Press 2018 

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

1000 Genomes Project Consortium, Auton, A, Brooks, LD, Durbin, RM, Garrison, EP, Kang, HM, Korbel, JO, Marchini, JL, McCarthy, S, McVean, GA and Abecasis, GR (2015). A global reference for human genetic variation. Nature 526, 68–74.Google Scholar
Anderson, CA, Pettersson, FH, Clarke, GM, Cardon, LR, Morris, AP and Zondervan, KT (2010). Data quality control in genetic case-control association studies. Nature Protocols 5, 1564–1573.Google Scholar
Ashenhurst, JR, Harden, KP, Corbin, WR and Fromme, K (2015). Trajectories of binge drinking and personality change across emerging adulthood. Psychology Of Addictive Behaviors 29, 978–991.Google Scholar
Belsky, DW and Israel, S (2014). Integrating genetics and social science: genetic risk scores. Biodemography and Social Biology 60, 137–155.Google Scholar
Benjamin, DJ, Berger, JO, Johannesson, M, Nosek, BA, Wagenmakers, E-J, Berk, R, Bollen, KA, Brembs, B, Brown, L, Camerer, C, Cesarini, D, Chambers, CD, Clyde, M, Cook, TD, De Boeck, P, Dienes, Z, Dreber, A, Easwaran, K, Efferson, C, Fehr, E, Fidler, F, Field, AP, Forster, M, George, EI, Gonzalez, R, Goodman, S, Green, E, Green, DP, Greenwald, AG, Hadfield, JD, Hedges, LV, Held, L, Hua Ho, T, Hoijtink, H, Hruschka, DJ, Imai, K, Imbens, G, Ioannidis, JPA, Jeon, M, Jones, JH, Kirchler, M, Laibson, D, List, J, Little, R, Lupia, A, Machery, E, Maxwell, SE, McCarthy, M, Moore, DA, Morgan, SL, Munafó, M, Nakagawa, S, Nyhan, B, Parker, TH, Pericchi, L, Perugini, M, Rouder, J, Rousseau, J, Savalei, V, Schönbrodt, FD, Sellke, T, Sinclair, B, Tingley, D, Van Zandt, T, Vazire, S, Watts, DJ, Winship, C, Wolpert, RL, Xie, Y, Young, C, Zinman, J and Johnson, VE (2018). Redefine statistical significance. Nature Human Behaviour 2, 6–10.Google Scholar
Carey, CE, Agrawal, A, Bucholz, KK, Hartz, SM, Lynskey, MT, Nelson, EC, Bierut, LJ and Bogdan, R (2016). Associations between polygenic risk for psychiatric disorders and substance involvement. Frontiers in Genetics 7, 1–10.Google Scholar
Caspi, A, Houts, RM, Belsky, DW, Goldman-Mellor, SJ, Harrington, H, Israel, S, Meier, MH, Ramrakha, S, Shalev, I, Poulton, R and Moffitt, TE (2014). The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clinical Psychological Science 2, 119–137.Google Scholar
Chambers, RA, Krystal, JH and Self, DW (2001). A neurobiological basis for substance abuse comorbidity in schizophrenia. Biological Psychiatry 50, 71.Google Scholar
Chang, CC, Chow, CC, Tellier, LCAM, Vattikuti, S, Purcell, SM and Lee, JJ (2015). Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 1–16.Google Scholar
Christie, KA, Burke, JD, Regier, DA, Rae, DS, Boyd, JH and Locke, BZ (1988). Epidemiologic evidence for early onset of mental disorders and higher risk of drug abuse in young adults. American Journal of Psychiatry 145, 971–975.Google Scholar
Clarke, T-K, Adams, MJ, Davies, G, Howard, DM, Hall, LS, Padmanabhan, S, Murray, AD, Smith, BH, Campbell, A, Hayward, C, Porteous, DJ, Deary, IJ and McIntosh, AM (2017). Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N = 112117). Molecular Psychiatry 22, 1376–1384.Google Scholar
Cross-Disorder Group of the Psychiatric Genomics Consortium (2013). Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. The Lancet 381, 1371–1379.Google Scholar
Das, S, Forer, L, Schönherr, S, Sidore, C, Locke, AE, Kwong, A, Vrieze, SI, Chew, EY, Levy, S, McGue, M, Schlessinger, D, Stambolian, D, Loh, PR, Iacono, WG, Swaroop, A, Scott, LJ, Cucca, F, Kronenberg, F, Boehnke, M, Abecasis, GR and Fuchsberger, C (2016). Next-generation genotype imputation service and methods. Nature Genetics 48, 1284–1287.Google Scholar
Edwards, AC and Kendler, KS (2013). Alcohol consumption in men is influenced by qualitatively different genetic factors in adolescence and adulthood. Psychological Medicine 43, 1857–1868.Google Scholar
Fromme, K, Corbin, WR and Kruse, MI (2008). Behavioral risks during the transition from high school to college. Developmental Psychology 44, 1497–1504.Google Scholar
Grant, BF, Goldstein, RB, Saha, TD, Chou, SP, Jung, J, Zhang, H, Pickering, RP, Ruan, WJ, Smith, SM, Huang, B and Hasin, DS (2015). Epidemiology of DSM-5 alcohol use disorder: results from the national epidemiologic survey on alcohol and related conditions-III. JAMA Psychiatry 72, 757.Google Scholar
Grant, BF, Saha, TD, Ruan, WJ, Goldstein, RB, Chou, SP, Jung, J, Zhang, H, Smith, SM, Pickering, RP, Huang, B and Hasin, DS (2016). Epidemiology of DSM-5 drug use disorder: results from the national epidemiologic survey on alcohol and related conditions-III. JAMA Psychiatry 73, 39.Google Scholar
Gratten, J, Wray, NR, Keller, MC and Visscher, PM (2014). Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nature Neuroscience 17, 782–790.Google Scholar
Gurriarán, X, Rodriguez-López, J, Flórez, G, Pereiro, C, Fernández, JM, Fariñas, E, Estévez, V, Group, GS, Arrojo, M and Costas, J (2018). Relationships between substance abuse/dependence and psychiatric disorders based on polygenic scores. Genes, Brain and Behavior, e12504.Google Scholar
Hartz, SM, Horton, A, Oehlert, M, Carey, CE, Agrawal, A, Bogdan, R, Chen, L-S, Hancock, DB, Johnson, EO, Pato, C, Pato, M, Rice, JP and Bierut, LJ (2017). Association between substance use disorder and polygenic liability to schizophrenia. Biological Psychiatry 82, 709–715.Google Scholar
Johnson, W, Hicks, BM, McGue, M and Iacono, WG (2009). How intelligence and education contribute to substance use: hints from the Minnesota Twin family study. Intelligence 37, 613–624.Google Scholar
Johnson, NB, Hayes, LD, Brown, K, Hoo, EC and Ethier, KA (2014). CDC national health report: leading causes of morbidity and mortality and associated behavioral risk and protective factors – United States, 2005–2013. MMWR. Surveillance Summaries 63(Suppl 4), 3–27.Google Scholar
Johnston, LD, O'malley, PM, Bachman, JG and Schulenberg, JE (2011). Monitoring the future national survey results on drug use, 1975–2010. Volume II, college students & adults ages 19–50. Institute for Social Research.Google Scholar
Kalsi, G, Euesden, J, Coleman, JRI, Ducci, F, Aliev, F, Newhouse, SJ, Liu, X, Ma, X, Wang, Y, Collier, DA, Asherson, P, Li, T and Breen, G (2016). Genome-wide association of heroin dependence in Han Chinese. PLoS Genetics 11, e0167388.Google Scholar
Karlsson Linnér, R, Biroli, P, Kong, E, Meddens, SFW, Wedow, R, Fontana, MA, Lebreton, M, Abdellaoui, A, Hammerschlag, AR, Nivard, MG, Okbay, A, Rietveld, CA, Timshel, PN, Tino, SP, Trzaskowski, M, de Vlaming, R, Zünd, CL, Bao, Y, Buzdugan, L, Caplin, AH, Chen, C-Y, Eibich, P, Fontanillas, P, Gonzalez, JR, Joshi, PK, Karhunen, V, Kleinman, A, Levin, RZ, Lill, CM, Meddens, GA, Muntané, G, Sanchez-Roige, S, van Rooij, FJ, Taskesen, E, Wu, Y, Zhang, F, Team 23andMe Research, eQTLgen Consortium, International Cannabis Consortium, Psychiatric Genomics Consortium, Social Science Genetic Association Consortium, Auton, A, Boardman, JD, Clark, DW, Conlin, A, Dolan, CC, Fischbacher, U, Groenen, PJ, Harris, KM, Hasler, G, Hofman, A, Ikram, MA, Jain, S, Karlsson, R, Kessler, RC, Kooyman, M, MacKillop, J, Männikkö, M, Morcillo-Suarez, C, McQueen, MB, Schmidt, KM, Smart, MC, Sutter, M, Thurik, AR, Uitterlinden, AG, White, J, de Wit, H, Yang, J, Bertram, L, Boomsma, D, Esko, T, Fehr, E, Hinds, DA, Johannesson, M, Kumari, M, Laibson, D, Magnusson, PK, Meyer, MN, Navarro, A, Palmer, AA, Pers, TH, Posthuma, D, Schunk, D, Stein, MB, Svento, R, Tiemeier, H, Timmers, PR, Turley, P, Ursano, RJ, Wagner, GG, Wilson, JF, Gratten, J, Lee, JJ, Cesarini, D, Benjamin, DJ, Koellinger, P and Beauchamp, JP (2018). Genome-wide study identifies 611 loci associated with risk tolerance and risky behaviors. bioRxiv. https://doi.org/10.1101/261081Google Scholar
Kendler, KS, Prescott, CA, Myers, J and Neale, MC (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry 60, 929.Google Scholar
Kendler, KS, Schmitt, E, Aggen, SH and Prescott, CA (2008). Genetic and environmental influences on alcohol, caffeine, cannabis, and nicotine use from early adolescence to middle adulthood. Archives of General Psychiatry 65, 674.Google Scholar
Kendler, KS, Aggen, SH, Prescott, CA, Crabbe, J and Neale, MC (2012). Evidence for multiple genetic factors underlying the DSM-IV criteria for alcohol dependence. Molecular Psychiatry 17, 1306–1315.Google Scholar
Kessler, RC, Berglund, P, Demler, O, Jin, R, Merikangas, KR and Walters, EE (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry 62, 593.Google Scholar
Kessler, RC, Amminger, GP, Aguilar-Gaxiola, S, Alonso, J, Lee, S and Ustün, TB (2007). Age of onset of mental disorders: a review of recent literature. Current Opinion in Psychiatry 20, 359–364.Google Scholar
Krapohl, E, Euesden, J, Zabaneh, D, Pingault, J-B, Rimfeld, K, von Stumm, S, Dale, PS, Breen, G, O'Reilly, PF and Plomin, R (2016). Phenome-wide analysis of genome-wide polygenic scores. Molecular Psychiatry 21, 1188–1193.Google Scholar
Loh, P-R, Palamara, PF and Price, AL (2016). Fast and accurate long-range phasing in a UK Biobank cohort. Nature Genetics 48, 811–816.Google Scholar
Mallard, TT, Ashenhurst, JR, Harden, KP and Fromme, K, (2018). GABRA2, alcohol, and illicit drug use: an event-level model of genetic risk for polysubstance use. Journal of Abnormal Psychology 127, 190–201.Google Scholar
Molenaar, PCM and Campbell, CG (2009). The new person-specific paradigm in psychology. Psychological Science 18, 112–117.Google Scholar
Network and Pathway Analysis Subgroup of the Psychiatric Genomics Consortium (2015). Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nature Neuroscience 18, 199–209.Google Scholar
Pasman, JA, Verweij, KJH, Gerring, Z, Stringer, S, Sanchez-Roige, S, Treur, JL, Abdellaoui, A, Nivard, MG, Baselmans, BML, Ong, J-S, Ip, HF, van der Zee, MD, Bartels, M, Day, FR, Fontanillas, P, Elson, SL, Team the 23andMe Research, de Wit, H, Davis, LK, MacKillop, J, Consortium, IC, Derringer, JL, Branje, SJT, Hartman, CA, Heath, AC, van Lier, PAC, Madden, PAF, Magi, R, Meeus, W, Montgomery, GW, Oldehinkel, AJ, Pausova, Z, Ramos-Quiroga, JA, Paus, T, Ribases, M, Kaprio, J, Boks, MPM, Bell, JT, Spector, TD, Gelernter, J, Boomsma, DI, Martin, NG, MacGregor, S, Perry, JRB, Palmer, AA, Posthuma, D, Munafo, MR, Gillespie, NA, Derks, EM and Vink, JM (2018). Genome-wide association analysis of lifetime cannabis use (N = 184 765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use. bioRxiv. https://doi.org/10.1101/234294Google Scholar
Pettersson, E, Larsson, H and Lichtenstein, P (2016). Common psychiatric disorders share the same genetic origin: a multivariate sibling study of the Swedish population. Molecular Psychiatry 21, 717–721.Google Scholar
Polderman, TJC, Benyamin, B, de Leeuw, CA, Sullivan, PF, van Bochoven, A, Visscher, PM and Posthuma, D (2015). Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nature Genetics 47, 702–709.Google Scholar
Polimanti, R, Agrawal, A and Gelernter, J (2017). Schizophrenia and substance use comorbidity: a genome-wide perspective. Genome Medicine 9, 25.Google Scholar
Power, RA, Verweij, KJH, Zuhair, M, Montgomery, GW, Henders, AK, Heath, AC, Madden, PAF, Medland, SE, Wray, NR and Martin, NG (2014). Genetic predisposition to schizophrenia associated with increased use of cannabis. Molecular Psychiatry 19, 1201–1204.Google Scholar
Raudenbush, SW and Bryk, AS (2002). Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd Edn. London: Sage.Google Scholar
Reginsson, GW, Ingason, A, Euesden, J, Bjornsdottir, G, Olafsson, S, Sigurdsson, E, Oskarsson, H, Tyrfingsson, T, Runarsdottir, V, Hansdottir, I, Steinberg, S, Stefansson, H, Gudbjartsson, DF, Thorgeirsson, TE and Stefansson, K (2017). Polygenic risk scores for schizophrenia and bipolar disorder associate with addiction. Addiction Biology 23, 485–492.Google Scholar
Roshyara, NR, Kirsten, H, Horn, K, Ahnert, P and Scholz, M (2014). Impact of pre-imputation SNP-filtering on genotype imputation results. BMC Genetics 15, 88.Google Scholar
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427.Google Scholar
Turner, S, Armstrong, LL, Bradford, Y, Carlson, CS, Dana, C, Crenshaw, AT, De Andrade, M, Doheny, KF, Jonathan, L, Hayes, G, Jarvik, G, Jiang, L, Kullo, IJ, Li, R, Manolio, TA, Matsumoto, M, Mccarty, CA, Andrew, N, Mirel, DB, Paschall, JE, Pugh, EW, Luke, V, Wilke, RA, Zuvich, RL and Ritchie, MD (2011). Quality control procedures for genome wide association studies. Current Proceedings in Human Genetics 68, 1–24.Google Scholar
van Beek, JHDA, Kendler, KS, de Moor, MHM, Geels, LM, Bartels, M, Vink, JM, van den Berg, SM, Willemsen, G and Boomsma, DI (2012). Stable genetic effects on symptoms of alcohol abuse and dependence from adolescence into early adulthood. Behavior Genetics 42, 40–56.Google Scholar
Verweij, KJH, Abdellaoui, A, Nivard, MG, Sainz Cort, A, Ligthart, L, Draisma, HHM and Minică, CC (2017). Short communication: genetic association between schizophrenia and cannabis use. Drug and Alcohol Dependence 171, 117–121.Google Scholar
Whiteford, HA, Degenhardt, L, Rehm, J, Baxter, AJ, Ferrari, AJ, Erskine, HE, Charlson, FJ, Norman, RE, Flaxman, AD, Johns, N, Burstein, R, Murray, CJ and Vos, T (2013). Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. The Lancet 382, 1575–1586.Google Scholar
Supplementary material: File

Mallard et al. supplementary material

Tables S1-S3

Download Mallard et al. supplementary material(File)
File 38.2 KB