Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6,7,8,9,10,11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.
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All software used to perform these analyses is available online.
GWAS summary statistics can be downloaded online (https://genome.psych.umn.edu/index.php/GSCAN). We provide association results for all SNPs that passed quality-control filters in a GWAS meta-analysis of each of our five substance use phenotypes that excludes the research participants from 23andMe.
Ezzati, M. et al. Selected major risk factors and global and regional burden of disease. Lancet 360, 1347–1360 (2002).
Hicks, B. M., Schalet, B. D., Malone, S. M., Iacono, W. G. & McGue, M. Psychometric and genetic architecture of substance use disorder and behavioral disinhibition measures for gene association studies. Behav. Genet. 41, 459–475 (2011).
Polderman, T. J. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).
Kendler, K. S., Schmitt, E., Aggen, S. H. & Prescott, C. A. Genetic and environmental influences on alcohol, caffeine, cannabis, and nicotine use from early adolescence to middle adulthood. Arch. Gen. Psychiatry 65, 674–682 (2008).
Kendler, K. S., Prescott, C. A., Myers, J. & Neale, M. C. The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Arch. Gen. Psychiatry 60, 929–937 (2003).
Bierut, L. J. et al. ADH1B is associated with alcohol dependence and alcohol consumption in populations of European and African ancestry. Mol. Psychiatry 17, 445–450 (2012).
Thorgeirsson, T. E. et al. Sequence variants at CHRNB3–CHRNA6 and CYP2A6 affect smoking behavior. Nat. Genet. 42, 448–453 (2010).
Thorgeirsson, T. E. et al. A rare missense mutation in CHRNA4 associates with smoking behavior and its consequences. Mol. Psychiatry 21, 594–600 (2016).
Furberg, H. et al. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).
Schumann, G. et al. KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference. Proc. Natl Acad. Sci. USA 113, 14372–14377 (2016).
Jorgenson, E. et al. Genetic contributors to variation in alcohol consumption vary by race/ethnicity in a large multi-ethnic genome-wide association study. Mol. Psychiatry 22, 1359–1367 (2017).
Polesskaya, O. O., Smith, R. F. & Fryxell, K. J. Chronic nicotine doses down-regulate PDE4 isoforms that are targets of antidepressants in adolescent female rats. Biol. Psychiatry 61, 56–64 (2007).
Boyden, L. M. et al. Mutations in kelch-like 3 and cullin 3 cause hypertension and electrolyte abnormalities. Nature 482, 98–102 (2012).
Wang, W. et al. Forced expiratory volume in the first second and aldosterone as mediators of smoking effect on stroke in African Americans: the Jackson Heart Study. J. Am. Heart Assoc. 5, e002689 (2016).
Aoun, E. G. et al. A relationship between the aldosterone-mineralocorticoid receptor pathway and alcohol drinking: preliminary translational findings across rats, monkeys and humans. Mol. Psychiatry 23, 1466–1473 (2018).
Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).
Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Yang, J. A., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).
Harris, K. M., Halpern, C. T., Haberstick, B. C. & Smolen, A. The National Longitudinal Study of Adolescent Health (Add Health) sibling pairs data. Twin Res. Hum. Genet. 16, 391–398 (2013).
Sonnega, A. et al. Cohort profile: the Health and Retirement Study (HRS). Int. J. Epidemiol. 43, 576–585 (2014).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Wilson, S., Bair, J. L., Thomas, K. M. & Iacono, W. G. Problematic alcohol use and reduced hippocampal volume: a meta-analytic review. Psychol. Med. 47, 2288–2301 (2017).
Ewing, S. W. F., Sakhardande, A. & Blakemore, S. J. The effect of alcohol consumption on the adolescent brain: a systematic review of MRI and fMRI studies of alcohol-using youth. Neuroimage Clin. 5, 420–437 (2014).
Goldstein, R. Z. & Volkow, N. D. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12, 652–669 (2011).
Volkow, N. D. & Morales, M. The brain on drugs: from reward to addiction. Cell 162, 712–725 (2015).
Koob, G. F. & Volkow, N. D. Neurocircuitry of addiction. Neuropsychopharmacology 35, 217–238 (2010).
Koob, G. F. & Volkow, N. D. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry 3, 760–773 (2016).
Fernandez, E., Schiappa, R., Girault, J. A. & Le Novere, N. DARPP-32 is a robust integrator of dopamine and glutamate signals. PLoS Comput. Biol. 2, 1619–1633 (2006).
Yagishita, S. et al. A critical time window for dopamine actions on the structural plasticity of dendritic spines. Science 345, 1616–1620 (2014).
Zhu, H. W. et al. DARPP-32 phosphorylation opposes the behavioral effects of nicotine. Biol. Psychiatry 58, 981–989 (2005).
Stoker, A. K. & Markou, A. Unraveling the neurobiology of nicotine dependence using genetically engineered mice. Curr. Opin. Neurobiol. 23, 493–499 (2013).
Litten, R. Z. et al. A double-blind, placebo-controlled trial assessing the efficacy of varenicline tartrate for alcohol dependence. J. Addiction Med. 7, 277–286 (2013).
Hyman, S. E., Malenka, R. C. & Nestler, E. J. Neural mechanisms of addiction: the role of reward-related learning and memory. Annu. Rev. Neurosci. 29, 565–598 (2006).
Kalivas, P. W. The glutamate homeostasis hypothesis of addiction. Nat. Rev. Neurosci. 10, 561–572 (2009).
Szumlinski, K. K. et al. Methamphetamine addiction vulnerability: the glutamate, the bad, and the ugly. Biol. Psychiatry 81, 959–970 (2017).
Gass, J. T. & Olive, M. F. Glutamatergic substrates of drug addiction and alcoholism. Biochem. Pharmacol. 75, 218–265 (2008).
Vaughan, J. et al. Urocortin, a mammalian neuropeptide related to fish urotensin I and to corticotropin-releasing factor. Nature 378, 287–292 (1995).
Logrip, M. L., Koob, G. F. & Zorrilla, E. P. Role of corticotropin-releasing factor in drug addiction: potential for pharmacological intervention. CNS Drugs 25, 271–287 (2011).
Volkow, N. D., Koob, G. F. & McLellan, A. T. Neurobiologic advances from the brain disease model of addiction. N. Engl. J. Med. 374, 363–371 (2016).
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
Lassi, G. et al. The CHRNA5–A3–B4 gene cluster and smoking: from discovery to therapeutics. Trends Neurosci. 39, 851–861 (2016).
Edenberg, H. J. The genetics of alcohol metabolism: role of alcohol dehydrogenase and aldehyde dehydrogenase variants. Alcohol Res. Health 30, 5–13 (2007).
Sallese, M. et al. The G-protein-coupled receptor kinase GRK4 mediates homologous desensitization of metabotropic glutamate receptor 1. FASEB J. 14, 2569–2580 (2000).
Perroy, J., Adam, L., Qanbar, R., Chenier, S. & Bouvier, M. Phosphorylation-independent desensitization of GABAB receptor by GRK4. EMBO J. 22, 3816–3824 (2003).
Yang, J., Villar, V. M., Armando, I., Jose, P. A. & Zeng, C. Y. G. G protein–coupled receptor kinases: crucial regulators of blood pressure. J. Am. Heart Assoc. 5, e003519 (2016).
GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017). erratum 553, 530 (2018).
Costas, J. The highly pleiotropic gene SLC39A8 as an opportunity to gain insight into the molecular pathogenesis of schizophrenia. Am. J. Med. Genet. B Neuropsychiatr. Genet. 177, 274–283 (2018).
Kong, A. et al. The nature of nurture: effects of parental genotypes. Science 359, 424–428 (2018).
Vrieze, S. I., Hicks, B. M., Iacono, W. G. & McGue, M. Decline in genetic influence on the co-occurrence of alcohol, marijuana, and nicotine dependence symptoms from age 14 to 29. Am. J. Psychiatry 169, 1073–1081 (2012).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).
Zhan, X., Hu, Y., Li, B., Abecasis, G. R. & Liu, D. J. RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data. Bioinformatics 32, 1423–1426 (2016).
Kang, H. M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).
Jiang, Y. et al. Proper conditional analysis in the presence of missing data identified novel independently associated low frequency variants in nicotine dependence genes. PLoS Genet. 14, e1007452 (2018).
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, S1–S3 (2012).
Grotzinger, A. D. et al. Genomic sem provides insights into the multivariate genetic architecture of complex traits. Preprint at https://doi.org/10.1101/305029 (2018).
Li, J. & Ji, L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95, 221–227 (2005).
Gao, X. Y., Becker, L. C., Becker, D. M., Starmer, J. D. & Province, M. A. Avoiding the high Bonferroni penalty in genome-wide association studies. Genet. Epidemiol. 34, 100–105 (2010).
Chen, Z. X. & Liu, Q. Z. a new approach to account for the correlations among single nucleotide polymorphisms in genome-wide association studies. Hum. Hered. 72, 1–9 (2011).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Wu, Y., Zheng, Z. L., Visscher, P. M. & Yang, J. Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data. Genome Biol. 18, 86 (2017).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
Vilhjalmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).
Li, Y., Davila-Velderrain, J. & Kellis, M. A probabilistic framework to dissect functional cell-type-specific regulatory elements and risk loci underlying the genetics of complex traits. Preprint at https://doi.org/10.1101/059345 (2017).
Zhan, X. & Liu, D. J. SEQMINER: an R-package to facilitate the functional interpretation of sequence-based associations. Genet. Epidemiol. 39, 619–623 (2015).
Lamparter, D., Marbach, D., Rueedi, R., Kutalik, Z. & Bergmann, S. Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLoS Comput. Biol. 12, e1004714 (2016).
Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
Frey, B. J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).
This study was designed and carried out by the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN). It was conducted by using the UK Biobank Resource under application number 16651. This study was supported by funding from US National Institutes of Health awards R01DA037904 to S.V., R01HG008983 to D. J. Liu., and R21DA040177 to D. J. Liu. Ethical review and approval was provided by the University of Minnesota institutional review board; all human subjects provided informed consent. A full list of acknowledgements is provided in the Supplementary Note.
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Michelle Agee11, Babak Alipanahi11, Adam Auton11, Robert K. Bell11, Katarzyna Bryc11, Sarah L. Elson11, Pierre Fontanillas11, Nicholas A. Furlotte11, David A. Hinds11, Bethann S. Hromatka11, Karen E. Huber11, Aaron Kleinman11, Nadia K. Litterman11, Matthew H. McIntyre11, Joanna L. Mountain11, Carrie A.M. Northover11, J. Fah Sathirapongsasuti11, Olga V. Sazonova11, Janie F. Shelton11, Suyash Shringarpure11, Chao Tian11, Joyce Y. Tung11, Vladimir Vacic11, Catherine H. Wilson11 and Steven J. Pitts11.
HUNT All-In Psychiatry
Amy Mitchell65, Anne Heidi Skogholt20, Bendik S Winsvold65,78, Børge Sivertsen79,80,81, Eystein Stordal80,82, Gunnar Morken80,83, Håvard Kallestad80,83, Ingrid Heuch81, John-Anker Zwart65,78,84, Katrine Kveli Fjukstad85,86, Linda M Pedersen65, Maiken Elvestad Gabrielsen20, Marianne Bakke Johnsen65,84, Marit Skrove87, Marit Sæbø Indredavik80,87, Ole Kristian Drange80,83, Ottar Bjerkeset80,88, Sigrid Børte65,84 and Synne Øien Stensland65,89
L.J.B. and the spouse of N.L.S. are listed as inventors on issued US patent number 8,080,371, ‘Markers for Addiction’, covering the use of certain SNPs in determining the diagnosis, prognosis, and treatment of addiction. S.P.D. is a scientific advisor to BaseHealth, Inc. G.B., D.F.G., G.W.R., H.S., K.S., and T.E.T. are employees of deCODE Genetics/Amgen, Inc. C.T. and D.H. are employees of 23andMe, Inc.
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Liu, M., Jiang, Y., Wedow, R. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet 51, 237–244 (2019) doi:10.1038/s41588-018-0307-5
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