Genetic Liability to Addiction Underlies Comorbid Bipolar and Substance Use Disorders
Year: 2026
Type: article
Abstract: BACKGROUND: Bipolar disorder (BIP) frequently co-occurs with heightened substance use (SU) and SU disorders (SUDs). Although the strong co-occurrence of these heritable traits points to shared genetic susceptibility, the extent to which there are differences in how SU and SUD overlap with the genetic architecture of BIP remains unclear. METHODS: We quantified the polygenic overlap between BIP and SUDs (alcohol, cannabis, opioid, and tobacco) and BIP and SU traits (drinks per week, lifetime cannabis use, prescription opioid use, and smoking initiation) using genome-wide association study (GWAS) summary statistics and trivariate MiXeR. Then, we isolated the general and unique genetic contributions of SUD and SU using GWAS by subtraction via genomic structural equation modeling. Next, we tested associations between polygenic risk scores (PRSs) derived from these latent factors and diagnostic and behavioral outcomes in the MoBa (Norwegian Mother, Father and Child Cohort Study). Finally, we applied GSA-MiXeR to explore pleiotropic pathway enrichment shared between the latent factors and BIP. RESULTS: We found extensive polygenic overlap between traits, with SUDs being more genetically correlated with BIP than SU traits. The unique SUD factor positively correlated with psychiatric disorders, whereas the unique SU factor negatively correlated with psychiatric disorders. PRSs for BIP, shared SUD/SU, and unique SUD were significantly associated with BIP, SUD, and comorbid SUD-BIP; PRS for unique SU was associated only with self-reported lifetime SU. GSA-MiXeR revealed richer gene-set enrichment for SUD/BIP than for SU/BIP, implicating dopamine signaling and interneuron function. CONCLUSIONS: By dissecting the genetic liability to SUD and SU and investigating their relationship with BIP, we found a genetic signature correlated with substance dependence but not SU more broadly.
Source: Biological Psychiatry
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $5,489
Funders: Stiftelsen Kristian Gerhard Jebsen, Trond Mohn stiftelse, European Commission, Helse- og Omsorgsdepartementet, Novo Nordisk, Helse Vest, Universitetet i Bergen, Universitetet i Oslo, Norges Forskningsråd, Novo Nordisk Fonden, Utdannings- og forskningsdepartementet, National Institutes of Health, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, Helse Sør-Øst RHF, EEA Grants, Norwegian Institute of Public Health, National Institute of Mental Health
Awards: G1300865376, G1470873464, G2230720350, G225017805, G304045356, G3096137445, G3277956970, G3433676821, G3458941969, G3742410397, G3761261155, G434337789, G4949420144, G5351531748, G5511928619, G5721558492, G5908129080, G6488308326, G6517481153, G6966598969, Predicting comorbid cardiovascular disease in individuals with mental disorder by decoding disease mechanisms, G7984825499, Using real-world big data from eHealth, biobanks and national registries, integrated with clinical trial data to improve outcome of severe mental disorders, SCIENTIA-FELLOWS II: International Postdoctoral Fellowship Programme, G879810234, G9000112882, G9000318151, 3/7 Psychiatric Genomics Consortium: Advancing Discovery and Impact
OpenAlex ID: W7127885442