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Identification of novel risk loci with shared effects on alcoholism, heroin, and methamphetamine dependence

Abstract

Different substance dependences have common effects on reward pathway and molecular adaptations, however little is known regarding their shared genetic factors. We aimed to identify the risk genetic variants that are shared for substance dependence (SD). First, promising genome-wide significant loci were identified from 3296 patients (521 alcoholic/1026 heroin/1749 methamphetamine) vs 2859 healthy controls and independently replicated using 1954 patients vs 1904 controls. Second, the functional effects of promising variants on gene expression, addiction characteristics, brain structure (gray and white matter), and addiction behaviors in addiction animal models (chronic administration and self-administration) were assessed. In addition, we assessed the genetic correlation among the three SDs using LD score regression. We identified and replicated three novel loci that were associated with the common risk of heroin, methamphetamine addiction, and alcoholism: ANKS1B rs2133896 (Pmeta = 3.60 × 10−9), AGBL4 rs147247472 (Pmeta = 3.40 × 10−12), and CTNNA2 rs10196867 (Pmeta = 4.73 × 10−9). Rs2133896 in ANKS1B was associated with ANKS1B gene expression and had effects on gray matter of the left calcarine and white matter of the right superior longitudinal fasciculus in heroin dependence. Overexpression of anks1b gene in the ventral tegmental area decreased addiction vulnerability for heroin and methamphetamine in self-administration rat models. Our findings could shed light on the root cause for substance dependence and will be helpful for the development of cost-effective prevention strategies for general addiction disorders.

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References

  1. United Nations publication. World Drug Report. 2018; https://www.unodc.org/wdr2018/.

  2. Vanyukov MM, Tarter RE, Kirisci L, Kirillova GP, Maher BS, Clark DB. Liability to substance use disorders: Common mechanisms and manifestations. Neurosci Biobehav Rev. 2003;27:507–15.

    Article  Google Scholar 

  3. Nestler EJ. Is there a common molecular pathway for addiction? Nat Neurosci. 2005;8:1445–9.

    CAS  Article  Google Scholar 

  4. Krueger RF, Hicks BM, Patrick CJ, Carlson SR, Iacono WG, McGue M. Etiologic connections among substance dependence, antisocial behavior, and personality: Modeling the externalizing spectrum. J Abnorm Psychol. 2002;111:411–24.

    Article  Google Scholar 

  5. Uhl GR. Molecular genetic underpinnings of human substance abuse vulnerability: likely contributions to understanding addiction as a mnemonic process. Neuropharmacology. 2004;47(Suppl 1):140–7.

    CAS  Article  Google Scholar 

  6. Palmer RHC, Brick L, Nugent NR, Bidwell LC, McGeary JE, Knopik VS, et al. Examining the role of common genetic variants on alcohol, tobacco, cannabis and illicit drug dependence: genetics of vulnerability to drug dependence. Addiction. 2015;110:530–7.

    Article  Google Scholar 

  7. Agrawal A, Neale MC, Prescott CA, Kendler KS. Cannabis and other illicit drugs: comorbid use and abuse/dependence in males and females (vol 34, pg 217, 2004). Behav Genet. 2004;34:557–557.

    Article  Google Scholar 

  8. Kendler KS, Jacobson KC, Prescott CA, Neale MC. Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am J Psychiatry. 2003;160:687–95.

    Article  Google Scholar 

  9. Buhler KM, Gine E, Echeverry-Alzate V, Calleja-Conde J, de Fonseca FR, Lopez-Moreno JA. Common single nucleotide variants underlying drug addiction: more than a decade of research. Addict Biol. 2015;20:845–71.

    Article  Google Scholar 

  10. Uhl GR, Drgon T, Johnson C, Fatusin OO, Liu QR, Contoreggi C, et al. “Higher order” addiction molecular genetics: Convergent data from genome-wide association in humans and mice. Biochem Pharmacol. 2008;75:98–111.

    CAS  Article  Google Scholar 

  11. Li MD, Burmeister M. New insights into the genetics of addiction. Nat Rev Genet. 2009;10:225–31.

    CAS  Article  Google Scholar 

  12. Reyes-Gibby CC, Yuan C, Wang J, Yeung SCJ, Shete S. Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: alcohol, smoking and opioid addiction. Bmc Syst Biol. 2015;9:25.

    Article  Google Scholar 

  13. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J. Schizophrenia Working Group of the Psychiatric Genomics C et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

    CAS  Article  Google Scholar 

  14. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51:237–44.

    CAS  Article  Google Scholar 

  15. Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacology. 2010;35:217–38.

    Article  Google Scholar 

  16. Xue Y-X, Wu P, Shi HS, Xue LF, Chen C, Zhu WL, et al. A memory retrieval-extinction procedure to prevent drug craving and relapse. Science. 2012;336:241–5.

    CAS  Article  Google Scholar 

  17. Zhang Y, Xue Y, Meng S, Luo Y, Liang J, Li J, et al. Inhibition of lactate transport erases drug memory and prevents drug relapse. Biol Psychiatry. 2016;79:928–39.

    CAS  Article  Google Scholar 

  18. Hoseth EZ, Krull F, Dieset I, Morch RH, Hope S, Gardsjord ES, et al. Exploring the Wnt signaling pathway in schizophrenia and bipolar disorder. Transl Psychiatry. 2018;8:55.

    Article  Google Scholar 

  19. Grupp LA. An investigation of intravenous ethanol self-administration in rats using a fixed-ratio schedule of reinforcement. Physiol Psychol. 1981;9:359–63.

    CAS  Article  Google Scholar 

  20. Crowley TJ. The reinforcers for drug abuse: why people take drugs. Compr Psychiatry. 1972;13:51–62.

    CAS  Article  Google Scholar 

  21. Tsuang MT, Lyons MJ, Meyer JM, Doyle T, Eisen SA, Goldberg J, et al. Co-occurrence of abuse of different drugs in men: the role of drug-specific and shared vulnerabilities. Arch Gen Psychiatry. 1998;55:967–72.

    CAS  Article  Google Scholar 

  22. Ghersi E, Vito P, Lopez P, Abdallah M, D’Adamio L. The intracellular localization of amyloid beta protein precursor (A beta PP) intracellular domain associated protein-1 (AIDA-1) is regulated by A beta PP and alternative splicing. J Alzheimers Dis. 2004;6:67–78.

    CAS  Article  Google Scholar 

  23. Jordan BA, Fernholz BD, Boussac M, Xu C, Grigorean G, Ziff EB, et al. Identification and verification of novel rodent postsynaptic density proteins. Mol Cell Proteom. 2004;3:857–71.

    CAS  Article  Google Scholar 

  24. Jordan BA, Fernholz BD, Khatri L, Ziff EB. Activity-dependent AIDA-1 nuclear signaling regulates nucleolar numbers and protein synthesis in neurons. Nat Neurosci. 2007;10:427–35.

    CAS  Article  Google Scholar 

  25. Luykx JJ, Bakker SC, Lentjes E, Neeleman M, Strengman E, Mentink L, et al. Genome-wide association study of monoamine metabolite levels in human cerebrospinal fluid. Mol psychiatry. 2014;19:228–34.

    CAS  Article  Google Scholar 

  26. Enga RM, Rice AC, Weller P, Subler MA, Lee D, Hall CP, et al. Initial characterization of behavior and ketamine response in a mouse knockout of the post-synaptic effector gene Anks1b. Neurosci Lett. 2017;641:26–32.

    CAS  Article  Google Scholar 

  27. Cross-Disorder Group of the Psychiatric Genomics C, Smoller JW, Craddock N, Kendler K, Lee PH, Neale BM, et al. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381:1371–9.

    Article  Google Scholar 

  28. McClay JL, Adkins DE, Aberg K, Stroup S, Perkins DO, Vladimirov VI, et al. Genome-wide pharmacogenomic analysis of response to treatment with antipsychotics. Mol psychiatry. 2011;16:76–85.

    CAS  Article  Google Scholar 

  29. McClay JL, Adkins DE, Aberg K, Bukszar J, Khachane AN, Keefe RSE, et al. genome-wide pharmacogenomic study of neurocognition as an indicator of antipsychotic treatment response in schizophrenia. Neuropsychopharmacolog. 2011;36:616–26.

    CAS  Article  Google Scholar 

  30. Garriock HA, Kraft JB, Shyn SI, Peters EJ, Yokoyama JS, Jenkins GD, et al. A genomewide association study of citalopram response in major depressive disorder. Biol psychiatry. 2010;67:133–8.

    CAS  Article  Google Scholar 

  31. Jia T, Macare C, Desrivieres S, Gonzalez DA, Tao C, Ji X, et al. Neural basis of reward anticipation and its genetic determinants. Proc Natl Acad Sci USA. 2016;113:3879–84.

    CAS  Article  Google Scholar 

  32. Yalachkov Y, Kaiser J, Naumer MJ. Functional neuroimaging studies in addiction: multisensory drug stimuli and neural cue reactivity. Neurosci Biobehav Rev. 2012;36:825–35.

    Article  Google Scholar 

  33. Wang X, Pathak S, Stefaneanu L, Yeh FC, Li S, Fernandez-Miranda JC. Subcomponents and connectivity of the superior longitudinal fasciculus in the human brain. Brain Struct Funct. 2016;221:2075–92.

    Article  Google Scholar 

  34. Starnawska A, Tan Q, McGue M, Mors O, Borglum AD, Christensen K, et al. Epigenome-wide association study of cognitive functioning in middle-aged monozygotic twins. Front Aging Neurosci. 2017;9:413.

    Article  Google Scholar 

  35. Johnson C, Drgon T, Liu QR, Zhang PW, Walther D, Li CY, et al. Genome wide association for substance dependence: convergent results from epidemiologic and research volunteer samples. BMC Med Genet. 2008;9:113.

    Article  Google Scholar 

  36. Drgon T, Zhang PW, Johnson C, Walther D, Hess J, Nino M, et al. Genome wide association for addiction: replicated results and comparisons of two analytic approaches. PLoS ONE. 2010;5:e8832.

    Article  Google Scholar 

  37. Abe K, Chisaka O, van Roy F, Takeichi M. Stability of dendritic spines and synaptic contacts is controlled by alpha N-catenin. Nat Neurosci. 2004;7:357–63.

    CAS  Article  Google Scholar 

  38. Park C, Falls W, Finger JH, Longo-Guess CM, Ackerman SL. Deletion in Catna2, encoding alpha N-catenin, causes cerebellar and hippocampal lamination defects and impaired startle modulation. Nat Genet. 2002;31:279–84.

    CAS  Article  Google Scholar 

  39. Lesch KP, Timmesfeld N, Renner TJ, Halperin R, Roser C, Nguyen TT, et al. Molecular genetics of adult ADHD: converging evidence from genome-wide association and extended pedigree linkage studies. J Neural Transm. 2008;115:1573–85.

    CAS  Article  Google Scholar 

  40. Terracciano A, Esko T, Sutin AR, de Moor MH, Meirelles O, Zhu G, et al. Meta-analysis of genome-wide association studies identifies common variants in CTNNA2 associated with excitement-seeking. Transl Psychiatry. 2011;1:e49.

    CAS  Article  Google Scholar 

  41. Hall FS, Drgonova J, Jain S, Uhl GR. Implications of genome wide association studies for addiction: are our a priori assumptions all wrong? Pharmacol Ther. 2013;140:267–79.

    CAS  Article  Google Scholar 

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Acknowledgements

This work was supported by grants from the National Basic Research Program of China (2015CB553503), the National Natural Science Foundation of China (U180220091, 81821092, 81601165), the National Key Research and Development Program of China (2017YFC0803608, 2017YFC0803609, 2016YFC0800908), Beijing Municipal Science & Technology Commission (Z181100001518005 and Z161100002616006), and Youth Elite Scientists Sponsorship Program by CASR (CSTQT2017002). We are grateful to Beijing Compass Biotechnology Company for technical assistance with the microarray experiments.

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JS, YS, and SC designed the study and obtained financial support; YS, FW, WY, HS, ZN, and XC conducted cohort recruitment, collected biological samples, and phenotypic data. JL performed the genotype microarray experiments. SC and YS performed genetic data processing, statistical, and bioinformatics analysis. ZL performed the brain imaging analysis. LZ, YZ, and YC performed the animal and in vitro experiments. YS and SC drafted the manuscript. JS and LL supervised the experiments and data analysis. All authors critically reviewed the manuscript and approved the final version.

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Correspondence to Jie Shi.

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Sun, Y., Chang, S., Liu, Z. et al. Identification of novel risk loci with shared effects on alcoholism, heroin, and methamphetamine dependence. Mol Psychiatry 26, 1152–1161 (2021). https://doi.org/10.1038/s41380-019-0497-y

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