A multivariate approach to understanding the genetic overlap between externalizing phenotypes and substance use disorders.
- Authors
- Poore, Holly E; Hatoum, Alexander; Mallard, Travis T; Sanchez-Roige, Sandra; Waldman, Irwin D; Palmer, Abraham A; Harden, K Paige; Barr, Peter B; Dick, Danielle M
- Year
- 2023
- Journal
- Addiction biology
- PMID
- 37644899
- DOI
- 10.1111/adb.13319
- PMCID
- PMC11010459
Substance use disorders (SUDs) are phenotypically and genetically correlated with each other and with other psychological traits characterized by behavioural under-control, termed externalizing phenotypes. In this study, we used genomic structural equation modelling to explore the shared genetic architecture among six externalizing phenotypes and four SUDs used in two previous multivariate genome-wide association studies of an externalizing and an addiction risk factor, respectively. We first evaluated five confirmatory factor analytic models, including a common factor model, alternative parameterizations of two-factor structures and a bifactor model. We next explored the genetic correlations between factors identified in these models and other relevant psychological traits. Finally, we quantified the degree of polygenic overlap between externalizing and addiction risk using MiXeR. We found that the common and two-factor structures provided the best fit to the data, evidenced by high factor loadings, good factor reliability and no evidence of concerning model characteristics. The two-factor models yielded high genetic correlations between factors (r s ≥ 0.87), and between the effect sizes of genetic correlations with external traits (r ≥ 0.95). Nevertheless, 21 of the 84 correlations with external criteria showed small, significant differences between externalizing and addiction risk factors. MiXer results showed that approximately 81% of influential externalizing variants were shared with addiction risk, whereas addiction risk shared 56% of its influential variants with externalizing. These results suggest that externalizing and addiction genetic risk are largely shared, though both constructs also retain meaningful unshared genetic variance. These results can inform future efforts to identify specific genetic influences on externalizing and SUDs.
Path diagrams and standardized factor loadings of all confirmatory models tested (A) common factor, (B) EXT-ARF model, (C) BD-SUB model, (D) EXT-resARF model, and (E) bifactor BD-SUB model.
LLM interpretation
This figure consists of five path diagrams (A-E) representing different confirmatory factor models. Each diagram shows latent variables (ovals) and their standardized factor loadings (numerical values) onto observed indicators (rectangles), such as RISK, ADHD, and various substance use measures. The models vary in structure, ranging from a single common factor (EXT) to multi-factor and bifactor models (EXT-ARF, BD-SUB, EXT-resARF, and Bifactor BD-SUB) with indicated correlations between latent factors.
Genetic correlations among externalizing phenotypes and SUDs.
LLM interpretation
This is a correlation heatmap showing genetic correlations between various externalizing phenotypes and substance use disorders (SUDs). The visualization uses a blue-to-red color scale to represent correlation coefficients, with most values being positive (blue). The strongest correlation is observed between Opioid Use Disorder and Cannabis Use Disorder (0.77), while the only negative correlation is between Cannabis Initiation and Problematic Tobacco Use (-0.06).
(A) Indicators’ loadings in the common factor, EXT-ARF and BD-SUB models. Variability of (B) factor loadings and (C) standard errors of the factor loadings of indicators when one indicator was dropped from the model at a time.
LLM interpretation
This figure consists of three panels analyzing factor loadings across three models: Common Factor, EXT-ARF, and BD-SUB. Panel (A) is a radar chart showing the loadings of various indicators (e.g., RISK, CUD, ADHD), while panels (B) and (C) are box plots displaying the standardized factor loadings and their corresponding standard errors, respectively, when indicators are dropped one at a time. Across all panels, the three models show similar patterns and magnitudes for each indicator, with CUD generally exhibiting the highest factor loading and OUD showing the highest standard error.
Comparison of genetic correlations of factors in the EXT-ARF and BD-SUB models with psychological, personality and substance-use traits. * = Statistically significant decrement in model fit when the genetic correlations were constrained to be equal.
LLM interpretation
This figure consists of two horizontal bar charts (A, C) and two scatter plots (B, D) comparing genetic correlations between different risk factors and various psychological or substance-use traits. Panels A and C show zero-order genetic correlations for the EXT-ARF and BD-SUB models, respectively, with asterisks indicating statistically significant decrements in model fit when correlations were constrained. Panels B and D plot the genetic correlations of two factors against each other (Externalizing vs. Addiction Risk Factor and Behavioral Disinhibition vs. Substance Use), with point colors representing the difference in genetic correlation (rG).
Genetic correlations between externalizing factor in the common factor model and psychological, personality and substance-use traits.
LLM interpretation
This is a horizontal bar chart showing zero-order genetic correlations between an externalizing factor and various psychological, personality, and substance-use traits. Most traits, including antisocial behavior, suicide attempts, and stress-related disorders, show positive genetic correlations, while agreeableness and conscientiousness show negative correlations. The x-axis represents the genetic correlation coefficient ranging from -1.0 to 1.0, with error bars indicating confidence intervals for each trait.
Polygenic overlap between externalizing and addiction risk (A) Venn diagram depicting the estimated number of influencing variants in thousands shared (grey) between and unique to addiction risk (blue) and externalizing (orange). Subparts (B) and (C) show conditional quantile–quantile (Q–Q) plots of observed versus expected −log10 p values in addiction risk and externalizing as a function of the significance of association with the other trait. (D) Log likelihood curves showing goodness of model fit as a function of the number of shared influential variants.
LLM interpretation
This figure analyzes the polygenic overlap between addiction risk and externalizing traits across four panels. (A) A Venn diagram shows shared (9.0k) and unique variants for addiction risk (7.2k) and externalizing (2.1k), with a genetic correlation of $r_g = 0.66$. (B) and (C) are conditional Q-Q plots showing that observed $-\log_{10} p$-values increase as the significance of association with the other trait increases. (D) A line plot shows the log-likelihood of model fit increasing as the number of shared influential variants increases from 8k to 11k.
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| Citation | PMID | DOI | Status |
|---|---|---|---|
| BarrPB, DickDM. The genetics of externalizing problems. Curr Top Behav Neurosci. 2020;47:93–112. doi:10.1007/7854_2019_12031845132 | — | — | — |
| BarrPB, MallardTT, Sanchez-RoigeS, Parsing genetically influenced risk pathways: genetic loci impact problematic alcohol use via externalizing and specific risk. Transl Psychiatry. 2022;12:420. doi:10.1038/s41398-022-02171-x36180423 PMC9525649 | — | — | — |
| BlancoC, WallMM, HeJ-P, The space of common psychiatric disorders in adolescents: comorbidity structure and individual latent liabilities. J am Acad Child Adolesc Psychiatry. 2015;54(1):45–52.25524789 10.1016/j.jaac.2014.10.007PMC4299666 | — | — | — |
| Bulik-SullivanBK, LohPR, FinucaneHK, LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291–295.25642630 10.1038/ng.3211PMC4495769 | — | — | — |
| CardNA, LittleTD. Proactive and reactive aggression in childhood and adolescence: a meta-analysis of differential relations with psychosocial adjustment. Int J Behav Dev. 2006;30(5):466–480. doi:10.1177/0165025406071904 | — | — | — |
| Castellanos-RyanN, BriereFN, O’Leary-BarrettM, The structure of psychopathology in adolescence and its common personality and cognitive correlates. J Abnorm Psychol. 2016;125(8):1039–1052.27819466 10.1037/abn0000193PMC5098414 | — | — | — |
| DeakJD, JohnsonEC. Genetics of substance use disorders: a review. Psychol Med. 2021;51(13):2189–2200.33879270 10.1017/S0033291721000969PMC8477224 | — | — | — |
| DemangePA, MalanchiniM, MallardTT, Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction. Nat Genet. 2021;53(1):35–44. doi:10.1038/s41588-020-00754-233414549 PMC7116735 | — | — | — |
| DemontisD, WaltersRK, MartinJ, Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51(1):63–75.30478444 10.1038/s41588-018-0269-7PMC6481311 | — | — | — |
| DickDM, AdkinsAE, KuoSIC. Genetic influences on adolescent behavior. Neurosci Biobehav Rev. 2016;70:198–205. doi:10.1016/j.neubiorev.2016.07.00727422449 PMC5074858 | — | — | — |
| FlorenceCS, ZhouC, LuoF, XuL. The economic burden of prescription opioid overdose, abuse, and dependence in the United States, 2013. Medicine. 2016;54(10):901–906. doi:10.1097/MLR.0000000000000625PMC597535527623005 | — | — | — |
| ForbesMK, GreeneAL, Levin-AspensonHF, Three recommendations based on a comparison of the reliability and validity of the predominant models used in research on the empirical structure of psychopathology. J Abnorm Psychol. 2021;130(3):297–317. doi:10.1037/abn000053333539117 | — | — | — |
| FreiO, HollandD, SmelandOB, Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nat Commun. 2019;10(1):2417. .31160569 10.1038/s41467-019-10310-0PMC6547727 | — | — | — |
| GrahamJM. Congeneric and (essentially) tau-equivalent estimates of score reliability: what they are and how to use them. Educ Psychol Meas. 2006;66(6):930–944. doi:10.1177/0013164406288165 | — | — | — |
| GreeneAL, EatonNR. The temporal stability of the bifactor model of comorbidity: an examination of moderated continuity pathways. Compr Psychiatry. 2017;72:74–82. doi:10.1016/j.comppsych.2016.09.01027764677 | — | — | — |
| GrotzingerAD, RhemtullaM, de VlamingR, Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav. 2019;3(5):513–525.30962613 10.1038/s41562-019-0566-xPMC6520146 | — | — | — |
| HancockGR, MuellerRO. Rethinking construct reliability within latent variable systems. In: CudeckR, du ToitS, SorbomD, eds. Structural equation modeling: present and future—a festschrift in honor of Karl Jöreskog. Scientific Software International; 2001:195–216. | — | — | — |
| HatoumAS, ColbertSMC, JohnsonEC, Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders. Nature Mental Health. 2023;1(3):210–223. doi:10.1038/s44220-023-00034-yPMC1021779237250466 | — | — | — |
| HatoumAS, JohnsonEC, ColbertSMC, The addiction risk factor: a unitary genetic vulnerability characterizes substance use disorders and their associations with common correlates. Neuropsychopharmacology. 2022;47:1739–1745. doi:10.1038/s41386-021-01209-w34750568 PMC9372072 | — | — | — |
| HuL, BentlerPM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1–55. doi:10.1080/10705519909540118 | — | — | — |
| JohnsonEC, DemontisD, ThorgeirssonTE, A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry. 2020;7(12):1032–1045.33096046 10.1016/S2215-0366(20)30339-4PMC7674631 | — | — | — |
| Karlsson LinnérR, BiroliP, KongE, Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat Genet. 2019;51(2):245–257. doi:10.1038/s41588-018-0309-330643258 PMC6713272 | — | — | — |
| Karlsson LinnérR, MallardTT, BarrPB, Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nat Neurosci. 2021;24(10):1367–1376.34446935 10.1038/s41593-021-00908-3PMC8484054 | — | — | — |
| KendlerKS, JacobsonKC, PrescottCA, NealeMC. 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. 2003;160(4):687–695. doi:10.1176/appi.ajp.160.4.68712668357 | — | — | — |
| KendlerKS, MyersJ. The boundaries of the internalizing and externalizing genetic spectra in men and women. Psychol Med. 2014;44(3):647–655. .23574685 10.1017/S0033291713000585PMC3825740 | — | — | — |
| KeyesKM, EatonNR, KruegerRF, Thought disorder in the meta-structure of psychopathology. Psychol Med. 2013;43(8):1673–1683. .23171498 10.1017/S0033291712002292PMC3767418 | — | — | — |
| KruegerH, PatrickC, IaconoMG. Etiological connections among substance dependence, antisocial behavior, and personality: modeling the externalizing Spectrum. J Abnorm Psychol. 2002;111(3):411–424. doi: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. 2007;116(4):645–666. doi:10.1037/0021-843X.116.4.64518020714 PMC2242625 | — | — | — |
| KruegerRF, HobbsKA, ConwayCC, Validity and utility of hierarchical taxonomy of psychopathology (HiTOP): II. Externalizing super-spectrum. World Psychiatry. 2021;20(2):171–193.34002506 10.1002/wps.20844PMC8129870 | — | — | — |
| LaheyBB, KruegerRF, RathouzPJ, WaldmanID, ZaldDH. A hierarchical causal taxonomy of psychopathology across the life span. Psychol Bull. 2017;143(2):142–186.28004947 10.1037/bul0000069PMC5269437 | — | — | — |
| LiuM, JiangY, WedowR, Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51(2):237–244. doi:10.1038/s41588-018-0307-530643251 PMC6358542 | — | — | — |
| MallardTT, SavageJE, JohnsonEC, Item-level genome-wide association study of the alcohol use disorders identification test in three population-based cohorts. Am J Psychiatry. 2022;179(1):58–70.33985350 10.1176/appi.ajp.2020.20091390PMC9272895 | — | — | — |
| PasmanJA, VerweijKJH, GerringZ, GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability. Nat Neurosci. 2018;21(9):1161–1170. .30150663 10.1038/s41593-018-0206-1PMC6386176 | — | — | — |
| RevelleW, ZinbargRE. Coefficients alpha, Beta, omega, and the glb: comments on Sijtsma. Psychometrika. 2009;74(1):145–154. doi:10.1007/s11336-008-9102-z | — | — | — |
| SacksJJ, GonzalesKR, BoucheryEE, TomediLE, BrewerRD. 2010 national and state costs of excessive alcohol consumption. Am J Prev Med. 2015;49(5):e73–e79. doi:10.1016/j.amepre.2015.05.03126477807 | — | — | — |
| Sanchez-RoigeS, PalmerAA, FontanillasP, Genome-wide association study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts. Am J Psychiatry. 2019;176(2):107–118. .30336701 10.1176/appi.ajp.2018.18040369PMC6365681 | — | — | — |
| SchoelerT, BaldwinJ, AllegriniA, Novel biological insights into the common heritable liability to substance involvement: a multivariate genome-wide association study. Biol Psychiatry. 2022;93(6):524–535. doi:10.1016/j.biopsych.2022.07.027 | — | — | — |
| TeamRC. R: A Language and Environment for Statistical Computing. Vienna, Austria; 2022. | — | — | — |
| VerhulstB, NealeMC, KendlerKS. The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies. Psychol Med. 2015;45(5):1061–1072.25171596 10.1017/S0033291714002165PMC4345133 | — | — | — |
| VeronaE, JavdaniS, SpragueJ. Comparing factor structures of adolescent psychopathology. Psychol Assess. 2011;23(2):545–551. doi:10.1037/a002205521280958 | — | — | — |
| VerweijKJ, ZietschBP, LynskeyMT, Genetic and environmental influences on cannabis use initiation and problematic use: a meta-analysis of twin studies. Addiction. 2010;105(3):417–430. doi:10.1111/j.1360-0443.2009.02831.x20402985 PMC2858354 | — | — | — |
| VolleberghWA, IedemaJ, BijlRV, de GraafR, SmitF, OrmelJ. The structure and stability of common mental disorders: the NEMESIS study. Arch Gen Psychiatry. 2001;58(6):597–603. doi:10.1001/archpsyc.58.6.59711386990 | — | — | — |
| WaldmanID, KingCD, PooreHE, Recommendations for adjudicating among alternative structural models of psychopathology. Clin Psychol Sci. 2023;11(4):616–640. doi:10.1177/21677026221144256 | — | — | — |
| WaldmanID, PooreHE, LuninghamJM, YangJ. Testing structural models of psychopathology at the genomic level. World Psychiatry. 2020;19(3):350–359.32931100 10.1002/wps.20772PMC7491626 | — | — | — |
| WaltersRK, PolimantiR, JohnsonEC, Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat Neurosci. 2018;21(12):1656–1669. .30482948 10.1038/s41593-018-0275-1PMC6430207 | — | — | — |
| WattsAL, PooreHE, LilienfeldSO, WaldmanID. Clarifying the associations between big five personality domains and higher-order psychopathology dimensions in youth. J Res Pers. 2019;82:103844. doi:10.1016/j.jrp.2019.07.002 | — | — | — |
| WattsAL, PooreHE, WaldmanID. Riskier tests of the validity of the bifactor model of psychopathology. Clin Psychol Sci. 2019;7(6):1285–1303. doi:10.1177/2167702619855035 | — | — | — |
| YoungStallings, CorleyKrauter, Hewitt. Genetic and environmental influences on behavioral disinhibition. Am J Med Genet. 2000;96(5):684–695. doi:10.1002/1096-8628(20001009)96:53.0.CO;2-G11054778 | — | — | — |
| ZhouH, RentschCT, ChengZ, Association of OPRM1 functional coding variant with opioid use disorder: a genome-wide association study. JAMA Psychiatry. 2020;77(10):1072–1080.32492095 10.1001/jamapsychiatry.2020.1206PMC7270886 | — | — | — |
| ZhouH, SealockJM, Sanchez-RoigeS, Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat Neurosci. 2020;23(7):809–818.32451486 10.1038/s41593-020-0643-5PMC7485556 | — | — | — |
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External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Are polygenic scores for psychiatric and substance use outcomes "ready" for clinical application? Current state and next steps. | Dick DM et al. | — | 2026 | → |
| Genome-wide association studies of lifetime and frequency of cannabis use in 131,895 individuals. | Thorpe HHA et al. | — | 2026 | → |
| Multivariate genetic analyses of 2.2 million individuals reveal broad and substance-specific pathways of addiction risk. | Poore HE et al. | — | 2026 | → |
| Development of the Comprehensive Addiction Risk Evaluation System: Initial Participant Response to an Online Personalized Feedback Program Integrating Genomic, Behavioral, and Environmental Risk Information. | Dick DM et al. | — | 2025 | → |
| Drinking motives and alcohol sensitivity mediate multidimensional genetic influences on alcohol use behaviors. | Savage JE et al. | — | 2025 | → |
| The broad structure of psychopathology in the All of Us Research Program. | Ehteshami A et al. | — | 2025 | → |
| Drinking motives and alcohol sensitivity mediate multi-dimensional genetic influences on alcohol use behaviors | Savage JE et al. | — | 2024 | — |
| Examining intergenerational risk factors for conduct problems using polygenic scores in the Norwegian Mother, Father and Child Cohort Study. | Frach L et al. | — | 2024 | → |
| Generalized genetic liability to substance use disorders. | Miller AP et al. | — | 2024 | → |
| Genetic and shared environmental factors explain the association between adolescent polysubstance use and high school noncompletion. | Davis CN et al. | — | 2024 | → |
| Investigating Trivariate Associations Between Risky Sexual Behavior, Internalizing Problems, and Externalizing Problems: A Twin Study. | Paulich KN et al. | — | 2024 | → |
| Partitioning the Genomic Components of Behavioral Disinhibition and Substance Use (Disorder) Using Genomic Structural Equation Modeling. | Horwitz TB et al. | — | 2024 | → |
| The neurobiology of stress: Vulnerability, resilience, and major depression. | Akil H et al. | — | 2023 | → |