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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.

Introduction: Relative to the general population, patients with schizophrenia or schizoaffective disorder have higher rates of suicide attempts and mortality from COVID-19 infection. Therefore, determining whether a history of suicide attempt is associated with COVID-19 in patients with schizophrenia or schizoaffective disorder has implications for COVID-19 vulnerability stratification in this patient population. Methods: We carried out cross-sectional analyses of electronic health records of veterans with a diagnosis of schizophrenia or schizoaffective disorder that received treatment at any United States Veterans Affairs Medical Center between January 1, 2020, and January 31, 2021. We used logistic regression to estimate unadjusted and adjusted (including age, sex, race, marital status, body mass index (BMI), and a medical comorbidity score) odds ratios (ORs) for COVID-19 positivity in suicide attempters relative to nonattempters. Results: A total of 101,032 veterans (mean age 56.67 ± 13.13 years; males 91,715 [90.8%]) were included in the analyses. There were 2,703 (2.7%) suicide attempters and 719 (0.7%) patients were positive for COVID-19. The association between history of suicide attempt and COVID-19 positivity was modified by age and BMI, such that the relationship was only significant in patients younger than 59 years, and in obese (BMI ≥30) patients (adjusted OR 3.42, 95% CI 2.02–5.79 and OR 2.85, 95% CI 1.65–4.94, respectively). Conclusions: Higher rates of COVID-19 in young or obese suicide attempters with a diagnosis of schizophrenia or schizoaffective disorder might be due to the elevated risk for the infection in this subgroup of patients.

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.

Contains fulltext : 237013.pdf (Publisher’s version ) (Closed access) Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental trait with complex and far-reaching social and health correlates. 27 p.

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.

BackgroundAlcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders.MethodsWe used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific.ResultsWe identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001).ConclusionsOur findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.

BACKGROUND AND AIMS: The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire screens for harmful drinking using a 12-month timeframe. A score of 0 is assigned to individuals who report abstaining from alcohol in the past year. However, many middle-age individuals reporting current abstinence are former drinkers (FDs). Because FDs may be more genetically prone to harmful alcohol use than lifelong abstainers (LAs) and are often combined with LAs, we evaluated the impact of differentiating them on the identification of genetic association. DESIGN AND SETTING: The United Kingdom Biobank (UKBB) includes AUDIT-C and alcohol drinker status. PARTICIPANTS: 131 510 Europeans, including 5135 FDs. MEASUREMENTS: We compared three genome-wide association (GWAS) analyses to explore the effects of removing FDs: the full AUDIT-C data, AUDIT-C data without FDs, and data from a random sample numerically matched to the data without FDs. Because prior studies show a consistent association of the ADH1B polymorphism rs1229984 with both alcohol consumption and alcohol use disorder, we compared allele frequencies for rs1229984 stratified by AUDIT-C value and FD versus LA status. Additionally, we calculated polygenic risk scores (PRS) of related diseases. FINDINGS: The rs1229984 allele frequencies among FDs were numerically comparable to those with high AUDIT-C scores and very different from those of LAs. Removing FDs from GWAS yielded a stronger association with rs1229984 (P value after removal: 1.9 × 10(−70) vs 1.7 × 10(−65) and 2.5 × 10(−62)), more statistically significant single nucleotide polymorphisms (SNPs) (after removal: 11 vs 9 and 8), and genomic loci (after removal: 11 vs 9 and 7). Additional independent SNPs were identified after removal of FDs: rs2817866 (PTGER3), rs7105867 (ANO3), and rs17601612 (DRD2). For PRS of alcohol use disorder and major depressive disorder, there are statistically significant differences between FDs and LAs. CONCLUSIONS: Differentiating between former drinkers and lifelong abstainers can improve Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) genome-wide association results.

OBJECTIVE. Genome-wide association studies (GWASs) of the Alcohol Use Disorders Identification Test (AUDIT), a ten-item screener for alcohol use disorders (AUD), have elucidated novel loci for alcohol consumption and misuse. However, these studies also revealed that GWASs can be influenced by numerous biases (e.g., measurement error, selection bias), which may have led to inconsistent genetic correlations between alcohol involvement and AUD, as well as paradoxically negative genetic correlations between alcohol involvement and psychiatric disorders/medical conditions. METHODS. To explore these unexpected differences in genetic correlations, we conducted the first item-level and largest GWAS of AUDIT items (N=160,824), and applied a multivariate framework to mitigate previous biases. RESULTS. We identified novel patterns of similarity (and dissimilarity) among the AUDIT items, and found evidence of a correlated two-factor structure at the genetic level (Consumption and Problems, r(g)=.80). Moreover, by applying empirically-derived weights to each of the AUDIT items, we constructed an aggregate measure of alcohol consumption that is strongly associated with alcohol dependence (r(g)=.67) and several other psychiatric disorders, and no longer positively associated with health and positive socioeconomic outcomes. Lastly, by conducting polygenic analyses in three independent cohorts that differed in their ascertainment and prevalence of AUD, we identified novel genetic associations between alcohol consumption, alcohol misuse, and human health. CONCLUSIONS. Our work further emphasizes the value of AUDIT for both clinical and genetic studies of AUD, and the importance of using multivariate methods to study genetic associations that are more closely related to AUD.

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.

Background Several studies have reported conflicting evidence on the inclusion of testicular germ cell tumors (TGCT) in the DICER1 tumor-predisposition phenotype. We evaluated the relationship between DICER1 and TGCT by reviewing scrotal ultrasounds of males with pathogenic germline variants in DICER1 and queried exome data from TGCT-affected men for DICER1 variants. Methodology Fifty-four male DICER1-carriers and family controls (n=41) enrolled in the National Cancer Institute (NCI) DICER1 Natural History Study were offered scrotal ultrasounds. These studies were examined by a single radiologist for abnormalities. In parallel, DICER1 variants from two large exome-sequenced TGCT cohorts were extracted. We used previously published AMG-AMP criteria to characterize rare DICER1 variants. Results There was no observed difference in frequency of testicular cystic structures in DICER1-carriers versus controls. DICER1 variation was not associated with TGCT in the NCI DICER1-carriers. In 1,264 exome-sequenced men with TGCT, none harbored ClinVar- or InterVar-determined pathogenic or likely pathogenic variants in DICER1. Three DICER1 variants of uncertain significance (one case and two controls) were predicted “damaging” based on a priori criteria. Conclusion Using two complementary approaches, we found no evidence of an association between pathogenic DICER1 variants and TGCT.

Polygenic risk scores (PRS) for coronary artery disease (CAD) identify high-risk individuals more likely to benefit from primary prevention statin therapy. Whether polygenic CAD risk is captured by conventional paradigms for assessing clinical cardiovascular risk remains unclear.This study sought to intersect polygenic risk with guideline-based recommendations and management patterns for CAD primary prevention.A genome-wide CAD PRS was applied to 47,108 individuals across 3 U.S. health care systems. The authors then assessed whether primary prevention patients at high polygenic risk might be distinguished on the basis of greater guideline-recommended statin eligibility and higher rates of statin therapy.Of 47,108 study participants, the mean age was 60 years, and 11,020 (23.4%) had CAD. The CAD PRS strongly associated with prevalent CAD (odds ratio: 1.4 per SD increase in PRS; p 0.0001). High polygenic risk (top 20% of PRS) conferred 1.9-fold odds of developing CAD (p 0.0001). However, among primary prevention patients (n = 33,251), high polygenic risk did not correspond with increased recommendations for statin therapy per the American College of Cardiology/American Heart Association (46.2% for those with high PRS vs. 46.8% for all others, p = 0.54) or U.S. Preventive Services Task Force (43.7% vs. 43.7%, p = 0.99) or higher rates of statin prescriptions (25.0% vs. 23.8%, p = 0.04). An additional 4.1% of primary prevention patients may be recommended for statin therapy if high CAD PRS were considered a guideline-based risk-enhancing factor.Current paradigms for primary cardiovascular prevention incompletely capture a polygenic susceptibility to CAD. An opportunity may exist to improve CAD prevention efforts by integrating both genetic and clinical risk.

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.

BackgroundDespite the large toll of opioid use disorder (OUD), genome-wide association studies (GWAS) of OUD to date have yielded few susceptibility loci.MethodsWe 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 (MVP), Psychiatric Genomics Consortium (PGC), iPSYCH, FinnGen, Partners Biobank, BioVU, and Yale-Penn 3, resulting in a total N=639,709 (Ncases=20,858) across ancestries. OUD cases were defined as having 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).ResultsThe 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=1.37×10−08; r2=0.02). Rs1799971 (p=4.91×10−08) and another OPRM1 variant (rs9478500; p=1.95×10−8; 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 18 GWS loci, some of which map to genes or gene regions that have previously been associated with psychiatric or addiction phenotypes.ConclusionsWe identified multiple OUD variant associations at OPRM1, single variant associations with FURIN, and 18 GWS associations in the OUD-MTAG. OUD is likely influenced by both OUD-specific loci and loci shared across SUDs.

BackgroundElectronic health records (EHR) are useful tools for understanding complex medical phenotypes, but they have been underutilized for opioid use disorders (OUD). Patterns of prescription opioid use might provide an objective measure of OUD risk.MethodsWe extracted data 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 Prescription, Minimal Exposure (2 prescriptions within 90 days at least once, but never 3 prescriptions ResultsThe prevalence of substance (alcohol, tobacco, cannabis) use disorders was higher in patients with OUD and Chronic Exposure than those with No Prescription or Minimal Exposure. Patients in the OUD and Chronic Exposure groups had more psychiatric (anxiety, depression, schizophrenia, bipolar disorder) and medical comorbidities (pain, hepatitis C, HIV) than those in the Minimal Exposure group. Notably, patients in the Minimal Exposure group had different comorbidity profiles (higher rates of substance use and psychiatric disorders, more pain conditions) than those in the Unscreened or No Prescription groups, highlighting the value of including opioid exposure in studies of OUD.ConclusionsLong-term opioid prescription use may serve as an additional tool to characterize OUD risk.

Major depressive disorder (MDD) and opioid use disorder (OUD) are common, potentially fatal, polygenic disorders that are moderately heritable and often co-occur. We examined the unique and shared associations of polygenic risk scores (PRS) for these disorders with µ-opioid receptor (MOR) concentration and endogenous opioid response during a stressful stimulus. Participants were 144 healthy European-ancestry (EA) subjects (88 females) who underwent MOR quantification scans with [

Polygenic risk scores (PRS) represent an individual’s summed genetic risk for a trait and can serve as biomarkers for disease. Less is known about the utility of PRS as a means to quantify genetic risk for substance use disorders (SUDs) than for many other traits. Nonetheless, the growth of large, electronic health record-based biobanks makes it possible to evaluate the association of SUD PRS with other traits. We calculated PRS for smoking initiation, alcohol use disorder (AUD), and opioid use disorder (OUD) using summary statistics from the Million Veteran Program sample. We then tested the association of each PRS with its primary phenotype in the Penn Medicine BioBank (PMBB) using all available genotyped participants of African or European ancestry (AFR and EUR, respectively) (N=18,612). Finally, we conducted phenome-wide association analyses (PheWAS) separately by ancestry and sex to test for associations across disease categories. Tobacco use disorder was the most common SUD in the PMBB, followed by AUD and OUD, consistent with the population prevalence of these disorders. All PRS were associated with their primary phenotype in both ancestry groups. PheWAS results yielded cross-trait associations across multiple domains, including psychiatric disorders and medical conditions. SUD PRS were associated with their primary phenotypes, however they are not yet predictive enough to be useful diagnostically. The cross-trait associations of the SUD PRS are indicative of a broader genetic liability. Future work should extend findings to additional population groups and for other substances of abuse.

ImportanceAs patients with schizophrenia or schizoaffective disorder have a high risk of suicide, and a history of suicide attempt is a strong predictor of suicide, determining whether history of suicide attempt is associated with COVID-19 in patients with schizophrenia or schizoaffective disorder has implications for suicide prevention in this patient population.ObjectiveTo determine whether a history of suicide attempt is associated with COVID-19 in Veterans with schizophrenia or schizoaffective disorder.DesignCross-sectional analyses of nation-wide electronic health records (EHR).SettingUnited States Veterans Health Administration.ParticipantsVeterans with a diagnosis of schizophrenia or schizoaffective disorder that received treatment at any United States Veterans Affairs Medical Center from January 1, 2020 to January 31, 2021.ExposureHistory of suicide attempt.Main OutcomeAdjusted and unadjusted odds ratios (ORs) for COVID-19 positivity in suicide attempters relative to non-attempters. Adjusted analyses included age, sex, race, marital status, BMI, and a medical comorbidity score.ResultsA total of 101,032 Veterans [mean age 56.67 ± 13.13 years; males 91,715 (90.8%)] were included in the analyses. There were 2,703 (2.7%) suicide attempters and 719 (0.7%) patients were positive for COVID-19. There was effect modification by age and BMI in the association of history of suicide attempt with COVID-19 positivity such that the association was only significant in obese (BMI ≥ 30) patients and patients younger than 59 years respectively. In the entire sample, the unadjusted OR for COVID-19 positivity in attempters was 1.42 (95% CI 0.97 to 2.10) and the adjusted odds ratio was 1.90 (95% CI 1.28 to 2.80). In patients younger than 59 years, and in the obese patients respectively, history of suicide attempt was associated with COVID-positive status in unadjusted analyses [OR 3.53 (95% CI 2.10 to 5.94); OR 2.22 (95% CI 1.29 to 3.81)] and adjusted analyses [OR 3.42 (95% CI 2.02 to 5.79); OR 2.85 (95% CI 1.65 to 4.94)].Conclusions and RelevanceYoung or obese suicide attempters with a diagnosis of schizophrenia or schizoaffective disorders have higher rates of COVID-19 diagnosis; due to possible long-term neuropsychiatric sequelae of infection with SARS-CoV-2, such patients should be monitored closely.

Background: Approximately 13% of black individuals carry 2 copies of the apolipoprotein L1 ( APOL1 ) risk alleles G1 or G2, which are associated with 1.5- to 2.5-fold increased risk of chronic kidney disease. There have been conflicting reports as to whether an association exists between APOL1 risk alleles and cardiovascular disease (CVD) that is independent of the effects of APOL1 on kidney disease. We sought to test the association of APOL1 G1/G2 alleles with coronary artery disease, peripheral artery disease, and stroke among black individuals in the Million Veteran Program. Methods: We performed a time-to-event analysis of retrospective electronic health record data using Cox proportional hazard and competing-risks Fine and Gray subdistribution hazard models. The primary exposure was APOL1 risk allele status. The primary outcome was incident coronary artery disease among individuals without chronic kidney disease during the 12.5-year follow-up period. We separately analyzed the cross-sectional association of APOL1 risk allele status with lipid traits and 115 cardiovascular diseases using phenome-wide association. Results: Among 30 903 black Million Veteran Program participants, 3941 (13%) carried the 2 APOL1 risk allele high-risk genotype. Individuals with normal kidney function at baseline with 2 risk alleles had slightly higher risk of developing coronary artery disease compared with those with no risk alleles (hazard ratio, 1.11 [95% CI, 1.01–1.21]; P =0.039). Similarly, modest associations were identified with incident stroke (hazard ratio, 1.20 [95% CI, 1.05–1.36; P =0.007) and peripheral artery disease (hazard ratio, 1.15 [95% CI, 1.01–1.29l; P =0.031). When both cardiovascular and renal outcomes were modeled, APOL1 was strongly associated with incident renal disease, whereas no significant association with the CVD end points could be detected. Cardiovascular phenome-wide association analyses did not identify additional significant associations with CVD subsets. Conclusions: APOL1 risk variants display a modest association with CVD, and this association is likely mediated by the known APOL1 association with chronic kidney disease.

Objective Pleiotropy, where 1 genetic locus affects multiple phenotypes, can offer significant insights in understanding the complex genotype–phenotype relationship. Although individual genotype–phenotype associations have been thoroughly explored, seemingly unrelated phenotypes can be connected genetically through common pleiotropic loci or genes. However, current analyses of pleiotropy have been challenged by both methodologic limitations and a lack of available suitable data sources. Materials and Methods In this study, we propose to utilize a new regression framework, reduced rank regression, to simultaneously analyze multiple phenotypes and genotypes to detect pleiotropic effects. We used a large-scale biobank linked electronic health record data from the Penn Medicine BioBank to select 5 cardiovascular diseases (hypertension, cardiac dysrhythmias, ischemic heart disease, congestive heart failure, and heart valve disorders) and 5 mental disorders (mood disorders; anxiety, phobic and dissociative disorders; alcohol-related disorders; neurological disorders; and delirium dementia) to validate our framework. Results Compared with existing methods, reduced rank regression showed a higher power to distinguish known associated single-nucleotide polymorphisms from random single-nucleotide polymorphisms. In addition, genome-wide gene-based investigation of pleiotropy showed that reduced rank regression was able to identify candidate genetic variants with novel pleiotropic effects compared to existing methods. Conclusion The proposed regression framework offers a new approach to account for the phenotype and genotype correlations when identifying pleiotropic effects. By jointly modeling multiple phenotypes and genotypes together, the method has the potential to distinguish confounding from causal genotype and phenotype associations.

Purpose: To assess the contribution of rare variants in the genetic background toward variability of neurodevelopmental phenotypes in individuals with rare copy-number variants (CNVs) and gene-disruptive variants. Methods: We analyzed quantitative clinical information, exome sequencing, and microarray data from 757 probands and 233 parents and siblings who carry disease-associated variants. Results: The number of rare likely deleterious variants in functionally intolerant genes (“other hits”) correlated with expression of neurodevelopmental phenotypes in probands with 16p12.1 deletion (n=23, p=0.004) and in autism probands carrying gene-disruptive variants (n=184, p=0.03) compared with their carrier family members. Probands with 16p12.1 deletion and a strong family history presented more severe clinical features (p=0.04) and higher burden of other hits compared with those with mild/no family history (p=0.001). The number of other hits also correlated with severity of cognitive impairment in probands carrying pathogenic CNVs (n=53) or de novo pathogenic variants in disease genes (n=290), and negatively correlated with head size among 80 probands with 16p11.2 deletion. These co-occurring hits involved known disease-associated genes such as SETD5, AUTS2, and NRXN1, and were enriched for cellular and developmental processes. Conclusion: Accurate genetic diagnosis of complex disorders will require complete evaluation of the genetic background even after a candidate disease-associated variant is identified.

Alcohol consumption level and alcohol use disorder (AUD) diagnosis are moderately heritable traits. We conduct genome-wide association studies of these traits using longitudinal Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) scores and AUD diagnoses in a multi-ancestry Million Veteran Program sample (N = 274,424). We identify 18 genome-wide significant loci: 5 associated with both traits, 8 associated with AUDIT-C only, and 5 associated with AUD diagnosis only. Polygenic Risk Scores (PRS) for both traits are associated with alcohol-related disorders in two independent samples. Although a significant genetic correlation reflects the overlap between the traits, genetic correlations for 188 non-alcohol-related traits differ significantly for the two traits, as do the phenotypes associated with the traits’ PRS. Cell type group partitioning heritability enrichment analyses also differentiate the two traits. We conclude that, although heavy drinking is a key risk factor for AUD, it is not a sufficient cause of the disorder., The genetic underpinnings of alcohol use disorder and consumption are incompletely understood. Here, the authors perform GWAS for Alcohol Use Disorder (AUD) Identification Test-Consumption scores and AUD diagnosis from electronic health records of 274,424 individuals and identify a total of 18 associated loci.

BackgroundStudies show that Black and Hispanic Veterans have a higher prevalence of alcohol use disorder (AUD) than White Veterans. We examined whether the relationship between self-reported race/ethnicity and AUD diagnosis varies by self-reported alcohol consumption.MethodsThe sample included 700,013 Black, Hispanic, and White Veterans enrolled in the Million Veteran Program cohort. Alcohol consumption was defined as an individual’s maximum score on the Alcohol Use Disorders Identification Test–Consumption (AUDIT-C) questionnaire, a screen for hazardous or harmful drinking. The primary outcome, AUD, was defined by the presence of ICD-9/10 codes in the electronic health record. We used logistic regression with interactions to assess the association between race/ethnicity and AUD by maximum AUDIT-C score.ResultsBlack and Hispanic Veterans were more likely to have an AUD diagnosis than White Veterans despite similar levels of alcohol consumption. The difference was greatest between Black and White men. At all but the lowest and highest levels of alcohol consumption, Black men had 24%-111% greater odds of an AUD diagnosis. The association between race/ethnicity and AUD diagnosis remained after adjustment for alcohol consumption, alcohol-related disorders, and other potential confounders.ConclusionsThe large discrepancy in AUD diagnosis across groups despite a similar distribution of alcohol consumption measures suggests that Veterans are differentially assigned an AUD diagnosis by race/ethnicity. Efforts are needed to examine the causes of the observed differences and to implement changes, such as structured diagnostic methods, to address a likely contributor to racial differences (i.e., bias) in AUD diagnosis.

PurposeGenome-wide association studies (GWAS) have identified hundreds of single nucleotide polymorphisms (SNPs) significantly associated with several cancers, but the predictive ability of polygenic risk scores (PRS) is unclear, especially among non-Whites.MethodsGenome-wide genotype data was available for 20,079 individuals enrolled in an academic biobank. PRS were derived from significant DNA variants for 15 cancers. We determined the discriminatory accuracy of each cancer-specific PRS in patients of genetically-determined African and European ancestry separately.ResultsAmong individuals of European genetic ancestry, PRS for breast, colon, melanoma, and prostate were significantly associated with their respective cancers (OR 1.25-1.47). Among individuals of African genetic ancestry, PRS for breast, colon, and prostate were significantly associated with their respective cancers. The AUC of a model comprised of age, sex, and principal components was 0.617–0.709 and increased by 1-4% with the PRS in individuals of European genetic ancestry. In individuals of African genetic ancestry, AUC was overall higher in the model without PRS (0.740-0.811) but increased < 1% with the PRS in the majority of cancers.ConclusionPRS constructed from SNPs moderately increased discriminatory ability for cancer status in individuals of European but not African ancestry. Further large-scale studies are needed to identify ancestry-specific genetic factors in non-White populations to incorporate PRS into cancer risk assessment.

Genome-wide association studies have identified hundreds of single nucleotide variations (formerly single nucleotide polymorphisms) associated with several cancers, but the predictive ability of polygenic risk scores (PRSs) is unclear, especially among non-Whites.PRSs were derived from genome-wide significant single-nucleotide variations for 15 cancers in 20,079 individuals in an academic biobank. We evaluated the improvement in discriminatory accuracy by including cancer-specific PRS in patients of genetically-determined African and European ancestry.Among the individuals of European genetic ancestry, PRSs for breast, colon, melanoma, and prostate were significantly associated with their respective cancers. Among the individuals of African genetic ancestry, PRSs for breast, colon, prostate, and thyroid were significantly associated with their respective cancers. The area under the curve of the model consisting of age, sex, and principal components was 0.621 to 0.710, and it increased by 1% to 4% with the inclusion of PRS in individuals of European genetic ancestry. In individuals of African genetic ancestry, area under the curve was overall higher in the model without the PRS (0.723-0.810) but increased by1% with the inclusion of PRS for most cancers.PRS moderately increased the ability to discriminate the cancer status in individuals of European but not African ancestry. Further large-scale studies are needed to identify ancestry-specific genetic factors in non-White populations to incorporate PRS into cancer risk assessment.