Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders

Abstract

Excessive alcohol consumption is one of the main causes of death and disability worldwide. Alcohol consumption is a heritable complex trait. Here we conducted a meta-analysis of genome-wide association studies of alcohol consumption (g d−1) from the UK Biobank, the Alcohol Genome-Wide Consortium and the Cohorts for Heart and Aging Research in Genomic Epidemiology Plus consortia, collecting data from 480,842 people of European descent to decipher the genetic architecture of alcohol intake. We identified 46 new common loci and investigated their potential functional importance using magnetic resonance imaging data and gene expression studies. We identify genetic pathways associated with alcohol consumption and suggest genetic mechanisms that are shared with neuropsychiatric disorders such as schizophrenia.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Results of the discovery genome-wide association meta-analysis with alcohol consumption.
Fig. 2: Association of alcohol intake loci with other traits.
Fig. 3: Mediation effect of the grey-matter volume of bilateral putamen on the relationship between SNP rs13107325 and alcohol intake.
Fig. 4: Comparison of ZIP8 alcohol phenotypes in Drosophila.

Data availability

The UKB GWAS data can be assessed from the UKB data repository (https://biota.osc.ox.ac.uk/). The genetic and phenotypic UKB data are available through application to the UKB (https://www.ukbiobank.ac.uk). Summary GWAS data can be assessed by request to the corresponding authors and are available at LDHub (http://ldsc.broadinstitute.org/ldhub/).

References

  1. 1.

    GBD 2016 Alcohol Collaborators. Alcohol use and burden for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 392, 1015–1035 (2018).

  2. 2.

    Poznyak, V. & Rekve, D. (eds) Global Status Report on Alcohol and Health 2018. https://www.who.int/substance_abuse/publications/global_alcohol_report/gsr_2018/en/(WHO, 2018).

  3. 3.

    Wood, A. M. et al. Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. Lancet 391, 1513–1523 (2018).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Verhulst, B., Neale, M. C. & Kendler, K. S. The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies. Psychol. Med. 45, 1061–1072 (2015).

    CAS  PubMed  Google Scholar 

  5. 5.

    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).

    CAS  PubMed  Google Scholar 

  6. 6.

    Clarke, T. K. et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112 117). Mol. Psychiatr. 22, 1376–1384 (2017).

    CAS  Google Scholar 

  7. 7.

    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. Psychiatr. 22, 1359–1367 (2017).

    CAS  Google Scholar 

  8. 8.

    Baik, I., Cho, N. H., Kim, S. H., Han, B. G. & Shin, C. Genome-wide association studies identify genetic loci related to alcohol consumption in Korean men. Am. J. Clin. Nutr. 93, 809–816 (2011).

    CAS  PubMed  Google Scholar 

  9. 9.

    Jackson, B. et al. Update on the aldehyde dehydrogenase gene (ALDH) superfamily. Hum. Genomics 5, 283–303 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    McCarthy, S. et al. Haplotype Reference Consortium. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Evangelou, E. & Ioannidis, J. P. Meta-analysis methods for genome-wide association studies and beyond. Nat. Rev. Genet. 14, 379–389 (2013).

    CAS  PubMed  Google Scholar 

  14. 14.

    Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Desikan, R. S. et al. Genetic overlap between Alzheimer’s disease and Parkinson’s disease at the MAPT locus. Mol. Psychiatr. 20, 1588–1595 (2015).

    CAS  Google Scholar 

  16. 16.

    Do, C. B. et al. Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson’s disease. PLoS Genet. 7, e1002141 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Pankratz, N. et al. Meta-analysis of Parkinson’s disease: identification of a novel locus, RIT2. Ann. Neurol. 71, 370–384 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Couch, F. J. et al. Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk. PLoS Genet. 9, e1003212 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Ikram, M. A. et al. Common variants at 6q22 and 17q21 are associated with intracranial volume. Nat. Genet. 44, 539–544 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    van der Harst, P. et al. Seventy-five genetic loci influencing the human red blood cell. Nature 492, 369–375 (2012).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Samuel, A. et al. Six3 regulates optic nerve development via multiple mechanisms. Sci. Rep. 6, 20267 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  24. 24.

    Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    International Consortium for Blood Pressure Genome-Wide Association Studies. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).

  26. 26.

    Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Lim, C. S. & Alkon, D. L. Protein kinase C stimulates HuD-mediated mRNA stability and protein expression of neurotrophic factors and enhances dendritic maturation of hippocampal neurons in culture. Hippocampus 22, 2303–2319 (2012).

    CAS  PubMed  Google Scholar 

  29. 29.

    Barker, J. M., Taylor, J. R., De Vries, T. J. & Peters, J. Brain-derived neurotrophic factor and addiction: pathological versus therapeutic effects on drug seeking. Brain Res. 1628, 68–81 (2015).

    CAS  PubMed  Google Scholar 

  30. 30.

    Tanaka, T. et al. Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake. Am. J. Clin. Nutr. 97, 1395–1402 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Talukdar, S. et al. FGF21 regulates sweet and alcohol preference. Cell Metab. 23, 344–349 (2016).

    CAS  PubMed  Google Scholar 

  32. 32.

    Grant, S. F. et al. Association analysis of the FTO gene with obesity in children of Caucasian and African ancestry reveals a common tagging SNP. PLoS One 3, e1746 (2008).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Elliott, P. et al. The airwave health monitoring study of police officers and staff in Great Britain: rationale, design and methods. Environ. Res. 134, 280–285 (2014).

    CAS  PubMed  Google Scholar 

  34. 34.

    Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018).

    PubMed  Google Scholar 

  35. 35.

    Stipanovich, A. et al. A phosphatase cascade by which rewarding stimuli control nucleosomal response. Nature 453, 879–884 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Yang, B. Z. et al. Association of haplotypic variants in DRD2, ANKK1, TTC12 and NCAM1 to alcohol dependence in independent case control and family samples. Hum. Mol. Genet. 16, 2844–2853 (2007).

    CAS  PubMed  Google Scholar 

  37. 37.

    Gelernter, J. et al. Haplotype spanning TTC12 and ANKK1, flanked by the DRD2 and NCAM1 loci, is strongly associated to nicotine dependence in two distinct American populations. Hum. Mol. Genet. 15, 3498–3507 (2006).

    CAS  PubMed  Google Scholar 

  38. 38.

    Treutlein, J. et al. Genetic association of the human corticotropin releasing hormone receptor 1 (CRHR1) with binge drinking and alcohol intake patterns in two independent samples. Mol. Psychiatr. 11, 594–602 (2006).

    CAS  Google Scholar 

  39. 39.

    Timpl, P. et al. Impaired stress response and reduced anxiety in mice lacking a functional corticotropin-releasing hormone receptor 1. Nat. Genet. 19, 162–166 (1998).

    CAS  PubMed  Google Scholar 

  40. 40.

    Ruggeri, B. et al. Association of protein phosphatase PPM1G with alcohol use disorder and brain activity during behavioral control in a genome-wide methylation analysis. Am. J. Psychiatr. 172, 543–552 (2015).

    PubMed  Google Scholar 

  41. 41.

    Wang, J., Vasaikar, S., Shi, Z., Greer, M. & Zhang, B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 45, W130–W137 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Gonzalez, D. A. et al. The Arf6 activator Efa6/PSD3 confers regional specificity and modulates ethanol consumption in Drosophila and humans. Mol. Psychiatr. 23, 621–628 (2018).

    CAS  Google Scholar 

  43. 43.

    Ojelade, S. A. et al. Rsu1 regulates ethanol consumption in Drosophila and humans. Proc. Natl Acad. Sci. USA 112, E4085–E4093 (2015).

    CAS  PubMed  Google Scholar 

  44. 44.

    Rademakers, R., Cruts, M. & van Broeckhoven, C. The role of tau (MAPT) in frontotemporal dementia and related tauopathies. Hum. Mutat. 24, 277–295 (2004).

    CAS  PubMed  Google Scholar 

  45. 45.

    Higashi, Y. et al. Influence of extracellular zinc on M1 microglial activation. Sci. Rep. 7, 43778 (2017).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Chen, G. et al. Striatal involvement in human alcoholism and alcohol consumption, and withdrawal in animal models. Alcohol. Clin. Exp. Res. 35, 1739–1748 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Okada, N. et al. Abnormal asymmetries in subcortical brain volume in schizophrenia. Mol. Psychiatr. 21, 1460–1466 (2016).

    CAS  Google Scholar 

  48. 48.

    van Erp, T. G. et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol. Psychiatr. 21, 547–553 (2016).

    Google Scholar 

  49. 49.

    Meyers, J. L. et al. The association between DRD2/ANKK1 and genetically informed measures of alcohol use and problems. Addict. Biol. 18, 523–536 (2013).

    CAS  PubMed  Google Scholar 

  50. 50.

    Logrip, M. L., Barak, S., Warnault, V. & Ron, D. Corticostriatal BDNF and alcohol addiction. Brain Res. 1628, 60–67 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Boschen, K. E., Criss, K. J., Palamarchouk, V., Roth, T. L. & Klintsova, A. Y. Effects of developmental alcohol exposure vs. intubation stress on BDNF and TrkB expression in the hippocampus and frontal cortex of neonatal rats. Int. J. Dev. Neurosci. 43, 16–24 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Monaco, A. et al. A complex network approach reveals a pivotal substructure of genes linked to schizophrenia. PLoS One 13, e0190110 (2018).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Nielsen, S. M., Toftdahl, N. G., Nordentoft, M. & Hjorthoj, C. Association between alcohol, cannabis, and other illicit substance abuse and risk of developing schizophrenia: a nationwide population based register study. Psychol. Med. 47, 1668–1677 (2017).

    CAS  PubMed  Google Scholar 

  54. 54.

    Nivard, M. G. et al. Connecting the dots, genome-wide association studies in substance use. Mol. Psychiatr. 21, 733–735 (2016).

    CAS  Google Scholar 

  55. 55.

    Gaziano, J. M. et al. Moderate alcohol intake, increased levels of high-density lipoprotein and its subfractions, and decreased risk of myocardial infarction. N. Engl. J. Med. 329, 1829–1834 (1993).

    CAS  PubMed  Google Scholar 

  56. 56.

    Linn, S. et al. High-density lipoprotein cholesterol and alcohol consumption in US white and black adults: data from NHANES II. Am. J. Publ. Health 83, 811–816 (1993).

    CAS  Google Scholar 

  57. 57.

    Vu, K. N. et al. Causal role of alcohol consumption in an improved lipid profile: the atherosclerosis risk in communities (ARIC) study. PLoS One 11, e0148765 (2016).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Chaput, J. P., McNeil, J., Despres, J. P., Bouchard, C. & Tremblay, A. Short sleep duration is associated with greater alcohol consumption in adults. Appetite 59, 650–655 (2012).

    PubMed  Google Scholar 

  59. 59.

    Walters, R. K. et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat. Neurosci. 21, 1656–1669 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Bagnardi, V. et al. Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis. Br. J. Cancer 112, 580–593 (2015).

    CAS  PubMed  Google Scholar 

  61. 61.

    Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Boniface, S., Kneale, J. & Shelton, N. Drinking pattern is more strongly associated with under-reporting of alcohol consumption than socio-demographic factors: evidence from a mixed-methods study. BMC Publ. Health 14, 1297 (2014).

    Google Scholar 

  63. 63.

    Greenfield, T. K. & Kerr, W. C. Alcohol measurement methodology in epidemiology: recent advances and opportunities. Addiction 103, 1082–1099 (2008).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Grotz, A. K., Gloyn, A. L. & Thomsen, S. K. Prioritising causal genes at type 2 diabetes risk loci. Curr. Diab. Rep. 17, 76 (2017).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Hambrecht, M. & Hafner, H. Substance abuse and the onset of schizophrenia. Biol. Psychiatr. 40, 1155–1163 (1996).

    CAS  Google Scholar 

  67. 67.

    Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Georgiopoulos, G. & Evangelou, E. Power considerations for λ inflation factor in meta-analyses of genome-wide association studies. Genet. Res. 98, e9 (2016).

    Google Scholar 

  69. 69.

    Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Stacey, D. et al. RASGRF2 regulates alcohol-induced reinforcement by influencing mesolimbic dopamine neuron activity and dopamine release. Proc. Natl Acad. Sci. USA 109, 21128–21133 (2012).

    CAS  PubMed  Google Scholar 

  72. 72.

    Bakshi, A. et al. Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Sci. Rep. 6, 32894 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  74. 74.

    Ramasamy, A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418–1428 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).

    CAS  PubMed  Google Scholar 

  76. 76.

    Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    CAS  PubMed  Google Scholar 

  77. 77.

    Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Brown, C. A. et al. Development, validation and application of a new fornix template for studies of aging and preclinical Alzheimer’s disease. Neuroimage Clin. 13, 106–115 (2017).

    PubMed  Google Scholar 

  79. 79.

    Diedrichsen, J. et al. Imaging the deep cerebellar nuclei: a probabilistic atlas and normalization procedure. Neuroimage 54, 1786–1794 (2011).

    CAS  PubMed  Google Scholar 

  80. 80.

    Zhang, Y., Brady, M. & Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001).

    CAS  PubMed  Google Scholar 

  81. 81.

    Wager, T. D., Davidson, M. L., Hughes, B. L., Lindquist, M. A. & Ochsner, K. N. Prefrontal-subcortical pathways mediating successful emotion regulation. Neuron 59, 1037–1050 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Wager, T. D. et al. Brain mediators of cardiovascular responses to social threat: part I: reciprocal dorsal and ventral sub-regions of the medial prefrontal cortex and heart-rate reactivity. Neuroimage 47, 821–835 (2009).

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    Petersen, S. E. et al. UK Biobank’s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18, 8 (2016).

    PubMed  PubMed Central  Google Scholar 

  84. 84.

    Bai, W. et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J. Cardiovasc. Magn. Reson. 20, 65 (2018).

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Linge, J. et al. Body composition profiling in the UK Biobank imaging study. Obesity (2018).

  86. 86.

    Peru y Colón de Portugal, R. L. et al. Adult neuronal Arf6 controls ethanol-induced behavior with Arfaptin downstream of Rac1 and RhoGAP18B. J. Neurosci. 32, 17706–17713 (2012).

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Dimou, N. L. & Tsilidis, K. K. A primer in Mendelian randomization methodology with a focus on utilizing published summary association data. Methods Mol. Biol. 1793, 211–230 (2018).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

H.G. was funded by the NIHR Imperial College Health Care NHS Trust and Imperial College London Biomedical Research Centre. I.K. was supported by the EU PhenoMeNal project (Horizon 2020, grant no. 654241) and the UK Dementia Research Institute, which is supported by the MRC, the Alzheimer’s Society and Alzheimer’s Research UK. S. Thériault was supported by the Canadian Institutes of Health Research and Université Laval (Quebec City, Canada). L.R. was supported by Forschungs- und Förder-Stiftung INOVA, Vaduz, Liechtenstein. D.C. holds a McMaster University Department of Medicine Mid-Career Research Award. M.B. is supported by NIH grant R01-DK062370. P.v.d.H. was supported by ICIN-NHI and Marie Skłodowska-Curie GF (call: H2020-MSCA-IF-2014; Project ID: 661395). C.H. was supported by a core MRC grant to the MRCHGU QTL in Health and Disease research programme. N.V. was supported by Marie Skłodowska-Curie GF (grant no. 661395) and ICIN-NHI. Q.L. is partially supported by the National Natural Sciences Foundation of China (No. 81873909), Shanghai Municipal Science and Technology Major Project(No.2018SHZDZX01) and ZJLab. P.E. acknowledges support from the NIHR Biomedical Research Centre at Imperial College Healthcare NHS Trust and Imperial College London, the NIHR Health Protection Research Unit in Health Impact of Environmental Hazards (HPRU-2012-10141), the Medical Research Council (MRC) and Public Health England (PHE) Centre for Environment and Health (MR/L01341X/1) and Health Data Research (HDR) UK. P.E. is supported by a UK Dementia Research Institute (DRI) professorship, UK DRI at Imperial College London, funded by the MRC, Alzheimer’s Society and Alzheimer’s Research UK. This work received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology; LSHM-CT- 2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders; grant no. 695313), ERANID (Understanding the interplay between cultural, biological and subjective factors in drug use pathways; PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics; MR/N027558/1), the FP7 projects IMAGEMEND (grant no. 602450; IMAging GEnetics for MENtal Disorders) and MATRICS (grant no. 603016), the Innovative Medicine Initiative Project EU-AIMS (grant. no115300-2), the Medical Research Council Grant ‘c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions; MR/N000390/1), the Swedish Research Council FORMAS, the Medical Research Council, the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; eMED SysAlc01ZX1311A; and Forschungsnetz AERIAL 01EE1406A and 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2 and SFB 940/2), the Medical Research Foundation and Medical Research Council (MR/R00465X/1), the Human Brain Project (HBP SGA 2). Further support was provided by grants from ANR (project AF12-NEUR0008-01-WM2NA, and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche Médicale, the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1); and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

Authors

Contributions

Central analysis: E.E., H.G., C.C., G.N., P.B., A.R.B., R.P., H. Suzuki, F.K., A.M.Y., I.K., J.E., Q.L., N.D., D.L., I.T., J.D.B., P.M.M., A.R., S.D., G.S. and P.E. Writing of the manuscript: E.E., H.G., C.C., G.N., P.B., A.R.B., R.P., H. Suzuki, F.K., A.M.Y., I.K., D.L., I.T., J.D.B., P.M.M., A.R., S.D., G.S. and P.E. Association of MRI analysis: C.C., H. Suzuki, A.M.Y., A.I.B., J.D.B., P.M.M. and G.S. AlcGen and Charge+ contributors, by study: (ARIC): A.C.M., M.R.B., B.Y. and D.E.A. (CHS): B.M.P., R.N.L., T.M.B. and J.A.B. (FHS): D.L. and C.L. (GAPP/Swiss-AF/Beat-AF): S. Thériault, S.A., D.C., L.R. and M. Kühne. (GENOA): S.L.R.K., J.A.S., W.Z. and S.M.R., (GRAPHIC): N.J.S., C.P.N. and P.S.B., (GS): A.M.M., T.-K.C., C.H. and D.P., (HBCS): J.L., S. Tuominen, M.-M.P. and J.G.E. (HRS): D.R.W., S.L.R.K., J.D.F., W.Z. and J.A.S. (MESA): X.G., J.Y., A.W. and J.I.R. (METSIM): M.L., A.S., J. Vangipurapu and J.K. (FUSION): M.B., K.L.M., L.J.S. and A.U.J. (NESDA): B.W.J.H.P. and Y.M. (NFBC): M.-R.J., J. Veijola, M. Männikkö and J.A. (ORCADES): H.C. and P.K.J. (VIKING): J.F.W. and K.A.K. (Croatia-VIS): I.R. and O.P. (Croatia-KORCULA): C.H. (PREVEND): N.V. and P.v.d.H. (OZALC): N.G.M., J.B.W., P.A.L. and A.C.H. (SHIP): A.T., H.J.G., S.E.B. and G.H. (TRAILS-pop): A.J.O. and I.M.N. (TRAILS-CC): C.A.H. and H. Snieder. (TwinsUK): T.D.S. and M. Mangino. (YFS): L.-P.L., M. Kähönen, O.T.R. and T.L. All authors critically reviewed and approved the final version of the manuscript

Corresponding authors

Correspondence to Gunter Schumann or Paul Elliott.

Ethics declarations

Competing interests

B.M.P. serves on the DSMB of a clinical trial funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. B.W.J.H.P. has received research funding (unrelated to the work reported here) from Jansen Research and Boehringer Ingelheim. The other authors declare no competing interests.

Additional information

Peer review information: Primary Handling Editor: Stavroula Kousta.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and Supplementary Note.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–16.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Evangelou, E., Gao, H., Chu, C. et al. New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders. Nat Hum Behav 3, 950–961 (2019). https://doi.org/10.1038/s41562-019-0653-z

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing