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A multi-omics Mendelian randomization study identifies new therapeutic targets for alcohol use disorder and problem drinking

Nature Human Behaviour volume 9, pages 188–207 (2025)Cite this article

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Abstract

Integrating proteomic and transcriptomic data with genetic architectures of problematic alcohol use and alcohol consumption behaviours can advance our understanding and help identify therapeutic targets. We conducted systematic screens using genome-wise association study data from ~3,500 cortical proteins (N = 722) and ~6,100 genes in 8 canonical brain cell types (N = 192) with 4 alcohol-related outcomes (N ≤ 537,349), identifying 217 cortical proteins and 255 cell-type genes associated with these behaviours, with 36 proteins and 37 cell-type genes being new. Although there was limited overlap between proteome and transcriptome targets, downstream neuroimaging revealed shared neurophysiological pathways. Colocalization with independent genome-wise association study data further prioritized 16 proteins, including CAB39L and NRBP1, and 12 cell-type genes, implicating mechanisms such as mTOR signalling. In addition, genes such as SAMHD1, VIPAS39, NUP160 and INO80E were identified as having favourable neuropsychiatric profiles. These findings provide insights into the genetic landscapes governing problematic alcohol use and alcohol consumption behaviours, highlighting promising therapeutic targets for future research.

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Fig. 1: Study overview.
Fig. 2: Manhattan plots for cis-MR screens of cortical proteomic and single-cell transcriptomic architecture in PAU and alcohol consumption behaviours.
Fig. 3: Colocalization results and number of overlapping neuroimaging traits between the cortical proteins and cell-type genes.
Fig. 4: Results of colocalized, high-confidence proteins and genes on the physical and psychiatric consequences of alcohol consumption in the FinnGen cohort.
Fig. 5: Selected results of neuropsychiatric contextualization of cortical proteins and cell-type genes.

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Data availability

All analyses were based on publicly available data. Brain cortex protein data are available from Synapse (https://www.synapse.org/#!Synapse:syn24172458). Single-cell eQTL data from eight brain cell types are available at https://zenodo.org/records/7276971 (ref. 150). PAU data are available through dbGaP with the accession number phs001672.v3.p1. Summary statistics for DPW is available from the GSCAN study page at https://conservancy.umn.edu/items/ca7ed549-636b-41c0-ae79-97c57e266417. Binge drinking frequency from the Neale Lab Round 2 UK Biobank release is available at https://docs.google.com/spreadsheets/u/0/d/1kvPoupSzsSFBNSztMzl04xMoSC3Kcx3CrjVf4yBmESU/edit?usp=sharing&pli=1. AIF is available through the Open GWAS Project (https://gwas.mrcieu.ac.uk/datasets/ukb-b-5779/). Cortical and subcortical grey matter summary statistics are available through application at https://enigma.ini.usc.edu/research/download-enigma-gwas-results/. White matter tract diffusor tensor imaging summary statistics are available at https://www.med.unc.edu/bigs2/data/gwas-summary-statistics/. The R9 FinnGen alcohol-related outcomes used for replication are available at https://www.finngen.fi/en/data-freeze-9-results-and-summary-statistics-now-available, with outcome-specific links in Supplementary Table 1 (our data sources table). Similarly, our neuropsychiatric contextualization used the Open GWAS Project (https://gwas.mrcieu.ac.uk/) and their study links are also available in Supplementary Table 1. H-MAGMA annotation data are available at https://github.com/thewonlab/H-MAGMA/tree/master/Input_Files. FUSION eQTL weights for transcriptomic imputation are available at http://gusevlab.org/projects/fusion/. Source data are provided with this paper.

References

  1. Hingson, R. W., Zha, W. & White, A. M. Drinking beyond the binge threshold: predictors, consequences, and changes in the U.S. Am. J. Prev. Med. 52, 717–727 (2017).

    Article  PubMed  Google Scholar 

  2. Azagba, S., Shan, L., Latham, K. & Manzione, L. Trends in binge and heavy drinking among adults in the United States, 2011–2017. Subst. Use Misuse 55, 990–997 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Global Status Report on Alcohol and Health 2018 (World Health Organization, 2018); https://www.who.int/publications/i/item/9789241565639

  4. Charlet, K. & Heinz, A. Harm reduction—a systematic review on effects of alcohol reduction on physical and mental symptoms. Addict. Biol. 22, 1119–1159 (2017).

    Article  PubMed  Google Scholar 

  5. Saitz, R., Larson, M. J., Labelle, C., Richardson, J. & Samet, J. H. The case for chronic disease management for addiction. J. Addict. Med. 2, 55–65 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lohoff, F. W. Targeting unmet clinical needs in the treatment of alcohol use disorder. Front. Psychiatry 13, 767506 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bogenschutz, M. P. et al. Percentage of heavy drinking days following psilocybin-assisted psychotherapy vs placebo in the treatment of adult patients with alcohol use disorder: a randomized clinical trial. JAMA Psychiatry 79, 953–962 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Witkiewitz, K. et al. Drinking risk level reductions associated with improvements in physical health and quality of life among individuals with alcohol use disorder. Alcohol Clin. Exp. Res. 42, 2453–2465 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Witkiewitz, K., Litten, R. Z. & Leggio, L. Advances in the science and treatment of alcohol use disorder. Sci. Adv. 5, eaax4043 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Sanderson, E. et al. Mendelian randomization. Nat. Rev. Methods Prim. 2, 6 (2022).

    Article  CAS  Google Scholar 

  11. Schmidt, A. F. et al. Genetic drug target validation using Mendelian randomisation. Nat. Commun. 11, 3255 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    Article  PubMed  CAS  Google Scholar 

  14. Tawa, E. A., Hall, S. D. & Lohoff, F. W. Overview of the genetics of alcohol use disorder. Alcohol Alcohol. 51, 507–514 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Sanchez-Roige, S. & Palmer, A. A. Emerging phenotyping strategies will advance our understanding of psychiatric genetics. Nat. Neurosci. 23, 475–480 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Gupta, I., Dandavate, R., Gupta, P., Agrawal, V. & Kapoor, M. Recent advances in genetic studies of alcohol use disorders. Curr. Genet Med Rep. 8, 27–34 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kranzler, H. R. et al. Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nat. Commun. 10, 1499 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Liu, M. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Zhou, H. et al. Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat. Neurosci. 23, 809–818 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Mallard, T. T. et al. Item-level genome-wide association study of the alcohol use disorders identification test in three population-based cohorts. Am. J. Psychiatry https://doi.org/10.1176/appi.ajp.2020.20091390 (2021).

  22. Mavromatis, L. A. et al. Association between brain structure and alcohol use behaviors in adults: a Mendelian randomization and multiomics study. JAMA Psychiatry 79, 869–878 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  23. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Lin, Z., Nie, C., Zhang, Y., Chen, Y. & Yang, T. Evidence accumulation for value computation in the prefrontal cortex during decision making. Proc. Natl Acad. Sci. USA 117, 30728–30737 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Holmes, M. V., Richardson, T. G., Ference, B. A., Davies, N. M. & Davey Smith, G. Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development. Nat. Rev. Cardiol. 18, 435–453 (2021).

    Article  PubMed  Google Scholar 

  26. Johnson, E. C. B. et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat. Med. 26, 769–780 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Wingo, A. P. et al. Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age. Nat. Commun. 10, 1619 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Wingo, T. S. et al. Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder. Mol. Psychiatry 27, 3075–3084 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Pathak, G. A. et al. Genetically regulated multi-omics study for symptom clusters of posttraumatic stress disorder highlights pleiotropy with hematologic and cardio-metabolic traits. Mol. Psychiatry 27, 1394–1404 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Liu, J., Li, X. & Luo, X. J. Proteome-wide association study provides insights into the genetic component of protein abundance in psychiatric disorders. Biol. Psychiatry 90, 781–789 (2021).

    Article  PubMed  CAS  Google Scholar 

  31. Marees, A. T. et al. Potential influence of socioeconomic status on genetic correlations between alcohol consumption measures and mental health. Psychol. Med. 50, 484–498 (2020).

    Article  PubMed  Google Scholar 

  32. Kapoor, M. et al. Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases. Nat. Commun. 12, 5071 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Trevino, A. E. et al. Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell 184, 5053–5069.e23 (2021).

    Article  PubMed  CAS  Google Scholar 

  34. Schwartzentruber, J. et al. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes. Nat. Genet. 53, 392–402 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Neavin, D. et al. Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells. Genome Biol. 22, 76 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Walker, R. L. et al. Genetic control of expression and splicing in developing human brain informs disease mechanisms. Cell 179, 750–771.e22 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Erickson, E. K., Grantham, E. K., Warden, A. S. & Harris, R. A. Neuroimmune signaling in alcohol use disorder. Pharm. Biochem. Behav. 177, 34–60 (2019).

    Article  CAS  Google Scholar 

  39. Bryois, J. et al. Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nat. Neurosci. 25, 1104–1112 (2022).

    Article  PubMed  CAS  Google Scholar 

  40. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Lim, Y. et al. Exploration of alcohol use disorder-associated brain miRNA–mRNA regulatory networks. Transl. Psychiatry 11, 504 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Lohoff, F. W. et al. Epigenome-wide association study of alcohol consumption in N = 8161 individuals and relevance to alcohol use disorder pathophysiology: identification of the cystine/glutamate transporter SLC7A11 as a top target. Mol. Psychiatry 27, 1754–1764 (2022).

    Article  PubMed  CAS  Google Scholar 

  43. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Burgess, S. & Thompson, S. G. Avoiding bias from weak instruments in Mendelian randomization studies. Int J. Epidemiol. 40, 755–764 (2011).

    Article  PubMed  Google Scholar 

  45. Ehinger, Y. et al. Brain-specific inhibition of mTORC1 eliminates side effects resulting from mTORC1 blockade in the periphery and reduces alcohol intake in mice. Nat. Commun. 12, 4407 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Steegen, S., Tuerlinckx, F., Gelman, A. & Vanpaemel, W. Increasing transparency through a multiverse analysis. Perspect. Psychol. Sci. 11, 702–712 (2016).

    Article  PubMed  Google Scholar 

  47. Henry, A. et al. Therapeutic targets for heart failure identified using proteomics and Mendelian randomization. Circulation 145, 1205–1217 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Yang, C. et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat. Neurosci. 24, 1302–1312 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Hatoum, A. S. et al. Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders. Nat. Ment. Health 1, 210–223 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ma, S. et al. Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. Science 377, eabo7257 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    Article  PubMed  CAS  Google Scholar 

  52. Griffith, M. et al. DGIdb: mining the druggable genome. Nat. Methods 10, 1209–1210 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Noble, E. P. Alcoholism and the dopaminergic system: a review. Addict. Biol. 1, 333–348 (1996).

    Article  PubMed  CAS  Google Scholar 

  54. Kishi, T., Sevy, S., Chekuri, R. & Correll, C. U. Antipsychotics for primary alcohol dependence: a systematic review and meta-analysis of placebo-controlled trials. J. Clin. Psychiatry 74, e642–e654 (2013).

    Article  PubMed  Google Scholar 

  55. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Sey, N. Y. A. et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat. Neurosci. 23, 583–593 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Feng, H. et al. Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies. PLoS Genet. 17, e1008973 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Wingo, T. S. et al. Shared mechanisms across the major psychiatric and neurodegenerative diseases. Nat. Commun. 13, 4314 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Toikumo, S., Xu, H., Gelernter, J., Kember, R. L. & Kranzler, H. R. Integrating human brain proteomic data with genome-wide association study findings identifies novel brain proteins in substance use traits. Neuropsychopharmacology 47, 2292–2299 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Huggett, S. B. et al. Genome- and transcriptome-wide splicing associations with alcohol use disorder. Sci. Rep. https://doi.org/10.1038/s41598-023-30926-z (2023).

  61. Marees, A. T. et al. Post-GWAS analysis of six substance use traits improves the identification and functional interpretation of genetic risk loci. Drug Alcohol Depend. 206, 107703 (2020).

    Article  PubMed  CAS  Google Scholar 

  62. Grasby, K. L. et al. The genetic architecture of the human cerebral cortex. Science 367, eaay6690 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 8, 13624 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. McColl, E. R. & Piquette-Miller, M. SLC neurotransmitter transporters as therapeutic targets for alcohol use disorder: a narrative review. Alcohol Clin. Exp. Res. 44, 1965–1976 (2020).

    Article  PubMed  CAS  Google Scholar 

  67. Li, J. et al. Integration of transcriptome-wide association study with neuronal dysfunction assays provides functional genomics evidence for Parkinson’s disease genes. Hum. Mol. Genet. 32, 685–695 (2023).

    Article  PubMed  CAS  Google Scholar 

  68. Chatzinakos, C., Georgiadis, F. & Daskalakis, N. P. GWAS meets transcriptomics: from genetic letters to transcriptomic words of neuropsychiatric risk. Neuropsychopharmacology 46, 255–256 (2021).

    Article  PubMed  Google Scholar 

  69. Wingo, T. S. et al. Brain proteome-wide association study implicates novel proteins in depression pathogenesis. Nat. Neurosci. 24, 810–817 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Hall, L. S. et al. A transcriptome-wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia. Hum. Mol. Genet. 29, 159–167 (2020).

    Article  PubMed  CAS  Google Scholar 

  71. Lichou, F. & Trynka, G. Functional studies of GWAS variants are gaining momentum. Nat. Commun. 11, 6283 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Carnicella, S. et al. Cabergoline decreases alcohol drinking and seeking behaviors via glial cell line-derived neurotrophic factor. Biol. Psychiatry 66, 146–153 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Spoelder, M., Baars, A. M., Rotte, M. D., Vanderschuren, L. J. & Lesscher, H. M. Dopamine receptor agonists modulate voluntary alcohol intake independently of individual levels of alcohol intake in rats. Psychopharmacology 233, 2715–2725 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Su, W. J. et al. Antidiabetic drug glyburide modulates depressive-like behavior comorbid with insulin resistance. J. Neuroinflammation. 14, 210 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Sonsalla, M. M. et al. Acarbose ameliorates western diet-induced metabolic and cognitive impairments in the 3xTg mouse model of Alzheimer’s disease. Alzheimer’s Dement. 19, e078561 (2023).

    Article  Google Scholar 

  76. Rajkumar, M., Kannan, S. & Thangaraj, R. Voglibose attenuates cognitive impairment, Aβ aggregation, oxidative stress, and neuroinflammation in streptozotocin-induced Alzheimer’s disease rat model. Inflammopharmacology 31, 2751–2771 (2023).

    Article  PubMed  CAS  Google Scholar 

  77. Jerlhag, E. Alcohol-mediated behaviours and the gut-brain axis; with focus on glucagon-like peptide-1. Brain Res. 1727, 146562 (2020).

    Article  PubMed  CAS  Google Scholar 

  78. Finan, C. et al. The druggable genome and support for target identification and validation in drug development. Sci. Transl. Med. 9, eaag1166 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Paunovska, K., Loughrey, D. & Dahlman, J. E. Drug delivery systems for RNA therapeutics. Nat. Rev. Genet. 23, 265–280 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Milivojevic, V., Angarita, G. A., Hermes, G., Sinha, R. & Fox, H. C. Effects of prazosin on provoked alcohol craving and autonomic and neuroendocrine response to stress in alcohol use disorder. Alcohol Clin. Exp. Res. 44, 1488–1496 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Rogawski, M. A. & Wenk, G. L. The neuropharmacological basis for the use of memantine in the treatment of Alzheimer’s disease. CNS Drug Rev. 9, 275–308 (2003).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Montemitro, C., Angebrandt, A., Wang, T.-Y., Pettorruso, M. & Abulseoud, O. A. Mechanistic insights into the efficacy of memantine in treating certain drug addictions. Prog. Neuropsychopharmacol. Biol. Psychiatry 111, 110409 (2021).

    Article  PubMed  CAS  Google Scholar 

  83. Lawlor, D. A., Tilling, K. & Davey Smith, G. Triangulation in aetiological epidemiology. Int. J. Epidemiol. 45, 1866–1886 (2016).

    PubMed  Google Scholar 

  84. Martin, E. et al. Loss of function of glucocerebrosidase GBA2 is responsible for motor neuron defects in hereditary spastic paraplegia. Am. J. Hum. Genet. 92, 238–244 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Ji, C. Advances and new concepts in alcohol-induced organelle stress, unfolded protein responses and organ damage. Biomolecules 5, 1099–1121 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Gissen, P. in Epstein’s Inborn Errors of Development: The Molecular Basis of Clinical Disorders of Morphogenesis 3rd edn (eds Erickson, R. P. & Wynshaw-Boris, A. J.) Ch. 193 (Oxford Univ. Press, 2016); https://doi.org/10.1093/med/9780199934522.003.0193

  87. Worzfeld, T. & Schwaninger, M. Apicobasal polarity of brain endothelial cells. J. Cereb. Blood Flow. Metab. 36, 340–362 (2016).

    Article  PubMed  CAS  Google Scholar 

  88. Morris, G. et al. Leaky brain in neurological and psychiatric disorders: drivers and consequences. Aust. N. Z. J. Psychiatry 52, 924–948 (2018).

    Article  PubMed  Google Scholar 

  89. Mushtaq, Z. et al. Madm/NRBP1 mediates synaptic maintenance and neurodegeneration-induced presynaptic homeostatic potentiation. Cell Rep. 41, 111710 (2022).

    Article  PubMed  CAS  Google Scholar 

  90. Nalberczak-Skóra, M. et al. Impaired synaptic transmission in dorsal dentate gyrus increases impulsive alcohol seeking. Neuropsychopharmacology 48, 436–447 (2023).

    Article  PubMed  Google Scholar 

  91. Heymann, D. et al. The association between alcohol use and the progression of Alzheimer’s disease. Curr. Alzheimer Res. 13, 1356–1362 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Belgareh, N. et al. An evolutionarily conserved NPC subcomplex, which redistributes in part to kinetochores in mammalian cells. J. Cell Biol. 154, 1147–1160 (2001).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Coyne, A. N. & Rothstein, J. D. Nuclear pore complexes—a doorway to neural injury in neurodegeneration. Nat. Rev. Neurol. 18, 348–362 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Cullen, K. M. & Halliday, G. M. Neurofibrillary tangles in chronic alcoholics. Neuropathol. Appl. Neurobiol. 21, 312–318 (1995).

    Article  PubMed  CAS  Google Scholar 

  95. Johnson, E. C. et al. Investigation of convergent and divergent genetic influences underlying schizophrenia and alcohol use disorder. Psychol. Med. 53, 1196–1204 (2023).

    Article  PubMed  Google Scholar 

  96. Castillo-Carniglia, A., Keyes, K. M., Hasin, D. S. & Cerdá, M. Psychiatric comorbidities in alcohol use disorder. Lancet Psychiatry 6, 1068–1080 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Drake, R. E. & Wallach, M. A. Substance abuse among the chronic mentally ill. Hosp. Community Psychiatry 40, 1041–1046 (1989).

    PubMed  CAS  Google Scholar 

  98. Khurana, S. & Oberdoerffer, P. Replication stress: a lifetime of epigenetic change. Genes 6, 858–877 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Jung, J. et al. Additive effects of stress and alcohol exposure on accelerated epigenetic aging in alcohol use disorder. Biol. Psychiatry 93, 331–341 (2022).

    Article  PubMed  Google Scholar 

  101. Jung, J. et al. Alcohol use disorder is associated with DNA methylation-based shortening of telomere length and regulated by TESPA1: implications for aging. Mol. Psychiatry 27, 3875–3884 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  102. Luo, A. et al. Epigenetic aging is accelerated in alcohol use disorder and regulated by genetic variation in APOL2. Neuropsychopharmacology 45, 327–336 (2020).

    Article  PubMed  CAS  Google Scholar 

  103. Kessler, R. C. et al. Prevalence, persistence, and sociodemographic correlates of DSM-IV disorders in the National Comorbidity Survey Replication Adolescent Supplement. Arch. Gen. Psychiatry 69, 372–380 (2012).

    Article  PubMed  Google Scholar 

  104. Marees, A. T. et al. Potential influence of socioeconomic status on genetic correlations between alcohol consumption measures and mental health. Psychol. Med. https://doi.org/10.1017/s0033291719000357 (2019).

  105. Rosoff, D. B., Yoo, J. & Lohoff, F. W. Smoking is significantly associated with increased risk of COVID-19 and other respiratory infections. Commun. Biol. 4, 1230 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Harrison, R. K. Phase II and phase III failures: 2013–2015. Nat. Rev. Drug Discov. 15, 817–818 (2016).

    Article  PubMed  CAS  Google Scholar 

  107. Gordillo-Marañón, M. et al. Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics. Nat. Commun. 12, 6120 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Chong, M. et al. Novel drug targets for ischemic stroke identified through Mendelian randomization analysis of the blood proteome. Circulation 140, 819–830 (2019).

    Article  PubMed  CAS  Google Scholar 

  109. Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Prapiadou, S. et al. Proteogenomic data integration reveals CXCL10 as a potentially downstream causal mediator for IL-6 signaling on atherosclerosis. Circulation 149, 669–683 (2024).

    Article  PubMed  CAS  Google Scholar 

  111. Burgess, S. et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 4, 186 (2019).

    Article  PubMed  Google Scholar 

  112. Lähnemann, D. et al. Eleven grand challenges in single-cell data science. Genome Biol. 21, 31 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Hicks, S. C., Townes, F. W., Teng, M. & Irizarry, R. A. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 19, 562–578 (2018).

    Article  PubMed  Google Scholar 

  114. Williams, C. G., Lee, H. J., Asatsuma, T., Vento-Tormo, R. & Haque, A. An introduction to spatial transcriptomics for biomedical research. Genome Med. 14, 68 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  115. Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Egervari, G., Siciliano, C. A., Whiteley, E. L. & Ron, D. Alcohol and the brain: from genes to circuits. Trends Neurosci. 44, 1004–1015 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. Wallace, C. A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genet. 17, e1009440 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  118. Millwood, I. Y. et al. Conventional and genetic evidence on alcohol and vascular disease aetiology: a prospective study of 500 000 men and women in China. Lancet 393, 1831–1842 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  119. Britton, A., Ben-Shlomo, Y., Benzeval, M., Kuh, D. & Bell, S. Life course trajectories of alcohol consumption in the United Kingdom using longitudinal data from nine cohort studies. BMC Med. 13, 47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  121. Sudhinaraset, M., Wigglesworth, C. & Takeuchi, D. T. Social and cultural contexts of alcohol use: influences in a social–ecological framework. Alcohol Res. 38, 35–45 (2016).

    PubMed  PubMed Central  Google Scholar 

  122. Neale Lab. UK Biobank GWAS http://www.nealelab.is/uk-biobank/ (2018).

  123. Hatoum, A. S., Johnson, E. C., Agrawal, A. & Bogdan, R. Brain structure and problematic alcohol use: a test of plausible causation using latent causal variable analysis. Brain Imaging Behav. 15, 2741–2745 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Cartas-Cejudo, P. et al. Mapping the human brain proteome: opportunities, challenges, and clinical potential. Expert Rev. Proteom. 21, 55–63 (2024).

    Article  CAS  Google Scholar 

  125. Schmidt, A. F. et al. Druggable proteins influencing cardiac structure and function: implications for heart failure therapies and cancer cardiotoxicity. Sci. Adv. 9, eadd4984 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  126. Storm, C. S. et al. Finding genetically-supported drug targets for Parkinson’s disease using Mendelian randomization of the druggable genome. Nat. Commun. 12, 7342 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  127. Rosoff, D. B. et al. Multivariate genome-wide analysis of aging-related traits identifies novel loci and new drug targets for healthy aging. Nat. Aging 3, 1020–1035 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Bouras, E. et al. Circulating inflammatory cytokines and risk of five cancers: a Mendelian randomization analysis. BMC Med. 20, 3 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  129. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  PubMed  Google Scholar 

  130. Burgess, S., Zuber, V., Valdes-Marquez, E., Sun, B. B. & Hopewell, J. C. Mendelian randomization with fine-mapped genetic data: choosing from large numbers of correlated instrumental variables. Genet. Epidemiol. 41, 714–725 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  131. Burgess, S., Small, D. S. & Thompson, S. G. A review of instrumental variable estimators for Mendelian randomization. Stat. Methods Med. Res. 26, 2333–2355 (2017).

    Article  PubMed  Google Scholar 

  132. Smith, G. D. & Ebrahim, S. Mendelian randomization: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003).

    Article  PubMed  Google Scholar 

  133. Yavorska, O. O. & Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 46, 1734–1739 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  134. Bowden, J. et al. Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int J. Epidemiol. 48, 728–742 (2019).

    Article  PubMed  Google Scholar 

  135. Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  136. Koopmans, F. et al. SynGO: an evidence-based, expert-curated knowledge base for the synapse. Neuron 103, 217–234.e4 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  137. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  138. Piñero, J. et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 48, D845–D855 (2020).

    PubMed  Google Scholar 

  139. Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587–D592 (2023).

    Article  PubMed  CAS  Google Scholar 

  140. Sollis, E. et al. The NHGRI-EBI GWAS catalog: knowledgebase and deposition resource. Nucleic Acids Res. 51, D977–D985 (2023).

    Article  PubMed  CAS  Google Scholar 

  141. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  142. Gaziano, L. et al. Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19. Nat. Med. 27, 668–676 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  143. Zuber, V. et al. Combining evidence from Mendelian randomization and colocalization: review and comparison of approaches. Am. J. Hum. Genet. 109, 767–782 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Tissink, E. et al. The genetic architectures of functional and structural connectivity properties within cerebral resting-state networks. eNeuro https://doi.org/10.1523/ENEURO.0242-22.2023 (2023).

  145. Foley, C. N. et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Nat. Commun. 12, 764 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  146. Thom, C. S. & Voight, B. F. Genetic colocalization atlas points to common regulatory sites and genes for hematopoietic traits and hematopoietic contributions to disease phenotypes. BMC Med. Genomics 13, 89 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  147. Satizabal, C. L. et al. Genetic architecture of subcortical brain structures in 38,851 individuals. Nat. Genet. 51, 1624–1636 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  148. Zhao, B. et al. Common genetic variation influencing human white matter microstructure. Science 372, eabf3736 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  149. Rosenman, R., Tennekoon, V. & Hill, L. G. Measuring bias in self-reported data. Int J. Behav. Health. Res. 2, 320–332 (2011).

    Article  Google Scholar 

  150. Bryois, J. Summary statistics of cell-type specific cis-eQTLs in eight brain cell-types. Zenodo https://zenodo.org/records/7276971 (2021).

  151. Stelzer, G. et al. The GeneCards Suite: from gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinforma. 54, 1.30.1–1.30.33 (2016).

    Article  Google Scholar 

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Acknowledgements

We acknowledge the participants and investigators of the many studies, including the UK Biobank, Psychiatric Genomics Consortium, Million Veterans Program, FinnGen, ROSMAP, Banner Health, Mount Sinai Brain Bank, Netherlands Brain Bank, the MS UK Tissue Bank the Edinburgh Brain Bank and the GTEx Project, used in this research, without whom this effort would not be possible. We also acknowledge the Medical Research Council Integrative Epidemiology Unit (MRC-IEU, University of Bristol), especially the developers of the MRC-IEU UK Biobank GWAS Pipeline. This work was supported by the National Institutes of Health intramural funding (ZIA-AA000242 to F.W.L) as part of the Division of Intramural Clinical and Biological Research of the National Institute on Alcohol Abuse and Alcoholism. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors received no specific funding for this work.

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Authors and Affiliations

  1. Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA

    Daniel B. Rosoff, Josephin Wagner, Andrew S. Bell, Lucas A. Mavromatis, Jeesun Jung & Falk W. Lohoff

  2. NIH Oxford–Cambridge Scholars Program, National Institutes of Health, Bethesda, MD, USA

    Daniel B. Rosoff

  3. Radcliffe Department of Medicine, University of Oxford, Oxford, UK

    Daniel B. Rosoff

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  1. Daniel B. Rosoff
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  2. Josephin Wagner
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  3. Andrew S. Bell
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  4. Lucas A. Mavromatis
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  6. Falk W. Lohoff
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Contributions

D.B.R. and F.W.L. conceptualized the study and interpreted the results. D.B.R. was responsible for data acquisition, preparation, analyses, and visualization and drafted the manuscript. All authors reviewed the manuscript. F.W.L. provided project supervision.

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Correspondence to Falk W. Lohoff.

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Nature Human Behaviour thanks Xiong-Jian Luo, Michael Salling and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Rosoff, D.B., Wagner, J., Bell, A.S. et al. A multi-omics Mendelian randomization study identifies new therapeutic targets for alcohol use disorder and problem drinking. Nat Hum Behav 9, 188–207 (2025). https://doi.org/10.1038/s41562-024-02040-1

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