Multi-ancestral genome-wide association study of clinically defined nicotine dependence reveals strong genetic correlations with other substance use disorders and health-related traits.
- Authors
- Johnson, Emma C; Lai, Dongbing; Miller, Alex P; Hatoum, Alexander S; Deak, Joseph D; Balbona, Jared V; Baranger, David Aa; Galimberti, Marco; Sanichwankul, Kittipong; Thorgeirsson, Thorgeir; Colbert, Sarah Mc; Sanchez-Roige, Sandra; Adhikari, Keyrun; Docherty, Anna; Degenhardt, Louisa; Edwards, Tobias; Fox, Louis; Giannelis, Alexandros; Jeffries, Paul; Korhonen, Tellervo; Morrison, Claire; Nunez, Yaira Z; Palviainen, Teemu; Su, Mei-Hsin; Villela, Pamela N Romero; Wetherill, Leah; Willoughby, Emily A; Zellers, Stephanie; Bierut, Laura; Buchwald, Jadwiga; Copeland, William; Corley, Robin; Friedman, Naomi P; Foroud, Tatiana M; Gillespie, Nathan A; Gizer, Ian R; Heath, Andrew C; Hickie, Ian B; Kaprio, Jaakko A; Keller, Matthew C; Lee, James L; Lind, Penelope A; Madden, Pamela A; Maes, Hermine Hm; Martin, Nicholas G; McGue, Matt; Medland, Sarah E; Nelson, Elliot C; Pearson, John V; Porjesz, Bernice; Stallings, Michael; Vrieze, Scott; Wilhelmsen, Kirk C; Walters, Raymond K; Polimanti, Renato; Malison, Robert T; Zhou, Hang; Stefansson, Kari; Potenza, Marc N; Mutirangura, Apiwat; Shotelersuk, Vorasuk; Kalayasiri, Rasmon; Edenberg, Howard J; Gelernter, Joel; Agrawal, Arpana
- Year
- 2025
- Journal
- medRxiv : the preprint server for health sciences
- PMID
- 39974067
- DOI
- 10.1101/2025.01.29.25320962
- PMCID
- PMC11838619
- Published as
- Multi-ancestral genome-wide association study of clinically defined nicotine dependence reveals strong genetic correlations with other substance use disorders and health-related traits. →
Genetic research on nicotine dependence has utilized multiple assessments that are in weak agreement. We conducted a genome-wide association study of nicotine dependence defined using the Diagnostic and Statistical Manual of Mental Disorders (DSM-NicDep) in 61,861 individuals (47,884 of European ancestry, 10,231 of African ancestry, 3,746 of East Asian ancestry) and compared the results to other nicotine-related phenotypes. We replicated the well-known association at the locus (lead SNP: rs147144681, p =1.27E-11 in European ancestry; 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 and 6 of 11 individual diagnostic criteria, but none of the Fagerström Test for Nicotine Dependence (FTND) items, in the independent NESARC-III sample. In genomic structural equation models, DSM-NicDep loaded more strongly on a previously identified factor of general addiction liability than did a "problematic tobacco use" factor (a combination of cigarettes per day and nicotine dependence defined by the FTND). Finally, DSM-NicDep was strongly genetically correlated with a GWAS of tobacco use disorder as defined in electronic health records, suggesting 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.
Comparing genetic correlations (rg) for DSM-NicDep, FTND, ICD-TUD, and PTU with other traits in European ancestry data.Traits include other substance use disorders (CanUD = cannabis use disorder25; OUD = opioid use disorder26; PAU = problematic alcohol use24, ICD-TUD = ICD-based tobacco use disorder6), substance use behaviors (CanUse = cannabis ever-use33; DPW = drinks per week3; SmkInit = smoking initiation3, SmkCessation = smoking cessation3, CPD = cigarettes per day3), psychiatric disorders and other mental health phenotypes (ADHD = attention deficit hyperactivity disorder34; PTSD = post-traumatic stress disorder35), biomarkers (Cot+HC = cotinine + 3-hydroxycotinine36), lung health-related traits (FEV1 = forced expiratory volume in 1 second), risk tolerance37, socioeconomic status-related traits (Edu attainment = educational attainment38; TDI = Townsend deprivation index), executive function (EF39), and anthropometric measures (BMI = body mass index40; height41). * indicates rgs that significantly differ between DSM-NicDep and FTND at = 0.002 (Bonferroni correction for 24 comparisons).
LLM interpretation
This forest plot displays genetic correlations ($r_g$) and 95% confidence intervals for four nicotine-related traits (DSM-NicDep, FTND, ICD-TUD, and PTU) across various substance use, psychiatric, and health phenotypes. The x-axis represents the genetic correlation coefficient ($r_g$), with a vertical line at 0 indicating no correlation. Asterisks (*) denote traits where the genetic correlation for DSM-NicDep significantly differs from that of FTND (e.g., CanUD, PAU, CanUse, SmkInit, and TDI).
A modified Addiction-Risk-Factor model.This model is patterned upon the common factor model in Figure 1A of Hatoum et al., 2022, but updated with new, larger versions of the OUD26, PAU24,25, and CanUD GWAS and using 3 different phenotypes for tobacco GWAS. Panel A: DSM-NicDep. Panel B: PTU5 GWAS. Panel C: ICD-TUD6. Significant loadings at p < 0.05 are represented by *. Addiction-rf = The Addiction-Risk-Factor; OUD = opioid use disorder; PAU = problematic alcohol use; CanUD = cannabis use disorder; DSM-NicDep = nicotine dependence; ICD-TUD = ICD-based tobacco use disorder.
LLM interpretation
This figure presents two structural equation models (Panels A and C) illustrating the "Addiction-Risk-Factor" (Addiction-rf) as a common factor influencing various substance use disorders. Both panels show significant positive loadings (p < 0.05, marked by *) from the Addiction-rf to OUD, PAU, and CanUD, with varying strengths for tobacco-related phenotypes (PTU in Panel A and ICD-TUD in Panel C). Model fit statistics, including $\chi^2$, CFI, AIC, and SRMR, are provided for both visualizations.
Polygenic scores (PGS) for FTND and DSM-NicDep predict individual DSM-5 nicotine use disorder and FTND criteria in the European ancestry subset of the NESARC-III sample.Filled circles represent estimates that were significant after FDR correction, while open circles represent estimates that were not significant after FDR correction. Hazardous = Recurrent use in physically hazardous situations; Fail = Recurrent use resulting in failure to fulfill major role obligations at work, school or home; Tolerance = Marked need for increased amount to get same effect, or diminished effect of same amount; TimeSpent = Great deal of time spent in activities necessary to obtain or use; GiveUp = Important recreational, social or occupational activities given up or reduced; Problems = Use despite knowledge of persistent/recurrent physical/psychological problems; Larger = Taken over larger amounts/longer periods than intended; Withdrawal = Withdrawal syndrome or use to relieve/avoid syndrome; Cutdown = Persistent desire or unsuccessful attempts to cutdown or control use; Crave = Craving, or strong urge or desire to use; Social = Persistent use despite recurring social/interpersonal problems caused or exacerbated by use; FTND1_within30min = How soon after you wake up do you smoke your first cigarette?; FTND2_prohibited = Do you find it difficult to refrain from smoking in places where it is forbidden?; FTND3_morning = Which cigarette would you hate most to give up?; FTND5_waking = Do you smoke more frequently during the first hours after waking than during the rest of the day?
LLM interpretation
This figure is a forest plot showing the Odds Ratios (95% CI) for two polygenic scores (PGS), DSM-NicDep (blue) and FTND (orange), across various nicotine use disorder and FTND criteria. The x-axis lists specific outcomes, while the y-axis represents the Odds Ratio relative to a baseline of 1.0. Filled circles indicate estimates that remained significant after FDR correction, while open circles indicate those that were not.
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