Abstract
To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = −5.39, p = 7.2 × 10–8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10–8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10–6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10–8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10–7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10–5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10–5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10–5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.
Original language | English |
---|---|
Pages (from-to) | 1673-1687 |
Number of pages | 15 |
Journal | Molecular Psychiatry |
Volume | 25 |
Issue number | 8 |
DOIs | |
State | Published - 1 Aug 2020 |
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In: Molecular Psychiatry, Vol. 25, No. 8, 01.08.2020, p. 1673-1687.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium
AU - on behalf of the Psychiatric Genomics Consortium Substance Use Disorders Workgroup
AU - Polimanti, Renato
AU - Walters, Raymond K.
AU - Johnson, Emma C.
AU - McClintick, Jeanette N.
AU - Adkins, Amy E.
AU - Adkins, Daniel E.
AU - Bacanu, Silviu Alin
AU - Bierut, Laura J.
AU - Bigdeli, Tim B.
AU - Brown, Sandra
AU - Bucholz, Kathleen K.
AU - Copeland, William E.
AU - Costello, E. Jane
AU - Degenhardt, Louisa
AU - Farrer, Lindsay A.
AU - Foroud, Tatiana M.
AU - Fox, Louis
AU - Goate, Alison M.
AU - Grucza, Richard
AU - Hack, Laura M.
AU - Hancock, Dana B.
AU - Hartz, Sarah M.
AU - Heath, Andrew C.
AU - Hewitt, John K.
AU - Hopfer, Christian J.
AU - Johnson, Eric O.
AU - Kendler, Kenneth S.
AU - Kranzler, Henry R.
AU - Krauter, Kenneth
AU - Lai, Dongbing
AU - Madden, Pamela A.F.
AU - Martin, Nicholas G.
AU - Maes, Hermine H.
AU - Nelson, Elliot C.
AU - Peterson, Roseann E.
AU - Porjesz, Bernice
AU - Riley, Brien P.
AU - Saccone, Nancy
AU - Stallings, Michael
AU - Wall, Tamara L.
AU - Webb, Bradley T.
AU - Wetherill, Leah
AU - Edenberg, Howard J.
AU - Agrawal, Arpana
AU - Gelernter, Joel
N1 - Funding Information: Acknowledgements The Psychiatric Genomics Consortium Substance Use Disorders Working Group receives support from the National Institute on Drug Abuse and the National Institute of Mental Health via U01 MH109532 and U01 MH109528. We gratefully acknowledge prior support from the National Institute on Alcohol Abuse and Alcoholism. Statistical analyses for the PGC were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara and financially supported by the Netherlands Scientific Organization (NWO 480-05-003) along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam. A.A. acknowledges DA032573; A.C.H. acknowledges support from NIH grants AA07535, AA07729, AA13320, AA13321, and AA11998; A. E.A. acknowledges support from AA011408 and AA017828; A.M.G. acknowledges support from U10 AA08401; B.P.R. was supported by AA011408, AA017828, and AA022537; B.T.W. acknowledges support from AA011408, AA017828, and AA022537; C.J.H. acknowledges DA032555, DA035804, DA011015, and DA042755; D.B.H. acknowledges support from R01DA036583; E.J.C. acknowledges support from DA023026, DA011301, and DA024413; E.O.J. acknowledges support from R01 DA044014; H.M. acknowledges support from DA025109, DA024413, and DA016977; J.G. acknowledges support from DA12690 and DA047527; J.K.H. acknowledges support from DA011015; K.S.K. acknowledges support from AA011408, AA017828, and AA022537; L.D. is supported by an Australian National Health and Medical Research Council (NHMRC) Principal Research Fellowship; L.J.B. acknowledges support from R01DA036583; L.M.H. acknowledges support from AA011408 and AA017828; L.M.H. acknowledges support from AA011408 and AA017828; M.C.S. acknowledges support from DA035804; P.A.F.M. acknowledges funding support from NIH grants: DA012854 and R25DA027995; R.A.G. acknowledges support from AA017444; R.E. P. is supported by NIH K01 grant MH113848; R.P. acknowledges support from DA12690 and DA047527; S.A.B. acknowledges support from AA011408, AA017828, AA022537, and AA022717; S.M.H. acknowledges support from R21AA024888 and K08DA032680; T.B. B. acknowledges support from MH100549; T.L.W. acknowledges support from R01 DA021905 and R01 DA035804; W.E.C. acknowledges support from R01HD093651, R01DA036523, and P30DA023026, R01MH117559. Alcohol Dependence in African Americans (ADAA) study was funded by NIH grant R01 AA017444. Funding support for the Comorbidity and Trauma Study (CATS) (dbGAP accession number: phs000277.v1.p1) was provided by the National Institute on Drug Abuse (R01 DA17305); GWAS genotyping services at the CIDR at The Johns Hopkins University were supported by the National Institutes of Health (contract N01-HG-65403). The data collection and analysis of the Center on Antisocial Drug Dependence (CADD) was supported by the following grants: DA011015, DA012845, DA021913, DA021905, DA032555, and DA035804. The Collaborative Study on the Genetics of Alcoholism (COGA) is supported by NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). Funding support for this GWAS genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the National Institute on Alcohol Abuse and Alcoholism, the NIH GEI (U01HG004438), and the NIH contract “High throughput genotyping for studying the genetic contributions to human disease” (HHSN268200782096C). COGA Principal Investigators: B. Porjesz, Funding Information: V. Hesselbrock, H. Edenberg, L. Bierut, includes eleven different centers: University of Connecticut (V. Hesselbrock); Indiana University (H.J. Edenberg, J. Nurnberger Jr., T. Foroud); University of Iowa (S. Kuperman, J. Kramer); SUNY Downstate (B. Porjesz); Washington University in St. Louis (L. Bierut, J. Rice, K. Bucholz, A. Agrawal); University of California at San Diego (M. Schuckit); Rut-gers University (J. Tischfield, A. Brooks); Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA (L. Almasy), Virginia Commonwealth University (D. Dick), Icahn School of Medicine at Mount Sinai (A. Goate), and Howard University (R. Taylor). Other COGA collaborators include: L. Bauer (University of Connecticut); J. McClintick, L. Wetherill, X. Xuei, Y. Liu, D. Lai, S. O’Connor, M. Plawecki, S. Lourens (Indiana University); G. Chan (University of Iowa; University of Connecticut); J. Meyers, D. Chorlian, C. Kamarajan, A. Pandey, J. Zhang (SUNY Downstate); J.-C. Wang, M. Kapoor, S. Bertelsen (Icahn School of Medicine at Mount Sinai); A. Anokhin, V. McCutcheon, S. Saccone (Washington University); J. Salvatore, F. Aliev, B. Cho (Virginia Commonwealth University); and Mark Kos (University of Texas Rio Grande Valley). A. Parsian are the NIAAA Staff Collaborators. M. Reilly was an NIAAA staff collaborator. We continue to be inspired by our memories of Henri Begleiter and Theodore Reich, founding PI and Co-PI of COGA, and also owe a debt of gratitude to other past organizers of COGA, including Ting-Kai Li, currently a consultant with COGA, P. Michael Conneally, Raymond Crowe, and Wendy Reich, for their critical contributions. We thank Kim Doheny and Elizabeth Pugh from CIDR and Justin Paschall from the NCBI dbGaP staff for valuable assistance with genotyping and quality control in developing the dataset available at dbGaP (phs000125.v1.p1; also: phs000763.v1.p1; phs000976.v1.p1). Support for the Study of Addiction: Genetics and Environment (SAGE) was provided through the NIH Genes, Environment and Health Initiative [GEI; U01 HG004422; dbGaP study accession phs000092.v1.p1]. SAGE is one of the genome-wide association studies funded as part of the Gene Environment Association Studies (GENEVA) under GEI. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GENEVA Coordinating Center [U01 HG004446]. Assistance with data cleaning was provided by the National Center for Biotechnology Information. Support for collection of datasets and samples was provided by the Collaborative Study on the Genetics of Alcoholism [COGA; U10 AA008401], the Collaborative Genetic Study of Nicotine Dependence [COGEND; P01 CA089392; see also phs000404.v1.p1], and the Family Study of Cocaine Dependence [FSCD; R01 DA013423, R01 DA019963]. Funding support for gen-otyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR), was provided by the NIH GEI [U01HG004438], the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse, and the NIH contract “High throughput genotyping for studying the genetic contributions to human disease” [HHSN268200782096C]. The Gene–Environment Development Initiative: Great Smoky Mountains Study (phs000852. v1.p1) was supported by the National Institute on Drug Abuse (U01DA024413, R01DA11301), the National Institute of Mental Health (R01MH063970, R01MH063671, R01MH048085, K01MH093731, and K23MH080230), NARSAD, and the William T. Grant Foundation. We are grateful to all the GSMS and CCC study participants who contributed to this work. The following grants supported data collection and analysis of CADD: DA011015, DA012845, DA021913, DA021905, DA032555, and DA035804. Gene-Environment-Development Initiative -GEDI – Virginia Commonwealth University (VTSABD; dbGAP in progress) was supported by the National Institute on Drug Abuse (U01DA024413, R01DA025109), the National Institute of Mental Health (R01MH045268, R01MH055557, and R01MH068521), and the Virginia Tobacco Settlement Foundation grant 8520012. We are grateful to all the VTSABD-YAFU-TSA study participants who contributed to this work. Yale-Penn (phs000425.v1.p1; phs000952.v1. p1) was supported by National Institutes of Health Grants RC2 DA028909, R01 DA12690, R01 DA12849, R01 DA18432, R01 AA11330, and R01 AA017535 and the Veterans Affairs Connecticut and Philadelphia Veterans Affairs Mental Illness Research, Educational, and Clinical Centers. Australian Alcohol and Nicotine studies (OZALC; phs000181.v1.p1) were supported by National Institutes of Health Grants AA07535,AA07728, AA13320, AA13321, AA14041, AA11998, AA17688,DA012854, and DA019951; by Grants from the Australian National Health and Medical Research Council (241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, and 552498); by Grants from the Australian Research Council (A7960034, A79906588, A79801419, DP0770096, DP0212016, and DP0343921); and by the 5th Framework Programme (FP-5) GenomEUtwin Project (QLG2-CT-2002-01254). Genome-wide association study genotyping at Center for Inherited Disease Research was supported by a Grant to the late Richard Todd, M.D., Ph.D., former Principal Investigator of Grant AA13320. Substance Use Disorder Working Group of the Psychiatric Genomics Consortium: Raymond K. Walters, Renato Polimanti, Emma C. Johnson, Jeanette N. McClintick, Mark J. Adams, Amy E. Adkins, Fazil Aliev, Silviu-Alin Bacanu, Anthony Batzler, Sarah Bertelsen, Joanna M. Biernacka, Tim B. Bigdeli, Li-Shiun Chen, Toni-Kim Clarke, Yi-Ling Chou, Franziska Degenhardt, Anna R. Docherty, Alexis C. Edwards, Pierre Fontanillas, Jerome C. Foo, Louis Fox, Josef Frank, Ina Giegling, Scott Gordon, Laura M. Hack, Annette M. Hartmann, Sarah M. Hartz, Stefanie Heilmann-Heimbach, Stefan Herms, Colin Hodgkinson, Per Hoffmann, Jouke Jan Hottenga, Martin A. Kennedy, Mervi Alanne-Kinnunen, Bettina Konte, Jari Lahti, Marius Lahti-Pulkkinen, Dongbing Lai, Lannie Ligthart, Anu Loukola, Brion S. Maher, Hamdi Mbarek, Andrew M. McIntosh, Matthew B. McQueen, Jacquelyn L. Meyers, Yuri Milaneschi, Teemu Palviainen, John F. Pearson, Roseann E. Peterson, Samuli Ripatti, Euijung Ryu, Nancy L. Saccone, Jessica E. Salvatore, Sandra Sanchez-Roige, Melanie Schwandt, Richard Sherva, Fabian Streit, Jana Strohmaier, Nathaniel Thomas, Jen-Chyong Wang, Bradley T. Webb, Robbee Wedow, Leah Wetherill, Amanda G. Wills, 23andMe Research Team, Jason D. Boardman, Danfeng Chen, Doo-Sup Choi, William E. Copeland, Robert C. Culverhouse, Norbert Dahmen, Louisa Degenhardt, Benjamin W. Domingue, Sarah L. Elson, Mark A. Frye, Wolfgang Gäbel, Caroline Hayward, Marcus Ising, Margaret Keyes, Falk Kiefer, John Kramer, Samuel Kuperman, Susanne Lucae, Michael T. Lynskey, Wolfgang Maier, Karl Mann, Satu Männistö, Bertram Müller-Myhsok, Alison D. Murray, John I. Nurnberger, Aarno Palotie, Ulrich Preuss, Katri Räikkönen, Maureen D. Reynolds, Monika Ridinger, Norbert Scherbaum, Marc A. Schuckit, Michael Soyka, Jens Treutlein, Stephanie Witt, Norbert Wodarz, Peter Zill, Daniel E. Adkins, Joseph M. Boden, Dorret I. Boomsma, Laura J. Bierut, Sandra A. Brown, Kathleen K. Bucholz, Sven Cichon, E. Jane Costello, Harriet de Wit, Nancy Diazgranados, Danielle M. Dick, Johan G. Eriksson, Lindsay A. Farrer, Tatiana M. Foroud, Nathan A. Gillespie, Alison M. Goate, David Goldman, Richard A. Grucza, Dana B. Hancock, Kathleen Mullan Harris, Andrew C. Heath, Victor Hesselbrock, John K. Hewitt, Christian J. Hopfer, John Horwood, William Iacono, Eric O. Johnson, Jaakko A. Kaprio, Victor M. Karpyak, Kenneth S. Kendler, Henry R. Kranzler, Kenneth Krauter, Paul Lichtenstein, Penelope A. Lind, Matt McGue, James MacKillop, Pamela A. F. Madden, Hermine H. Maes, Patrik Magnusson, Nicholas G. Martin, Sarah E. Medland, Grant W. Montgomery, Elliot C. Nelson, Markus M. Nöthen, Abraham A. Palmer, Nancy L. Pedersen, Brenda W. J. H. Penninx, Bernice Porjesz, John P. Rice, Marcella Rietschel, Brien P. Riley, Richard Rose, Dan Rujescu, Pei-Hong Shen, Judy Silberg, Michael C. Stallings, Ralph E. Tarter, Michael M. Vanyukov, Scott Vrieze, Tamara L. Wall, John B. Publisher Copyright: © 2020, Springer Nature Limited.
PY - 2020/8/1
Y1 - 2020/8/1
N2 - To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = −5.39, p = 7.2 × 10–8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10–8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10–6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10–8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10–7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10–5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10–5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10–5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.
AB - To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = −5.39, p = 7.2 × 10–8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10–8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10–6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10–8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10–7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10–5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10–5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10–5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.
UR - http://www.scopus.com/inward/record.url?scp=85080030785&partnerID=8YFLogxK
U2 - 10.1038/s41380-020-0677-9
DO - 10.1038/s41380-020-0677-9
M3 - Article
C2 - 32099098
AN - SCOPUS:85080030785
SN - 1359-4184
VL - 25
SP - 1673
EP - 1687
JO - Molecular Psychiatry
JF - Molecular Psychiatry
IS - 8
ER -