Large-scale genomic studies have revealed how common behavioral traits and substance-specific biology work together to shape addiction risk, providing new directions for prediction, prevention, and treatment.
Study: Multivariate genetic analysis of 2.2 million people reveals broad and substance-specific pathways of addiction risk. Image credit: OlgaKan/Shutterstock.com
Substance use disorders (SUDs) cause significant personal, social, and economic costs. These involve both genetic and environmental predispositions. In recent research, natural mental health Improved detection of genetic risk factors for SUDs Integrative analysis of genes associated with externalizing traits and SUD.
Shared genetic risk is associated with multiple substance use disorders
Twin studies suggest that overall SUD risk is significantly influenced by shared genes. Up to 80% of the genetic influence on alcohol use disorder (AUD) is shared with other SUDs, and up to 74% with other SUDs. Therefore, the remaining genetic effects are substance-specific and do not represent the total non-genetic risk.
Further support for this view comes from genome-wide association studies (GWAS) focused on SUDs that target a large number of genes. This indicated a common genomic risk for such diseases.
SUDs form one component of the externalizing spectrum of behaviors and disorders characterized by behavioral disinhibition. This includes conduct disorders in childhood and antisocial behavior in adults. Multiple SUDs and other externalizing traits often co-occur.
They all share a common underlying genetic framework, and latent externalizing tendencies are estimated to be up to 80% heritable, far exceeding the magnitude of genetic associations with individual diseases. Nevertheless, existing genetic studies often focus on identifying genes that confer risk for a single SUD.
Previous research by the same authors suggested that although there is significant genetic overlap between SUDs and other externalizing traits, there are several factors that confer risk for specific SUDs. In the current study, we aimed to determine whether genes associated with externalizing are distinct from those influencing SUD risk or whether they are shared across the externalizing spectrum. This will help improve research methods dealing with SUD origins.
Research tests whether SUD risk is individual or shared.
This study focused on two alternative latent genomic models. One is a single externalizing factor that includes SUD, and the other is a two-factor model that separates behavioral disinhibition and SUD and allows for a high degree of correlation between the two. The researchers used multivariate genomic analysis on data from more than 2.2 million people, including more than 5.9 million genetic variations.
They inherited two models from their previous work. One model treats SUD as part of an externalizing spectrum, whereas a second model treats SUD as distinct from, but highly correlated with, externalizing behaviors characterized by behavioral disinhibition. This helps clarify the role of externalizing traits in the origin of SUD.
Identify both substance-specific and shared genetic pathways
The researchers compared the results of these models to determine whether isolating SUD-specific genetic risk would improve findings beyond externalizing shared responsibility. They found that analyzing SUDs in parallel with other externalizing traits can help reveal the neurobiological pathways and genetic architecture underlying risk for SUDs overall and specific SUDs. This advancement in knowledge has not come at the expense of the specificity of genetic markers for SUD risk.
Of note, models focusing only on SUD did not identify novel genetic signals, highlighting the importance of incorporating a broader range of externalizing traits to improve discovery.
For example, the first (externalizing spectrum) model yielded 708 loci, 26% of which were novel in their association with externalizing traits or addiction risk. Moreover, 57% of them were associated with substance use characteristics for the first time.
Researchers also identified genomic risk loci for residual genetic risk for certain SUDs, such as problematic alcohol use and tobacco use. Many of these are genes involved in the metabolism of these substances, suggesting a genetically encoded susceptibility to each substance that increases the risk of addiction.
The second model included two factors: disinhibition and SUD. Genomic analysis identified 631 and 48 genomic risk loci associated with these factors, respectively.
The researchers also identified genes unique to each genomic factor.
- 37 Externalizing Traits – More than 80% are already associated with substance use phenotypes, highlighting a common underlying genetic structure.
- 21 for behavioral disinhibition
- 3 for SUDs – one of which is linked to multiple SUDs
They also identified remaining problematic tobacco and alcohol use genes, most of which were already known to be associated with SUD risk. However, some of them were selectively associated with specific SUDs. This type of residual risk was captured by polygenic scores (PGS), which best predict specific SUDs.
This highlights the potential translational utility of broad and specific PGS. Broader indicators of risk thereby capture an individual’s general propensity for addiction, while specific indicators can provide insight into the risk of problems with specific substances.
Additionally, there are residual genetic influences acting through non-externalizing mechanisms, indicating that internalizing and thought disorders should be looked for in SUD patients as well.
The results also revealed neurobiological pathways involved in the externalizing and behavioral disinhibition networks, both of which contained more genes associated with mental illness and substance abuse.
In addition, the scientists identified more than 100 druggable targets (genes that can be targeted by drugs) that are suitable for drug repurposing, particularly targets that have been approved for pharmacotherapeutic assistance (MAT) for SUDs. However, these are only potential targets rather than immediately validated treatments.
Addiction risk reflects common traits and substance sensitivities
Genetic risk for SUDs may be explained by a combination of increased behavioral disinhibition through common genetic loci associated with a wide range of externalizing behaviors and biological responses that increase an individual’s susceptibility to certain substances.
This study highlights the need to recognize the important role of externalizing in SUD in both clinical practice and research. Conversely, because of their shared genetic makeup, people who are at risk for or currently have SUD may develop other externalizing disorders. This knowledge will improve prevention and treatment of both types of disorders.
Integrated genetic analyzes may help detect more genes associated with SUD susceptibility, its mechanisms, and associated risks, and evidence shows that broader externalization-based models improve gene discovery over SUD-only analyses.
Research limitations
This study included only participants of European descent, limiting generalizability to the broader population. Age at first sexual intercourse is a factor in GWAS analysis and may introduce bias between groups. Residual risk analysis depends on the statistical power of the underlying GWAS and the remaining variance after accounting for shared genetic liability. Finally, SUD is intertwined with non-externalizing forms of mental illness, which are not modeled here.
Externalizing traits and biology shape addiction risk
This study suggests that genetic risk for SUD operates through both widespread externalizing and substance-specific genetic variation. An integrative approach to SUD genetic research using externalizing trait networks will improve genetic power and expand current knowledge about the underlying neurobiological pathways and genetic mechanisms involved in these diseases.
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Reference magazines:
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Poore, H. E., Chatzinakos, C., Leger, B. et al. (2026). Multivariate genetic analysis of 2.2 million individuals reveals broad and substance-specific pathways of addiction risk. Natural mental health. Toi: https://doi.org/10.1038/s44220-026-00608-6. https://www.nature.com/articles/s44220-026-00608-6
