Multivariate genetic of 2.2 million individuals demonstrate genetic influences on substance use disorders operate via behavioral disinhibition and substance-specific risk.
- Authors
- Poore, Holly E; Chatzinakos, Chris; Leger, Brittany; Gonzalez, Jean; Mallard, Travis T; Sanchez-Roige, Sandra; Aliev, Fazil; Hatoum, Alexander; COGA Collaborators; Waldman, Irwin D; Palmer, Abraham A; Harden, K Paige; Dick, Danielle M; Barr, Peter B
- Year
- 2025
- Journal
- medRxiv : the preprint server for health sciences
- PMID
- 39649581
- DOI
- 10.1101/2024.11.26.24318011
- PMCID
- PMC11623735
Ongoing efforts to identify genes involved in substance use disorders (SUDs) often focus on individual disorders despite high rates of co-occurrence with each other and other externalizing traits. Here, we investigate whether incorporating data on other externalizing traits can boost power to detect without sacrificing specificity of SUD genetic signal. We used multivariate genomic analyses and downstream biological annotation and genetic association analyses to explore this question. We found that joint analysis of SUDs and other externalizing traits resulted in increased insights into the neurobiology of broad and substance-specific SUD risk. We found no evidence of loss of specificity for SUD genetic signal but note improvements in our ability to characterize the neurobiology of broad and substance-specific SUD genetic effects. Our findings suggest that genetic risk for SUDs operates largely via pathways shared with other behaviors characterized by behavioral disinhibition, with additional substance-specific risk, and that modeling this shared disposition improves gene discovery.
Path diagrams of models used in the current analyses. Box A represents the broad Externalizing factor onto which all behavioral disinhibition and SUD phenotypes load. Boxes B-E represent residual SUD phenotypes. Boxes F and G represent narrower factors reflecting Behavioral Disinhibition and SUD phenotypes, respectively. Single headed arrows indicate factor loadings whereas the double headed arrow indicates a correlation between the two factors. RISK = risk taking, ADHD = attention deficit hyperactivity disorder, NSEX = number of sexual partners, FSEX = age at first sexual intercourse, SMOK = smoking initiation, CANN = cannabis initiation, PAU = problematic alcohol use, OUD = opioid use disorder, CUD = cannabis use disorder, PTU = problematic tobacco use.
Manhattan plots of (top to bottom) Externalizing, Behavioral Disinhibition, and SUD. Brown points represent novel SUD loci (loci not previously associated with a substance use trait). Top loci are mapped to the nearest gene using ANNOVAR28 annotation.
(a) Venn diagrams showing the overlap of genes identified by the MetaXcan, SMR, MAGMA, and H-MAGMA analyses for Externalizing (blue), Behavioral Disinhibition (orange), and SUD (yellow). (b) upset plot showing intersecting sets of high confidence genes (those identified by all three gene-based methods) across Externalizing, Behavioral Disinhibition, and SUD.
Network Annotation.(a) Upset plot showing the overlap in genes identified from genes mapping algorithms (see Figure 3a), and network analysis. (b) System map for the Externalizing network. Community labels represent aggregated significant gene ontology enrichment labels, and node size indicates log-transformed community size, measured by number of genes. Color indicates the odds ratio of the enrichment of that community for genes annotated in the GWAS catalog for substance abuse. Only labelled communities and those connecting labeled communities are shown.
Genetic association analyses with residual SUDs.(a) genetic correlations and their 95% confidence intervals of the original SUD indicator GWAS (blue) and the residual SUD genetic effects (red) with relevant outcomes, (b) R2 estimates and their 95% confidence intervals of variance explained by residual SUD PGS in SUDs in COGA and (c) corresponding relative risk of moderate substance use disorder across quintiles of PGS. Matched SUD phenotype and residual SUD PGS are shown in a darker color relative to unmatched PGS.
No entities extracted from this document yet.
No uploaded files.
| Citation | PMID | DOI | Status |
|---|---|---|---|
| AshburnerM. Gene Ontology: tool for the unification of biology. Nature Genetics 25, 25–29 (2000). 10.1038/7555610802651 PMC3037419 | — | — | — |
| BarbeiraA. N. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nature Communications 9, 1825 (2018). 10.1038/s41467-018-03621-1 | — | — | — |
| BaurleyJ. W., EdlundC. K., PardameanC. I., ContiD. V. & BergenA. W. Smokescreen: a targeted genotyping array for addiction research. BMC Genomics 17, 145 (2016). 10.1186/s12864-016-2495-726921259 PMC4769529 | — | — | — |
| Blair GC. J., CoppockA, HumphreysM, SonnetL. estimatr: Fast Estimators for Design-Based Inference. (2025). | — | — | — |
| CannonM. DGIdb 5.0: rebuilding the drug–gene interaction database for precision medicine and drug discovery platforms. Nucleic Acids Research 52, D1227–D1235 (2024). 10.1093/nar/gkad104037953380 PMC10767982 | — | — | — |
| CantyA. a. R., B. boot: Bootstrap R (S-Plus) Functions. (2024). | — | — | — |
| CarlsonM. D., MendleJ. & HardenK. P. Early adverse environments and genetic influences on age at first sex: Evidence for gene × environment interaction. Developmental Psychology 50, 1532–1542 (2014). 10.1037/a003547924417626 | — | — | — |
| ConsortiumG. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020). 10.1126/science.aaz177632913098 PMC7737656 | — | — | — |
| de LeeuwC. A., MooijJ. M., HeskesT. & PosthumaD. MAGMA: Generalized Gene-Set Analysis of GWAS Data. PLOS Computational Biology 11, e1004219 (2015). 10.1371/journal.pcbi.100421925885710 PMC4401657 | — | — | — |
| DeakJ. D. & JohnsonE. C. Genetics of substance use disorders: a review. Psychol Med 51, 2189–2200 (2021). 10.1017/S003329172100096933879270 PMC8477224 | — | — | — |
| DeakJ. D. Genome-wide association study in individuals of European and African ancestry and multi-trait analysis of opioid use disorder identifies 19 independent genome-wide significant risk loci. Molecular Psychiatry 27, 3970–3979 (2022). 10.1038/s41380-022-01709-135879402 PMC9718667 | — | — | — |
| DemontisD. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet 51, 63–75 (2019). 10.1038/s41588-018-0269-730478444 PMC6481311 | — | — | — |
| DickD. M. The collaborative study on the genetics of alcoholism: Sample and clinical data. Genes, Brain and Behavior 22, e12860 (2023).37581339 10.1111/gbb.12860PMC10550787 | — | — | — |
| FagerstromK. Determinants of tobacco use and renaming the FTND to the Fagerstrom Test for Cigarette Dependence. Nicotine Tob Res 14, 75–78 (2012). 10.1093/ntr/ntr13722025545 | — | — | — |
| GeronimusA. T. Teenage childbearing as cultural prism. Br Med Bull 69, 155–166 (2004). 10.1093/bmb/ldh01915226204 | — | — | — |
| GeT., ChenC. Y., NiY., FengY. A. & SmollerJ. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nature Communications 10, 1776 (2019). 10.1038/s41467-019-09718-5 | — | — | — |
| GrotzingerA. D. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nature Human Behavior 3, 513–525 (2019). 10.1038/s41562-019-0566-x | — | — | — |
| HardenK. P. A Sex-Positive Framework for Research on Adolescent Sexuality. Perspect Psychol Sci 9, 455–469 (2014). 10.1177/174569161453593426186753 | — | — | — |
| HatoumA. S. Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders. Nature Mental Health 1, 210–223 (2023). 10.1038/s44220-023-00034-y37250466 PMC10217792 | — | — | — |
| HatoumA. S. The addiction risk factor: A unitary genetic vulnerability characterizes substance use disorders and their associations with common correlates. Neuropsychopharmacol (2021). 10.1038/s41386-021-01209-w | — | — | — |
| HussongA. M., JonesD. J., SteinG. L., BaucomD. H. & BoedingS. An internalizing pathway to alcohol use and disorder. Psychol Addict Behav 25, 390–404 (2011). 10.1037/a002451921823762 PMC3178003 | — | — | — |
| Investigators, A. o. U. R. P. G. Genomic data in the All of Us Research Program. Nature 627, 340–346 (2024). 10.1038/s41586-023-06957-x38374255 PMC10937371 | — | — | — |
| Investigators, T. A. o. U. R. P. The “All of Us” Research Program. New England Journal of Medicine 381, 668–676 (2019). 10.1056/NEJMsr180993731412182 PMC8291101 | — | — | — |
| JohnsonE. C. A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry 7, 1032–1045 (2020). 10.1016/s2215-0366(20)30339-433096046 PMC7674631 | — | — | — |
| JohnsonE. C. The collaborative study on the genetics of alcoholism: Genetics. Genes, Brain and Behavior 22, e12856 (2023). 10.1111/gbb.1285637387240 PMC10550788 | — | — | — |
| Karlsson LinnérR. Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nature Neuroscience 24, 1367–1376 (2021). 10.1038/s41593-021-00908-334446935 PMC8484054 | — | — | — |
| KendlerK. S. A Swedish Population-Based Multivariate Twin Study of Externalizing Disorders. Behavior Genetics 46, 183–192 (2016). 10.1007/s10519-015-9741-726494460 PMC4752872 | — | — | — |
| KendlerK. S., JacobsonK. C., PrescottC. A. & NealeM. C. Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am J Psychiatry 160, 687–695 (2003). 10.1176/appi.ajp.160.4.68712668357 | — | — | — |
| KendlerK. S., MyersJ. & PrescottC. A. Specificity of genetic and environmental risk factors for symptoms of cannabis, cocaine, alcohol, caffeine, and nicotine dependence. Archives of general psychiatry 64, 1313–1320 (2007).17984400 10.1001/archpsyc.64.11.1313 | — | — | — |
| KhanY. Transdiagnostic and Disorder-Level Genome-Wide Association Studies Enhance Precision of Substance Use and Psychiatric Genetic Risk Profiles in African and European Ancestries. Biol Psychiatry (2025). 10.1016/j.biopsych.2025.04.021 | — | — | — |
| KingS. M., IaconoW. G. & McGueM. Childhood externalizing and internalizing psychopathology in the prediction of early substance use. Addiction 99, 1548–1559 (2004).15585046 10.1111/j.1360-0443.2004.00893.x | — | — | — |
| KotovR. The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology 126, 454–477 (2017). 10.1037/abn000025828333488 | — | — | — |
| Krueger Etiological Connections among Substance Dependence, Antisocial Behavior, and Personality: Modeling the Externalizing Spectrum. Journal of Abnormal Psychology 111, 411–424 (2002). 10.1037/0021-843X.111.3.41112150417 | — | — | — |
| KruegerMarkon, PatrickBenning & Kramer. Linking Antisocial Behavior, Substance Use, and Personality: An Integrative Quantitative Model of the Adult Externalizing Spectrum. J Abnorm Psychol 116, 645–666 (2007). 10.1037/0021-843X.116.4.64518020714 PMC2242625 | — | — | — |
| KruegerR. F. Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): II. Externalizing superspectrum. World Psychiatry 20, 171–193 (2021). 10.1002/wps.2084434002506 PMC8129870 | — | — | — |
| LaiD. Genome-wide association studies of alcohol dependence, DSM-IV criterion count and individual criteria. Genes, Brain and Behavior 18, e12579 (2019). 10.1111/gbb.1257931090166 PMC6612573 | — | — | — |
| LindbergL. D., Maddow-ZimetI. & MarcellA. V. Prevalence of Sexual Initiation Before Age 13 Years Among Male Adolescents and Young Adults in the United States. JAMA Pediatr 173, 553–560 (2019). 10.1001/jamapediatrics.2019.045830958512 PMC6547075 | — | — | — |
| LinnérR., Karlsson Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nature Genetics 51, 245–257 (2019). 10.1038/s41588-018-0309-330643258 PMC6713272 | — | — | — |
| LiuM. 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). 10.1038/s41588-018-0307-530643251 PMC6358542 | — | — | — |
| MillerA. P., BogdanR., AgrawalA. & HatoumA. S. Generalized genetic liability to substance use disorders. The Journal of Clinical Investigation 134 (2024). 10.1172/JCI172881 | — | — | — |
| MojtabaiR. Estimating the Prevalence of Substance Use Disorders in the US Using the Benchmark Multiplier Method. JAMA Psychiatry 79, 1074–1080 (2022). 10.1001/jamapsychiatry.2022.275636129721 PMC9494265 | — | — | — |
| PasmanJ. A. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat Neurosci 21, 1161–1170 (2018). 10.1038/s41593-018-0206-130150663 PMC6386176 | — | — | — |
| PooreH. E. A Multivariate Approach to Understanding the Genetic Overlap between Externalizing Phenotypes and Substance Use Disorders. Addiction Biology 28, e13319 (2023). 10.1111/adb.1331937644899 PMC11010459 | — | — | — |
| PriceA. L. Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics 38, 904–909 (2006). 10.1038/ng184716862161 | — | — | — |
| PurcellS. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American journal of human genetics 81, 559–575 (2007).17701901 10.1086/519795PMC1950838 | — | — | — |
| RamirezA. H. The All of Us Research Program: Data quality, utility, and diversity. Patterns (N Y) 3, 100570 (2022). 10.1016/j.patter.2022.10057036033590 PMC9403360 | — | — | — |
| RosenthalS. B. Mapping the common gene networks that underlie related diseases. Nature Protocols 18, 1745–1759 (2023). 10.1038/s41596-022-00797-136653526 PMC10257754 | — | — | — |
| SacksJ. J., GonzalesK. R., BoucheryE. E., TomediL. E. & BrewerR. D. 2010 National and State Costs of Excessive Alcohol Consumption. Am J Prev Med 49, e73–e79 (2015) 10.1016/j.amepre.2015.05.03126477807 | — | — | — |
| SaundersG. R. B. Genetic diversity fuels gene discovery for tobacco and alcohol use. Nature 612, 720–724 (2022). 10.1038/s41586-022-05477-436477530 PMC9771818 | — | — | — |
| SeyN. Y. A. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nature Neuroscience 23, 583–593 (2020). 10.1038/s41593-020-0603-032152537 PMC7131892 | — | — | — |
| SinghalA. Multiscale community detection in Cytoscape. PLOS Computational Biology 16, e1008239 (2020). 10.1371/journal.pcbi.100823933095781 PMC7584444 | — | — | — |
| SollisE. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Research 51, D977–D985 (2023). 10.1093/nar/gkac101036350656 PMC9825413 | — | — | — |
| The Gene Ontology, C. The Gene Ontology knowledgebase in 2023. Genetics 224, iyad031 (2023). 10.1093/genetics/iyad03136866529 PMC10158837 | — | — | — |
| VerhulstB., NealeM. C. & KendlerK. S. The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies. Psychol Med 45, 1061–1072 (2015). 10.1017/s003329171400216525171596 PMC4345133 | — | — | — |
| VerweijK. J. Genetic and environmental influences on cannabis use initiation and problematic use: a meta-analysis of twin studies. Addiction 105, 417–430 (2010). 10.1111/j.1360-0443.2009.02831.x20402985 PMC2858354 | — | — | — |
| WangK., LiM. & HakonarsonH. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38, e164 (2010). 10.1093/nar/gkq60320601685 PMC2938201 | — | — | — |
| WatanabeK., TaskesenE., van BochovenA. & PosthumaD. Functional mapping and annotation of genetic associations with FUMA. Nature Communications 8, 1826 (2017). 10.1038/s41467-017-01261-5 | — | — | — |
| WrightS. N. Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks. Cell Rep 42, 112873 (2023). 10.1016/j.celrep.2023.11287337527041 PMC10546330 | — | — | — |
| WrightS. N. State of the interactomes: an evaluation of molecular networks for generating biological insights. Molecular Systems Biology 21, 1–29–29 (2025). 10.1038/s44320-024-00077-y39653848 PMC11697402 | — | — | — |
| ZhengF. HiDeF: identifying persistent structures in multiscale ‘omics data. Genome Biology 22, 21 (2021). 10.1186/s13059-020-02228-433413539 PMC7789082 | — | — | — |
| ZhouH. 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). 10.1038/s41593-020-0643-532451486 PMC7485556 | — | — | — |
| ZhouH. Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals. Nature Medicine 29, 3184–3192 (2023). 10.1038/s41591-023-02653-5 | — | — | — |
| ZhuZ. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature genetics 48, 481–487 (2016).27019110 10.1038/ng.3538 | — | — | — |
No papers in this knowledge base cite this source.
No citations found for this source.