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Background & aimsGiven ongoing challenges in non-invasive non-alcoholic liver disease (NAFLD) diagnosis, we sought to validate an ALT-based NAFLD phenotype using measures readily available in electronic health records (EHRs) and population-based studies by leveraging the clinical and genetic data in the Million Veteran Program (MVP), a multi-ethnic mega-biobank of US Veterans.MethodsMVP participants with alanine aminotransferases (ALT) >40 units/L for men and >30 units/L for women without other causes of liver disease were compared to controls with normal ALT. Genetic variants spanning eight NAFLD risk or ALT-associated loci (LYPLAL1, GCKR, HSD17B13, TRIB1, PPP1R3B, ERLIN1, TM6SF2, PNPLA3) were tested for NAFLD associations with sensitivity analyses adjusting for metabolic risk factors and alcohol consumption. A manual EHR review assessed performance characteristics of the NAFLD phenotype with imaging and biopsy data as gold standards. Genetic associations with advanced fibrosis were explored using FIB4, NAFLD Fibrosis Score and platelet counts.ResultsAmong 322,259 MVP participants, 19% met non-invasive criteria for NAFLD. Trans-ethnic meta-analysis replicated associations with previously reported genetic variants in all but LYPLAL1 and GCKR loci (PConclusionsWe validate a simple, non-invasive ALT-based NAFLD phenotype using EHR data by leveraging previously established NAFLD risk-associated genetic polymorphisms.

Abstract Substance Use Disorders (SUDs) manifest as persistent drug-seeking behavior despite adverse consequences, with Alcohol Use Disorder (AUD) and Opioid Use Disorder (OUD) representing prevalent forms associated with significant mortality rates and economic burdens. The co-occurrence of AUD and OUD is common, necessitating a deeper comprehension of their intricate interactions. While the causal link between these disorders remains elusive, shared genetic factors are hypothesized. Leveraging public datasets, we employed genomic and transcriptomic analyses to explore conserved and distinct molecular pathways within the dorsolateral prefrontal cortex associated with AUD and OUD. Our findings unveil modest transcriptomic overlap at the gene level between the two disorders but substantial convergence on shared biological pathways. Notably, these pathways predominantly involve inflammatory processes, synaptic plasticity, and key intracellular signaling regulators. Integration of transcriptomic data with the latest genome-wide association studies (GWAS) for problematic alcohol use (PAU) and OUD not only corroborated our transcriptomic findings but also confirmed the limited shared heritability between the disorders. Overall, our study indicates that while alcohol and opioids induce diverse transcriptional alterations at the gene level, they converge on select biological pathways, offering promising avenues for novel therapeutic targets aimed at addressing both disorders simultaneously.

Abstract There is a high prevalence of antisocial personality disorder (ASPD) in individuals affected by substance use disorders (SUD). However, there is limited information on the specific patterns of association of ASPD with SUD severity and specific SUD diagnostic criteria. We investigated the association of alcohol, cannabis, cocaine, opioid, and tobacco use disorders (AUD, CanUD, CocUD, OUD, and TUD, respectively) in 1660 individuals with ASPD and 6640 controls matched by sex (24% female), age, and racial/ethnic background in a sample ascertained for addiction-related traits. Generalized linear regressions were used to test ASPD with respect to the five DSM-5 SUD diagnoses, their severity (i.e., mild, moderate, severe), and their diagnostic criteria. We found that ASPD is associated with the diagnosis and severity of AUD (Odds Ratio, ORs = 1.89 and 1.25), CanUD (ORs = 2.13 and 1.32), and TUD (ORs = 1.50 and 1.21) (ps

Abstract Neural processing of rewarding stimuli involves several distinct regions, including the nucleus accumbens (NAc). The majority of NAc neurons are GABAergic projection neurons known as medium spiny neurons (MSNs). MSNs are broadly defined by dopamine receptor expression, but evidence suggests that a wider array of subtypes exist. To study MSN heterogeneity, we analyzed single-nucleus RNA sequencing data from the largest available rat NAc dataset. Analysis of 48,040 NAc MSN nuclei identified major populations belonging to the striosome and matrix compartments. Integration with mouse and human data indicated consistency across species and disease-relevance scoring using genome-wide association study results revealed potentially differential roles for MSN populations in substance use disorders. Additional high-resolution clustering identified 34 transcriptomically distinct subtypes of MSNs definable by a limited number of marker genes. Together, these data demonstrate the diversity of MSNs in the NAc and provide a basis for more targeted genetic manipulation of specific populations.

Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach.

The increased vulnerability to alcohol dependence (AD) seen in individuals with childhood adversity (CA) may result in part from CA-induced epigenetic changes. To examine CA-associated DNA methylation changes in AD patients, we examined peripheral blood DNA methylation levels of 384 CpGs in promoter regions of 82 candidate genes in 279 African Americans [AAs; 88 with CA (70.5% with AD) and 191 without CA (38.2% with AD)] and 239 European Americans [EAs; 61 with CA (86.9% with AD) and 178 without CA (46.6% with AD)] using Illumina GoldenGate Methylation Array assays. The effect of CA on methylation of individual CpGs and overall methylation in promoter regions of genes was evaluated using a linear regression analysis (with consideration of sex, age, and ancestry proportion of subjects) and a principal components-based analysis, respectively. In EAs, hypermethylation of 10 CpGs in seven genes (ALDH1A1, CART, CHRNA5, HTR1B, OPRL1, PENK, and RGS19) were cross validated in AD patients and healthy controls who were exposed to CA. P values of two CpGs survived Bonferroni correction when all EA samples were analyzed together to increase statistical power [CHRNA5_cg17108064: P(adjust) = 2.54×10(-5); HTR1B_cg06031989: P(adjust) = 8.98×10(-5)]. Moreover, overall methylation levels in the promoter regions of three genes (ALDH1A1, OPRL1 and RGS19) were elevated in both EA case and control subjects who were exposed to CA. However, in AAs, CA-associated DNA methylation changes in AD patients were not validated in healthy controls. Our findings suggest that CA could induce population-specific methylation alterations in the promoter regions of specific genes, thus leading to changes in gene transcription and an increased risk for AD and other disorders.

Risk of substance dependence (SD) and obesity has been linked to the function of melanocortin peptides encoded by the proopiomelanocortin gene (POMC).POMC exons were Sanger sequenced in 280 African Americans (AAs) and 308 European Americans (EAs). Among them, 311 (167 AAs and 114 EAs) were affected with substance (alcohol, cocaine, opioid and/or marijuana) dependence and 277 (113 AAs and164 EAs) were screened controls. We identified 23 variants, including two common polymorphisms (rs10654394 and rs1042571) and 21 rare variants; 12 of which were novel. We used logistic regression to analyze the association between the two common variants and SD or body mass index (BMI), with sex, age, and ancestry proportion as covariates. The common variant rs1042571 in the 3'UTR was significantly associated with BMI in EAs (Overweight: P(adj) = 0.005; Obese: P(adj) = 0.018; Overweight+Obese: P(adj) = 0.002) but not in AAs. The common variant, rs10654394, was not associated with BMI and neither common variant was associated with SD in either population. To evaluate the association between the rare variants and SD or BMI, we collapsed rare variants and tested their prevalence using Fisher's exact test. In AAs, rare variants were nominally associated with SD overall and with specific SD traits (SD: P(FET,1df) = 0.026; alcohol dependence: P(FET,1df) = 0.027; cocaine dependence: P(FET,1df) = 0.007; marijuana dependence: P(FET,1df) = 0.050) (the P-value from cocaine dependence analysis survived Bonferroni correction). There was no such effect in EAs. Although the frequency of the rare variants did not differ significantly between the normal-weight group and the overweight or obese group in either population, certain rare exonic variants occurred only in overweight or obese subjects without SD.These findings suggest that POMC exonic variants may influence risk for both SD and elevated BMI, in a population-specific manner. However, common and rare variants in this gene may exert different effects on these two phenotypes.

Individuals with schizophrenia tend to be heavy smokers and are at high risk for tobacco dependence. However, the nature of the comorbidity is not entirely clear. We previously reported evidence for association of schizophrenia with SNPs and SNP haplotypes in a region of chromosome 5q containing the SPEC2, PDZ-GEF2 and ACSL6 genes. In this current study, analysis of the control subjects of the Molecular Genetics of Schizophrenia (MGS) sample showed similar pattern of association with number of cigarettes smoked per day (numCIG) for the same region. To further test if this locus is associated with tobacco smoking as measured by numCIG and FTND, we conducted replication and meta-analysis in 12 independent samples (n>16,000) for two markers in ACSL6 reported in our previous schizophrenia study. In the meta-analysis of the replication samples, we found that rs667437 and rs477084 were significantly associated with numCIG (p = 0.00038 and 0.00136 respectively) but not with FTND scores. We then used in vitro and in vivo techniques to test if nicotine exposure influences the expression of ACSL6 in brain. Primary cortical culture studies showed that chronic (5-day) exposure to nicotine stimulated ACSL6 mRNA expression. Fourteen days of nicotine administration via osmotic mini pump also increased ACSL6 protein levels in the prefrontal cortex and hippocampus of mice. These increases were suppressed by injection of the nicotinic receptor antagonist mecamylamine, suggesting that elevated expression of ACSL6 requires nicotinic receptor activation. These findings suggest that variations in the ACSL6 gene may contribute to the quantity of cigarettes smoked. The independent associations of this locus with schizophrenia and with numCIG in non-schizophrenic subjects suggest that this locus may be a common liability to both conditions.

Abstract Smoking behaviors and alcohol use disorder (AUD), both moderately heritable traits, commonly co-occur in the general population. Single-trait genome-wide association studies (GWAS) have identified multiple loci for smoking and AUD. However, GWASs that have aimed to identify loci contributing to co-occurring smoking and AUD have used small samples and thus have not been highly informative. Applying multi-trait analysis of GWASs (MTAG), we conducted a joint GWAS of smoking and AUD with data from the Million Veteran Program (N = 318,694). By leveraging GWAS summary statistics for AUD, MTAG identified 21 genome-wide significant (GWS) loci associated with smoking initiation and 17 loci associated with smoking cessation compared to 16 and 8 loci, respectively, identified by single-trait GWAS. The novel loci for smoking behaviors identified by MTAG included those previously associated with psychiatric or substance use traits. Colocalization analysis identified 10 loci shared by AUD and smoking status traits, all of which achieved GWS in MTAG, including variants on SIX3, NCAM1, and near DRD2. Functional annotation of the MTAG variants highlighted biologically important regions on ZBTB20, DRD2, PPP6C, and GCKR that contribute to smoking behaviors. In contrast, MTAG of smoking behaviors and alcohol consumption (AC) did not enhance discovery compared with single-trait GWAS for smoking behaviors. We conclude that using MTAG to augment the power of GWAS enables the identification of novel genetic variants for commonly co-occuring phenotypes, providing new insights into their pleiotropic effects on smoking behavior and AUD.

Summary: To characterize polysubstance addiction (PSA) patterns of cocaine use disorder (CoUD), we performed a latent class analysis (LCA) in 7,989 participants with a lifetime DSM-5 diagnosis of CoUD. This analysis identified three PSA subgroups among CoUD participants (i.e., low, 17%; intermediate, 38%; high, 45%). While these subgroups varied by age, sex, and racial-ethnic distribution (p 0.05). After accounting for sex, age, and race-ethnicity, the CoUD subgroup with high PSA had higher odds of antisocial personality disorder (OR = 21.96 vs. 6.39, difference-p = 8.08✕10−6), agoraphobia (OR = 4.58 vs. 2.05, difference-p = 7.04✕10−4), mixed bipolar episode (OR = 10.36 vs. 2.61, difference-p = 7.04✕10−4), posttraumatic stress disorder (OR = 11.54 vs. 5.86, difference-p = 2.67✕10−4), antidepressant medication use (OR = 13.49 vs. 8.02, difference-p = 1.42✕10−4), and sexually transmitted diseases (OR = 5.92 vs. 3.38, difference-p = 1.81✕10−5) than the low-PSA CoUD subgroup. These findings underscore the importance of modeling PSA severity and comorbidities when examining the clinical, molecular, and neuroimaging correlates of CoUD.

Abstract Opioid addiction (OA) is moderately heritable, yet only rs1799971, the A118G variant in OPRM1, has been identified as a genome-wide significant association with OA and independently replicated. We applied genomic structural equation modeling to conduct a GWAS of the new Genetics of Opioid Addiction Consortium (GENOA) data together with published studies (Psychiatric Genomics Consortium, Million Veteran Program, and Partners Health), comprising 23,367 cases and effective sample size of 88,114 individuals of European ancestry. Genetic correlations among the various OA phenotypes were uniformly high (rg > 0.9). We observed the strongest evidence to date for OPRM1: lead SNP rs9478500 (p = 2.56 × 10–9). Gene-based analyses identified novel genome-wide significant associations with PPP6C and FURIN. Variants within these loci appear to be pleiotropic for addiction and related traits.

Abstract Integrating data across institutions can improve learning efficiency. To integrate data efficiently while protecting privacy, we propose A one-shot, summary-statistics-based, Distributed Algorithm for fitting Penalized (ADAP) regression models across multiple datasets. ADAP utilizes patient-level data from a lead site and incorporates the first-order (ADAP1) and second-order gradients (ADAP2) of the objective function from collaborating sites to construct a surrogate objective function at the lead site, where model fitting is then completed with proper regularizations applied. We evaluate the performance of the proposed method using both simulation and a real-world application to study risk factors for opioid use disorder (OUD) using 15,000 patient data from the OneFlorida Clinical Research Consortium. Our results show that ADAP performs nearly the same as the pooled estimator but achieves higher estimation accuracy and better variable selection than the local and average estimators. Moreover, ADAP2 successfully handles heterogeneity in covariate distributions.

Abstract We developed a One-shot Distributed Algorithm for Cox proportional-hazards model to analyze Heterogeneous multi-center time-to-event data (ODACH) circumventing the need for sharing patient-level information across sites. This algorithm implements a surrogate likelihood function to approximate the Cox log-partial likelihood function that is stratified by site using patient-level data from a lead site and aggregated information from other sites, allowing the baseline hazard functions and the distribution of covariates to vary across sites. Simulation studies and application to a real-world opioid use disorder study showed that ODACH provides estimates close to the pooled estimator, which analyzes patient-level data directly from all sites via a stratified Cox model. Compared to the estimator from meta-analysis, the inverse variance-weighted average of the site-specific estimates, ODACH estimator demonstrates less susceptibility to bias, especially when the event is rare. ODACH is thus a valuable privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data.

Abstract Stimulant dependence is heritable, but specific genetic factors underlying the trait have not been identified. A genome-wide association study for stimulant dependence was performed in a discovery cohort of African- (AA) and European-ancestry (EA) subjects ascertained for genetic studies of alcohol, opioid, and cocaine use disorders. The sample comprised individuals with DSM-IV stimulant dependence (393 EA cases, 5288 EA controls; 155 AA cases, 5603 AA controls). An independent cohort from the family-based Collaborative Study on the Genetics of Alcoholism (532 EA cases, 7635 EA controls; 53 AA cases, AA 3352 controls) was used for replication. One variant in SLC25A16 (rs2394476, p = 3.42 × 10−10, odds ratio [OR] = 3.70) was GWS in AAs. Four other loci showed suggestive evidence, including KCNA4 in AAs (rs11500237, p = 2.99 × 10−7, OR = 2.31) which encodes one of the potassium voltage-gated channel protein that has been linked to several other substance use disorders, and CPVL in the combined population groups (rs1176440, p = 3.05 × 10−7, OR = 1.35), whose expression was previously shown to be upregulated in the prefrontal cortex from users of cocaine, cannabis, and phencyclidine. Analysis of the top GWAS signals revealed a significant enrichment with nicotinic acetylcholine receptor genes (adjusted p = 0.04) and significant pleiotropy between stimulant dependence and alcohol dependence in EAs (p adj = 3.6 × 10−3), an anxiety disorder in EAs (p adj = 2.1 × 10−4), and ADHD in both AAs (p adj = 3.0 × 10−33) and EAs (p adj = 6.7 × 10−35). Our results implicate novel genes and pathways as having roles in the etiology of stimulant dependence.

Aim: Substance use disorders (SUD) result in substantial morbidity and mortality worldwide. Opioids, and to a lesser extent cocaine, contribute to a large percentage of this health burden. Despite their high heritability, few genetic risk loci have been identified for either opioid or cocaine dependence (OD or CD, respectively). A genome-wide association study of OD and CD related phenotypes reflecting the time between first self-reported use of these substances and a first DSM-IV dependence diagnosis was conducted. Methods: Cox proportional hazards regression in a discovery sample of 6,188 African-Americans (AAs) and 6,835 European-Americans (EAs) participants in a genetic study of multiple substance dependence phenotypes were used to test for association between genetic variants and these outcomes. The top findings were tested for replication in two independent cohorts. Results: In the discovery sample, three independent regions containing variants associated with time to dependence at P < 5 × 10−8 were identified, one (rs61835088 = 1.03 × 10−8) for cocaine in the combined EA-AA meta-analysis in the gene FAM78B on chromosome 1, and two for opioids in the AA portion of the sample in intergenic regions of chromosomes 4 (rs4860439, P = 1.37 × 10−8) and 9 (rs7032521, P = 3.30 × 10−8). After meta-analysis with data from the replication cohorts, the signal at rs61835088 improved (HR = 0.87, P = 3.71 × 10−9 and an intergenic SNP on chromosome 21 (rs2825295, HR = 1.14, P = 2.57 × 10−8) that missed the significance threshold in the AA discovery sample became genome-wide significant (GWS) for CD. Conclusions: Although the two GWS variants are not in genes with obvious links to SUD biology and have modest effect sizes, they are statistically robust and show evidence for association in independent samples. These results may point to novel pathways contributing to disease progression and highlight the utility of related phenotypes to better understand the genetics of SUDs.

Chronic excessive alcohol use has neurotoxic effects, which may contribute to cognitive decline and the risk of early-onset dementia. Elevated peripheral iron levels have been reported in individuals with alcohol use disorder (AUD), but its association with brain iron loading has not been explored. We evaluated whether (1) serum and brain iron loading are higher in individuals with AUD than non-dependent healthy controls and (2) serum and brain iron loading increase with age. A fasting serum iron panel was obtained and a magnetic resonance imaging scan with quantitative susceptibility mapping (QSM) was used to quantify brain iron concentrations. Although serum ferritin levels were higher in the AUD group than in controls, whole-brain iron susceptibility did not differ between groups. Voxel-wise QSM analyses revealed higher susceptibility in a cluster in the left globus pallidus in individuals with AUD than controls. Whole-brain iron increased with age and voxel-wise QSM indicated higher susceptibility with age in various brain areas including the basal ganglia. This is the first study to analyze both serum and brain iron loading in individuals with AUD. Larger studies are needed to examine the effects of alcohol use on iron loading and its associations with alcohol use severity, structural and functional brain changes, and alcohol-induced cognitive impairments.

Abstract Substance dependence diagnoses (SDs) are important risk factors for suicidality. We investigated the associations of multiple SDs with different suicidality outcomes, testing how genetic background moderates these associations. The Yale-Penn cohort (N = 15,557) was recruited to investigate the genetics of SDs. The Army STARRS (Study to Assess Risk and Resilience in Servicemembers) cohort (N = 11,236) was recruited to evaluate mental health risk and resilience among Army personnel. We applied multivariate logistic regression to investigate the associations of SDs with suicidality and, in the Yale-Penn cohort, we used the structured linear mixed model (StructLMM) to study multivariate gene–environment interactions. In Yale-Penn, lifetime polysubstance dependence was strongly associated with lifetime suicidality: having five SDs showed an association with suicidality, from odds ratio (OR) = 6.77 (95% confidence interval, CI = 5.74–7.99) for suicidal ideation (SI) to OR = 3.61 (95% CI = 2.7–4.86) for suicide attempt (SA). In Army STARRS, having multiple substance use disorders for alcohol and/or drugs was associated with increased suicidality ranging from OR = 2.88 (95% CI = 2.6–3.19) for SI to OR = 3.92 (95% CI = 3.19–4.81) for SA. In Yale-Penn, we identified multivariate gene–environment interactions (Bayes factors, BF > 0) of SI with respect to a gene cluster on chromosome 16 (LCAT, p = 1.82 × 10–7; TSNAXIP1, p = 2.13 × 10−7; CENPT, p = 2.32 × 10−7; PARD6A, p = 5.57 × 10−7) for opioid dependence (BF = 12.2), cocaine dependence (BF = 12.1), nicotine dependence (BF = 9.2), and polysubstance dependence (BF = 2.1). Comorbidity of multiple SDs is a significant associated with suicidality and heritability of suicidality is partially moderated by multivariate gene interactions.

Genome-wide association studies (GWASs) for cigarette smoking have identified several hundred loci that account for a small proportion of the overall genetic risk. Here, the authors report a large GWAS for smoking trajectories and meta-analysis for smoking status, finding multiple plausible loci.

Aim: Racial disparities in opioid use disorder (OUD) management exist, however, and there is limited research on factors that influence opioid cessation in different population groups. Methods: We employed multiple machine learning prediction algorithms least absolute shrinkage and selection operator, random forest, deep neural network, and support vector machine to assess factors associated with ceasing opioid use in a sample of 1,192 African Americans (AAs) and 2,557 individuals of European ancestry (EAs) who met Diagnostic and Statistical Manual of Mental Disorders, 5th Edition criteria for OUD. Values for nearly 4,000 variables reflecting demographics, alcohol and other drug use, general health, non-drug use behaviors, and diagnoses for other psychiatric disorders, were obtained for each participant from the Semi-Structured Assessment for Drug Dependence and Alcoholism, a detailed semi-structured interview. Results: Support vector machine models performed marginally better on average than other machine learning methods with maximum prediction accuracies of 75.4% in AAs and 79.4% in EAs. Subsequent stepwise regression considered the 83 most highly ranked variables across all methods and models and identified less recent cocaine use (AAs: odds ratio (OR) = 1.82, P = 9.19 × 10−5; EAs: OR = 1.91, P = 3.30 × 10−15), shorter duration of opioid use (AAs: OR = 0.55, P = 5.78 × 10−6; EAs: OR = 0.69, P = 3.01 × 10−7), and older age (AAs: OR = 2.44, P = 1.41 × 10−12; EAs: OR = 2.00, P = 5.74 × 10−9) as the strongest independent predictors of opioid cessation in both AAs and EAs. Attending self-help groups for OUD was also an independent predictor (P < 0.05) in both population groups, while less gambling severity (OR = 0.80, P = 3.32 × 10−2) was specific to AAs and post-traumatic stress disorder recovery (OR = 1.93, P = 7.88 × 10−5), recent antisocial behaviors (OR = 0.64, P = 2.69 × 10−3), and atheism (OR = 1.45, P = 1.34 × 10−2) were specific to EAs. Factors related to drug use comprised about half of the significant independent predictors in both AAs and EAs, with other predictors related to non-drug use behaviors, psychiatric disorders, overall health, and demographics. Conclusions: These proof-of-concept findings provide avenues for hypothesis-driven analysis, and will lead to further research on strategies to improve OUD management in EAs and AAs.

Epidemiologic studies recognize that trauma and posttraumatic stress are associated with heightened suicidal behavior severity, yet examination of these associations from a genetic perspective is limited. We performed a multivariate gene-by-environment genome-wide interaction study (GEWIS) of suicidality in 123,633 individuals using a covariance matrix based on 26 environments related to traumatic experiences, posttraumatic stress, social support, and socioeconomic status. We discovered five suicidality risk loci, including the male-associated rs2367967 (CWC22), which replicated in an independent cohort. All GEWIS-significant loci exhibited interaction effects where at least 5% of the sample had environmental profiles conferring opposite SNP effects from the majority. We identified PTSD as a primary driving environment for GxE at suicidality risk loci. The male suicidality GEWIS was enriched for three middle-temporal-gyrus inhibitory neuron transcriptomic profiles: SCUBE- and PVALB-expressing cells (β = 0.028, p = 3.74 × 10−4), OPRM1-expressing cells (β = 0.030, p = 0.001), and SPAG17-expressing cells (β = 0.029, p = 9.80 × 10−4). Combined with gene-based analyses (CNTN5 passociation = 2.38 × 10−9, pinteraction = 1.51 × 10−3; PSMD14 passociation = 2.04 × 10−7, pinteraction = 7.76 × 10−6; HEPACAM passociation = 2.43 × 10−6, pinteraction = 3.82 × 10−7) including information about brain chromatin interaction profiles (UBE2E3 in male neuron p = 1.07 × 10−5), our GEWIS points to extracellular matrix biology and synaptic plasticity as biological interactors with the effects of potentially modifiable lifetime traumatic experiences on genetic risk for suicidality. Characterization of molecular basis for the effects of traumatic experience and posttraumatic stress on risk of suicidal behaviors may help to identify novel targets for which more effective treatments can be developed for use in high-risk populations.