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Background: Venous thromboembolism (VTE) is a life-threatening vascular event with environmental and genetic determinants. Recent VTE genome-wide association studies (GWAS) meta-analyses involved nearly 30 000 VTE cases and identified up to 40 genetic loci associated with VTE risk, including loci not previously suspected to play a role in hemostasis. The aim of our research was to expand discovery of new genetic loci associated with VTE by using cross-ancestry genomic resources. Methods: We present new cross-ancestry meta-analyzed GWAS results involving up to 81 669 VTE cases from 30 studies, with replication of novel loci in independent populations and loci characterization through in silico genomic interrogations. Results: In our genetic discovery effort that included 55 330 participants with VTE (47 822 European, 6320 African, and 1188 Hispanic ancestry), we identified 48 novel associations, of which 34 were replicated after correction for multiple testing. In our combined discovery-replication analysis (81 669 VTE participants) and ancestry-stratified meta-analyses (European, African, and Hispanic), we identified another 44 novel associations, which are new candidate VTE-associated loci requiring replication. In total, across all GWAS meta-analyses, we identified 135 independent genomic loci significantly associated with VTE risk. A genetic risk score of the significantly associated loci in Europeans identified a 6-fold increase in risk for those in the top 1% of scores compared with those with average scores. We also identified 31 novel transcript associations in transcriptome-wide association studies and 8 novel candidate genes with protein quantitative-trait locus Mendelian randomization analyses. In silico interrogations of hemostasis and hematology traits and a large phenome-wide association analysis of the 135 GWAS loci provided insights to biological pathways contributing to VTE, with some loci contributing to VTE through well-characterized coagulation pathways and others providing new data on the role of hematology traits, particularly platelet function. Many of the replicated loci are outside of known or currently hypothesized pathways to thrombosis. Conclusions: Our cross-ancestry GWAS meta-analyses identified new loci associated with VTE. These findings highlight new pathways to thrombosis and provide novel molecules that may be useful in the development of improved antithrombosis treatments.

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.

Background Identification of germline mutations in DNA repair genes has significant implications for the personalized treatment of individuals with prostate cancer (PrCa). Objective To determine DNA repair genes associated with localized PrCa in a diverse academic biobank and to determine genetic testing burden. Design, setting, and participants A cross-sectional study of 2391 localized PrCa patients was carried out. Outcome measurements and statistical analysis Genetic ancestry and mutation rates (excluding somatic interference) in 17 DNA repair genes were determined in 1588 localized PrCa patients and 3273 cancer-free males. Burden testing within individuals of genetically determined European (EUR) and African (AFR) ancestry was performed between biobank PrCa cases and cancer-free biobank and gnomAD males. Results and limitations AFR individuals with localized PrCa had lower DNA repair gene mutation rates than EUR individuals (1.4% vs 4.0%, p = 0.02). Mutation rates in localized PrCa patients were similar to those in biobank and gnomAD controls (EUR: 4.0% vs 2.8%, p = 0.15, vs 3.1%, p = 0.04; AFR: 1.4% vs 1.8%, p = 0.8, vs 2.1%, p = 0.5). Gene-based rare variant association testing revealed that only BRCA2 mutations were significantly enriched compared with gnomAD controls of EUR ancestry (1.0% vs 0.28%, p = 0.03). Of the participants, 21% and 11% met high-risk and very-high-risk criteria; of them, 3.7% and 6.2% had any germline genetic mutation and 1.0% and 2.5% had a BRCA2 mutation, respectively. Limitations of this study include an analysis of a relatively small, single-institution cohort. Conclusions DNA repair gene germline mutation rates are low in an academic biobank cohort of localized PrCa patients, particularly among individuals of AFR genetic ancestry. Mutation rates in genes with published evidence of association with PrCa exceed 2.5% only in high-risk, very-high-risk localized, and node-positive PrCa patients. These findings highlight the importance of risk stratification in localized PrCa patients to identify appropriate patients for germline genetic testing. Patient summary In the majority of patients who develop localized prostate cancer, germline genetic testing is unlikely to reveal an inherited DNA repair mutation, regardless of race. High-risk features increase the possibility of a germline DNA repair mutation.

BackgroundEpidemiologic studies have shown an association between sleep abnormalities and alcohol-related traits. Recent genome-wide association studies (GWAS) have identified genetic variants associated with sleep-related traits, including insomnia and sleep duration, and with alcohol-related phenotypes, including alcohol use disorder (AUD) and level of alcohol consumption.ObjectivesWe investigated whether genetic risk for insomnia and sleep duration abnormalities are associated with AUD and alcohol consumption. We also evaluated the causal relationships between sleep- and alcohol-related traits.MethodsIndividual level phenotype and genotype data from the Million Veteran Program was used. Polygenic risk scores (PRS) were computed using summary statistics from two recent discovery GWAS of insomnia (N=453,379 European-ancestry (EA) individuals) and sleep duration (N=446,118 EAs) and tested for association with lifetime AUD diagnosis (cases, N=34,658 EAs) and past-year Alcohol Use Disorders Identification Test-Consumption scale scores (AUDIT-C, N=200,680 EAs). Bi-directional two-sample Mendelian Randomization (MR) analyses assessed causal associations between the two sleep traits and the two alcohol-related traits.ResultsInsomnia PRS was positively associated with AUD at 2/9 PRS thresholds, with p−5). Conversely, insomnia PRS was negatively associated with AUDIT-C at 6/9 PRS thresholds (most significant threshold being p=0.001 (β = - 0.02, p = 5.6 × 10−8). Sleep duration PRS was not associated with AUD, but was positively associated with AUDIT-C at 2/9 PRS thresholds, with the most significant threshold being p = 1 × 10−6(β = 0.01, p = 0.0009). MR analyses supported a significant positive causal effect of insomnia on AUD (14 SNPs; beta = 104.14; SE = 16.19; p = 2.22 × 10−5), although with significant heterogeneity. MR analyses also provided nominal evidence of a causal effect of AUD on insomnia (10 SNPs; beta = 0.01; SE = 0.007; p = 0.01). Finally, MR analyses showed that decreased sleep duration had a causal effect on the risk of AUD (27 SNPs; beta = -63.05; SE = 3.54; p = 4.55 × 10−16) and was robust to sensitivity analyses.ConclusionThe genetic risk for insomnia shows pleiotropy with AUD, and sleep continuity abnormalities have a causal influence on the development of AUD.

Alcohol use disorders (AUDs) and related electrophysiological endophenotypes have been associated with theGABRA2gene. However, the causal variants inGABRA2and their mechanisms of influence on AUD and its correlates have not been established. Here we investigate the phenotypic spectrum of aGABRA2coding variant (rs279858) through a phenome-wide association study (PheWAS) in two open-source datasets.We applied the PheWAS approach to identify a broad range of phenotypes associated with rs279858 in the MRC IEU OpenGWAS PheWAS and the Open Targets Genetics Portal. These databases extend the array of phenotypes beyond those available in electronic health records (EHR) to include numerous non-medical phenotypes and traits. We then followed up the results from those exploratory associations by examining the genetic correlations between our “top hits” and alcohol- and smoking-related phenotypes.In both data sources, rs279858 (C effect allele) was associated with anxiety-related phenotypes, including reduced risk-taking behavior and an increase in nervous feelings, as well as reduced number of lifetime sexual partners. Follow-up analyses revealed that these phenotypes were genetically correlated with each other and with alcohol- and smoking-related phenotypes.This work illustrates the utility of the PheWAS approach, particularly for phenotypes that extend beyond those that are typically captured in EHR data. In fact, the associations described here are all behavioral rather than clinical phenotypes. We postulate that these traits may be related to anxiety or behavioral inhibition that has been identified as a risk factor for AUD, and may represent pathophysiological intermediaries betweenGABRA2and AUD.

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.

The Penn Medicine BioBank (PMBB) is an electronic health record (EHR)-linked biobank at the University of Pennsylvania (Penn Medicine). A large variety of health-related information, ranging from diagnosis codes to laboratory measurements, imaging data and lifestyle information, is integrated with genomic and biomarker data in the PMBB to facilitate discoveries and translational science. To date, 174,712 participants have been enrolled into the PMBB, including approximately 30% of participants of non-European ancestry, making it one of the most diverse medical biobanks. There is a median of seven years of longitudinal data in the EHR available on participants, who also consent to permission to recontact. Herein, we describe the operations and infrastructure of the PMBB, summarize the phenotypic architecture of the enrolled participants, and use body mass index (BMI) as a proof-of-concept quantitative phenotype for PheWAS, LabWAS, and GWAS. The major representation of African-American participants in the PMBB addresses the essential need to expand the diversity in genetic and translational research. There is a critical need for a “medical biobank consortium” to facilitate replication, increase power for rare phenotypes and variants, and promote harmonized collaboration to optimize the potential for biological discovery and precision medicine.

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.

BackgroundCharting the clinical course of substance use disorders (SUDs) to identify etiologic contributors to milestone onset and progression could inform intervention efforts.MethodsWe calculated polygenic risk scores (PRS) in 5,692 European-ancestry individuals (EUR) (56.2% male) and 4,918 African-ancestry (AFR) individuals (54.9% male) using genome-wide association studies (GWAS) of alcohol use disorder (AUD), opioid use disorder (OUD), and smoking trajectory (SMK). Using Cox regression, we examined the association of polygenic risk with age of first substance use, regular use, reported problems, and dependence diagnosis and with progression from regular use to onset of problems and dependence.ResultsEUR and males reported earlier onset and shorter progression times than AFR and females, respectively. Among EUR, higher AUD PRS predicted earlier onset and more rapid progression to alcohol-related milestones (p’sp=0.0165), it was more predictive of the progression to problems among males (p=0.0054). OUD and SMK PRS in EUR also predicted earlier onset of the respective milestones (p’s=0.0002). Among AFR, where power is lower, AUD PRS predicted age of regular alcohol use (p=0.039) and dependence (p=0.001) and progression from regular use to diagnosis (p=0.045), while SMK PRS predicted earlier age of initiation (p=0.036).ConclusionsGenetic risk for SUDs predicts milestones and symptom progression in EUR and, to a lesser extent, among AFR. Larger, diverse discovery GWAS and target samples are needed to enhance the power of PRS to personalize interventions for individuals at genetic risk of serious substance-related outcomes.DisclosureDr. Kranzler is a member of advisory boards for Dicerna Pharmaceuticals, Sophrosyne Pharmaceuticals, and Enthion Pharmaceuticals; a consultant to Sobrera Pharmaceuticals; the recipient of research funding and medication supplies for an investigator-initiated study from Alkermes; and a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported in the last three years by Alkermes, Dicerna, Ethypharm, Lundbeck, Mitsubishi, and Otsuka. Drs. Gelernter and Kranzler hold U.S. Patent 10,900,082: Genotype-guided Dosing of Opioid Receptor Agonists, 26 Jan. 2021. The other authors have no disclosures to make.

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.

IntroductionA large majority of genome-wide significant (GWS) loci identified for substance use traits (SUTs) in genome-wide association studies (GWAS) have been for alcohol and smoking-related phenotypes. GWAS of opioid use disorder (OUD) and cannabis use disorder (CUD) have lagged those of the two historically legal substances.MethodsWe applied multi-trait analysis of GWAS (MTAG) to 2,888,727 single nucleotide polymorphisms (SNPs) common to GWAS of four SUTs (OUD, CUD, alcohol use disorder [AUD], and smoking initiation [SMK]) in European-ancestry (EUR) subjects. We calculated polygenic risk scores (PRS) for the four traits in an independent sample (i.e., the Yale-Penn sample; N=5,692 EUR) and examined the power increment for each set of MTAG-GWAS summary statistics relative to those of the input GWAS.ResultsMTAG increased the effective sample size for all four SUTs, which showed high pairwise genetic correlations. After clumping, MTAG identified independent GWS SNPs for all 4 traits: 41 SNPs in 36 loci (including 5 novel loci not previously associated with any SUT) for OUD; 74 SNPs in 60 loci (including 4 novel loci) for CUD; 63 SNPs in 52 loci (including 10 novel loci) for AUD; and 183 SNPs in 144 loci (including 8 novel loci) for SMK. In PRS analyses in the Yale-Penn sample, the MTAG-derived PRS consistently yielded more significant associations with both the corresponding substance use disorder diagnosis and multiple related phenotypes than each of the 4 GWAS-derived PRS.ConclusionsMTAG boosted the number of GWS loci for the 4 SUTs, including identifying genes not previously linked to any SUT. MTAG-derived PRS also showed stronger associations with expected phenotypes than PRS for the input GWAS. MTAG can be used to identify novel associations for SUTs, especially those with sample sizes smaller than for historically legal substances.

Despite the identification of a growing number of genetic risk loci for substance use traits (SUTs), the impact of these loci on protein abundance and the potential utility of relevant proteins as therapeutic targets are unknown. We conducted a proteome-wide association study (PWAS) in which we integrated human brain proteomes from discovery (Banner; N = 152) and validation (ROSMAP; N = 376) datasets with genome-wide association study (GWAS) summary statistics for 4 SUTs. The 4 samples comprised GWAS of European-ancestry individuals for smoking initiation [Smk] (N = 1,232,091), alcohol use disorder [AUD] (N = 313,959), cannabis use disorder [CUD] (N = 384,032), and opioid use disorder [OUD] (N = 302,585). We conducted transcriptome-wide association studies (TWAS) with human brain transcriptomic data to examine the overlap of genetic effects at the proteomic and transcriptomic levels and characterize significant genes through conditional, colocalization, and fine-mapping analyses. We identified 27 genes (Smk = 21, AUD = 3, CUD = 2, OUD = 1) that were significantly associated with cis-regulated brain protein abundance. Of these, 7 showed evidence for causality (Smk: NT5C2, GMPPB, NQO1, RHOT2, SRR and ACTR1B; and AUD: CTNND1). Cis-regulated transcript levels for 8 genes (Smk = 6, CUD = 1, OUD = 1) were associated with SUTs, indicating that genetic loci could confer risk for these SUTs by modulating both gene expression and proteomic abundance. Functional studies of the high-confidence risk proteins identified here are needed to determine whether they are modifiable targets and useful in developing medications and biomarkers for these SUTs.

BackgroundSubstance use disorders (SUDs) are associated with a variety of co-occurring psychiatric disorders and other SUDs, which partly reflects genetic pleiotropy. Polygenic risk scores (PRS) and phenome-wide association studies (PheWAS) are useful in evaluating pleiotropic effects. The comparatively low prevalence of SUDs and lack of detailed information available in electronic health records limits their informativeness for such analyses.MethodsWe used the deeply-phenotyped Yale-Penn sample [(N=10,610; 46.3% African ancestry (AFR), 53.7% European ancestry (EUR)], recruited for genetic studies of substance dependence, to examine pleiotropy for 4 major substance-related traits: alcohol use disorder (AUD), opioid use disorder (OUD), smoking initiation (SMK), and lifetime cannabis use (CAN). The sample includes both affected and control subjects interviewed using the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA), a comprehensive psychiatric interview.ResultsIn AFR individuals PRS for AUD, and in EUR individuals PRS for AUD, OUD, and SMK, were associated with their respective primary DSM diagnoses. These PRS were also associated with additional phenotypes involving the same substance. PheWAS analyses of PRS in EUR individuals identified associations across multiple phenotypic domains, including phenotypes not commonly assessed in PheWAS analyses, such as family environment and early childhood experiences.ConclusionsSmaller, deeply-phenotyped samples can complement large biobank genetic studies with limited phenotyping by providing greater phenotypic granularity. These efforts allow associations to be identified between specific features of disorders and genetic liability for SUDs, which help to inform our understanding of the pleiotropic pathways underlying them.

Background: Despite the growing number of genetic risk loci identified for substance use traits (SUTs), the impact of these loci on protein abundance and their potential as therapeutic targets are unknown. Methods: To address this, we performed a proteome-wide association study (PWAS) by integrating human brain proteomes from discovery (Banner; N = 152) and validation (ROSMAP; N = 376) datasets with genome-wide association study (GWAS) summary statistics for 4 SUTs. The sample comprised 4 GWAS of European-ancestry individuals for smoking initiation [Smk] (N = 1,232,091), alcohol use disorder [AUD] (N = 313,959), cannabis use disorder [CUD] (N = 384,032), and opioid use disorder [OUD] (N = 302,585). We conducted transcriptome-wide association studies (TWAS) with human brain transcriptomic data to examine the overlap of genetic effects at the proteomic and transcriptomic levels and tested significant genes for causality through Colocalization analysis. Results: Twenty-seven genes (Smk=21, AUD=3, CUD=2, OUD=1) were significantly associated with cis-regulated brain protein abundance. There was evidence for causality in 6 genes (Smk: NT5C2, GMPPB, NQO1, SRR, and ACTR1B; AUD: CTNND1), which act by regulating brain protein abundance. Cis-regulated transcript levels for 8 genes (Smk=6, CUD=1, OUD=1) were associated with SUTs, indicating that genetic loci could confer risk for these SUTs by modulating both gene expression and proteomic abundance. Conclusions: Functional studies of the high-confidence risk proteins (SRR for Smk and CTNND1 for AUD) identified here are needed to determine whether they are modifiable targets and useful in developing medications and biomarkers for these SUTs.