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Gene × environment effects and mediation involving adverse childhood events, mood and anxiety disorders, and substance dependence

Nature Human Behaviour volume 8, pages 1616–1627 (2024)Cite this article

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Abstract

Adverse childhood events (ACEs) contribute to the development of mood and anxiety disorders and substance dependence. However, the extent to which these effects are direct or indirect and whether genetic risk moderates them is unclear. We examined associations among ACEs, mood/anxiety disorders and substance dependence in 12,668 individuals (44.9% female, 42.5% African American/Black, 42.1% European American/white). Using latent variables for each phenotype, we modelled direct and indirect associations of ACEs with substance dependence, mediated by mood/anxiety disorders (the forward or ‘self-medication’ model) and of ACEs with mood/anxiety disorders, mediated by substance dependence (the reverse or ‘substance-induced’ model). In a subsample, we tested polygenic scores for the substance dependence and mood/anxiety disorder factors as moderators in the mediation models. Although there were significant indirect paths in both directions, mediation by mood/anxiety disorders (the forward model) was greater than that by substance dependence (the reverse model). Greater genetic risk for substance use disorders was associated with a weaker direct association between ACEs and substance dependence in both ancestry groups (reflecting gene × environment interactions) and a weaker indirect association in European-ancestry individuals (reflecting moderated mediation). We found greater evidence that substance dependence reflects self-medication of mood/anxiety disorders than that mood/anxiety disorders are substance induced. Among individuals at higher genetic risk for substance dependence, ACEs were less associated with that outcome. Following exposure to ACEs, multiple pathways appear to underlie the associations between mood/anxiety disorders and substance dependence. Specification of these pathways could inform individually targeted prevention and treatment approaches.

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Fig. 1: Path diagram depicting hypothesized associations.
Fig. 2: Path diagrams of the mediation models.
Fig. 3: Path diagrams of the moderated mediation model results.

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Data availability

The GWAS summary statistics used for the analyses can be accessed at the following locations: the iPSYCH website (https://ipsych.dk/en/research/downloads/), dbGAP (study accession no. phs001672.v11.p1; https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001672.v11.p1), the PGC website (https://www.med.unc.edu/pgc/results-and-downloads) and via the GWAS Catalog (https://www.ebi.ac.uk/gwas/summary-statistics). Summary statistics for the Yale–Penn sample can be obtained by emailing the corresponding author.

Code availability

This study used openly available software and code, including GenomicSEM (v.0.0.5c; https://github.com/GenomicSEM/GenomicSEM), LDSC (implemented in GenomicSEM v.0.0.5c; https://github.com/bulik/ldsc/) and PRS-CSx (v.1.1.0; https://github.com/getian107/PRScsx). Custom scripts for the mediation model analyses conducted in Mplus can be obtained by emailing the corresponding author.

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Acknowledgements

This work was supported by the Veterans Integrated Service Network 4 Mental Illness Research, Education and Clinical Center and by Department of Veterans Affairs grant nos. I01 BX004820 to H.R.K. and IK2 CX002336 to E.E.H., and National Institute on Alcohol Abuse and Alcoholism grant no. AA028292 to R.L.K. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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  1. Daniel Levey & Joel Gelernter

    Present address: VA CT Healthcare Center, West Haven, CT, USA

Authors and Affiliations

  1. Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Henry R. Kranzler, Christal N. Davis, Zeal Jinwala, Yousef Khan, Ariadni Oikonomou, Damaris Silva-Lopez, Isabel Burton, Morgan Dixon, Jackson Milone, Sarah Ramirez, Naomi Shifman, Emily E. Hartwell & Rachel L. Kember

  2. Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA

    Henry R. Kranzler, Christal N. Davis, Zeal Jinwala, Emily E. Hartwell & Rachel L. Kember

  3. Department of Medical Sciences, Frank H. Netter School of Medicine at Quinnipiac University, North Haven, CT, USA

    Richard Feinn

  4. Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

    Daniel Levey & Joel Gelernter

  5. Departments of Genetics and Neurobiology, Yale University School of Medicine, New Haven, CT, USA

    Joel Gelernter

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Contributions

H.R.K., R.F. and C.N.D. designed the study. R.F., Y.K., Z.J., C.N.D. and D.L. analysed the data. H.R.K., C.N.D., R.F., A.O., D.S.-L., I.B., M.D., J.M., S.R., N.S., D.L., J.G., E.E.H. and R.L.K. wrote the paper and/or edited it for scientific content.

Corresponding author

Correspondence to Henry R. Kranzler.

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Competing interests

H.R.K. is a member of advisory boards for Dicerna Pharmaceuticals, Sophrosyne Pharmaceuticals, Enthion Pharmaceuticals and Clearmind Medicine; a consultant to Sobrera Pharmaceuticals; the recipient of research funding and medication supplies for an investigator-initiated study from Alkermes and a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported in the past three years by Alkermes, Dicerna, Ethypharm, Lundbeck, Mitsubishi, Otsuka and Pear Therapeutics. H.R.K. and J.G. hold US patent 10,900,082, titled ‘Genotype-guided dosing of opioid agonists’, issued 26 January 2021. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Distribution of substance use disorder PRS by ancestry and substance dependence diagnosis status.

EA = European ancestry and AA = African ancestry.

Extended Data Fig. 2 Distribution of mood and anxiety PRS by ancestry and mood/anxiety disorder diagnosis status.

EA = European ancestry and AA = African ancestry.

Extended Data Fig. 3 Mood and anxiety trait loadings onto common genetic factor, AFR ancestry.

The common genetic factor was produced using GenomicSEM-0.0.5c from GWAS of each input trait shown in the diagram. Because African ancestry reference files are not provided with the GenomicSEM-0.0.5c suite, we used LD scores from the Pan-UK Biobank (https://pan.ukbb.broadinstitute.org 2020) to compute LD matrices and correlations. Analysis was then performed after filtering SNPs to include those with MAF > 0.01 in the 1000 Genomes African ancestry population (https://doi.org/10.1093/nar/gkz836). Values attached to arrows from Fg to trait represent loading of the trait onto the common factor. Values at the bottom represent residual variance in each trait that is unexplained by the common genetic factor. Standard error values are in parentheses. GAD2 = generalized anxiety disorder-2 scores; MDD = major depressive disorder; PTSD = posttraumatic stress disorder; BIP = bipolar disorder.

Extended Data Fig. 4 Mood and anxiety trait loadings onto common genetic factor, EUR ancestry.

The common genetic factor was produced using GenomicSEM-0.0.5c from GWAS of each input trait shown in the diagram. The GAD trait was constructed from three GWAS of anxiety-related traits that were jointly analyzed using MTAG (methods). Values attached to arrows from Fg to trait represent loading of trait onto the common factor. Bottom values represent residual variance in each trait that is unexplained by the common genetic factor. Standard error values are in parentheses. GAD = generalized anxiety disorder; MDD = major depressive disorder; PTSD = posttraumatic stress disorder; BIP = bipolar disorder.

Extended Data Fig. 5 Factor loadings for the adverse childhood events (ACEs) latent variable.

This measure comprised 10 variables that reflected participants’ experiences before age 13. All variables were dichotomized to ensure the same coding and equal weight among them. All 10variables loaded significantly onto a single ACEs latent variable, but fit was below acceptable ranges (RMSEA = 0.05, CFI = 0.86, SRMR = 0.07). Allowing the residuals of household substance use and household smoking to covary improved model fit considerably (RMSEA = 0.03, CFI = 0.96, SRMR = 0.05), with item loadings ranging from 0.16 (for no religious involvement) to 0.73 for physical abuse.

Extended Data Fig. 6 Factor loadings for the substance dependence latent variable.

The SD latent variable comprised DSM-IV SD diagnoses for alcohol, cocaine, opioids, tobacco, and cannabis. The five diagnoses loaded well onto a single factor (RMSEA = 0.09, CFI = 0.99, SRMR = 0.05), with all item loadings ≥0.69.

Extended Data Fig. 7 Substance use disorder trait loadings onto common genetic factor, AFR ancestry.

The common genetic factor was produced using GenomicSEM-0.0.5c from GWAS of each input trait shown in the diagram. Because African ancestry reference files are not provided with the GenomicSEM-0.0.5c suite, we used LD scores from the Pan-UK Biobank (https://pan.ukbb.broadinstitute.org 2020.) to compute LD matrices and correlations. Analysis was then performed after filtering SNPs to include those with MAF > 0.01 in the 1000 Genomes African ancestry population (https://doi.org/10.1093/nar/gkz836). Values attached to arrows from Fg to trait represent loading of trait onto the common factor. Bottom values represent residual variance in each trait that is unexplained by the common genetic factor. Standard error values are in parentheses.

Extended Data Fig. 8 Substance use disorder trait loadings onto common genetic factor, EUR ancestry.

The common genetic factor was produced using GenomicSEM-0.0.5c from GWAS of each input trait shown in the diagram. Values attached to arrows from Fg to trait represent loading of trait onto the common factor. Bottom values represent residual variance in each trait that is unexplained by the common genetic factor. Standard error values are in parentheses.

Extended Data Fig. 9 Factor loadings for the mood and anxiety disorders latent variable.

Including 8 psychiatric disorders (major depressive disorder [MDD], bipolar disorder, posttraumatic stress disorder [PTSD], generalized anxiety disorder [GAD], obsessive-compulsive disorder [OCD], social phobia, agoraphobia, and panic disorder) as indicators for a single M/AD latent variable demonstrated acceptable fit (RMSEA = 0.02, CFI = 0.98, SRMR = 0.13). All item loadings were significant and ranged from 0.11 for MDD to 1.00 for PTSD.

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Kranzler, H.R., Davis, C.N., Feinn, R. et al. Gene × environment effects and mediation involving adverse childhood events, mood and anxiety disorders, and substance dependence. Nat Hum Behav 8, 1616–1627 (2024). https://doi.org/10.1038/s41562-024-01885-w

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  • DOI: https://doi.org/10.1038/s41562-024-01885-w

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