Unique and shared genetics in psychiatry and SUD
A high correlation between risk for substance use disorder and occurrence of other mental health conditions is well known. Little is known about specific causal factors contributing to that risk.
This earlier post reviews some of the basic principles of genetics and how it relates to SUDs
Currently the DSM utilizes lists of symptoms to diagnose and define disorders separately. As research methods improve it becomes increasingly apparent that behavioral traits, psychiatric disorders, and substance use share common underpinnings along with distinct genetic and phenotypical differences.
There is a high incidence of co-occurring mental health conditions estimated at 45% in populations diagnosed with substance use disorders. The risk of SUD when considering a psychiatric diagnosis is high at 35.1%.
Advances in technology allow for large scale genome wide studies. Databases involving up to a million or more subjects can be analyzed to include rare variants and previously overlooked correlations. With the advent of newer algorithms, AI, and machine learning multi factorial analysis is possible.
Genomics can be integrated with biological data. Tissue specific enrichment, gene expression, and transcription factors add new information and insight into the role of genetics in risk and disease progression.
In recent years research has shifted away from diagnosis based on symptom lists to focus on causality and domain constructs. The NIMH Research Domain Construct initiative is an example.
https://www.nimh.nih.gov/research/research-funded-by-nimh/rdoc
More detailed information concerning causation and specific bio markers have been discussed in a previous post here.
A more comprehensive review of genetics as it pertains to addiction in a previous post here
In a broad sense psychiatric disorders and associated behavioral traits may be categorized as either internalizing or externalizing. Internalizing disorders are inwardly directed. Withdrawal, isolation, and anhedonia are common manifestations. Anxiety disorders, PTSD, and specific phobias are examples
Externalizing disorders are outwardly directed. Impulsivity, novelty seeking, and risk taking are typical. Examples include antisocial conduct disorders, ADHD and Oppositional Defiant Disorder. Substance use disorders have considerable overlap with externalizing traits and disorders although not exclusively so. These are not sharp distinctions, however they are useful for heuristic purposes.
This post is a review of a study published in the journal Nature Mental Health in January 2026. The study combined a number of genomic databases in people of European ancestry. The combined number of individual subjects was 2.2 million resulting in a large degree of statistical power allowing for multivariate analysis using several different approaches.
The study combined genetic factors influencing select externalizing traits and specific substance use disorders. The aims were to find if a multivariate approach would allow for discovery of previously unidentified genetic factors influencing SUDs. The study also examined the extent to which genetic factors were shared or specific. Analysis was also carried out to identify specific biological pathways involved.
The next section in green background is a technical note.
The primary analysis used was Genomic Structural Equation Modeling (SEM). The technique first involves identification of significant genetic foci influencing the traits involved.
Linkage Disequilibrium (LD) occurs because during crossover of chromosomes as cells begin to divide some DNA segments do not migrate by random assortment. When a genetic marker is close to a gene of interest they will tend to remain in proximity. This violates the Mendelian genetic principle of independent assortment. The non random occurrence of LD can be calculated and correlated to specific trait(s) of interest. Genomic SEM utilizes LD score along with summary GWAS data to combine studies and calculate the genetic association of a phenotype such as alcohol use disorder with a trait such as externalizing behavior and map weight to specific associated genes or gene networks.
This study looked at two models shown to have the best fit based on earlier research. The single factor model (upper) looked at linkage to externalizing as a factor. The second model (lower) looked at two linked factors, behavioral disinhibition and SUD.
Externalizing traits utilized were as follows:
RISK – Risk taking propensity
ADHD – Attention Deficit Hyperactivity Disorder
NSEX – Number of sexual partners
FSEX – Age at first sexual encounter
SMOK – Age of smoking initiation
CAN – Age at first cannabis use
PAU – Problematic alcohol use
OUD – Opiate Use Disorder
CUD – Cannabis use disorder
PTU – Problematic tobacco use
Both models are illustrated by the above diagrams with weighting linked to specific traits and related phenotypes. Overall the two factor model had the greatest fit to data.
Genome Wide Association Study (GWAS) showing specific genes identified using the single factor externalizing model. Foci above the dashed brown line reached statistical significance.
A total of 708 loci were identified, a significantly higher number of genetic factors than found in previous GWAS studies of substance use disorders.
(Note: In this plot the ‘spikes’ indicate a genetic marker occuring almost exclusively in the trait evaluated).
GWAS results using the two factor model. The upper plot shows genetic foci linked with behavioral disinhibition. There were 631 loci identified. The lower plot shows SUD linked genetic loci. There were 48 SUD related loci by this method.
Overall using externalizing as an analytical factor 182 genetic loci associated with SUD were found which were not identified using previous single factor analysis. This supports the hypothesis that SUD and other disorders are best understood in a larger context rather than as isolated entities.
This map shows specific functional characteristics of SUD related genes linked to externalizing systems. Darker colors reflect greater levels of gene expression with tissue enrichment. Larger circles represent larger gene “communities” related to a particular cellular function.
The pathways highlighted include proteins related to cytoskeletal formation essential in neuroplasticity, receptor proteins involved in neurotransmission, transcription factors and RNA synthesis essential in gene activation and expression.
Taken from another study.
Pleiotropy refers to genes resulting in more than one phenotype. This study looked at genetic expression in specific regions of the brain associated with SUD, psychiatric disorders, or both.
Statistical significance was highest for pleiotropic genes implicated in both psychiatric disorders and SUDs. All three patterns were heterogeneous across specific brain regions. This suggests that whole brain analysis may convey limited information with respect to molecular genetic influences.
Linkage Disequilibrium Score regression analysis. Colored circles reflect the genetic correlation between SUDs, psychiatric conditions, and other factors. The highest scores were age of initiation of addictive substance use, anxiety related disorders and SUDs, socioeconomic factors and risk tolerance.
Negative correlations were observed for OCD and educational attainment.
Based on composite genetic findings the authors propose factors with a causative role in these four SUDs. They also distinguish consequential factors resulting from SUDs.
Of note lower educational attainment has been consistently correlated to increased risk and a consequential outcome for SUDs. Schizophrenia is bidirectional for cannabis use disorder. Structural brain changes are noted as both causal and consequential sequela of alcohol use disorder.
Recent advances in genomics, data processing, and translational research in neuro psychiatry and substance use disorders has shifted away from viewing conditions as isolated entities influenced by a limited set of genetic and environmental circumstances.
It is increasingly clear that many of the genetic influences contributing to Substance Use Disorders also play a role in externalizing traits and conditions such as ADHD and schizophrenia. Using combined analytic tools there is a higher yield of novel loci and biological information.
Much of this work is still in the early investigational stages. The studies presented here have some limitations and serve to expand the field for further research. One significant limitation is that most databases are skewed to people of European origin and more heterogeneous studies may yield additional insight. Tissue specific gene expression rather than whole brain analysis is more likely to reveal biological pathways of interest.
For information and educational use only. Data and images obtained from sources freely available on the world wide web. This post should not be considered medical or professional advice. Thank you for your interest in this post and sobersynthesis. Feedback is always welcome. jeffk072261@gmail.com
Shared genetics
Multivariate genetic analyses of 2.2 million individuals reveal broad and substance-specific pathways of addiction risk
Holly E. Poore ,Travis T. Mallard, Sandra Sanchez-Roige, Danielle M. Dick
Nature Mental Health | Volume 4 | April 2026 | 582–593
https://www.nature.com/articles/s44220-026-00608-6.pdf
https://www.nature.com/articles/s44277-025-00031-2.pdf
Signs and symptoms of internalizing and externalizing
disorders and opportunities for clinical translation
Adrienne L. Romer
NPP – Digital Psychiatry and Neuroscience; https://doi.org/10.1038/s44277-025-00031-2
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https://www.nimh.nih.gov/research/research-funded-by-nimh/rdoc
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MGenetic correlation, pleiotropy, and causal associations between
substance use and psychiatric disorder
Seon-Kyeong Jang, Gretchen Saunders, MengZhen Liu
Psychol Med . 2022 April ; 52(5): 968–978. doi:10.1017/S003329172000272X.
https://pmc.ncbi.nlm.nih.gov/articles/PMC8759148/pdf/nihms-1638565.pdf
Grotzinger AD, Rhemtulla M, de Vlaming R, Ritchie SJ, Mallard TT, Hill WD, Ip HF, Marioni RE, McIntosh AM, Deary IJ, Koellinger PD, Harden KP, Nivard MG, Tucker-Drob EM. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav. 2019 May;3(5):513-525. doi: 10.1038/s41562-019-0566-x. Epub 2019 Apr 8. PMID: 30962613; PMCID: PMC6520146.
https://pmc.ncbi.nlm.nih.gov/articles/PMC6520146
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The genetic landscape of substance use disorders
Zachary F. Gerring, Jackson G. Thorp
Molecular Psychiatry (2024) 29:3694–3705; https://doi.org/10.1038/s41380-024-02547-z
https://www.nature.com/articles/s41380-024-02547-z.pdf
Jk 7/26
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