Hostname: page-component-7857688df4-mqpdw Total loading time: 0 Render date: 2025-11-13T00:41:40.628Z Has data issue: false hasContentIssue false
  • English
  • Français

Genetic correlation, pleiotropy, and causal associations between substance use and psychiatric disorder

Published online by Cambridge University Press:  07 August 2020

Seon-Kyeong Jang ,
Gretchen Saunders ,
MengZhen Liu ,
Yu Jiang ,
Dajiang J. Liu ,
Scott Vrieze  and
23andMe Research Team
Seon-Kyeong Jang
Affiliation:
Department of Psychology, University of Minnesota, Minneapolis, MN, USA
Gretchen Saunders
Affiliation:
Department of Psychology, University of Minnesota, Minneapolis, MN, USA
MengZhen Liu
Affiliation:
Department of Psychology, University of Minnesota, Minneapolis, MN, USA
Yu Jiang
Affiliation:
Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA Institute of Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA
Dajiang J. Liu
Affiliation:
Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA Institute of Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA
Scott Vrieze*
Affiliation:
Department of Psychology, University of Minnesota, Minneapolis, MN, USA
*
Author for correspondence: Scott Vrieze, E-mail: vrieze@umn.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Substance use occurs at a high rate in persons with a psychiatric disorder. Genetically informative studies have the potential to elucidate the etiology of these phenomena. Recent developments in genome-wide association studies (GWAS) allow new avenues of investigation.

Method

Using results of GWAS meta-analyses, we performed a factor analysis of the genetic correlation structure, a genome-wide search of shared loci, and causally informative tests for six substance use phenotypes (four smoking, one alcohol, and one cannabis use) and five psychiatric disorders (ADHD, anorexia, depression, bipolar disorder, and schizophrenia).

Results

Two correlated externalizing and internalizing/psychosis factor were found, although model fit was beneath conventional standards. Of 458 loci reported in previous univariate GWAS of substance use and psychiatric disorders, about 50% (230 loci) were pleiotropic with additional 111 pleiotropic loci not reported from past GWAS. Of the 341 pleiotropic loci, 152 were associated with both substance use and psychiatric disorders, implicating neurodevelopment, cell morphogenesis, biological adhesion pathways, and enrichment in 13 different brain tissues. Seventy-five and 114 pleiotropic loci were specific to either psychiatric disorders or substance use phenotypes, implicating neuronal signaling pathway and clathrin-binding functions/structures, respectively. No consistent evidence for phenotypic causation was found across different Mendelian randomization methods.

Conclusions

Genetic etiology of substance use and psychiatric disorders is highly pleiotropic and involves shared neurodevelopmental path, neurotransmission, and intracellular trafficking. In aggregate, the patterns are not consistent with vertical pleiotropy, more likely reflecting horizontal pleiotropy or more complex forms of phenotypic causation.

Keywords

AddictioncomorbidityGWASMendelian randomizationpleiotropypsychopathology

Information

Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 5 , April 2022 , pp. 968 - 978
Check for updates
Copyright
Copyright © The Author(s) 2020. 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.)

Article purchase

Temporarily unavailable

References

Aguet, F., Barbeira, A. N., Bonazzola, R., Brown, A., Castel, S. E., Jo, B., … Lappalainen, T. (2019). The GTEx Consortium atlas of genetic regulatory effects across human tissues. BioRxiv. doi:787903. 10.1101/787903.Google Scholar
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. doi:10.1109/TAC.1974.1100705.CrossRefGoogle Scholar
Barkus, E., & Murray, R. M. (2010). Substance use in adolescence and psychosis: Clarifying the relationship. Annual Review of Clinical Psychology, 6(1), 365–389. doi:10.1146/annurev.clinpsy.121208.131220.CrossRefGoogle ScholarPubMed
Berisa, T., & Pickrell, J. K. (2016). Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics (Oxford, England), 32(2), 283–285. doi:10.1093/bioinformatics/btv546.Google ScholarPubMed
Blanco, C., Hasin, D. S., Wall, M. M., Flórez-Salamanca, L., Hoertel, N., Wang, S., … Olfson, M. (2016). Cannabis use and risk of psychiatric disorders: Prospective evidence from a US national longitudinal study. JAMA Psychiatry, 73(4), 388–395. doi:10.1001/jamapsychiatry.2015.3229.CrossRefGoogle ScholarPubMed
Bowden, J., Hemani, G., & Davey Smith, G. (2018). Invited commentary: Detecting individual and global horizontal pleiotropy in Mendelian randomization – a job for the humble heterogeneity statistic? American Journal of Epidemiology, 187(12), 2681–2685. doi:10.1093/aje/kwy185.Google ScholarPubMed
Brebner, K., Wong, T. P., Liu, L., Liu, Y., Campsall, P., Gray, S., … Wang, Y. T. (2005). Nucleus accumbens long-term depression and the expression of behavioral sensitization. Science (New York, N.Y.), 310(5752), 1340–1343. doi:10.1126/science.1116894.CrossRefGoogle ScholarPubMed
Brook, J. S., Cohen, P., & Brook, D. W. (1998). Longitudinal study of co-occurring psychiatric disorders and substance use. Journal of the American Academy of Child & Adolescent Psychiatry, 37(3), 322–330. doi:10.1097/00004583-199803000-00018.CrossRefGoogle ScholarPubMed
Brown, S., Inskip, H., & Barraclough, B. (2000). Causes of the excess mortality of schizophrenia. The British Journal of Psychiatry, 177(3), 212–217. doi:10.1192/bjp.177.3.212.CrossRefGoogle ScholarPubMed
Conway, K. P., Swendsen, J., Husky, M. M., He, J.-P., & Merikangas, K. R. (2016). Association of lifetime mental disorders and subsequent alcohol and illicit drug use: Results from the national comorbidity survey–adolescent supplement. Journal of the American Academy of Child & Adolescent Psychiatry, 55(4), 280–288. doi:10.1016/j.jaac.2016.01.006.CrossRefGoogle ScholarPubMed
Dalton, E. J., Cate-Carter, T. D., Mundo, E., Parikh, S. V., & Kennedy, J. L. (2003). Suicide risk in bipolar patients: The role of co-morbid substance use disorders. Bipolar Disorders, 5(1), 58–61. doi:10.1034/j.1399-5618.2003.00017.x.CrossRefGoogle ScholarPubMed
Davey Smith, G., & Ebrahim, S. (2003). ‘Mendelian randomization’: Can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology, 32(1), 1–22. doi:10.1093/ije/dyg070.CrossRefGoogle Scholar
Demontis, D., Walters, R. K., Martin, J., Mattheisen, M., Als, T. D., Agerbo, E., … Neale, B. M. (2019). Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nature Genetics, 51(1), 63–75. doi:10.1038/s41588-018-0269-7.CrossRefGoogle ScholarPubMed
Ezzati, M., Lopez, A. D., Rodgers, A., Vander Hoorn, S., & Murray, C. J. (2002). Selected major risk factors and global and regional burden of disease. The Lancet, 360(9343), 1347–1360. doi:10.1016/S0140-6736(02)11403-6.CrossRefGoogle ScholarPubMed
Fluharty, M. E., Sallis, H., & Munafò, M. R. (2018). Investigating possible causal effects of externalizing behaviors on tobacco initiation: A Mendelian randomization analysis. Drug and Alcohol Dependence, 191, 338–342. doi:10.1016/j.drugalcdep.2018.07.015.CrossRefGoogle ScholarPubMed
Gage, S. H., Jones, H. J., Taylor, A. E., Burgess, S., Zammit, S., & Munafò, M. R. (2017). Investigating causality in associations between smoking initiation and schizophrenia using Mendelian randomization. Scientific Reports, 7(1), 1–8. doi:10.1038/srep40653.CrossRefGoogle ScholarPubMed
Gregg, L., Barrowclough, C., & Haddock, G. (2007). Reasons for increased substance use in psychosis. Clinical Psychology Review, 27(4), 494–510. doi:10.1016/j.cpr.2006.09.004.CrossRefGoogle ScholarPubMed
Grotzinger, A. D., Rhemtulla, M., de Vlaming, R., Ritchie, S. J., Mallard, T. T., Hill, W. D., … Tucker-Drob, E. M. (2019). Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nature Human Behaviour, 3(5), 513–525. doi:10.1038/s41562-019-0566-x.CrossRefGoogle ScholarPubMed
Hartz, S. M., Pato, C. N., Medeiros, H., Cavazos-Rehg, P., Sobell, J. L., Knowles, J. A., … Pato, M. T. (2014). Comorbidity of severe psychotic disorders with measures of substance use. JAMA Psychiatry, 71(3), 248–254. doi:10.1001/jamapsychiatry.2013.3726.CrossRefGoogle ScholarPubMed
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., … Haycock, P. C. (2018). The MR-Base platform supports systematic causal inference across the human phenome. Elife, 7, e34408. doi:10.7554/eLife.34408.CrossRefGoogle ScholarPubMed
Hicks, B. M., Krueger, R. F., Iacono, W. G., McGue, M., & Patrick, C. J. (2004). Family transmission and heritability of externalizing disorders: A twin-family study. Archives of General Psychiatry, 61(9), 922–928. doi:10.1001/archpsyc.61.9.922.CrossRefGoogle ScholarPubMed
Hjorthøj, C., Østergaard, M. L. D., Benros, M. E., Toftdahl, N. G., Erlangsen, A., Andersen, J. T., & Nordentoft, M. (2015). Association between alcohol and substance use disorders and all-cause and cause-specific mortality in schizophrenia, bipolar disorder, and unipolar depression: A nationwide, prospective, register-based study. The Lancet Psychiatry, 2(9), 801–808. doi:10.1016/S2215-0366(15)00207-2.CrossRefGoogle ScholarPubMed
Hodgson, K., Coleman, J. R., Hagenaars, S. P., Purves, K. L., Glanville, K., Choi, S. W., … Lewis, C. M. (2020). Cannabis use, depression and self-harm: Phenotypic and genetic relationships. Addiction, 115(3), 482–492. doi:10.1111/add.14845.CrossRefGoogle ScholarPubMed
Jamuar, S. S., Schmitz-Abe, K., D'Gama, A. M., Drottar, M., Chan, W. M., Peeva, M., … Yu, T. W. (2017). Biallelic mutations in human DCC cause developmental split-brain syndrome. Nature Genetics, 49(4), 606–612. doi:10.1038/ng.3804.CrossRefGoogle ScholarPubMed
Johnson, J. G., Cohen, P., Pine, D. S., Klein, D. F., Kasen, S., & Brook, J. S. (2000). Association between cigarette smoking and anxiety disorders during adolescence and early adulthood. JAMA, 284(18), 2348–2351. doi:10.1001/jama.284.18.2348.CrossRefGoogle ScholarPubMed
Jung, N., & Haucke, V. (2007). Clathrin-mediated endocytosis at synapses. Traffic (Copenhagen, Denmark), 8(9), 1129–1136. doi:10.1111/j.1600-0854.2007.00595.x.CrossRefGoogle ScholarPubMed
Kaksonen, M., & Roux, A. (2018). Mechanisms of clathrin-mediated endocytosis. Nature Reviews Molecular Cell Biology, 19(5), 313–326. doi:10.1038/nrm.2017.132.CrossRefGoogle ScholarPubMed
Kask, J., Ekselius, L., Brandt, L., Kollia, N., Ekbom, A., & Papadopoulos, F. C. (2016). Mortality in women with anorexia nervosa: The role of comorbid psychiatric disorders. Psychosomatic Medicine, 78(8), 910–919. doi:10.1097/PSY.0000000000000342.CrossRefGoogle ScholarPubMed
Kendler, K. S., Aggen, S. H., Knudsen, G. P., Røysamb, 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:10.1176/appi.ajp.2010.10030340.CrossRefGoogle ScholarPubMed
Khantzian, E. J. (1997). The self-medication hypothesis of substance use disorders: A reconsideration and recent applications. Harvard Review of Psychiatry, 4(5), 231–244. doi:10.3109/10673229709030550.CrossRefGoogle ScholarPubMed
King, S. M., Iacono, W. G., & McGue, M. (2004). Childhood externalizing and internalizing psychopathology in the prediction of early substance use. Addiction, 99(12), 1548–1559. doi:10.1111/j.1360-0443.2004.00893.x.CrossRefGoogle ScholarPubMed
Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nature Genetics, 46(3), 310–315. doi:10.1038/ng.2892.CrossRefGoogle ScholarPubMed
Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., Bagby, R.M., … Zimmerman, M. (2017). The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126(4), 454–477. doi:10.1037/abn0000258.CrossRefGoogle ScholarPubMed
Krueger, R. F., & Markon, K. E. (2006). Reinterpreting comorbidity: A model-based approach to understanding and classifying psychopathology. Annual Review of Clinical Psychology, 2, 111–133. doi:10.1146/annurev.clinpsy.2.022305.095213.CrossRefGoogle ScholarPubMed
Lasser, K., Boyd, J. W., Woolhandler, S., Himmelstein, D. U., McCormick, D., & Bor, D. H. (2000). Smoking and mental illness: A population-based prevalence study. JAMA, 284(20), 2606–2610. doi:10.1001/jama.284.20.2606.CrossRefGoogle ScholarPubMed
Lee, P. H., Anttila, V., Won, H., Feng, Y. C. A., Rosenthal, J., Zhu, Z., … Smoller, J. W. (2019). Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell, 179(7), 1469–1482. doi:10.1016/j.cell.2019.11.020.CrossRefGoogle Scholar
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., … Fontana, M. A. (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.CrossRefGoogle ScholarPubMed
Linnér, R. K., Biroli, P., Kong, E., Meddens, S. F. W., Wedow, R., Fontana, M. A., … Nivard, M. G. (2019). Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nature Genetics, 51(2), 245–257. doi:10.1038/s41588-018-0309-3.CrossRefGoogle Scholar
Liu, M., Jiang, Y., Wedow, R., Li, Y., Brazel, D. M., Chen, F., … Vrieze, S. (2019). Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nature Genetics, 51(2), 237–244. doi:10.1038/s41588-018-0307-5.CrossRefGoogle ScholarPubMed
Lo, M.-T., Hinds, D. A., Tung, J. Y., Franz, C., Fan, C.-C., Wang, Y., … Chen, C.-H. (2017). Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nature Genetics, 49(1), 152–156. doi:10.1038/ng.3736.CrossRefGoogle ScholarPubMed
Margolese, H. C., Malchy, L., Negrete, J. C., Tempier, R., & Gill, K. (2004). Drug and alcohol use among patients with schizophrenia and related psychoses: Levels and consequences. Schizophrenia Research, 67(2–3), 157–166. doi:10.1016/S0920-9964(02)00523-6.CrossRefGoogle ScholarPubMed
Miettunen, J., Murray, G. K., Jones, P. B., Mäki, P., Ebeling, H., Taanila, A., … Moilanen, I. (2014). Longitudinal associations between childhood and adulthood externalizing and internalizing psychopathology and adolescent substance use. Psychological Medicine, 44(8), 1727–1738. doi:10.1017/S0033291713002328.CrossRefGoogle ScholarPubMed
Mitchell, E. S., Conus, N., & Kaput, J. (2014). B vitamin polymorphisms and behavior: Evidence of associations with neurodevelopment, depression, schizophrenia, bipolar disorder and cognitive decline. Neuroscience and Biobehavioral Reviews, 47, 307–320. doi:10.1016/j.neubiorev.2014.08.006.CrossRefGoogle ScholarPubMed
Morón, J. A., Abul-Husn, N. S., Rozenfeld, R., Dolios, G., Wang, R., & Devi, L. A. (2007). Morphine administration alters the profile of hippocampal postsynaptic density-associated proteins: A proteomics study focusing on endocytic proteins. Molecular & Cellular Proteomics, 6(1), 29–42. doi:10.1074/mcp.M600184-MCP200.CrossRefGoogle ScholarPubMed
O'Connor, L. J., & Price, A. L. (2018). Distinguishing genetic correlation from causation across 52 diseases and complex traits. Nature Genetics, 50(12), 1728–1734. doi:10.1038/s41588-018-0255-0.CrossRefGoogle ScholarPubMed
Paaby, A. B., & Rockman, M. V. (2013). The many faces of pleiotropy. Trends in Genetics, 29(2), 66–73. doi:10.1016/j.tig.2012.10.010.CrossRefGoogle ScholarPubMed
Pardiñas, A. F., Holmans, P., Pocklington, A. J., Escott-Price, V., Ripke, S., Carrera, N., … Walters, J. T. R. (2018). Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nature Genetics, 50(3), 381–389. doi:10.1038/s41588-018-0059-2.CrossRefGoogle ScholarPubMed
Pasman, J. A., Verweij, K. J. H., Gerring, Z., Stringer, S., Sanchez-Roige, S., Treur, J. L., … Vink, J. M. (2018). GWAS Of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nature Neuroscience, 21(9), 1161–1170. doi:10.1038/s41593-018-0206-1.CrossRefGoogle Scholar
Pickrell, J. K., Berisa, T., Liu, J. Z., Ségurel, L., Tung, J. Y., & Hinds, D. A. (2016). Detection and interpretation of shared genetic influences on 42 human traits. Nature Genetics, 48(7), 709–717. doi:10.1038/ng.3570.CrossRefGoogle ScholarPubMed
Rosenström, T., Gjerde, L. C., Krueger, R. F., Aggen, S. H., Czajkowski, N. O., Gillespie, N. A., … Ystrom, E. (2019). Joint factorial structure of psychopathology and personality. Psychological Medicine, 49(13), 2158–2167. doi:10.1017/S0033291718002982.CrossRefGoogle Scholar
Ruderfer, D. M., Ripke, S., McQuillin, A., Boocock, J., Stahl, E. A., Pavlides, J. M. W., … Kendler, K. S. (2018). Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell, 173(7), 1705–1715.e16. doi:10.1016/j.cell.2018.05.046.CrossRefGoogle Scholar
Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M., & Smoller, J. W. (2013). Pleiotropy in complex traits: Challenges and strategies. Nature Reviews Genetics, 14(7), 483–495. doi:10.1038/nrg3461.CrossRefGoogle ScholarPubMed
Stahl, E. A., Breen, G., Forstner, A. J., McQuillin, A., Ripke, S., & Trubetskoy, V., … The Bipolar Disorder Working Group of the Psychiatric Genetics Consortium (2019). Genome-wide association study identifies 30 loci associated with bipolar disorder. Nature Genetics, 51(5), 793–803. doi:10.1038/s41588-019-0397-8.CrossRefGoogle ScholarPubMed
Sytnyk, V., Leshchyns'ka, I., & Schachner, M. (2017). Neural cell adhesion molecules of the immunoglobulin superfamily regulate synapse formation, maintenance, and function. Trends in Neurosciences, 40(5), 295–308. doi:10.1016/j.tins.2017.03.003.CrossRefGoogle Scholar
Taylor, A. E., Fluharty, M. E., Bjørngaard, J. H., Gabrielsen, M. E., Skorpen, F., Marioni, R. E., … Munafò, M. R. (2014). Investigating the possible causal association of smoking with depression and anxiety using Mendelian randomisation meta-analysis: The CARTA consortium. BMJ Open, 4(10), e006141. doi:10.1136/bmjopen-2014-006141.CrossRefGoogle ScholarPubMed
Treur, J. L., Demontis, D., Smith, G. D., Sallis, H., Richardson, T. G., Wiers, R. W., ... Munafò, M. R. (2019). Investigating causality between liability to ADHD and substance use, and liability to substance use and ADHD risk, using Mendelian randomization. Addiction Biology, e12849.Google ScholarPubMed
Van den Oever, M. C., Goriounova, N. A., Wan Li, K., Van der Schors, R. C., Binnekade, R., Schoffelmeer, A. N. M., … De Vries, T. J. (2008). Prefrontal cortex AMPA receptor plasticity is crucial for cue-induced relapse to heroin-seeking. Nature Neuroscience, 11(9), 1053–1058. doi:10.1038/nn.2165.CrossRefGoogle ScholarPubMed
van Hulzen, K. J. E., Scholz, C. J., Franke, B., Ripke, S., Klein, M., McQuillin, A., … Reif, A. (2017). Genetic overlap between attention-deficit/hyperactivity disorder and bipolar disorder: Evidence from genome-wide association study meta-analysis. Biological Psychiatry, 82(9), 634–641. doi:10.1016/j.biopsych.2016.08.040.CrossRefGoogle ScholarPubMed
van Os, J., Bak, M., Hanssen, M., Bijl, R. V., de Graaf, R., & Verdoux, H. (2002). Cannabis use and psychosis: A longitudinal population-based study. American Journal of Epidemiology, 156(4), 319–327. doi:10.1093/aje/kwf043.CrossRefGoogle ScholarPubMed
Verbanck, M., Chen, C.-Y., Neale, B., & Do, R. (2018). Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature Genetics, 50(5), 693–698. doi:10.1038/s41588-018-0099-7.CrossRefGoogle ScholarPubMed
Vrieze, S. I. (2012). Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychological Methods, 17(2), 228–243.CrossRefGoogle Scholar
Watanabe, K., Taskesen, E., van Bochoven, A., & Posthuma, D. (2017). Functional mapping and annotation of genetic associations with FUMA. Nature Communications, 8(1), 1–11. doi:10.1038/s41467-017-01261-5.CrossRefGoogle ScholarPubMed
Watson, H. J., Yilmaz, Z., Thornton, L. M., Hübel, C., Coleman, J. R., Gaspar, H. A., … Bulik, C. M. (2019). Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nature Genetics, 51(8), 1207–1214. doi:10.1038/s41588-019-0439-2.CrossRefGoogle ScholarPubMed
Weinberger, A. H., Kashan, R. S., Shpigel, D. M., Esan, H., Taha, F., Lee, C. J., … Goodwin, R. D. (2017). Depression and cigarette smoking behavior: A critical review of population-based studies. The American Journal of Drug and Alcohol Abuse, 43(4), 416–431. doi:10.3109/00952990.2016.1171327.CrossRefGoogle ScholarPubMed
Weiser, M., & Noy, S. (2005). Interpreting the association between cannabis use and increased risk for schizophrenia. Dialogues in Clinical Neuroscience, 7(1), 81–85. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3181719/.10.31887/DCNS.2005.7.1/mweiserCrossRefGoogle ScholarPubMed
Weitzman, E. R. (2004). Poor mental health, depression, and associations with alcohol consumption, harm, and abuse in a national sample of young adults in college. The Journal of Nervous and Mental Disease, 192(4), 269–277. doi:10.1097/01.nmd.0000120885.17362.94.CrossRefGoogle Scholar
Wootton, R., Richmond, R., Stuijfzand, B., Lawn, R., Sallis, H., Taylor, G., … Munafò, M. (2019). Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: A Mendelian randomisation study. Psychological Medicine, 1–9. doi:10.1017/S0033291719002678.Google ScholarPubMed
Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., & Abdellaoui, A., … The Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics, 50(5), 668–681. doi:10.1038/s41588-018-0090-3.CrossRefGoogle ScholarPubMed
Yang, J, Zaitlen, N. A., Goddard, M. E., Visscher, P. M., & Price, A. L. (2014). Advantages and pitfalls in the application of mixed-model association methods. Nature Genetics, 46(2), 100–106.CrossRefGoogle ScholarPubMed
Yao, Y., Xu, Y., Cai, Z., Liu, Q., Ma, Y., Li, A. N., … Li, M. D. (2020). Determination of shared genetic etiology and possible causal relations between tobacco smoking and depression. Psychological Medicine, 1–10. doi:10.1017/S003329172000063X.Google ScholarPubMed
Ye, T., Krebs, A. R., Choukrallah, M.-A., Keime, C., Plewniak, F., Davidson, I., & Tora, L. (2010). SeqMINER: An integrated ChIP-seq data interpretation platform. Nucleic Acids Research, 39(6), e35. doi:10.1093/nar/gkq1287.CrossRefGoogle ScholarPubMed
Yengo, L., Sidorenko, J., Kemper, K. E., Zheng, Z., Wood, A. R., & Weedon, M. N., … the GIANT Consortium (2018). Meta-analysis of genome-wide association studies for height and body mass index in~ 700000 individuals of European ancestry. Human Molecular Genetics, 27(20), 3641–3649. doi:10.1093/hmg/ddy271.CrossRefGoogle ScholarPubMed
Young, S. E., Friedman, N. P., Miyake, A., Willcutt, E. G., Corley, R. P., Haberstick, B. C., & Hewitt, J. K. (2009). Behavioral disinhibition: Liability for externalizing spectrum disorders and its genetic and environmental relation to response inhibition across adolescence. Journal of Abnormal Psychology, 118(1), 117–130. doi:10.1037/a0014657.CrossRefGoogle ScholarPubMed
Supplementary material: File

Jang et al. supplementary material

Jang et al. supplementary material 1

Download Jang et al. supplementary material(File)
File 20.5 KB
Supplementary material: File

Jang et al. supplementary material

Jang et al. supplementary material 2

Download Jang et al. supplementary material(File)
File 708.3 KB
Supplementary material: File

Jang et al. supplementary material

Jang et al. supplementary material 3

Download Jang et al. supplementary material(File)
File 646.9 KB