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Dash GF, Gizer IR, Martin NG, Slutske WS. Specificity in genetic and environmental risk for prescription opioid misuse and heroin use. Psychol Med 2023; 53:1-10. [PMID: 36946318 PMCID: PMC10514228 DOI: 10.1017/s003329172300034x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 01/23/2023] [Accepted: 02/01/2023] [Indexed: 03/23/2023]
Abstract
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.
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Affiliation(s)
- Genevieve F. Dash
- Department of Psychological Sciences, University of Missouri, Columbia, MO 65211, USA
| | - Ian R. Gizer
- Department of Psychological Sciences, University of Missouri, Columbia, MO 65211, USA
| | | | - Wendy S. Slutske
- Department of Family Medicine and Community Health and Center for Tobacco Research and Intervention, University of Wisconsin, Madison, WI 53711, USA
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2
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Chang XW, Sun Y, Muhai JN, Li YY, Chen Y, Lu L, Chang SH, Shi J. 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. Addiction 2022; 117:2515-2529. [PMID: 35491750 DOI: 10.1111/add.15908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 04/04/2022] [Indexed: 11/30/2022]
Abstract
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.
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Affiliation(s)
- Xiang-Wen Chang
- Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.,National Institute on Drug Dependence, Peking University, Beijing, China
| | - Yan Sun
- Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.,National Institute on Drug Dependence, Peking University, Beijing, China
| | - Jia-Na Muhai
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Yang-Yang Li
- Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.,National Institute on Drug Dependence, Peking University, Beijing, China
| | - Yun Chen
- Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.,National Institute on Drug Dependence, Peking University, Beijing, China
| | - Lin Lu
- National Institute on Drug Dependence, Peking University, Beijing, China.,Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Su-Hua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Jie Shi
- National Institute on Drug Dependence, Peking University, Beijing, China.,Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing, China.,The State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, China.,The Key Laboratory for Neuroscience of the Ministry of Education and Health, Peking University, Beijing, China
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3
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Freda PJ, Kranzler HR, Moore JH. Novel digital approaches to the assessment of problematic opioid use. BioData Min 2022; 15:14. [PMID: 35840990 PMCID: PMC9284824 DOI: 10.1186/s13040-022-00301-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 06/30/2022] [Indexed: 11/16/2022] Open
Abstract
The opioid epidemic continues to contribute to loss of life through overdose and significant social and economic burdens. Many individuals who develop problematic opioid use (POU) do so after being exposed to prescribed opioid analgesics. Therefore, it is important to accurately identify and classify risk factors for POU. In this review, we discuss the etiology of POU and highlight novel approaches to identifying its risk factors. These approaches include the application of polygenic risk scores (PRS) and diverse machine learning (ML) algorithms used in tandem with data from electronic health records (EHR), clinical notes, patient demographics, and digital footprints. The implementation and synergy of these types of data and approaches can greatly assist in reducing the incidence of POU and opioid-related mortality by increasing the knowledge base of patient-related risk factors, which can help to improve prescribing practices for opioid analgesics.
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Affiliation(s)
- Philip J Freda
- Cedars-Sinai Medical Center, Department of Computational Biomedicine, 700 N. San Vicente Blvd., Pacific Design Center Suite G540, West Hollywood, CA, 90069, USA.
| | - Henry R Kranzler
- University of Pennsylvania, Center for Studies of Addiction, 3535 Market St., Suite 500 and Crescenz VAMC, 3800 Woodland Ave., Philadelphia, PA, 19104, USA
| | - Jason H Moore
- Cedars-Sinai Medical Center, Department of Computational Biomedicine, 700 N. San Vicente Blvd., Pacific Design Center Suite G540, West Hollywood, CA, 90069, USA
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4
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Freda PJ, Moore JH, Kranzler HR. The phenomics and genetics of addictive and affective comorbidity in opioid use disorder. Drug Alcohol Depend 2021; 221:108602. [PMID: 33652377 PMCID: PMC8059867 DOI: 10.1016/j.drugalcdep.2021.108602] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 01/25/2021] [Accepted: 01/26/2021] [Indexed: 12/21/2022]
Abstract
Opioid use disorder (OUD) creates significant public health and economic burdens worldwide. Therefore, understanding the risk factors that lead to the development of OUD is fundamental to reducing both its prevalence and its impact. Significant sources of OUD risk include co-occurring lifetime and current diagnoses of both psychiatric disorders, primarily mood disorders, and other substance use disorders, and unique and shared genetic factors. Although there appears to be pleiotropy between OUD and both mood and substance use disorders, this aspect of OUD risk is poorly understood. In this review, we describe the prevalence and clinical significance of addictive and affective comorbidities as risk factors for OUD development as a basis for rational opioid prescribing and OUD treatment and to improve efforts to prevent the disorder. We also review the genetic variants that have been associated with OUD and other addictive and affective disorders to highlight targets for future study and risk assessment protocols.
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Affiliation(s)
- Philip J. Freda
- University of Pennsylvania, Biostatistics, Epidemiology, & Informatics, The Perelman School of Medicine, University of Pennsylvania A201 R…, Philadelphia, Pennsylvania 19104, United States
| | - Jason H. Moore
- Edward Rose Professor of Informatics, Director, Institute for Biomedical Informatics, Director, Division of Informatics, Department of Biostatistics, Epidemiology, & Informatics, Senior Associate Dean for Informatics, The Perelman School of Medicine, University of Pennsylvania, Contact Information: D202 Richards Building, 3700 Hamilton Walk, University of Pennsylvania, Philadelphia, PA 19104-6116
| | - Henry R. Kranzler
- Benjamin Rush Professor in Psychiatry, Department of Psychiatry, University of Pennsylvania, Treatment Research Center, 3535 Market Street, Suite 500, Philadelphia, PA 19104-6178
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5
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Höglund A, Henriksen R, Fogelholm J, Churcher AM, Guerrero-Bosagna CM, Martinez-Barrio A, Johnsson M, Jensen P, Wright D. The methylation landscape and its role in domestication and gene regulation in the chicken. Nat Ecol Evol 2020; 4:1713-1724. [PMID: 32958860 DOI: 10.1038/s41559-020-01310-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 08/26/2020] [Indexed: 01/06/2023]
Abstract
Domestication is one of the strongest examples of artificial selection and has produced some of the most extreme within-species phenotypic variation known. In the case of the chicken, it has been hypothesized that DNA methylation may play a mechanistic role in the domestication response. By inter-crossing wild-derived red junglefowl with domestic chickens, we mapped quantitative trait loci for hypothalamic methylation (methQTL), gene expression (eQTL) and behaviour. We find large, stable methylation differences, with 6,179 cis and 2,973 trans methQTL identified. Over 46% of the trans effects were genotypically controlled by five loci, mainly associated with increased methylation in the junglefowl genotype. In a third of eQTL, we find that there is a correlation between gene expression and methylation, while statistical causality analysis reveals multiple instances where methylation is driving gene expression, as well as the reverse. We also show that methylation is correlated with some aspects of behavioural variation in the inter-cross. In conclusion, our data suggest a role for methylation in the regulation of gene expression underlying the domesticated phenotype of the chicken.
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Affiliation(s)
- Andrey Höglund
- AVIAN Behavioural Genomics and Physiology Group, Linköping University, Linköping, Sweden
| | - Rie Henriksen
- AVIAN Behavioural Genomics and Physiology Group, Linköping University, Linköping, Sweden
| | - Jesper Fogelholm
- AVIAN Behavioural Genomics and Physiology Group, Linköping University, Linköping, Sweden
| | | | - Carlos M Guerrero-Bosagna
- AVIAN Behavioural Genomics and Physiology Group, Linköping University, Linköping, Sweden.,Evolutionary Biology Centrum, Dept of Organismal Biology, Uppsala University, Uppsala, Sweden
| | | | - Martin Johnsson
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, UK.,Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Per Jensen
- AVIAN Behavioural Genomics and Physiology Group, Linköping University, Linköping, Sweden
| | - Dominic Wright
- AVIAN Behavioural Genomics and Physiology Group, Linköping University, Linköping, Sweden.
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6
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Sumitani M, Nishizawa D, Hozumi J, Ikeda K. Genetic implications in quality palliative care and preventing opioid crisis in cancer-related pain management. J Neurosci Res 2020; 100:362-372. [PMID: 33174646 DOI: 10.1002/jnr.24756] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 09/23/2020] [Accepted: 10/25/2020] [Indexed: 12/11/2022]
Abstract
The prevalence of cancer-related pain is 64% among patients with metastatic, advanced, or terminal cancer, 59% among patients undergoing anticancer treatment, and 33% among patients who completed curative treatment. According to the World Health Organization cancer pain relief guidelines, opioid analgesics are the mainstay analgesic therapy in addition to conventional first-step analgesics, such as non-steroidal anti-inflammatory drugs and acetaminophen. The indications for strong opioids have recently been expanded to include mild-to-moderate pain in addition to moderate-to-severe pain. The U.S. Centers for Disease Control and Prevention guidelines emphasize that realistic expectations should be weighed against potential serious harm from opioids, rather than relying on the unrealized long-term benefits of these drugs. Therefore, treatment strategies for both cancer-related chronic or acute pain have been unfortunately deviated from opioid analgesics. The barriers hindering adequate cancer-related pain management with opioid analgesics are related to the inadequate knowledge of opioid analgesics (e.g., effective dose, adverse effects, and likelihood of addiction or tolerance). To achieve adequate opioid availability, these barriers should be overcome in a clinically suitable manner. Genetic assessments could play an important role in overcoming challenges in opioid management. To balance the improvement in opioid availability and the prevention of opioid misuse and addiction, the following two considerations concerning opioids and genetic polymorphisms warrant attention: (A) pain severity, opioid sensitivity, and opioid tolerance; and (B) vulnerability to opioid dependence and addiction.
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Affiliation(s)
- Masahiko Sumitani
- Department of Pain and Palliative Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Daisuke Nishizawa
- Addictive Substance Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Jun Hozumi
- Department of Pain and Palliative Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Kazutaka Ikeda
- Addictive Substance Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
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7
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Zhou H, Rentsch CT, Cheng Z, Kember RL, Nunez YZ, Sherva RM, Tate JP, Dao C, Xu K, Polimanti R, Farrer LA, Justice AC, Kranzler HR, Gelernter J. Association of OPRM1 Functional Coding Variant With Opioid Use Disorder: A Genome-Wide Association Study. JAMA Psychiatry 2020; 77:1072-1080. [PMID: 32492095 PMCID: PMC7270886 DOI: 10.1001/jamapsychiatry.2020.1206] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
IMPORTANCE With the current opioid crisis, it is important to improve understanding of the biological mechanisms of opioid use disorder (OUD). OBJECTIVES To detect genetic risk variants for OUD and determine genetic correlations and causal association with OUD and other traits. DESIGN, SETTING, AND PARTICIPANTS A genome-wide association study of electronic health record-defined OUD in the Million Veteran Program sample was conducted, comprising 8529 affected European American individuals and 71 200 opioid-exposed European American controls (defined by electronic health record trajectory analysis) and 4032 affected African American individuals and 26 029 opioid-exposed African American controls. Participants were enrolled from January 10, 2011, to May 21, 2018, with electronic health record data for OUD diagnosis from October 1, 1999, to February 7, 2018. Million Veteran Program results and additional OUD case-control genome-wide association study results from the Yale-Penn and Study of Addiction: Genetics and Environment samples were meta-analyzed (total numbers: European American individuals, 10 544 OUD cases and 72 163 opioid-exposed controls; African American individuals, 5212 cases and 26 876 controls). Data on Yale-Penn participants were collected from February 14, 1999, to April 1, 2017, and data on Study of Addiction: Genetics and Environment participants were collected from 1990 to 2007. The key result was replicated in 2 independent cohorts: proxy-phenotype buprenorphine treatment in the UK Biobank and newly genotyped Yale-Penn participants. Genetic correlations between OUD and other traits were tested, and mendelian randomization analysis was conducted to identify potential causal associations. MAIN OUTCOMES AND MEASURES Main outcomes were International Classification of Diseases, Ninth Revision-diagnosed OUD or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision-diagnosed OUD (Million Veteran Program), and DSM-IV-defined opioid dependence (Yale-Penn and Study of Addiction: Genetics and Environment). RESULTS A total of 114 759 individuals (101 016 men [88%]; mean [SD] age, 60.1 [12.8] years) were included. In 82 707 European American individuals, a functional coding variant (rs1799971, encoding Asn40Asp) in OPRM1 (μ-opioid receptor gene, the main biological target for opioid drugs; OMIM 600018) reached genome-wide significance (G allele: β = -0.066 [SE = 0.012]; P = 1.51 × 10-8). The finding was replicated in 2 independent samples. Single-nucleotide polymorphism-based heritability of OUD was 11.3% (SE = 1.8%). Opioid use disorder was genetically correlated with 83 traits, including multiple substance use traits, psychiatric illnesses, cognitive performance, and others. Mendelian randomization analysis revealed the following associations with OUD: risk of tobacco smoking, depression, neuroticism, worry neuroticism subcluster, and cognitive performance. No genome-wide significant association was detected for African American individuals or in transpopulation meta-analysis. CONCLUSIONS AND RELEVANCE This genome-wide meta-analysis identified a significant association of OUD with an OPRM1 variant, which was replicated in 2 independent samples. Post-genome-wide association study analysis revealed associated pleiotropic characteristics. Recruitment of additional individuals with OUD for future studies-especially those of non-European ancestry-is a crucial next step in identifying additional significant risk loci.
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Affiliation(s)
- Hang Zhou
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut,Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven
| | - Christopher T. Rentsch
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven,Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut,Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Zhongshan Cheng
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut,Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven
| | - Rachel L. Kember
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia,Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania
| | - Yaira Z. Nunez
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut,Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven
| | - Richard M. Sherva
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts
| | - Janet P. Tate
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven,Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Cecilia Dao
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven,Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut,Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut,Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven
| | - Lindsay A. Farrer
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts,Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts,Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts,Department of Neurology, Boston University School of Medicine, Boston, Massachusetts,Department of Ophthalmology, Boston University School of Medicine, Boston, Massachusetts
| | - Amy C. Justice
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven,Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut,Yale School of Public Health, New Haven, Connecticut
| | - Henry R. Kranzler
- Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania,Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut,Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven,Department of Genetics, Yale University School of Medicine, New Haven, Connecticut,Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
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