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Identification of risk variants and cross-disorder pleiotropy through multi-ancestry genome-wide analysis of alcohol use disorder

Abstract

Alcohol use disorder (AUD) is highly heritable and burdensome worldwide. Genome-wide association studies can provide new evidence regarding the etiology of AUD. We report a multi-ancestry genome-wide association study focusing on a narrow AUD phenotype, using novel statistical tools in a total sample of 1,041,450 individuals (102,079 cases; European, 75,583; African, 20,689 (mostly African American); Hispanic American, 3,449; East Asian, 2,254; South Asian, 104; descent). Cross-ancestry functional analyses were performed with European and African samples. Thirty-seven genome-wide significant loci (105 variants) were identified, of which seven were novel for AUD and six for other alcohol phenotypes. Loci were mapped to genes, which show altered expression in brain regions relevant for AUD (striatum, hypothalamus and prefrontal cortex) and encode potential drug targets (GABAergic, dopaminergic and serotonergic neurons). African-specific analysis yielded a unique pattern of immune-related gene sets. Polygenic overlap and positive genetic correlations showed extensive shared genetic architecture between AUD and both mental and general medical phenotypes, suggesting that they are not only complications of alcohol use but also share genetic liability with AUD. Leveraging a cross-ancestry approach allowed identification of novel genetic loci for AUD and underscores the value of multi-ancestry genetic studies. These findings advance our understanding of AUD risk and clinically relevant comorbidities.

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Fig. 1: Ancestry-specific genetic architecture of AUD in the multi-ancestry analysis (top, red, AUD multi-ancestry) and for the EUR (bottom, blue, AUD EUR) and AFR (bottom, yellow, AUD AFR) samples.
Fig. 2: Independent cell types associated with the GWAS meta-analysis results in the AFR, EUR and multi-ancestry samples.
Fig. 3: Tissue-specific gene expression enrichment from AUD meta-analyses in the multi-ancestry, AFR and EUR analyses, and from the meta-analysis of the drinks/week phenotype in the EUR + AFR samples.
Fig. 4: Polygenic overlap between AUD (blue) and clinically relevant phenotypes, after filtering based on estimation of MiXeR stability using the Akaike Informant Criterion.
Fig. 5: Genetic correlation of AUD with mental traits and disorders (left) and with general medical conditions and risk factors (right), including neuropsychiatric diseases, for the AFR and the EUR samples, separately.

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Data availability

The summary statistics used for the current study were obtained from third party, and are thus not fully available to all authors. Two of them are publicly available (FINNGEN, https://www.finngen.fi/en/access_results, R6 public release; Psychiatric Genomics Consortium (PGC), https://pgc.unc.edu/for-researchers/download-results/). We accessed UK Biobank individual-level genotype to extend publicly available summary statistics for alcohol-related traits GWAS that are otherwise publicly available. Likewise, MVP data can be accessed through the dbGaP website, study phs001672.v9.p1. The summary statistics and data underlying the figures are thus only available to people with registered access to MVP data from the corresponding author or A.S. ([email protected]) upon request.

Code availability

The code for genetic correlation (https://github.com/brielin/Popcorn) and MiXeR (https://github.com/precimed/mixer) is publicly available.

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Acknowledgements

This work was partly performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo and operated and developed by the TSD service group at the University of Oslo, IT Department (USIT). Computations were also performed on resources provided by UNINETT Sigma2, the National Infrastructure for High-Performance Computing and Data Storage in Norway. We gratefully acknowledge support from the NIH (NS057198, EB000790 and 1R01MH124839), the Research Council of Norway (229129, 213837, 223273, 324252, 248980 and 334920), the South-East Norway Regional Health Authority (2017-112 and 2019-108) and KG Jebsen Stiftelsen (SKGJ-MED-021). This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement numbers 847776, 964874 and 801133 (Marie Skłodowska-Curie grant agreement). R.I. thanks the INTPART program (principal investigator T.V.L.) for supporting his fellowship at NORMENT. Core funding of third party data acquisition: Million Veteran Program (MVP), United States Department of Veterans Affairs; FINNGEN, Business Finland and the pharmaceutical industry partners (https://www.finngen.fi/en/funding); UK Biobank (UKB), Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/our-funding); Psychiatric Genomics Consortium (PGC), US National Institute of Mental Health and the US National Institute of Drug Abuse (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5756100/).

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Authors

Contributions

R.I., A.S., G.H. and O.A.A. were responsible for the study concept and design. R.I., A.S. and G.H. contributed to the acquisition of summary data. A.S. was the main data analyst. B.H., N.K., O.B.S., S.D., H.J.E., H.Z. and O.A.A. assisted with data analysis and interpretation of findings. R.I., A.S., N.K. and B.H. prepared figures. B.H. drafted the paper. O.A.A., O.B.S., W.C., T.V.L., M.C.H., K.S.O., N.K., N.P., O.F., S.B., T.M.S., A.M.D. and J.G. provided critical revision of the paper for important intellectual content. All authors critically reviewed content and approved the final version for publication.

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Correspondence to Romain Icick.

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Competing interests

A.M.D. is a founder of and holds equity in Cortechs.ai and serves on its scientific advisory board; he is a member of the scientific advisory boards of HealthLytix and the Mohn Medical Imaging and Visualization Center (Bergen, Norway); and he receives funding through a research agreement between General Electric Healthcare and UCSD. O.A.A. has received speaking honoraria from Lundbeck and has served as a consultant for HealthLytix. The other authors report no financial relationships with commercial interests.

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Nature Mental Health thanks Colin Hodgkinson, El Cherif Ibrahim and Tianye Jia for their contribution to the peer review of this work.

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Supplementary Methods and Figs. 1 and 2.

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Supplementary Data 1

Tuning parameters of FUMA SNP2GENE for the main GWAS meta-analysis, by ancestry.

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Supplementary Tables 1–6.

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Statistical data for Table 1.

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Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

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Icick, R., Shadrin, A., Holen, B. et al. Identification of risk variants and cross-disorder pleiotropy through multi-ancestry genome-wide analysis of alcohol use disorder. Nat. Mental Health 3, 253–265 (2025). https://doi.org/10.1038/s44220-024-00353-8

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