Genetic research on nicotine dependence has utilized multiple assessments that are in weak agreement.

We conducted a genome-wide association study (GWAS) of nicotine dependence defined using the Diagnostic and Statistical Manual of Mental Disorders (DSM-NicDep) in 61,861 individuals (47,884 of European ancestry [EUR], 10,231 of African ancestry, and 3,746 of East Asian ancestry) and compared the results to other nicotine-related phenotypes.

We replicated the well-known association at the CHRNA5 locus (lead single-nucleotide polymorphism [SNP]: rs147144681, p = 1.27E−11 in EUR; lead SNP = rs2036527, p = 6.49e−13 in cross-ancestry analysis). DSM-NicDep showed strong positive genetic correlations with cannabis use disorder, opioid use disorder, problematic alcohol use, lung cancer, material deprivation, and several psychiatric disorders, and negative correlations with respiratory function and educational attainment. A polygenic score of DSM-NicDep predicted DSM-5 tobacco use disorder criterion count and all 11 individual diagnostic criteria in the independent National Epidemiologic Survey on Alcohol and Related Conditions-III sample. In genomic structural equation models, DSM-NicDep loaded more strongly on a previously identified factor of general addiction liability than a “problematic tobacco use” factor (a combination of cigarettes per day and nicotine dependence defined by the Fagerström Test for Nicotine Dependence). Finally, DSM-NicDep showed a strong genetic correlation with a GWAS of tobacco use disorder as defined in electronic health records (EHRs).

Our results suggest that combining the wide availability of diagnostic EHR data with nuanced criterion-level analyses of DSM tobacco use disorder may produce new insights into the genetics of this disorder.

Impulsivity is a multidimensional trait associated with substance use disorders (SUDs), but the relationship between distinct impulsivity facets and stages of substance use involvement remains unclear.

We used genomic structural equation modeling and genome-wide association studies (N = 79,729–903,147) to examine the latent genetic architecture of nine impulsivity traits and seven substance use (SU) and SUD traits.

We found that the SU and SUD factors were strongly genetically inter-correlated (rG=0.77) but their associations with impulsivity facets differed. Lack of premeditation, negative and positive urgency were equally positively genetically correlated with both the SU (rG=.0.30–0.50) and SUD (rG=0.38–0.46) factors; sensation seeking was more strongly genetically correlated with the SU factor (rG=0.27 versus rG=0.10); delay discounting was more strongly genetically correlated with the SUD factor (rG=0.31 versus rG=0.21); and lack of perseverance was only weakly genetically correlated with the SU factor (rG=0.10). After controlling for the genetic correlation between SU/SUD, we found that lack of premeditation was independently genetically associated with both the SU (β=0.42) and SUD factors (β=0.21); sensation seeking and positive urgency were independently genetically associated with the SU factor (β=0.48, β=0.33, respectively); and negative urgency and delay discounting were independently genetically associated with the SUD factor (β=0.33, β=0.36, respectively).

Our findings show that specific impulsivity facets confer risk for distinct stages of substance use involvement, with potential implications for SUDs prevention and treatment.

Studies suggest that alcohol consumption and alcohol use disorders have distinct genetic backgrounds.

We examined whether polygenic risk scores (PRS) for consumption and problem subscales of the Alcohol Use Disorders Identification Test (AUDIT-C, AUDIT-P) in the UK Biobank (UKB; N = 121 630) correlate with alcohol outcomes in four independent samples: an ascertained cohort, the Collaborative Study on the Genetics of Alcoholism (COGA; N = 6850), and population-based cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC; N = 5911), Generation Scotland (GS; N = 17 461), and an independent subset of UKB (N = 245 947). Regression models and survival analyses tested whether the PRS were associated with the alcohol-related outcomes.

In COGA, AUDIT-P PRS was associated with alcohol dependence, AUD symptom count, maximum drinks (R2 = 0.47–0.68%, p = 2.0 × 10−8–1.0 × 10−10), and increased likelihood of onset of alcohol dependence (hazard ratio = 1.15, p = 4.7 × 10−8); AUDIT-C PRS was not an independent predictor of any phenotype. In ALSPAC, the AUDIT-C PRS was associated with alcohol dependence (R2 = 0.96%, p = 4.8 × 10−6). In GS, AUDIT-C PRS was a better predictor of weekly alcohol use (R2 = 0.27%, p = 5.5 × 10−11), while AUDIT-P PRS was more associated with problem drinking (R2 = 0.40%, p = 9.0 × 10−7). Lastly, AUDIT-P PRS was associated with ICD-based alcohol-related disorders in the UKB subset (R2 = 0.18%, p < 2.0 × 10−16).

AUDIT-P PRS was associated with a range of alcohol-related phenotypes across population-based and ascertained cohorts, while AUDIT-C PRS showed less utility in the ascertained cohort. We show that AUDIT-P is genetically correlated with both use and misuse and demonstrate the influence of ascertainment schemes on PRS analyses.