Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium

Partitioning the Genomic Components of Behavioral Disinhibition and Substance Use (Disorder) Using Genomic Structural Equation Modeling

Externalizing behaviors encompass manifestations of risk-taking, self-regulation, aggression, sensation-/reward-seeking, and impulsivity. Externalizing research often includes substance use (SUB), substance use disorder (SUD), and other (non-SUB/SUD) "behavioral disinhibition" (BD) traits. Genome-wide and twin research have pointed to overlapping genetic architecture within and across SUB, SUD, and BD. We created single-factor measurement models-each describing SUB, SUD, or BD traits-based on mutually exclusive sets of European ancestry genome-wide association study (GWAS) statistics exploring externalizing variables. We then assessed the partitioning of genetic covariance among the three facets using correlated factors models and Cholesky decomposition. Even when the residuals for indicators relating to the same substance were correlated across the SUB and SUD factors, the two factors yielded a large correlation (rg = 0.803). BD correlated strongly with the SUD (rg = 0.774) and SUB (rg = 0.778) factors. In our initial decompositions, 33% of total BD variance remained after partialing out SUD and SUB. The majority of covariance between BD and SUB and between BD and SUD was shared across all factors, and, within these models, only a small fraction of the total variation in BD operated via an independent pathway with SUD or SUB outside of the other factor. When only nicotine/tobacco, cannabis, and alcohol were included for the SUB/SUD factors, their correlation increased to rg = 0.861; in corresponding decompositions, BD-specific variance decreased to 27%. Further research can better elucidate the properties of BD-specific variation by exploring its genetic/molecular correlates.

Cell type-specific Multi-Omics Analysis of Cocaine Use Disorder in the Human Caudate Nucleus

Structural and functional alterations in the brain's reward circuitry are present in cocaine use disorder (CocUD), but their molecular underpinnings remain unclear. To investigate these mechanisms, we performed single-nuclei multiome profiling on postmortem caudate nucleus tissue from six individuals with CocUD and eight controls. We profiled 31,178 nuclei, identifying 13 cell types including D1- and D2-medium spiny neurons (MSNs) and glial cells. We observed 1,383 differentially regulated genes and 10,235 differentially accessible peaks, with alterations in MSNs and astrocytes related to neurotransmitter activity and synapse organization. Gene regulatory network analysis identified the transcription factor ZEB1 as exhibiting distinct CocUD-specific subclusters, activating downstream expression of ion- and calcium-channels in MSNs. Further, PDE10A emerged as a potential drug target, showing conserved effects in a rat model. This study highlights cell type-specific molecular alterations in CocUD and provides targets for further investigation, demonstrating the value of multi-omics approaches in addiction research.

Shared and unique 3D genomic features of substance use disorders across multiple cell types

Recent genome-wide association studies (GWAS) have revealed shared genetic components among alcohol, opioid, tobacco and cannabis use disorders. However, the extent of the underlying shared causal variants and effector genes, along with their cellular context, remain unclear. We leveraged our existing 3D genomic datasets comprising high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq and RNA-seq across >50 diverse human cell types to focus on genomic regions that coincide with GWAS loci. Using stratified LD regression, we determined the proportion of genomewide SNP heritability attributable to the features assayed across our cell types by integrating recent GWAS summary statistics for the relevant traits: alcohol use disorder (AUD), tobacco use disorder (TUD), opioid use disorder (OUD) and cannabis use disorder (CanUD). Statistically significant enrichments (P<0.05) were observed in 14 specific cell types, with heritability reaching 9.2-fold for iPSC-derived cortical neurons and neural progenitors, confirming that they are crucial cell types for further functional exploration. Additionally, several pancreatic cell types, notably pancreatic beta cells, showed enrichment for TUD, with heritability enrichments up to 4.8-fold, suggesting genomic overlap with metabolic processes. Further investigation revealed significant positive genetic correlations between T2D with both TUD and CanUD (FDR<0.05) and a significant negative genetic correlation with AUD. Interestingly, after partitioning the heritability for each cell type's cis-regulatory elements, the correlation between T2D and TUD for pancreatic beta cells was greater (r=0.2) than the global genetic correlation value. Our study provides new genomic insights into substance use disorders and implicates cell types where functional follow-up studies could reveal causal variant-gene mechanisms underpinning these disorders.

Examining the Genetic Links between Clusters of Immune-mediated Diseases and Psychiatric Disorders

Importance

Autoimmune and autoinflammatory diseases have been linked to psychiatric disorders in the phenotypic and genetic literature. However, a comprehensive model that investigates the association between a broad range of psychiatric disorders and immune-mediated disease in a multivariate framework is lacking.

Objective

This study aims to establish a factor structure based on the genetic correlations of immune-mediated diseases and investigate their genetic relationships with clusters of psychiatric disorders.

Design Setting and Participants

We utilized Genomic Structural Equation Modeling (Genomic SEM) to establish a factor structure of 11 immune-mediated diseases. Genetic correlations between these immune factors were examined with five established factors across 13 psychiatric disorders representing compulsive, schizophrenia/bipolar, neurodevelopmental, internalizing, and substance use disorders. We included GWAS summary statistics of individuals of European ancestry with sample sizes from 1,223 cases for Addison's disease to 170,756 cases for major depressive disorder.

Main Outcomes and Measures

Genetic correlations between psychiatric and immune-mediated disease factors and traits to determine genetic overlap. We develop and validate a new heterogeneity metric, Q Factor , that quantifies the degree to which factor correlations are driven by more specific pairwise associations. We also estimate residual genetic correlations between pairs of psychiatric disorders and immune-mediated diseases.

Results

A four-factor model of immune-mediated diseases fit the data well and described a continuum from autoimmune to autoinflammatory diseases. The four factors reflected autoimmune, celiac, mixed pattern, and autoinflammatory diseases. Analyses revealed seven significant factor correlations between the immune and psychiatric factors, including autoimmune and mixed pattern diseases with the internalizing and substance use factors, and autoinflammatory diseases with the compulsive, schizophrenia/bipolar, and internalizing factors. Additionally, we find evidence of divergence in associations within factors as indicated by Q Factor . This is further supported by 14 significant residual genetic correlations between individual psychiatric disorders and immune-mediated diseases.

Conclusion and Relevance

Our results revealed genetic links between clusters of immune-mediated diseases and psychiatric disorders. Current analyses indicate that previously described relationships between specific psychiatric disorders and immune-mediated diseases often capture broader pathways of risk sharing indexed by our genomic factors, yet are more specific than a general association across all psychiatric disorders and immune-mediated diseases.

Genome-wide meta-analyses of cross substance use disorders in European, African, and Latino ancestry populations

Genetic risks for substance use disorders (SUDs) are due to both SUD-specific and SUD-shared genes. We performed the largest multivariate analyses to date to search for SUD-shared genes using samples of European (EA), African (AA), and Latino (LA) ancestries. By focusing on variants having cross-SUD and cross-ancestry concordant effects, we identified 45 loci. Through gene-based analyses, gene mapping, and gene prioritization, we identified 250 SUD-shared genes. These genes are highly expressed in amygdala, cortex, hippocampus, hypothalamus, and thalamus, primarily in neuronal cells. Cross-SUD concordant variants explained ~ 50% of the heritability of each SUD in EA. The top 5% individuals having the highest polygenic scores were approximately twice as likely to have SUDs as others in EA and LA. Polygenic scores had higher predictability in females than in males in EA. Using real-world data, we identified five drugs targeting identified SUD-shared genes that may be repurposed to treat SUDs.

Generalized genetic liability to substance use disorders

Lifetime and temporal co-occurrence of substance use disorders (SUDs) is common and compared with individual SUDs is characterized by greater severity, additional psychiatric comorbidities, and worse outcomes. Here, we review evidence for the role of generalized genetic liability to various SUDs. Coaggregation of SUDs has familial contributions, with twin studies suggesting a strong contribution of additive genetic influences undergirding use disorders for a variety of substances (including alcohol, nicotine, cannabis, and others). GWAS have documented similarly large genetic correlations between alcohol, cannabis, and opioid use disorders. Extending these findings, recent studies have identified multiple genomic loci that contribute to common risk for these SUDs and problematic tobacco use, implicating dopaminergic regulatory and neuronal development mechanisms in the pathophysiology of generalized SUD genetic liability, with certain signals demonstrating cross-species and translational validity. Overlap with genetic signals for other externalizing behaviors, while substantial, does not explain the entirety of the generalized genetic signal for SUD. Polygenic scores (PGS) derived from the generalized genetic liability to SUDs outperform PGS for individual SUDs in prediction of serious mental health and medical comorbidities. Going forward, it will be important to further elucidate the etiology of generalized SUD genetic liability by incorporating additional SUDs, evaluating clinical presentation across the lifespan, and increasing the granularity of investigation (e.g., specific transdiagnostic criteria) to ultimately improve the nosology, prevention, and treatment of SUDs.

The genetic landscape of substance use disorders

Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.

A novel multi-omics mendelian randomization method for gene set enrichment and its application to psychiatric disorders

Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often do not implicate specific genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS) that integrate xQTL and GWAS information, can link GWAS signals to effects on specific genes. To further increase detection power, gene signals are aggregated within relevant gene sets (GS) by performing gene set enrichment (GSE) analyses. Often GSE methods test for enrichment of "signal" genes in curated GS while overlooking their linkage disequilibrium (LD) structure, allowing for the possibility of increased false positive rates. Moreover, no GSE tool uses xQTL information to perform mendelian randomization (MR) analysis. To make causal inference on association between PD and GS, we develop a novel MR GSE (MR-GSE) procedure. First, we generate a "synthetic" GWAS for each MSigDB GS by aggregating summary statistics for x-level (mRNA, protein or DNA methylation (DNAm) levels) from the largest xQTL studies available) of genes in a GS. Second, we use synthetic GS GWAS as exposure in a generalized summary-data-based-MR analysis of complex trait outcomes. We applied MR-GSE to GWAS of nine important PD. When applied to the underpowered opioid use disorder GWAS, none of the four analyses yielded any signals, which suggests a good control of false positive rates. For other PD, MR-GSE greatly increased the detection of GO terms signals (2,594) when compared to the commonly used (non-MR) GSE method (286). Some of the findings might be easier to adapt for treatment, e.g., our analyses suggest modest positive effects for supplementation with certain vitamins and/or omega-3 for schizophrenia, bipolar and major depression disorder patients. Similar to other MR methods, when applying MR-GSE researchers should be mindful of the confounding effects of horizontal pleiotropy on statistical inference.

Neuronal-specific methylome and hydroxymethylome analysis reveal significant loci associated with alcohol use disorder

Background: Alcohol use disorder (AUD) is a complex condition associated with adverse health consequences that affect millions of individuals worldwide. Epigenetic modifications, including DNA methylation (5 mC), have been associated with AUD and other alcohol-related traits. Epigenome-wide association studies (EWAS) have identified differentially methylated genes associated with AUD in human peripheral and brain tissue. More recently, epigenetic studies of AUD have also evaluated DNA hydroxymethylation (5 hmC) in the human brain. However, most of the epigenetic work in postmortem brain tissue has examined bulk tissue. In this study, we investigated neuronal-specific 5 mC and 5 hmC alterations at CpG sites associated with AUD in the human orbitofrontal cortex (OFC). Methods: Neuronal nuclei from the OFC were evaluated in 34 human postmortem brain samples (10 AUD, 24 non-AUD). Reduced representation oxidative bisulfite sequencing was used to assess 5 mC and 5 hmC at the genome-wide level. Differential 5 mC and 5 hmC were evaluated using the methylKit R package and significance was set at false discovery rate < 0.05 and differential methylation > 2. Functional enrichment analyses were performed, and gene-level convergence was evaluated in an independent dataset that assessed 5 mC and 5 hmC of AUD in bulk cortical tissue. Results: We identified 417 5 mC and 363 5hmC significant differential CpG sites associated with AUD, with 59% in gene promoters. Some of the identified genes have been previously implicated in alcohol consumption, including SYK, DNMT3A for 5 mC, GAD1, DLX1, DLX2, for 5 hmC and GATA4 in both. Convergence with a previous AUD 5 mC and 5 hmC study was observed for 28 genes. We also identified 5 and 35 differential regions for 5 mC and 5 hmC, respectively. Lastly, GWAS enrichment analysis showed an association with AUD for differential 5 mC genes. Discussion: This study reveals neuronal-specific methylome and hydroxymethylome dysregulation associated with AUD, identifying both previously reported and potentially novel gene associations with AUD. Our findings provide new insights into the epigenomic dysregulation of AUD in the human brain.

The interaction effects of age, APOE and common environmental risk factors on human brain structure

Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.

Opioid trail: Tracking contributions to opioid use disorder from host genetics to the gut microbiome

Opioid use disorder (OUD) is a worldwide public health crisis with few effective treatment options. Traditional genetics and neuroscience approaches have provided knowledge about biological mechanisms that contribute to OUD-related phenotypes, but the complexity and magnitude of effects in the brain and body remain poorly understood. The gut-brain axis has emerged as a promising target for future therapeutics for several psychiatric conditions, so characterizing the relationship between host genetics and the gut microbiome in the context of OUD will be essential for development of novel treatments. In this review, we describe evidence that interactions between host genetics, the gut microbiome, and immune signaling likely play a key role in mediating opioid-related phenotypes. Studies in humans and model organisms consistently demonstrated that genetic background is a major determinant of gut microbiome composition. Furthermore, the gut microbiome is susceptible to environmental influences such as opioid exposure. Additional work focused on gene by microbiome interactions will be necessary to gain improved understanding of their effects on OUD-related behaviors.

Neuronal-specific methylome and hydroxymethylome analysis reveal replicated and novel loci associated with alcohol use disorder

Alcohol use disorder (AUD) is a complex condition associated with adverse health consequences that affect millions of individuals worldwide. Epigenetic modifications, including DNA methylation (5mC), have been associated with AUD and other alcohol-related traits. Epigenome-wide association studies (EWAS) have identified differentially methylated genes associated with AUD in human peripheral and brain tissue. More recently, epigenetic studies of AUD have also evaluated DNA hydroxymethylation (5hmC) in the human brain. However, most of the epigenetic work in postmortem brain tissue has examined bulk tissue. In this study, we investigated neuronal-specific 5mC and 5hmC alterations at CpG sites associated with AUD in the human orbitofrontal cortex (OFC). Neuronal nuclei from the OFC were evaluated in 34 human postmortem brain samples (10 AUD, 24 non-AUD). Reduced representation oxidative bisulfite sequencing was used to assess 5mC and 5hmC at the genome-wide level. Differential 5mC and 5hmC were evaluated using the methylKit R package and significance was set at false discovery rate <0.05 and differential methylation >2. Functional enrichment analyses were performed and replication was evaluated replication in an independent dataset that assessed 5mC and 5hmC of AUD in bulk cortical tissue. We identified 417 5mC and 363 5hmC genome-wide significant differential CpG sites associated with AUD, with 59% in gene promoters. We also identified genes previously implicated in alcohol consumption, such as SYK, CHRM2, DNMT3A, and GATA4, for 5mC and GATA4, and GAD1, GATA4, DLX1 for 5hmC. Replication was observed for 28 CpG sites from a previous AUD 5mC and 5hmC study, including FOXP1. Lastly, GWAS enrichment analysis showed an association with AUD for differential 5mC genes. This study reveals neuronal-specific methylome and hydroxymethylome dysregulation associated with AUD. We replicated previous findings and identified novel associations with AUD for both 5mC and 5hmC marks within the OFC. Our findings provide new insights into the epigenomic dysregulation of AUD in the human brain.

Predicting Heterogeneity in Patient Response to Morphine Treatment for Neonatal Opioid Withdrawal Syndrome

Infants with neonatal opioid withdrawal syndrome commonly receive morphine treatment to manage their withdrawal signs. However, the effectiveness of this pharmacotherapy in managing the infants' withdrawal signs vary widely. We sought to understand how information available early in infant monitoring can anticipate this treatment response, focusing on early modified Finnegan Neonatal Abstinence Scoring System (FNASS) scores, polygenic risk for opioid dependence (polygenic risk score (PRS)), and drug exposure. Using k-means clustering, we divided the 213 infants in our cohort into 3 groups based on their FNASS scores in the 12 hours before and after the initiation of pharmacotherapy. We found that these groups were pairwise significantly different for risk factors, including methadone exposure, and for in-hospital outcomes, including total morphine received, length of stay, and highest FNASS score. Whereas PRS was not predictive of receipt of treatment, PRS was pairwise significantly different between a subset of the groups. Using tree-based machine learning methods, we then constructed network graphs of the relationships among these groups, FNASS scores, PRS, drug exposures, and in-hospital outcomes. The resulting networks also showed meaningful connection between early FNASS scores and PRS, as well as between both of those and later in-hospital outcomes. These analyses present clinicians with the opportunity to better anticipate infant withdrawal progression and prepare accordingly, whether with expedited morphine treatment or non-pharmacotherapeutic alternative treatments.

Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition

The amygdala processes positive and negative valence and contributes to addiction, but the cell-type-specific gene regulatory programs involved are unknown. We generated an atlas of single-nucleus gene expression and chromatin accessibility in the amygdala of outbred rats with high and low cocaine addiction-like behaviors following prolonged abstinence. Differentially expressed genes between the high and low groups were enriched for energy metabolism across cell types. Rats with high addiction index (AI) showed increased relapse-like behaviors and GABAergic transmission in the amygdala. Both phenotypes were reversed by pharmacological inhibition of the glyoxalase 1 enzyme, which metabolizes methylglyoxal-a GABAA receptor agonist produced by glycolysis. Differences in chromatin accessibility between high and low AI rats implicated pioneer transcription factors in the basic helix-loop-helix, FOX, SOX and activator protein 1 families. We observed opposite regulation of chromatin accessibility across many cell types. Most notably, excitatory neurons had greater accessibility in high AI rats and inhibitory neurons had greater accessibility in low AI rats.

Shared genetic basis informs the roles of polyunsaturated fatty acids in brain disorders

The neural tissue is rich in polyunsaturated fatty acids (PUFAs), components that are indispensable for the proper functioning of neurons, such as neurotransmission. PUFA nutritional deficiency and imbalance have been linked to a variety of chronic brain disorders, including major depressive disorder (MDD), anxiety, and anorexia. However, the effects of PUFAs on brain disorders remain inconclusive, and the extent of their shared genetic determinants is largely unknown. Here, we used genome-wide association summary statistics to systematically examine the shared genetic basis between six phenotypes of circulating PUFAs (N = 114,999) and 20 brain disorders (N = 9,725-762,917), infer their potential causal relationships, identify colocalized regions, and pinpoint shared genetic variants. Genetic correlation and polygenic overlap analyses revealed a widespread shared genetic basis for 77 trait pairs between six PUFA phenotypes and 16 brain disorders. Two-sample Mendelian randomization analysis indicated potential causal relationships for 16 pairs of PUFAs and brain disorders, including alcohol consumption, bipolar disorder (BIP), and MDD. Colocalization analysis identified 40 shared loci (13 unique) among six PUFAs and ten brain disorders. Twenty-two unique variants were statistically inferred as candidate shared causal variants, including rs1260326 (GCKR), rs174564 (FADS2) and rs4818766 (ADARB1). These findings reveal a widespread shared genetic basis between PUFAs and brain disorders, pinpoint specific shared variants, and provide support for the potential effects of PUFAs on certain brain disorders, especially MDD, BIP, and alcohol consumption.

The Collaborative Study on the Genetics of Alcoholism: Overview

Alcohol use disorders (AUD) are commonly occurring, heritable and polygenic disorders with etiological origins in the brain and the environment. To outline the causes and consequences of alcohol-related milestones, including AUD, and their related psychiatric comorbidities, the Collaborative Study on the Genetics of Alcoholism (COGA) was launched in 1989 with a gene-brain-behavior framework. COGA is a family based, diverse (~25% self-identified African American, ~52% female) sample, including data on 17,878 individuals, ages 7-97 years, in 2246 families of which a proportion are densely affected for AUD. All participants responded to questionnaires (e.g., personality) and the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) which gathers information on psychiatric diagnoses, conditions and related behaviors (e.g., parental monitoring). In addition, 9871 individuals have brain function data from electroencephalogram (EEG) recordings while 12,009 individuals have been genotyped on genome-wide association study (GWAS) arrays. A series of functional genomics studies examine the specific cellular and molecular mechanisms underlying AUD. This overview provides the framework for the development of COGA as a scientific resource in the past three decades, with individual reviews providing in-depth descriptions of data on and discoveries from behavioral and clinical, brain function, genetic and functional genomics data. The value of COGA also resides in its data sharing policies, its efforts to communicate scientific findings to the broader community via a project website and its potential to nurture early career investigators and to generate independent research that has broadened the impact of gene-brain-behavior research into AUD.

Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism

Binge eating disorder (BED) is the most common eating disorder, yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank data sets. To address this limitation, we apply a supervised machine-learning approach (using 822 cases of individuals diagnosed with BED) to estimate the probability of each individual having BED based on electronic medical records from the Million Veteran Program. We perform a genome-wide association study of individuals of African (n = 77,574) and European (n = 285,138) ancestry while controlling for body mass index to identify three independent loci near the HFE, MCHR2 and LRP11 genes and suggest APOE as a risk gene for BED. We identify shared heritability between BED and several neuropsychiatric traits, and implicate iron metabolism in the pathophysiology of BED. Overall, our findings provide insights into the genetics underlying BED and suggest directions for future translational research.

Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing

Importance

Psychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy.

Objective

To identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes.

Design, Setting, and Participants

This genomic study applied a multivariate transcriptomic method, transcriptome-wide structural equation modeling (T-SEM), to investigate gene expression patterns associated with 5 genomic factors indexing shared risk across 13 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. The Broad Institute Connectivity Map Drug Repurposing Database and Drug-Gene Interaction Database public databases of drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk. Data were collected from database inception up to February 20, 2023.

Main Outcomes and Measures

Gene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes.

Results

In total, T-SEM identified 466 genes whose expression was significantly associated (z ≥ 5.02) with genomic factors and 36 genes with disorder-specific effects. Most associated genes were found for a thought disorders factor, defined by bipolar disorder and schizophrenia. Several existing pharmacological interventions were identified that could be repurposed to target genes whose expression was associated with the thought disorders factor or a transdiagnostic p factor defined by all 13 disorders.

Conclusions and Relevance

The findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.

Testing the causal relationships of physical activity and sedentary behaviour with mental health and substance use disorders: a Mendelian randomisation study

Observational studies suggest that physical activity can reduce the risk of mental health and substance use disorders. However, it is unclear whether this relationship is causal or explained by confounding bias (e.g., common underlying causes or reverse causality). We investigated the bidirectional causal relationship of physical activity (PA) and sedentary behaviour (SB) with ten mental health and substance use disorders, applying two-sample Mendelian Randomisation (MR). Genetic instruments for the exposures and outcomes were derived from the largest available, non-overlapping genome-wide association studies (GWAS). Summary-level data for objectively assessed PA (accelerometer-based average activity, moderate activity, and walking) and SB and self-reported moderate-to-vigorous PA were obtained from the UK Biobank. Data for mental health/substance use disorders were obtained from the Psychiatric Genomics Consortium and the GWAS and Sequencing Consortium of Alcohol and Nicotine Use. MR estimates were combined using inverse variance weighted meta-analysis (IVW). Sensitivity analyses were conducted to assess the robustness of the results. Accelerometer-based average PA was associated with a lower risk of depression (b = -0.043, 95% CI: -0.071 to -0.016, effect size[OR] = 0.957) and cigarette smoking (b = -0.026; 95% CI: -0.035 to -0.017, effect size[β] = -0.022). Accelerometer-based SB decreased the risk of anorexia (b = -0.341, 95% CI: -0.530 to -0.152, effect size[OR] = 0.711) and schizophrenia (b = -0.230; 95% CI: -0.285 to -0.175, effect size[OR] = 0.795). However, we found evidence of reverse causality in the relationship between SB and schizophrenia. Further, PTSD, bipolar disorder, anorexia, and ADHD were all associated with increased PA. This study provides evidence consistent with a causal protective effect of objectively assessed but not self-reported PA on reduced depression and cigarette smoking. Objectively assessed SB had a protective relationship with anorexia. Enhancing PA may be an effective intervention strategy to reduce depressive symptoms and addictive behaviours, while promoting sedentary or light physical activities may help to reduce the risk of anorexia in at-risk individuals.

Cross-phenotype relationship between opioid use disorder and suicide attempts: new evidence from polygenic association and Mendelian randomization analyses

Clinical epidemiological studies have found high co-occurrence between suicide attempts (SA) and opioid use disorder (OUD). However, the patterns of correlation and causation between them are still not clear due to psychiatric confounding. To investigate their cross-phenotype relationship, we utilized raw phenotypes and genotypes from >150,000 UK Biobank samples, and genome-wide association summary statistics from >600,000 individuals with European ancestry. Pairwise association and a potential bidirectional relationship between OUD and SA were evaluated with and without controlling for major psychiatric disease status (e.g., schizophrenia, major depressive disorder, and alcohol use disorder). Multiple statistical and genetics tools were used to perform epidemiological association, genetic correlation, polygenic risk score prediction, and Mendelian randomizations (MR) analyses. Strong associations between OUD and SA were observed at both the phenotypic level (overall samples [OR = 2.94, P = 1.59 ×10-14]; non-psychiatric subgroup [OR = 2.15, P = 1.07 ×10-3]) and the genetic level (genetic correlation rg = 0.38 and 0.5 with or without conditioning on psychiatric traits, respectively). Consistently, increasing polygenic susceptibility to SA is associated with increasing risk of OUD (OR = 1.08, false discovery rate [FDR] =1.71 ×10-3), and similarly, increasing polygenic susceptibility to OUD is associated with increasing risk of SA (OR = 1.09, FDR = 1.73 ×10-6). However, these polygenic associations were much attenuated after controlling for comorbid psychiatric diseases. A combination of MR analyses suggested a possible causal association from genetic liability for SA to OUD risk (2-sample univariable MR: OR = 1.14, P = 0.001; multivariable MR: OR = 1.08, P = 0.001). This study provided new genetic evidence to explain the observed OUD-SA comorbidity. Future prevention strategies for each phenotype needs to take into consideration of screening for the other one.

Identifying potential risk genes and pathways for neuropsychiatric and substance use disorders using intermediate molecular mediator information

Neuropsychiatric and substance use disorders (NPSUDs) have a complex etiology that includes environmental and polygenic risk factors with significant cross-trait genetic correlations. Genome-wide association studies (GWAS) of NPSUDs yield numerous association signals. However, for most of these regions, we do not yet have a firm understanding of either the specific risk variants or the effects of these variants. Post-GWAS methods allow researchers to use GWAS summary statistics and molecular mediators (transcript, protein, and methylation abundances) infer the effect of these mediators on risk for disorders. One group of post-GWAS approaches is commonly referred to as transcriptome/proteome/methylome-wide association studies, which are abbreviated as T/P/MWAS (or collectively as XWAS). Since these approaches use biological mediators, the multiple testing burden is reduced to the number of genes (∼20,000) instead of millions of GWAS SNPs, which leads to increased signal detection. In this work, our aim is to uncover likely risk genes for NPSUDs by performing XWAS analyses in two tissues-blood and brain. First, to identify putative causal risk genes, we performed an XWAS using the Summary-data-based Mendelian randomization, which uses GWAS summary statistics, reference xQTL data, and a reference LD panel. Second, given the large comorbidities among NPSUDs and the shared cis-xQTLs between blood and the brain, we improved XWAS signal detection for underpowered analyses by performing joint concordance analyses between XWAS results i) across the two tissues and ii) across NPSUDs. All XWAS signals i) were adjusted for heterogeneity in dependent instruments (HEIDI) (non-causality) p-values and ii) used to test for pathway enrichment. The results suggest that there were widely shared gene/protein signals within the major histocompatibility complex region on chromosome 6 (BTN3A2 and C4A) and elsewhere in the genome (FURIN, NEK4, RERE, and ZDHHC5). The identification of putative molecular genes and pathways underlying risk may offer new targets for therapeutic development. Our study revealed an enrichment of XWAS signals in vitamin D and omega-3 gene sets. So, including vitamin D and omega-3 in treatment plans may have a modest but beneficial effect on patients with bipolar disorder.

Transcriptional signatures of heroin intake and relapse throughout the brain reward circuitry in male mice

Opioid use disorder (OUD) looms as one of the most severe medical crises facing society. More effective therapeutics will require a deeper understanding of molecular changes supporting drug-taking and relapse. Here, we develop a brain reward circuit-wide atlas of opioid-induced transcriptional regulation by combining RNA sequencing (RNA-seq) and heroin self-administration in male mice modeling multiple OUD-relevant conditions: acute heroin exposure, chronic heroin intake, context-induced drug-seeking following abstinence, and relapse. Bioinformatics analysis of this rich dataset identified numerous patterns of transcriptional regulation, with both region-specific and pan-circuit biological domains affected by heroin. Integration of RNA-seq data with OUD-relevant behavioral outcomes uncovered region-specific molecular changes and biological processes that predispose to OUD vulnerability. Comparisons with human OUD RNA-seq and genome-wide association study data revealed convergent molecular abnormalities and gene candidates with high therapeutic potential. These studies outline molecular reprogramming underlying OUD and provide a foundational resource for future investigations into mechanisms and treatment strategies.

Exploratory factor analysis of shared and specific genetic associations in depression and anxiety

BACKGROUND

Previous genetic studies of anxiety and depression were mostly based on independent phenotypes. This study aims to investigate the shared and specific genetic structure between anxiety and depression.

METHOD

To identify the underlying factors of Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and their combined scale (joint scale), we employed exploratory factor analysis (EFA) using the eigenvalue of parallel analysis. Subsequently, we conducted a genome-wide association study (GWAS) for these factors. In addition, we utilized LD Score Regression (LDSC) to determine the genetic correlations between the identified factors and four common mental disorders, three sleep phenotypes, and other traits that have been previously linked to anxiety and depression.

RESULTS

The EFA uncovered two factors for the GAD-7 scale, namely nervousness and disturbance, two factors for the PHQ-9 scale, namely negative affect and sleep/appetite disturbance, and four factors for the joint scale, specifically nervousness, anhedonia, sleep/appetite disturbance, and fidget. We identified two genome-wide significant genomic loci, with overlap across GAD-7 factor 1 and joint scale factor 1: rs148579586 (P_GAD-7 = 1.365 × 10^-09, P_{Joint scale} = 1.434 × 1^0-09) and rs201074060 (P_GAD-7 = 3.672 × 10^-09, P_{Joint scale} = 3.824 × 10^-09). Genetic correlations in factors ranged from 0.722 to 1.000 (all p < 1.786 × 10^-3) with 27 of 28 correlations being significantly smaller than one. The genetic correlations with external phenotypes showed small variation across the eight factors.

CONCLUSION

Unidimensional structures can provide more precise scores, which can aid in identifying the shared and specific genetic associations between anxiety and depression. This is a crucial step in characterizing the genetic structure of these conditions and their co-occurrence.

Induction of dopaminergic neurons for neuronal subtype-specific modeling of psychiatric disease risk

Dopaminergic neurons are critical to movement, mood, addiction, and stress. Current techniques for generating dopaminergic neurons from human induced pluripotent stem cells (hiPSCs) yield heterogenous cell populations with variable purity and inconsistent reproducibility between donors, hiPSC clones, and experiments. Here, we report the rapid (5 weeks) and efficient (~90%) induction of induced dopaminergic neurons (iDANs) through transient overexpression of lineage-promoting transcription factors combined with stringent selection across five donors. We observe maturation-dependent increase in dopamine synthesis and electrophysiological properties consistent with midbrain dopaminergic neuron identity, such as slow-rising after- hyperpolarization potentials, an action potential duration of ~3 ms, tonic sub-threshold oscillatory activity, and spontaneous burst firing at a frequency of ~1.0-1.75 Hz. Transcriptome analysis reveals robust expression of genes involved in fetal midbrain dopaminergic neuron identity. Specifically expressed genes in iDANs, as well as those from isogenic induced GABAergic and glutamatergic neurons, were enriched in loci conferring heritability for cannabis use disorder, schizophrenia, and bipolar disorder; however, each neuronal subtype demonstrated subtype-specific heritability enrichments in biologically relevant pathways, and iDANs alone were uniquely enriched in autism spectrum disorder risk loci. Therefore, iDANs provide a critical tool for modeling midbrain dopaminergic neuron development and dysfunction in psychiatric disease.

Specificity in genetic and environmental risk for prescription opioid misuse and heroin use

BACKGROUND

Many studies aggregate prescription opioid misuse (POM) and heroin use into a single phenotype, but emerging evidence suggests that their genetic and environmental influences may be partially distinct.

METHODS

In total, 7164 individual twins (84.12% complete pairs; 59.81% female; mean age = 30.58 years) from the Australian Twin Registry reported their lifetime misuse of prescription opioids, stimulants, and sedatives, and lifetime use of heroin, cannabis, cocaine/crack, illicit stimulants, hallucinogens, inhalants, solvents, and dissociatives via telephone interview. Independent pathway models (IPMs) and common pathway models (CPMs) partitioned the variance of drug use phenotypes into general and drug-specific genetic (a), common environmental (c), and unique environmental factors (e).

RESULTS

An IPM with one general a and one general e factor and a one-factor CPM provided comparable fit to the data. General factors accounted for 55% (a = 14%, e = 41%) and 79% (a = 64%, e = 15%) of the respective variation in POM and heroin use in the IPM, and 25% (a = 12%, c = 8%, e = 5%) and 80% (a = 38%, c = 27%, e = 15%) of the respective variation in POM and heroin use in the CPM. Across both models, POM emerged with substantial drug-specific genetic influence (26-39% of total phenotypic variance; 69-74% of genetic variance); heroin use did not (0% of total phenotypic variance; 0% of genetic variance in both models). Prescription sedative misuse also demonstrated significant drug-specific genetic variance.

CONCLUSIONS

Genetic variation in POM, but not heroin use, is predominantly drug-specific. Misuse of prescription medications that reduce experiences of subjective distress may be partially influenced by sources of genetic variation separate from illicit drug use.

The Concept of Resistance to Substance Use and a Research Approach: The Resist! Project

Illicit substance use is dangerous in both acute and chronic forms, frequently resulting in lethal poisoning, addiction, and other negative consequences. Similar to research in other psychiatric conditions, whose ultimate goal is to enable effective prevention and treatment, studies in substance use are focused on factors elevating the risk for the disorder. The rapid growth of the substance use problem despite the effort invested in fighting it, however, suggests the need in changing the research approach. Instead of attempting to identify risk factors, whose neutralization is often infeasible if not impossible, it may be more promising to systematically reverse the perspective to the factors enhancing the aspect of liability to disorder that shares the same dimension but is opposite to risk, that is, resistance to substance use. Resistance factors, which enable the majority of the population to remain unaffected despite the ubiquity of psychoactive substances, may be more amenable to translation. While the resistance aspect of liability is symmetric to risk, the resistance approach requires substantial changes in sampling (high-resistance rather than high-risk) and using quantitative indices of liability. This article provides an overview and a practical approach to research in resistance to substance use/addiction, currently implemented in a NIH-funded project. The project benefits from unique opportunities afforded by the data originating from two longitudinal twin studies, the Virginia Twin Study of Adolescent and Behavioral Development and the Minnesota Twin Family Study. The methodology described is also applicable to other psychiatric disorders.

A review of the genomics of neonatal abstinence syndrome

Neonatal abstinence syndrome (NAS) is a constellation of signs of withdrawal occurring after birth following in utero exposure to licit or illicit opioids. Despite significant research and public health efforts, NAS remains challenging to diagnose, predict, and manage due to highly variable expression. Biomarker discovery in the field of NAS is crucial for stratifying risk, allocating resources, monitoring longitudinal outcomes, and identifying novel therapeutics. There is considerable interest in identifying important genetic and epigenetic markers of NAS severity and outcome that can guide medical decision making, research efforts, and public policy. A number of recent studies have suggested that genetic and epigenetic changes are associated with NAS severity, including evidence of neurodevelopmental instability. This review will provide an overview of the role of genetics and epigenetics in short and longer-term NAS outcomes. We will also describe novel research efforts using polygenic risk scores for NAS risk stratification and salivary gene expression to understand neurobehavioral modulation. Finally, emerging research focused on neuroinflammation from prenatal opioid exposure may elucidate novel mechanisms that could lead to development of future novel therapeutics.

Polygenic scores for psychiatric disorders in a diverse postmortem brain tissue cohort

A new era of human postmortem tissue research has emerged thanks to the development of 'omics technologies that measure genes, proteins, and spatial parameters in unprecedented detail. Also newly possible is the ability to construct polygenic scores, individual-level metrics of genetic risk (also known as polygenic risk scores/PRS), based on genome-wide association studies, GWAS. Here, we report on clinical, educational, and brain gene expression correlates of polygenic scores in ancestrally diverse samples from the Human Brain Collection Core (HBCC). Genotypes from 1418 donors were subjected to quality control filters, imputed, and used to construct polygenic scores. Polygenic scores for schizophrenia predicted schizophrenia status in donors of European ancestry (p = 4.7 × 10^-8, 17.2%) and in donors with African ancestry (p = 1.6 × 10^-5, 10.4% of phenotypic variance explained). This pattern of higher variance explained among European ancestry samples was also observed for other psychiatric disorders (depression, bipolar disorder, substance use disorders, anxiety disorders) and for height, body mass index, and years of education. For a subset of 223 samples, gene expression from dorsolateral prefrontal cortex (DLPFC) was available through the CommonMind Consortium. In this subgroup, schizophrenia polygenic scores also predicted an aggregate gene expression score for schizophrenia (European ancestry: p = 0.0032, African ancestry: p = 0.15). Overall, polygenic scores performed as expected in ancestrally diverse samples, given historical biases toward use of European ancestry samples and variable predictive power of polygenic scores across phenotypes. The transcriptomic results reported here suggest that inherited schizophrenia genetic risk influences gene expression, even in adulthood. For future research, these and additional polygenic scores are being made available for analyses, and for selecting samples, using postmortem tissue from the Human Brain Collection Core.

Transcriptional signatures of heroin intake and seeking throughout the brain reward circuit

Opioid use disorder (OUD) looms as one of the most severe medical crises currently facing society. More effective therapeutics for OUD requires in-depth understanding of molecular changes supporting drug-taking and relapse. Recent efforts have helped advance these aims, but studies have been limited in number and scope. Here, we develop a brain reward circuit-wide atlas of opioid-induced transcriptional regulation by combining RNA sequencing (RNAseq) and heroin self-administration in male mice modeling multiple OUD-relevant conditions: acute heroin exposure, chronic heroin intake, context-induced drug-seeking following prolonged abstinence, and heroin-primed drug-seeking (i.e., "relapse"). Bioinformatics analysis of this rich dataset identified numerous patterns of molecular changes, transcriptional regulation, brain-region-specific involvement in various aspects of OUD, and both region-specific and pan-circuit biological domains affected by heroin. Integrating RNAseq data with behavioral outcomes using factor analysis to generate an "addiction index" uncovered novel roles for particular brain regions in promoting addiction-relevant behavior, and implicated multi-regional changes in affected genes and biological processes. Comparisons with RNAseq and genome-wide association studies from humans with OUD reveal convergent molecular regulation that are implicated in drug-taking and relapse, and point to novel gene candidates with high therapeutic potential for OUD. These results outline broad molecular reprogramming that may directly promote the development and maintenance of OUD, and provide a foundational resource to the field for future research into OUD mechanisms and treatment strategies.

The Genetically Informed Neurobiology of Addiction (GINA) model

Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.

Substance abuse and the risk of severe COVID-19: Mendelian randomization confirms the causal role of opioids but hints a negative causal effect for cannabinoids

Since the start of the COVID-19 global pandemic, our understanding of the underlying disease mechanism and factors associated with the disease severity has dramatically increased. A recent study investigated the relationship between substance use disorders (SUD) and the risk of severe COVID-19 in the United States and concluded that the risk of hospitalization and death due to COVID-19 is directly correlated with substance abuse, including opioid use disorder (OUD) and cannabis use disorder (CUD). While we found this analysis fascinating, we believe this observation may be biased due to comorbidities (such as hypertension, diabetes, and cardiovascular disease) confounding the direct effect of SUD on severe COVID-19 illness. To answer this question, we sought to investigate the causal relationship between substance abuse and medication-taking history (as a proxy trait for comorbidities) with the risk of COVID-19 adverse outcomes. Our Mendelian randomization analysis confirms the causal relationship between OUD and severe COVID-19 illness but suggests an inverse causal effect for cannabinoids. Considering that COVID-19 mortality is largely attributed to disturbed immune regulation, the possible modulatory impact of cannabinoids in alleviating cytokine storms merits further investigation.

Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond

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.

Genetic overlap between mood instability and alcohol-related phenotypes suggests shared biological underpinnings

Alcohol use disorder (AUD) is a pervasive and devastating mental illness with high comorbidity rates with other mental disorders. Understanding the genetic architecture of this comorbidity could be improved by focusing on intermediate traits that show positive genetic correlation with the disorders. Thus, we aimed to characterize the shared vs. unique polygenicity of AUD, alcohol consumption (AC) and mood instability (MOOD) -beyond genetic correlation, and boost discovery for jointly-associated loci. Summary statistics for MOOD (a binary measure of the tendency to report frequent mood swings), AC (number of standard drinks over a typical consumption week) and AUD GWASs (Ns > 200,000) were analyzed to characterize the cross-phenotype associations between MOOD and AC, MOOD and AUD and AC and AUD. To do so, we used a newly established pipeline that combines (i) the bivariate causal mixture model (MiXeR) to quantify polygenic overlap and (ii) the conjunctional false discovery rate (conjFDR) to discover specific jointly associated genomic loci, which were mapped to genes and biological functions. MOOD was highly polygenic (10.4k single nucleotide polymorphisms, SNPs, SD = 2k) compared to AC (4.9k SNPs, SD = 0.6k) and AUD (4.3k SNPs, SD = 2k). The polygenic overlap of MOOD and AC was twice that of MOOD and AUD (98% vs. 49%), with opposite genetic correlation (-0.2 vs. 0.23), as confirmed in independent samples. MOOD&AUD associated SNPs were significantly enriched for brain genes, conversely to MOOD&AC. Among 38 jointly associated loci, fifteen were novel for MOOD, AC and AUD. MOOD, AC and AUD were also strongly associated at the phenotypic level. Overall, using multilevel polygenic quantification, joint loci discovery and functional annotation methods, we evidenced that the polygenic overlap between MOOD and AC/AUD implicated partly shared biological underpinnings, yet, clearly distinct functional patterns between MOOD&AC and MOOD&AUD, suggesting new mechanisms for the comorbidity of AUD with mood disorders.

Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects in brain regions associated with addiction

Despite an estimated heritability of ~50%, genome-wide association studies of opioid use disorder (OUD) have revealed few genome-wide significant loci. We conducted a cross-ancestry meta-analysis of OUD in the Million Veteran Program (N = 425,944). In addition to known exonic variants in OPRM1 and FURIN, we identified intronic variants in RABEPK, FBXW4, NCAM1 and KCNN1. A meta-analysis including other datasets identified a locus in TSNARE1. In total, we identified 14 loci for OUD, 12 of which are novel. Significant genetic correlations were identified for 127 traits, including psychiatric disorders and other substance use-related traits. The only significantly enriched cell-type group was CNS, with gene expression enrichment in brain regions previously associated with substance use disorders. These findings increase our understanding of the biological basis of OUD and provide further evidence that it is a brain disease, which may help to reduce stigma and inform efforts to address the opioid epidemic.

Genome-wide association study in individuals of European and African ancestry and multi-trait analysis of opioid use disorder identifies 19 independent genome-wide significant risk loci

Despite the large toll of opioid use disorder (OUD), genome-wide association studies (GWAS) of OUD to date have yielded few susceptibility loci. We performed a large-scale GWAS of OUD in individuals of European (EUR) and African (AFR) ancestry, optimizing genetic informativeness by performing MTAG (Multi-trait analysis of GWAS) with genetically correlated substance use disorders (SUDs). Meta-analysis included seven cohorts: the Million Veteran Program, Psychiatric Genomics Consortium, iPSYCH, FinnGen, Partners Biobank, BioVU, and Yale-Penn 3, resulting in a total N = 639,063 (Ncases = 20,686;Neffective = 77,026) across ancestries. OUD cases were defined as having a lifetime OUD diagnosis, and controls as anyone not known to meet OUD criteria. We estimated SNP-heritability (h2SNP) and genetic correlations (rg). Based on genetic correlation, we performed MTAG on OUD, alcohol use disorder (AUD), and cannabis use disorder (CanUD). A leave-one-out polygenic risk score (PRS) analysis was performed to compare OUD and OUD-MTAG PRS as predictors of OUD case status in Yale-Penn 3. The EUR meta-analysis identified three genome-wide significant (GWS; p ≤ 5 × 10-8) lead SNPs-one at FURIN (rs11372849; p = 9.54 × 10-10) and two OPRM1 variants (rs1799971, p = 4.92 × 10-09; rs79704991, p = 1.11 × 10-08; r2 = 0.02). Rs1799971 (p = 4.91 × 10-08) and another OPRM1 variant (rs9478500; p = 1.95 × 10-08; r2 = 0.03) were identified in the cross-ancestry meta-analysis. Estimated h2SNP was 12.75%, with strong rg with CanUD (rg = 0.82; p = 1.14 × 10-47) and AUD (rg = 0.77; p = 6.36 × 10-78). The OUD-MTAG resulted in a GWAS Nequivalent = 128,748 and 18 independent GWS loci, some mapping to genes or gene regions that have previously been associated with psychiatric or addiction phenotypes. The OUD-MTAG PRS accounted for 3.81% of OUD variance (beta = 0.61;s.e. = 0.066; p = 2.00 × 10-16) compared to 2.41% (beta = 0.45; s.e. = 0.058; p = 2.90 × 10-13) explained by the OUD PRS. The current study identified OUD variant associations at OPRM1, single variant associations with FURIN, and 18 GWS associations in the OUD-MTAG. The genetic architecture of OUD is likely influenced by both OUD-specific loci and loci shared across SUDs.

Pairwise genetic meta-analyses between schizophrenia and substance dependence phenotypes reveals novel association signals with pharmacological significance

Almost half of individuals diagnosed with schizophrenia also present with a substance use disorder, however, little is known about potential molecular mechanisms underlying this comorbidity. We used genetic analyses to enhance our understanding of the molecular overlap between these conditions. Our analyses revealed a positive genetic correlation between schizophrenia and the following dependence phenotypes: alcohol (rg = 0.368, SE = 0.076, P = 1.61 × 10-6), cannabis use disorder (rg = 0.309, SE = 0.033, P = 1.97 × 10-20) and nicotine (rg = 0.117, SE = 0.043, P = 7.0 × 10-3), as well as drinks per week (rg = 0.087, SE = 0.021, P = 6.36 × 10-5), cigarettes per day (rg = 0.11, SE = 0.024, P = 4.93 × 10-6) and life-time cannabis use (rg = 0.234, SE = 0.029, P = 3.74 × 10-15). We further constructed latent causal variable (LCV) models to test for partial genetic causality and found evidence for a potential causal relationship between alcohol dependence and schizophrenia (GCP = 0.6, SE = 0.22, P = 1.6 × 10-3). This putative causal effect with schizophrenia was not seen using a continuous phenotype of drinks consumed per week, suggesting that distinct molecular mechanisms underlying dependence are involved in the relationship between alcohol and schizophrenia. To localise the specific genetic overlap between schizophrenia and substance use disorders (SUDs), we conducted a gene-based and gene-set pairwise meta-analysis between schizophrenia and each of the four individual substance dependence phenotypes in up to 790,806 individuals. These bivariate meta-analyses identified 44 associations not observed in the individual GWAS, including five shared genes that play a key role in early central nervous system development. The results from this study further supports the existence of underlying shared biology that drives the overlap in substance dependence in schizophrenia, including specific biological systems related to metabolism and neuronal function.

Association of polygenic risk scores and hair cortisol with mental health trajectories during COVID lockdown

The COVID-19 pandemic is a global stressor with inter-individually differing influences on mental health trajectories. Polygenic Risk Scores (PRSs) for psychiatric phenotypes are associated with individual mental health predispositions. Elevated hair cortisol concentrations (HCC) and high PRSs are related to negative mental health outcomes. We analyzed whether PRSs and HCC are related to different mental health trajectories during the first COVID lockdown in Germany. Among 523 participants selected from the longitudinal resilience assessment study (LORA), we previously reported three subgroups (acute dysfunction, delayed dysfunction, resilient) based on weekly mental health (GHQ-28) assessment during COVID lockdown. DNA from blood was collected at the baseline of the original LORA study (n = 364) and used to calculate the PRSs of 12 different psychopathological phenotypes. An explorative bifactor model with Schmid-Leiman transformation was calculated to extract a general genetic factor for psychiatric disorders. Hair samples were collected quarterly prior to the pandemic for determining HCC (n = 192). Bivariate logistic regressions were performed to test the associations of HCC and the PRS factors with the reported trajectories. The bifactor model revealed 1 general factor and 4 sub-factors. Results indicate a significant association between increased values on the general risk factor and the allocation to the acute dysfunction class. The same was found for elevated HCC and the exploratorily tested sub-factor "childhood-onset neurodevelopmental disorders". Genetic risk and long-term cortisol secretion as a potential indicator of stress, indicated by PRSs and HCC, respectively, predicted different mental health trajectories. Results indicate a potential for future studies on risk prediction.

Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data

Introduction

Opioid use disorders (OUDs) constitute a major public health issue, and we urgently need alternative methods for characterizing risk for OUD. Electronic health records (EHRs) are useful tools for understanding complex medical phenotypes but have been underutilized for OUD because of challenges related to underdiagnosis, binary diagnostic frameworks, and minimally characterized reference groups. As a first step in addressing these challenges, a new paradigm is warranted that characterizes risk for opioid prescription misuse on a continuous scale of severity, i.e., as a continuum.

Methods

Across sites within the PsycheMERGE network, we extracted prescription opioid data and diagnoses that co-occur with OUD (including psychiatric and substance use disorders, pain-related diagnoses, HIV, and hepatitis C) for over 2.6 million patients across three health registries (Vanderbilt University Medical Center, Mass General Brigham, Geisinger) between 2005 and 2018. We defined three groups based on levels of opioid exposure: no prescriptions, minimal exposure, and chronic exposure and then compared the comorbidity profiles of these groups to the full registries and to those with OUD diagnostic codes.

Results

Our results confirm that EHR data reflects known higher prevalence of substance use disorders, psychiatric disorders, medical, and pain diagnoses in patients with OUD diagnoses and chronic opioid use. Comorbidity profiles that distinguish opioid exposure are strikingly consistent across large health systems, indicating the phenotypes described in this new quantitative framework are robust to health systems differences.

Conclusion

This work indicates that EHR prescription opioid data can serve as a platform to characterize complex risk markers for OUD using existing data.

The addiction risk factor: A unitary genetic vulnerability characterizes substance use disorders and their associations with common correlates

Substance use disorders commonly co-occur with one another and with other psychiatric disorders. They share common features including high impulsivity, negative affect, and lower executive function. We tested whether a common genetic factor undergirds liability to problematic alcohol use (PAU), problematic tobacco use (PTU), cannabis use disorder (CUD), and opioid use disorder (OUD) by applying genomic structural equation modeling to genome-wide association study summary statistics for individuals of European ancestry (Total N = 1,019,521; substance-specific Ns range: 82,707-435,563) while adjusting for the genetics of substance use (Ns = 184,765-632,802). We also tested whether shared liability across SUDs is associated with behavioral constructs (risk-taking, executive function, neuroticism; Ns = 328,339-427,037) and non-substance use psychopathology (psychotic, compulsive, and early neurodevelopmental disorders). Shared genetic liability to PAU, PTU, CUD, and OUD was characterized by a unidimensional addiction risk factor (termed The Addiction-Risk-Factor, independent of substance use. OUD and CUD demonstrated the largest loadings, while problematic tobacco use showed the lowest loading. The Addiction-Risk-Factor was associated with risk-taking, neuroticism, executive function, and non-substance psychopathology, but retained specific variance before and after accounting for the genetics of substance use. Thus, a common genetic factor partly explains susceptibility for alcohol, tobacco, cannabis, and opioid use disorder. The Addiction-Risk-Factor has a unique genetic architecture that is not shared with normative substance use or non-substance psychopathology, suggesting that addiction is not the linear combination of substance use and psychopathology.

Substance use and common contributors to morbidity: A genetics perspective

Excessive substance use and substance use disorders (SUDs) are common, serious and relapsing medical conditions. They frequently co-occur with other diseases that are leading contributors to disability worldwide. While heavy substance use may potentiate the course of some of these illnesses, there is accumulating evidence suggesting common genetic architectures. In this narrative review, we focus on four heritable medical conditions - cardiometabolic disease, chronic pain, depression and COVID-19, which are commonly overlapping with, but not necessarily a direct consequence of, SUDs. We find persuasive evidence of underlying genetic liability that predisposes to both SUDs and chronic pain, depression, and COVID-19. For cardiometabolic disease, there is greater support for a potential causal influence of problematic substance use. Our review encourages de-stigmatization of SUDs and the assessment of substance use in clinical settings. We assert that identifying shared pathways of risk has high translational potential, allowing tailoring of treatments for multiple medical conditions. FUNDING: SSR acknowledges T29KT0526, T32IR5226 and DP1DA054394; RLK acknowledges AA028292; AA acknowledges DA054869 & K02DA032573. The funders had no role in the conceptualization or writing of the paper.

Common and distinguishing genetic factors for substance use behavior and disorder: an integrated analysis of genomic and transcriptomic studies from both human and animal studies

BACKGROUND AND AIMS

Genomic and transcriptomic findings greatly broaden the biological knowledge regarding substance use. However, systematic convergence and comparison evidence of genome-wide findings is lacking for substance use. Here, we combined all the genome-wide findings from both substance use behavior and disorder (SUBD) and identified common and distinguishing genetic factors for different SUBDs.

METHODS

Systemic literature search for genome-wide association (GWAS) and RNA-seq studies of alcohol/nicotine/drug use behavior (partially meets or not reported diagnostic criteria) and alcohol use behavior and disorder (AUBD), nicotine use behavior and disorder (NUBD) and drug use behavior and disorder (DUBD) was performed using PubMed and the GWAS catalog. Drug use was focused upon cannabis, opioid, cocaine and methamphetamine use. GWAS studies required case-control or case/cohort samples. RNA-seq studies were based on brain tissues. The genes which contained significant single nucleotide polymorphism (P ≤ 1 × 10^-6 ) in GWAS and reported as significant in RNA-seq studies were extracted. Pathway enrichment was performed by using Metascape. Gene interaction networks were identified by using the Protein Interaction Network Analysis database.

RESULTS

Total SUBD-related 2910 genes were extracted from 75 GWAS studies (2 773 889 participants) and 17 RNA-seq studies. By overlapping the genes and pathways of AUBD, NUBD and DUBD, four shared genes (CACNB2, GRIN2B, PLXDC2 and PKNOX2), four shared pathways [two Gene Ontology (GO) terms of 'modulation of chemical synaptic transmission', 'regulation of trans-synaptic signaling', two Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of 'dopaminergic synapse', 'cocaine addiction'] were identified (significantly higher than random, P < 1 × 10^-5 ). The top shared KEGG pathways (Benjamini-Hochberg-corrected P-value < 0.05) in the pairwise comparison of AUBD versus DUBD, NUBD versus DUBD, AUBD versus NUBD were 'Epstein-Barr virus infection', 'protein processing in endoplasmic reticulum' and 'neuroactive ligand-receptor interaction', respectively. We also identified substance-specific genetic factors: i.e. ADH1B and ALDH2 were unique for AUBD, while CHRNA3 and CHRNA4 were unique for NUBD.

CONCLUSIONS

This systematic review identifies the shared and unique genes and pathways for alcohol, nicotine and drug use behaviors and disorders at the genome-wide level and highlights critical biological processes for the common and distinguishing vulnerability of substance use behaviors and disorders.

Genetics in psychiatry: Methods, clinical applications and future perspectives

Psychiatric disorders and related traits have a demonstrated genetic component, with heritability estimated by twin studies generally between 80% and 40%. Their pathogenesis is complex and multi-determined: environmental factors interact with a polygenic architecture, making difficult the development of models able to stratify patients or predict mental health outcomes. Despite this difficult challenge, relevant progress has been made in the field of psychiatric genetics in recent years. This review aims to present the main current methods in psychiatric genetics, their output, limitations, clinical applications, and possible future developments. Genome-wide association studies (GWASs) performed in increasingly large samples have led to the identification of replicated genetic loci associated with the risk of major psychiatric disorders, including schizophrenia and mood disorders. Statistical and biological approaches have been developed to improve our understanding of the etiopathogenetic mechanisms behind genome-wide significant associations, as well as for estimating the cumulative effect of risk variants at the individual level and the genetic overlap between different disorders, as pleiotropy is the rule rather than the exception. Clinical applications are available in the pharmacogenetics field. The main issues that remain to be addressed include improving ethnic diversity in genetic studies and the optimization of statistical power through methodological improvements, such as the definition of dimensional phenotypes with specific biological correlates and the integration of different types of omics data.

Multi-omics signatures of alcohol use disorder in the dorsal and ventral striatum

Alcohol Use Disorder (AUD) is a major contributor to global mortality and morbidity. Postmortem human brain tissue enables the investigation of molecular mechanisms of AUD in the neurocircuitry of addiction. We aimed to identify differentially expressed (DE) genes in the ventral and dorsal striatum between individuals with AUD and controls, and to integrate the results with findings from genome- and epigenome-wide association studies (GWAS/EWAS) to identify functionally relevant molecular mechanisms of AUD. DNA-methylation and gene expression (RNA-seq) data was generated from postmortem brain samples of 48 individuals with AUD and 51 controls from the ventral striatum (VS) and the dorsal striatal regions caudate nucleus (CN) and putamen (PUT). We identified DE genes using DESeq2, performed gene-set enrichment analysis (GSEA), and tested enrichment of DE genes in results of GWASs using MAGMA. Weighted correlation network analysis (WGCNA) was performed for DNA-methylation and gene expression data and gene overlap was tested. Differential gene expression was observed in the dorsal (FDR < 0.05), but not the ventral striatum of AUD cases. In the VS, DE genes at FDR < 0.25 were overrepresented in a recent GWAS of problematic alcohol use. The ARHGEF15 gene was upregulated in all three brain regions. GSEA in CN and VS pointed towards cell-structure associated GO-terms and in PUT towards immune pathways. The WGCNA modules most strongly associated with AUD showed strong enrichment for immune response and inflammation pathways. Our integrated analysis of multi-omics data sets provides further evidence for the importance of immune- and inflammation-related processes in AUD.

Epigenetic Signatures of Smoking in Five Brain Regions

(1) Background: Epigenome-wide association studies (EWAS) in peripheral blood have repeatedly found associations between tobacco smoking and aberrant DNA methylation (DNAm), but little is known about DNAm signatures of smoking in the human brain, which may contribute to the pathophysiology of addictive behavior observed in chronic smokers. (2) Methods: We investigated the similarity of DNAm signatures in matched blood and postmortem brain samples (n = 10). In addition, we performed EWASs in five brain regions belonging to the neurocircuitry of addiction: anterior cingulate cortex (ACC), Brodmann Area 9, caudate nucleus, putamen, and ventral striatum (n = 38-72). (3) Results: cg15925993 within the LOC339975 gene was epigenome-wide significant in the ACC. Of 16 identified differentially methylated regions, two (PRSS50 and LINC00612/A2M-AS1) overlapped between multiple brain regions. Functional enrichment was detected for biological processes related to neuronal development, inflammatory signaling and immune cell migration. Additionally, our results indicate the association of the well-known AHRR CpG site cg05575921 with smoking in the brain. (4) Conclusion: The present study provides further evidence of the strong relationship between aberrant DNAm and smoking.

Molecular rhythm alterations in prefrontal cortex and nucleus accumbens associated with opioid use disorder

Severe and persistent disruptions to sleep and circadian rhythms are common in people with opioid use disorder (OUD). Preclinical evidence suggests altered molecular rhythms in the brain modulate opioid reward and relapse. However, whether molecular rhythms are disrupted in the brains of people with OUD remained an open question, critical to understanding the role of circadian rhythms in opioid addiction. Using subjects' times of death as a marker of time of day, we investigated transcriptional rhythms in the brains of subjects with OUD compared to unaffected comparison subjects. We discovered rhythmic transcripts in both the dorsolateral prefrontal cortex (DLPFC) and nucleus accumbens (NAc), key brain areas involved in OUD, that were largely distinct between OUD and unaffected subjects. Fewer rhythmic transcripts were identified in DLPFC of subjects with OUD compared to unaffected subjects, whereas in the NAc, nearly double the number of rhythmic transcripts was identified in subjects with OUD. In NAc of subjects with OUD, rhythmic transcripts peaked either in the evening or near sunrise, and were associated with an opioid, dopamine, and GABAergic neurotransmission. Associations with altered neurotransmission in NAc were further supported by co-expression network analysis which identified OUD-specific modules enriched for transcripts involved in dopamine, GABA, and glutamatergic synaptic functions. Additionally, rhythmic transcripts in DLPFC and NAc of subjects with OUD were enriched for genomic loci associated with sleep-related GWAS traits, including sleep duration and insomnia. Collectively, our findings connect transcriptional rhythm changes in opioidergic, dopaminergic, GABAergic signaling in the human brain to sleep-related traits in opioid addiction.

Genomic and Personalized Medicine Approaches for Substance Use Disorders (SUDs) Looking at Genome-Wide Association Studies

Drug addiction, or substance use disorder (SUD), is a chronic, relapsing disorder in which compulsive drug-seeking and drug-taking behaviour persist despite serious negative consequences. Drug abuse represents a problem that deserves great attention from a social point of view, and focuses on the importance of genetic studies to help in understanding the genetic basis of addiction and its medical treatment. Despite the complexity of drug addiction disorders, and the high number of environmental variables playing a role in the onset, recurrence, and duration of the symptoms, several studies have highlighted the non-negligible role of genetics, as demonstrated by heritability and genome-wide association studies. A correlation between the relative risk of addiction to specific substances and heritability has been recently observed, suggesting that neurobiological mechanisms may be, at least in part, inherited. All these observations point towards a scenario where the core neurobiological factors of addiction, involving the reward system, impulsivity, compulsivity, stress, and anxiety response, are transmitted, and therefore, genes and mutations underlying their variation might be detected. In the last few years, the development of new and more efficient sequencing technologies has paved the way for large-scale studies in searching for genetic and epigenetic factors affecting drug addiction disorders and their treatments. These studies have been crucial to pinpoint single nucleotide polymorphisms (SNPs) in genes that affect the reaction to medical treatments. This is critically important to identify pharmacogenomic approaches for substance use disorder, such as OPRM1 SNPs and methadone required doses for maintenance treatment (MMT). Nevertheless, despite the promising results obtained by genome-wide association and pharmacogenomic studies, specific studies related to population genetics diversity are lacking, undermining the overall applicability of the preliminary findings, and thus potentially affecting the portability and the accuracy of the genetic studies. In this review, focusing on cannabis, cocaine and heroin use, we report the state-of-the-art genomics and pharmacogenomics of SUDs, and the possible future perspectives related to medical treatment response in people that ask for assistance in solving drug-related problems.

Genetics of substance use disorders in the era of big data

Substance use disorders (SUDs) are conditions in which the use of legal or illegal substances, such as nicotine, alcohol or opioids, results in clinical and functional impairment. SUDs and, more generally, substance use are genetically complex traits that are enormously costly on an individual and societal basis. The past few years have seen remarkable progress in our understanding of the genetics, and therefore the biology, of substance use and abuse. Various studies - including of well-defined phenotypes in deeply phenotyped samples, as well as broadly defined phenotypes in meta-analysis and biobank samples - have revealed multiple risk loci for these common traits. A key emerging insight from this work establishes a biological and genetic distinction between quantity and/or frequency measures of substance use (which may involve low levels of use without dependence), versus symptoms related to physical dependence.

Two data-driven approaches to identifying the spectrum of problematic opioid use: A pilot study within a chronic pain cohort

BACKGROUND

Although electronic health records (EHR) have significant potential for the study of opioid use disorders (OUD), detecting OUD in clinical data is challenging. Models using EHR data to predict OUD often rely on case/control classifications focused on extreme opioid use. There is a need to expand this work to characterize the spectrum of problematic opioid use.

METHODS

Using a large academic medical center database, we developed 2 data-driven methods of OUD detection: (1) a Comorbidity Score developed from a Phenome-Wide Association Study of phenotypes associated with OUD and (2) a Text-based Score using natural language processing to identify OUD-related concepts in clinical notes. We evaluated the performance of both scores against a manual review with correlation coefficients, Wilcoxon rank sum tests, and area-under the receiver operating characteristic curves. Records with the highest Comorbidity and Text-based scores were re-evaluated by manual review to explore discrepancies.

RESULTS

Both the Comorbidity and Text-based OUD risk scores were significantly elevated in the patients judged as High Evidence for OUD in the manual review compared to those with No Evidence (p = 1.3E-5 and 1.3E-6, respectively). The risk scores were positively correlated with each other (rho = 0.52, p < 0.001). AUCs for the Comorbidity and Text-based scores were high (0.79 and 0.76, respectively). Follow-up manual review of discrepant findings revealed strengths of data-driven methods over manual review, and opportunities for improvement in risk assessment.

CONCLUSION

Risk scores comprising comorbidities and text offer differing but synergistic insights into characterizing problematic opioid use. This pilot project establishes a foundation for more robust work in the future.

Transcriptional Alterations in Dorsolateral Prefrontal Cortex and Nucleus Accumbens Implicate Neuroinflammation and Synaptic Remodeling in Opioid Use Disorder

BACKGROUND

Prevalence rates of opioid use disorder (OUD) have increased dramatically, accompanied by a surge of overdose deaths. While opioid dependence has been extensively studied in preclinical models, an understanding of the biological alterations that occur in the brains of people who chronically use opioids and who are diagnosed with OUD remains limited. To address this limitation, RNA sequencing was conducted on the dorsolateral prefrontal cortex and nucleus accumbens, regions heavily implicated in OUD, from postmortem brains in subjects with OUD.

METHODS

We performed RNA sequencing on the dorsolateral prefrontal cortex and nucleus accumbens from unaffected comparison subjects (n = 20) and subjects diagnosed with OUD (n = 20). Our transcriptomic analyses identified differentially expressed transcripts and investigated the transcriptional coherence between brain regions using rank-rank hypergeometric orderlap. Weighted gene coexpression analyses identified OUD-specific modules and gene networks. Integrative analyses between differentially expressed transcripts and genome-wide association study datasets using linkage disequilibrium scores assessed the genetic liability of psychiatric-related phenotypes in OUD.

RESULTS

Rank-rank hypergeometric overlap analyses revealed extensive overlap in transcripts between the dorsolateral prefrontal cortex and nucleus accumbens in OUD, related to synaptic remodeling and neuroinflammation. Identified transcripts were enriched for factors that control proinflammatory cytokine, chondroitin sulfate, and extracellular matrix signaling. Cell-type deconvolution implicated a role for microglia as a potential driver for opioid-induced neuroplasticity. Linkage disequilibrium score analysis suggested genetic liabilities for risky behavior, attention-deficit/hyperactivity disorder, and depression in subjects with OUD.

CONCLUSIONS

Overall, our findings suggest connections between the brain's immune system and opioid dependence in the human brain.

Genetics of substance use disorders: a review

Substance use disorders (SUDs) are prevalent and result in an array of negative consequences. They are influenced by genetic factors (h2 = ~50%). Recent years have brought substantial progress in our understanding of the genetic etiology of SUDs and related traits. The present review covers the current state of the field for SUD genetics, including the epidemiology and genetic epidemiology of SUDs, findings from the first-generation of SUD genome-wide association studies (GWAS), cautions about translating GWAS findings to clinical settings, and suggested prioritizations for the next wave of SUD genetics efforts. Recent advances in SUD genetics have been facilitated by the assembly of large GWAS samples, and the development of state-of-the-art methods modeling the aggregate effect of genome-wide variation. These advances have confirmed that SUDs are highly polygenic with many variants across the genome conferring risk, the vast majority of which are of small effect. Downstream analyses have enabled finer resolution of the genetic architecture of SUDs and revealed insights into their genetic relationship with other psychiatric disorders. Recent efforts have also prioritized a closer examination of GWAS findings that have suggested non-uniform genetic influences across measures of substance use (e.g. consumption) and problematic use (e.g. SUD). Additional highlights from recent SUD GWAS include the robust confirmation of loci in alcohol metabolizing genes (e.g. ADH1B and ALDH2) affecting alcohol-related traits, and loci within the CHRNA5-CHRNA3-CHRNB4 gene cluster influencing nicotine-related traits. Similar successes are expected for cannabis, opioid, and cocaine use disorders as sample sizes approach those assembled for alcohol and nicotine.