Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits

Abstract

Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. Although genome-wide association studies have identified PAU risk genes, the genetic architecture of this trait is not fully understood. We conducted a proxy-phenotype meta-analysis of PAU, combining alcohol use disorder and problematic drinking, in 435,563 European-ancestry individuals. We identified 29 independent risk variants, 19 of them novel. PAU was genetically correlated with 138 phenotypes, including substance use and psychiatric traits. Phenome-wide polygenic risk score analysis in an independent biobank sample (BioVU, n = 67,589) confirmed the genetic correlations between PAU and substance use and psychiatric disorders. Genetic heritability of PAU was enriched in brain and in conserved and regulatory genomic regions. Mendelian randomization suggested causal effects on liability to PAU of substance use, psychiatric status, risk-taking behavior and cognitive performance. In summary, this large PAU meta-analysis identified novel risk loci and revealed genetic relationships with numerous other traits.

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Fig. 1: Overview of the analysis.
Fig. 2: Association results for AUD and PAU meta-analyses.
Fig. 3: Estimated SNP-based h2.
Fig. 4: Genetic correlations with published traits.
Fig. 5: Phenome-wide associations with PAU PRS in BioVU.

Data availability

The full summary-level association data from the meta-analysis are available through dbGaP at accession no. phs001672.v3.p1.

Code availability

Kinship analysis was performed using KING (http://people.virginia.edu/~wc9c/KING/). PCAs were performed using EIGENSOFT (https://data.broadinstitute.org/alkesgroup/EIGENSOFT/). Imputation was performed using EAGLE2 (https://data.broadinstitute.org/alkesgroup/Eagle/), Minimac3 (https://genome.sph.umich.edu/wiki/Minimac3), Sanger imputation server (https://imputation.sanger.ac.uk/) or RICOPILI (https://data.broadinstitute.org/mpg/ricopili/), the choice depending on the sample. GWAS was performed using PLINK (https://www.cog-genomics.org/plink2). Meta-analyses were performed using METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation). Polygenic risk score analyses were performed using PRSice-2 (https://www.prsice.info/) or PRS-CS (https://github.com/getian107/PRScs). GCTA (https://cnsgenomics.com/software/gcta/#Overview) was used for identification of independent loci (GCTA-COJO), multi-trait conditional analysis (GCTA-mtCOJO) and MR (GCTA-GSMR). LDSC (https://github.com/bulik/ldsc) was used for heritability estimation, genetic correlation analysis (also using LD Hub (http://ldsc.broadinstitute.org/)) and heritability enrichment analyses. FUMA (https://fuma.ctglab.nl/) was used for gene association, functional enrichment and gene set enrichment analyses. Transcriptomic analyses were performed using S-PrediXcan and S-MultiXcan (https://github.com/hakyimlab/MetaXcan). PheWAS analyses were run using the PheWAS R package (https://github.com/PheWAS/PheWAS). The Mendelian Randomization R Package (https://cran.r-project.org/web/packages/MendelianRandomization/index.html) and MR–PRESSO (https://github.com/rondolab/MR-PRESSO) were used for MR analyses. MTAG (https://github.com/omeed-maghzian/mtag) was used for multiple trait analysis.

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Acknowledgements

This research used data from MVP, and was supported by funding from the Department of Veterans Affairs Office of Research and Development, Million Veteran Program Grant nos. I01BX003341 and I01CX001849; and the VA Cooperative Studies Program study, no. 575B. This publication does not represent the views of the Department of Veterans Affairs or the United States Government. A list of members and affiliations of MVP appears in the Supplementary Information. Supported also by NIH (NIAAA) no. P50 AA12870 (to J.G.), a NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (to H.Z.) and NIH grants nos. 5T32GM080178 (to J.M.S.) and K02DA32573 (to A.A.), and the National Institute for Healthcare Research (NIHR) Imperial Biomedical Research Centre (BRC; to S.R.A. and M.R.T.). This research also used summary data from the PGC Substance Use Disorders (SUD) working group. PGC–SUD is supported by funds from NIDA and NIMH to MH109532 and, previously, had analyst support from NIAAA to U01AA008401 (COGA). PGC–SUD gratefully acknowledges its contributing studies and the participants in those studies, without whom this effort would not be possible. This research also used individual-level/summary data from UKB, a population-based sample of participants whose contributions we gratefully acknowledge; project ID 41910. We thank the iPSYCH-Broad Consortium for access to data on the iPSYCH cohort. The iPSYCH project is funded by the Lundbeck Foundation (nos. R102-A9118 and R155-2014-1724) and the universities and university hospitals of Aarhus and Copenhagen. Genotyping of iPSYCH samples was supported by grants from the Lundbeck Foundation and the Stanley Foundation, and The Danish National Biobank resource was supported by the Novo Nordisk Foundation. Data handling and analysis on the GenomeDK HPC facility was supported by NIMH (no. 1U01MH109514-01 to A.D.B.). High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Centre for Integrative Sequencing, Aarhus University, Denmark (grant to A.D.B.). The UCL and STOPAH case samples were genotyped with funding from the NIHR BRC. UCL cases and controls were collected with UK Medical Research Council project grants nos. G9623693N, G0500791, G0701007 and G1000708, and with support from the NIHR. Genotyping of the UCL control sample was supported by grants from the Stanley Foundation. The UK Household Longitudinal Study (Understanding Society) is led by the Institute for Social and Economic Research at the University of Essex and funded by the Economic and Social Research Council. The survey was conducted by NatCen, and the genome-wide scan data were analyzed and deposited by the Wellcome Trust Sanger Institute. Information on how to access the data can be found on the Understanding Society website: https://www.understandingsociety.ac.uk/. A.M. is supported by the University College London Hospitals NHS Foundation Trust NIHR BRC.

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H.Z., J.G., H.R.K. and A.A.P. conceived the analyses. H.Z. and J.G. wrote the first draft and prepared all drafts for submission. J.G. supervised and H.Z. accomplished primary analyses. J.M.S., S.S.-R., T.-K.C., D.F.L., Z.C., B.L. and A.M. conducted additional analyses. J.G., H.R.K., A.A.P., L.K.D., H.J.E. and A.A. supervised additional analyses. J.M.S., S.S.-R., T.-K.C., A.A.P., A.M. and L.K.D. prepared individual datasets and provided summary statistics or results. R.P., R.L.K., R.V.S., J.H.T., M.Y.M., S.R.A., M.R.T., M.N., M.M., A.D.B., E.C.J., A.C.J., A.M., L.K.D. and H.R.K. provided critical support regarding phenotypes and data in individual datasets. J.G., A.C.J. and H.R.K. provided resource support. All authors reviewed the manuscript and approved it for submission.

Corresponding author

Correspondence to Joel Gelernter.

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Competing interests

H.R.K. is a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported over the past 3 years by AbbVie, Alkermes, Ethypharm, Indivior, Lilly, Lundbeck, Otsuka, Pfizer, Arbor, and Amygdala Neurosciences. H.R.K. and J.G. are named as inventors on PCT patent application no. 15/878,640, entitled Genotype-guided dosing of opioid agonists, filed 24 January 2018.

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Peer review information Nature Neuroscience thanks Gerome Breen, Eske Derks, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

A list of members and affiliations in MVP, and Supplementary Figs. 1–6.

Reporting Summary

Supplementary Tables 1–16.

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Zhou, H., Sealock, J.M., Sanchez-Roige, S. 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). https://doi.org/10.1038/s41593-020-0643-5

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