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. 2025 Feb 13;11(1):1–11. doi: 10.1159/000543222

Clinical, Genomic, and Neurophysiological Correlates of Lifetime Suicide Attempts among Individuals with an Alcohol Use Disorder

Peter B Barr a,b,c,d,✉, Zoe Neale a,b,c, Chris Chatzinakos a,c, Jessica Schulman e, Niamh Mullins f,g,h, Jian Zhang a, David B Chorlian a, Chella Kamarajan a, Sivan Kinreich a, Ashwini K Pandey a, Gayathri Pandey a, Stacey Saenz de Viteri i, Laura Acion j, Lance Bauer a, Kathleen K Bucholz l, Grace Chan j,k, Danielle M Dick m,n, Howard J Edenberg o,p, Tatiana Foroud o,q, Alison Goate g, Victor Hesselbrock k, Emma C Johnson l, John R Kramer j, Dongbing Lai o, Martin H Plawecki q, Jessica Salvatore m, Leah Wetherill o, Arpana Agrawal l, Bernice Porjesz a, Jacquelyn L Meyers a,c
PMCID: PMC11888779  PMID: 40061584

Abstract

Introduction

Research has identified multiple risk factors associated with suicide attempt (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA.

Methods

We examined lifetime SA in 4,068 individuals with an AUD from the Collaborative Study on the Genetics of Alcoholism (23% lifetime SA; 53% female; mean age: 38). We explored risk for lifetime SA across other clinical conditions ascertained from a clinical interview, polygenic scores for comorbid psychiatric problems, and neurocognitive functioning.

Results

Participants with an AUD who attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, other substance use disorders (SUDs), and suicidal ideation. Polygenic scores for SA, depression, and PTSD were associated with increased odds of reporting an SA (ORs = 1.22–1.44). Participants who reported an SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small.

Conclusions

Overall, individuals with an AUD who report lifetime SA experience greater levels of trauma, have more severe comorbidities, and carry increased polygenic risk for other psychiatric problems. Our results demonstrate the need to further investigate SAs in the presence of SUDs.

Keywords: Suicide, Polygenic score, Alcohol use disorder

Introduction

Approximately 2–5% of US adults report having attempted suicide in their lifetimes [1–3], with the prevalence increasing in more recent birth cohorts [4]. Additionally, deaths by suicide are one of the leading causes in the recent decline in US life expectancy, alongside other “deaths of despair” such as drug- and alcohol-related deaths [5, 6]. While the rate of suicide attempts (SAs) in the general population is alarming, the rate of lifetime SAs is greater than triple (17.5%) among those with an alcohol use disorder (AUD) [7]. Among those seeking treatment for AUD, 40% report at least one SA at some point in their lives [8–11]. A history of past SAs is among the most prominent predictors of subsequent suicide death and contributes significant health care and disability costs per attempt [12]. Research focused on correlates of SAs can potentially help identify and treat those with nonfatal SAs, with the goal of reducing suicide deaths and saving lives [13]. Importantly, alcohol use is a consistent risk factor for death by suicide [14]; individuals with an AUD have emerged as a particularly high-risk group [15–17].

In addition to clinical and phenotypic correlations between substance use disorders (SUDs) and suicidal behaviors, there is also consistent evidence for genetic overlap between these outcomes, with AUD in particular. Evidence from large-scale genome-wide association studies (GWASs) of both AUD/problematic alcohol use [18–21] and SAs [22, 23] reveals robust genetic correlations across these outcomes. And while there are very limited GWASs of SA in the presence of AUD [24], two recent efforts using multivariate approaches, which harness existing GWAS, have shown that (1) a shared genetic liability toward all forms of SUD is correlated with suicidal ideation, attempt, and self-injurious behavior, independent of genetic liability toward depression [25], and (2) the shared genetic overlap between AUD and SAs is explained, in part, by underlying liability toward impulsive behaviors [26].

Similar to the genetics of AUD and SA, two separate literatures have explored neurocognitive differences between (a) individuals who have attempted suicide to those who have not [27–29] and (b) individuals with AUD [30–32] compared to those unaffected with AUD. Those with AUD exhibit deficits in many domains of brain functioning, including neuropsychological performance and neurophysiological indices [30–32]. Executive functioning is typically the primary focus of such studies, with a large literature demonstrating that individuals with AUD display poorer executive functioning and atypical neurophysiological profiles (e.g., EEG connectivity) than individuals without AUD [33–36]. Researchers have also examined these areas of brain functioning among individuals who have exhibited suicidal ideation and related mental health problems, such as depression [27–29], though research focused on SA is limited. While no previous studies have examined EEG connectivity and SA, EEG connectivity in depressed patients exhibited higher alpha and theta coherences in frontal, temporal, and parietal regions and higher beta coherence in frontal and temporal regions [37]. Further, a recent study found other neurophysiological differences associated with binge drinking and suicidal behaviors in adolescents [38]. To our knowledge, no prior study has examined neural connectivity among those with AUD who have attempted suicide.

Given the higher rates of SA observed among those with AUD, we explored whether there are clinical, genomic, and neurophysiological markers of SA within this population. Among participants diagnosed with an AUD (DSM-IV alcohol dependence) drawn from the Collaborative Study on the Genetics of Alcoholism (COGA), we examined whether clinical risk factors, polygenic scores (PGSs) for comorbid psychiatric problems, and neurocognitive functioning differed between those who have and have not reported a lifetime SA.

Methods

Sample and Measures

The Collaborative Study on the Genetics of Alcoholism (COGA) is a large, multi-site study of 2,255 families affected with AUD, designed to identify and understand genetic factors involved in the predisposition to AUD and related disorders, as previously described [39–41]. Probands along with all willing first-degree relatives were assessed; recruitment was extended to include additional relatives in families that contained 2 or more first-degree relatives with alcohol dependence and community-ascertained comparison families (N = 17,878). Participants 18 or older completed the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), which is a poly-diagnostic interview [42], and participants aged 12–17 completed an adolescent SSAGA. We currently have genome-wide data on 12,145 individuals. Our final analytic sample consisted of 4,068 COGA participants with an alcohol dependence diagnosis (lifetime) and GWAS data (including 3,270 individuals of European-like and 798 individuals of African-like genetic similarity, see following section for discussion on assignment of genetic similarity).

Lifetime SA

All participants were queried about whether they had “ever tried to kill” themselves (SA), regardless of a history of suicidal ideation (i.e., thoughts about killing yourself). For the current analyses, we included individuals reporting any SA, including those reporting drug-related SA (14% of all attempts). Importantly, SA items were not exclusively nested within the diagnostic section for major depressive disorder (MDD), although individuals who reported SAs in that section were coded accordingly as having reported the behavior.

Clinical Risk Factors and Comorbidities

We created lifetime diagnoses of other SUDs, psychiatric disorders, suicidal thoughts and behaviors, and trauma exposure based upon DSM-IV criteria using the child and adult versions of the SSAGA [41]. We assessed nicotine dependence using the Fagerström Test for Nicotine Dependence (FTND) scores [43]. Additionally, we included measures of extended family histories of AUD and other alcohol-related problems [44].

Polygenic Scores

Genotyping, imputation, and quality control have been described previously [45, 46]. Briefly, in order to limit the impact of population structure, genetic data were used to assign individuals into genetically similar groupings [47] based on the first two principal components and the 1,000 genomes reference panel (Phase 3, version 5) [46]. Families were classified as primarily European-like (EUR-like) or African-like (AFR-like) according to the genetic similarity of the greatest proportion of family members [45]. Genotyping of individuals in the analytic sample was performed using the Illumina 2.5M array (Illumina, San Diego, CA, USA), the Illumina OmniExpress [48], or the Illumina 1M array, or the Affymetrix Smokescreen array [49]. SNPs with a genotyping rate <98%, Hardy-Weinberg equilibrium violations (p < 10−6), or with minor allele frequency less than 3% were excluded from analyses. Data were imputed to 1,000 genomes (phase 3) using SHAPEIT [50] and IMPUTE2 [51]. Following imputation, dosage probabilities ≥0.90 were converted to hard calls. SNPs with an imputation information score <0.30 or minor allele frequency <0.03 were excluded from subsequent analysis.

We estimated PGSs, which are aggregate measures of the number of risk alleles individuals carry weighted by effect sizes from GWAS summary statistics, for a variety of psychiatric and substance use phenotypes. We included PGS derived from recent GWAS of (1) AUDs [52], (2) depression (DEP, 23andMe excluded) [53], (3) post-traumatic stress disorder (PTSD) [54], (4) bipolar disorders (BIP) [55, 56], (5) schizophrenia (SCZ) [56, 57], (6) smoking initiation (SMOK, as a proxy for externalizing risk) [58, 59], and (7) SA (SUI) [23]. For AUD and BIP, we meta-analyzed published GWAS results with corresponding results from FinnGen (release 9) [60]. We focus on these PGSs specifically because (1) these disorders are phenotypically correlated with SA and (2) they contain GWAS results for both European-like and African-like groupings. For GWAS that originally included COGA in the discovery sample, we obtained summary statistics with COGA removed.

To date, GWASs have been overwhelmingly limited to individuals of primarily European descent [61]. Because of variation in allele frequencies and linkage disequilibrium patterns, PGS often lose predictive accuracy when there is mismatch between the genetic similarity of the discovery GWAS and target sample [62]. COGA includes participants of both African-like and European-like groupings; thus, we used PRS-CSx [63], a method that integrates GWAS summary statistics from well-powered GWAS (typically of European-like individuals) with those from other populations to improve the predictive power of PGS in the participants of African-like groupings in COGA. PRS-CSx employs a Bayesian approach to correct GWAS summary statistics for the nonindependence of SNPs in linkage disequilibrium. We converted PGS into Z-scores for ease of interpretation.

Electroencephalogram Data

Electroencephalogram (EEG) recording and processing have been detailed previously [64]. Briefly, resting (eyes closed) EEG was recorded for 4.25 min; a continuous interval of 256 s was analyzed. Each subject wore a fitted electrode cap using the 61-channel montage as specified according to the extended 10–20 International system. The nose served as reference and the ground electrode was placed on the forehead. Electrode impedances were always maintained below 5 kΩ. EEG was recorded with subjects seated comfortably in a dimly lit sound-attenuated temperature-regulated booth. They were instructed to keep their eyes closed and remain relaxed but not to fall asleep. Electrical activity was amplified 10,000 times by Neuroscan and MASSCOMP amplifiers, with a bandpass between 0.02 Hz and 100 Hz, and recorded using the Neuroscan system (Compumedics Limited; El Paso, TX). EEG procedures were identical at all COGA collection sites. Bipolar electrode pairs were derived to reduce volume conduction effects, and 27 representative coherence pairs were selected based on previous EEG coherence work in COGA [64]. Magnitude-squared coherence was calculated from power spectral values derived from conventional Fourier transform methods [65]. Coherence measures were generated between bipolar pairs at the following frequency bands: theta (3–7 Hz), alpha (7–12 Hz), beta (12–28 Hz).

Statistical Analyses

We compared those with an AUD who reported an SA and those with an AUD who did not report an SA across a range of sociodemographic, psychiatric comorbidities, experiences of trauma, and other measures related to alcohol misuse. We use multiple-group, multi-level regression models in Mplus [66] and adjusted for sex, age (at time of psychiatric assessment), genetic similarity (AFR-like vs. EUR-like), family history of AUD, and family relatedness. We ran all models simultaneously (i.e., correlation among all variables accounted for) to limit multiple testing.

For PGSs, we first compared those with AUD who had reported an SA to those with AUD who had not reported an SA across all PGSs, independently, using logistic regression in R (version 4.2.1). Second, to ensure that results within those with AUD were not biased by conditioning on AUD [67], we also compared (1) those with AUD who had a reported SA, (2) those with AUD who had not reported an SA, and (3) those without AUD who had a reported SA to those who neither reported a SA nor meet criteria for AUD (see online suppl. Table 1 for sample description; for all online suppl. material, see https://doi.org/10.1159/000543222) using a multinomial logistic regression model in the nnet package in R [68]. In both analyses, we included sex, age, the first six genetic principal components, genotype array, and birth cohort as covariates. To adjust for familial clustering, we used cluster robust standard errors [69, 70]. We stratified analyses by genetic similarity and then meta-analyzed results (by PGS) within each of the analyses above. We corrected all analyses for multiple testing and report corrected 95% CI in the models described. We also performed a GWAS of SA but lacked the power to identify any individual variants associated with SA (see online suppl. Fig. 1 and online suppl. Tables 5–9).

Lastly, we compared those with AUD who reported an SA and those with AUD who did not report an SA across neurophysiological measures (resting state EEG coherence), again using multiple-group, multi-level regression models adjusted for sex, age (at time of psychiatric assessment), genetic similarity, family history of AUD, and family relatedness. We ran all models simultaneously to limit multiple testing. We also performed a series of exploratory analyses within a subset of individuals who had available neurocognitive measures (see online suppl. Fig. 2 for full description).

Results

Clinical Risk Factors Associated with SA in Participants with AUD

The main analytic sample was limited to the 4,068 participants with a DSM-IV diagnosis of alcohol dependence. We compared 3,138 COGA participants who met criteria for AUD and did not attempt suicide in their lifetime with 930 participants with AUD who attempted suicide. Table 1 presents the demographic characteristics. Overall, those with AUD who attempted suicide were more likely to be female (53% vs. 32%). Rates of SA and the age distribution of participants were similar across EUR-like and AFR-like groups (see online suppl. Table S2 for stratified results).

Table 1.

Sociodemographic characteristics in COGA participants with an AUD (N = 4,068)

No SA (N = 3,138) SA (N = 930)
Female, % 31.8 53.1*
Black or African American, % 20.5 16.7
Hispanic, % 6.2 8.5
Mean age at last interview (SD) 39.9 (11.9) 38.2 (10.5)
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*p < 0.05.

Figure 1 presents the means and rates for clinical and psychiatric comorbidities across those with and without a history of SA. The majority (58.4%) of the analytic sample endorsed suicidal ideation at some point in their lifetime; of those who attempted suicide, 97.6% endorsed prior suicidal ideation compared to 46.8% of those who did not attempt suicide. Participants with AUD who had attempted suicide were significantly more likely to have been exposed to traumatic events in their life, regardless of the type of trauma (sexual, assaultive, and non-assaultive). Additionally, those who reported SA also had significantly higher lifetime rates of MDD and PTSD relative to those who had not attempted suicide. In terms of comorbid substance use addition, participants that reported attempting suicide had higher family history densities of AUD [44], started drinking at an earlier age, had more severe indicators of alcohol-related problems, and had higher rates of meeting lifetime criteria for other SUDs (cocaine, nicotine, and, sedative). In total, those with AUD who report SA seem to be more severely affected for other psychiatric and substance use disorders.

Fig. 1.

Fig. 1.

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Clinical comorbidities across those who have and have not reported an SA. Percentages/means and 95% confidence intervals (CIs) for psychiatric and substance use comorbidities for those who have attempted (SA) and have not attempted (no SA) suicide. Significant differences in the combined, multivariate model indicated by dashed lines around bars (p < 0.05).

Polygenic Scores

Figure 2 (panel a) presents the meta-analyzed results for associations between each of the corresponding PGSs and lifetime SA within those meeting criteria for AUD. All 95% confidence intervals (CIs) were corrected for multiple testing. PGSs for DEP (ORMETA = 1.34, 95% CI = 1.18, 1.53), PTSD (ORMETA = 1.23, 95% CI = 1.03, 1.45), and SUI (ORMETA = 1.44, 95% CI = 1.22, 1.70) were associated with increased odds of reporting SA. However, the AUD, BIP, SCZ, and SMOK PGSs were not associated with SA. Importantly, both the DEP and SUI PGSs remained associated in the models that included all PGSs, simultaneously. The conditional models, which included all PGS, explained 4.2% and 1.3% of the variance in SA in EUR-like and AFR-like participants, respectively (full results in online suppl. Table S3).

Fig. 2.

Fig. 2.

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PGSs across those who have and have not reported an SA. a Odds ratios (ORs) and 95% confidence intervals (CIs) for AUD, DEP, and SUI PGSs from logistic regression models in persons with AUD who had and had not attempted suicide. b OR and 95% CI from multinomial logistic models (no AUD, no SA as reference group). All models include cohort, sex, PC1–PC6, array, and site as covariates. All 95% CIs are corrected for multiple testing. Standard errors (SEs) adjusted for familial clustering using cluster-robust SEs. AFR, African-like genetic similarity grouping; EUR, European-like genetic similarity grouping; AUD, alcohol use disorder polygenic score; DEP, depression polygenic score; SUI, suicide attempt polygenic score; SA−, no lifetime suicide attempt; AUD−, does not meet criteria for alcohol use disorder; SA+, lifetime suicide attempt; AUD+, meets criteria for alcohol use disorder.

Figure 2 (panel b) shows conditional PGS results from the multinomial logistic models comparing those with AUD who had attempted suicide (AUD+/SA+) and those with AUD who had not attempted suicide (AUD+/SA−), to those without an AUD diagnosis and who had not attempted suicide (AUD−/SA+ omitted for clarity, full results in online suppl. Table S4). Relative to the AUD−/SA− group, the AUD (ORMETA = 1.16, 95% CI = 1.05, 1.28), DEP (ORMETA = 1.29, 95% CI = 1.16, 1.44), SMOK (ORMETA = 1.30, 95% CI = 1.17, 1.44), and SUI (ORMETA = 1.40, 95% CI = 1.22, 1.60) PGSs were all associated with increased odds of being in the AUD+/SA+ group. By contrast, only the AUD (ORMETA = 1.15, 95% CI = 1.08, 1.22) and SMOK (ORMETA = 1.30, 95% CI = 1.21, 1.39) PGSs were associated with increased odds of being in the AUD+/SA− group relative to the reference group (AUD−/SA−).

Neurophysiological Findings

We observed nominal differences in resting state EEG coherence patterns. However, only two findings withstood multiple test correction. Results are available in the supplementary information.

Discussion

Researchers have begun to identify clinical, genomic, and neurophysiological correlates of SAs among individuals with and without psychiatric illnesses (i.e., SCZ, bipolar disorder, depression) [13–16]. However, few have examined these risk factors in tandem among those with AUD, despite the higher rates of SAs of this group. The current study identified distinct clinical, genomic, and neurophysiological associations with lifetime SA among individuals who meet criteria for DSM-IV alcohol dependence.

All participants with an AUD in COGA reported elevated rates of suicidal ideation, other SUDs, and trauma exposure compared to the general population [2, 71]. However, those who met criteria for an AUD and reported a lifetime SA had even greater levels of lifetime trauma (sexual, assaultive, and non-assaultive), other substance-related problems, suicidal ideation, and comorbid psychiatric conditions (PTSD and MDD) relative to those who had not attempted suicide. These results confirm that those with AUD who report a lifetime SA represent clinically high-risk group. Given the strong role early trauma plays in risk for SA [72], the elevated levels of exposure to trauma in this group, and the higher rates of PTSD, trauma exposure may play an even more important role for SA risk in those with AUD. Future work should utilize prospective information to determine whether early trauma exposure and psychiatric problems predate the onset of AUD and eventual SA in COGA.

In terms of genetic risk, the PGSs for SA, depression, and PTSD were associated with a lifetime SA in persons with an AUD in the meta-analyzed results. Exploration of the stratified results demonstrate these were primarily driven by the associations in the EUR-like participants. The lack of associations of PGSs in the AFR-like grouping likely stems from the relatively small sample sizes of the discovery GWASs [73] for the phenotypes included here. Importantly, in the multinomial logistic regression models, those with AUD did not differ in mean levels of AUD PGS regardless of whether they had reported a lifetime SA. Similar to the logistic regression models limited to persons with AUD, those who had attempted suicide had higher suicide and depression PGSs.

We note several important limitations. First, we focused on lifetime risk for all of the clinical measures and SA and cannot speak to the time order between the 2. Future research can harness the smaller subset of prospective participants in COGA to examine the longitudinal associations between psychiatric conditions and future SA, as well as frequency and means of attempt, among those with an SUD. These longitudinal designs will be critical for understanding antecedents of SA, especially as we move beyond the focus of risk factors in isolation, including genetic, clinical, and social risk factors, as has been done in SUD [74]. Second, we focused exclusively on AUD. There is also a high rate of SAs among individuals with other SUDs, particularly cocaine and opioid use disorders. Lastly, we did not have data on those who died by suicide, which may differ from those who have attempted but not taken their own lives. Lastly, though we included AFR-like participants in the current analyses, we were limited in the availability population-matched, well-powered GWAS in order to construct PGS in non-EUR samples. While there have been rapid advances in the availability of non-EUR GWAS, there is still significant work to be done before we reach parity in discovery sample sizes.

Understanding the antecedents for SA remains one of the top goals of psychiatric epidemiology. Persons with SUDs are a particularly at-risk group for lifetime SA. In the current analysis, we demonstrated that persons with an AUD that attempted suicide had particularly higher levels of trauma exposure and psychiatric comorbidities, elevated PGSs for SA, depression, and PTSD and lower neurophysiological functioning. Future work with larger and more diverse samples can examine additional risk factors, such as social and environmental conditions. Identifying robust predictive markers within an already high-risk group may allow for earlier intervention and prevention from unnecessary loss of human life.

Acknowledgments

The Collaborative Study on the Genetics of Alcoholism (COGA); Principal Investigators B.P., V.H., and T.F.; Scientific Director A.A.; Translational Director D.M.D. include ten different centers: University of Connecticut (V.H.); Indiana University (H.J.E., T.F., Y.L., and M.H.P.); University of Iowa Carver College of Medicine (S. Kuperman and J.R.K.); SUNY Downstate Health Sciences University (B.P., J.L.M., C.K., and A.K.P.); Washington University in St. Louis (L.B., J.R., K.K.B., and A.A.); University of California at San Diego (M.S.); Rutgers University (J.T., D.M.D., R.H., and J.S.); the Children’s Hospital of Philadelphia, University of Pennsylvania (L.A.); Icahn School of Medicine at Mount Sinai (A.G. and P.S.); and Howard University (D.S.). Other COGA collaborators include L.B. (University of Connecticut); J.N. Jr., L.W., X., Xuei, D.L., and S.O. (Indiana University); G.C. (University of Iowa; University of Connecticut); D.B.C., J.Z., P.B., S.K., and G.P. (SUNY Downstate); N.M. (Icahn School of Medicine at Mount Sinai); A.A., S.H., E.J., V.M., and S.S. (Washington University); J.M., F.A., Z.P., and S.K. (Rutgers University); and A.M. (the Children’s Hospital of Philadelphia and University of Pennsylvania). H.C. and A.P. are the NIAAA Staff Collaborators. We continue to be inspired by our memories of Henri Begleiter and Theodore Reich, founding PI and Co-PI of COGA, and also owe a debt of gratitude to other past organizers of COGA, including Ting-Kai Li, P.M. Conneally, Raymond Crowe, and Wendy Reich, for their critical contributions. This national collaborative study is supported by NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA).

This research also used summary data from the Psychiatric Genomics Consortium (PGC), the Million Veterans Program (MVP), and the International Suicide Genetics Consortium (ISGC, now part of the PGC). We would like to thank the many studies that made these consortia possible, the researchers involved, and the participants in those studies, without whom this effort would not be possible.

Statement of Ethics

All COGA participants provided written informed consent for participation in the broader COGA study. This study uses secondary data. Ethical approval is not required for this study in accordance with local or national guidelines.

Conflict of Interest Statement

The authors have no conflicts of interest to declare.

Funding Sources

Research reported in this publication was supported by the National Institute on Alcohol Abuse and Alcoholism under award number U01AA008401. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author Contributions

P.B.B., J.L.M., J.S., Z.N., and N.M. contributed to the conceptualization of this paper. P.B.B., Z.N., C.C., J.Z., J.S., and J.L.M. contributed to the analyses and data preparation. P.B.B., Z.N., and J.L.M. contributed to the drafting of the manuscript. Z.N., C.C., J.S., N.M., J.Z., D.B., C.K., S.K., A.K.P., G.P., S.S.V., L.A., L.B., K.K.B., G.C., D.M.D., H.J.E., T.F., A.G., V.H., E.C.J., J.R.K., D.L., M.H.P., J.S., L.W., A.A., B.P., and J.L.M. provided critical feedback and approved the final manuscript.

Funding Statement

Research reported in this publication was supported by the National Institute on Alcohol Abuse and Alcoholism under award number U01AA008401. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Data Availability Statement

All data sources are described in the manuscript and supplemental information. No new data were collected. COGA genetic data were available through dbGaP (Study Accession: phs000763.v1.p1). The process for obtaining the GWAS summary statistics used in these analyses is described in the corresponding original GWAS publications.

Supplementary Material.

Supplementary Material.

Supplementary Material.

Supplementary Material.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary_material-Suppl.1-s1.docx (1.5MB, docx)
Supplementary_material-Suppl.2-s2.xlsx (31.5KB, xlsx)
Supplementary_material-Suppl.3-s3.xlsx (10.3MB, xlsx)
Supplementary_material-Suppl.4-s4.xlsx (20.1MB, xlsx)

Data Availability Statement

All data sources are described in the manuscript and supplemental information. No new data were collected. COGA genetic data were available through dbGaP (Study Accession: phs000763.v1.p1). The process for obtaining the GWAS summary statistics used in these analyses is described in the corresponding original GWAS publications.

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