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Convergence of case-specific epigenetic alterations identify a confluence of genetic vulnerabilities tied to opioid overdose

Molecular Psychiatry volume 27, pages 2158–2170 (2022)Cite this article

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Abstract

Opioid use disorder is a highly heterogeneous disease driven by a variety of genetic and environmental risk factors which have yet to be fully elucidated. Opioid overdose, the most severe outcome of opioid use disorder, remains the leading cause of accidental death in the United States. We interrogated the effects of opioid overdose on the brain using ChIP-seq to quantify patterns of H3K27 acetylation in dorsolateral prefrontal cortical neurons isolated from 51 opioid-overdose cases and 51 accidental death controls. Among opioid cases, we observed global hypoacetylation and identified 388 putative enhancers consistently depleted for H3K27ac. Machine learning on H3K27ac patterns predicted case-control status with high accuracy. We focused on case-specific regulatory alterations, revealing 81,399 hypoacetylation events, uncovering vast inter-patient heterogeneity. We developed a strategy to decode this heterogeneity based on convergence analysis, which leveraged promoter-capture Hi-C to identify five genes over-burdened by alterations in their regulatory network or “plexus”: ASTN2, KCNMA1, DUSP4, GABBR2, ENOX1. These convergent loci are enriched for opioid use disorder risk genes and heritability for generalized anxiety, number of sexual partners, and years of education. Overall, our multi-pronged approach uncovers neurobiological aspects of opioid use disorder and captures genetic and environmental factors perpetuating the opioid epidemic.

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Fig. 1: Identification of homogeneous H3K27ac hypoacetylation events.
Fig. 2: Machine learning reveals models and key features that distinguish opioid cases from controls.
Fig. 3: Identification of Variable Enhancer Loci (VELs).
Fig. 4: VEL convergence of shared target genes with plexus analysis.
Fig. 5: Convergent VEL genes are enriched for disease heritability.

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Data availability

ChIP and RNA seq data are available through dbgap accession number: phs002724.v1.p1.

Code availability

Code for the machine learning and VEL analyses are available through: https://github.com/corradin-lab/DLPFC-opioid-overdose. Convergence analysis code is available through: https://axiotl.com/ and https://convergence.axiotl.com/.

References

  1. Center for Disease Control. Increase in fatal drug overdoses across the United States driven by synthetic opioids before and during the COVID-19 pandemic. Publication Number CDCHAN-00438 December. Available at https://emergency cdc gov/han/2020/han00438 asp (Accessed February 14, 2021). 2020.

  2. Zhou H, Rentsch CT, Cheng Z, Kember RL, Nunez YZ, Sherva RM, et al. Association of OPRM1 functional coding variant with opioid use disorder: A genome-wide association study. JAMA Psychiatry. 2020; Available from: https://doi.org/10.1001/jamapsychiatry.2020.1206.

  3. Hancock DB, Markunas CA, Bierut LJ, Johnson EO. Human genetics of addiction: New insights and future directions. Curr Psychiatry Rep. 2018;20:8.

    PubMed  PubMed Central  Google Scholar 

  4. Sullivan PF, Agrawal A, Bulik CM, Andreassen OA, Børglum AD, Breen G, et al. Psychiatric genomics: An update and an agenda. AJP 2018;175:15–27.

    Google Scholar 

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

    PubMed  Google Scholar 

  6. Browne CJ, Godino A, Salery M, Nestler EJ. Epigenetic mechanisms of opioid addiction. Biol Psychiatry 2020;87:22–33.

    CAS  PubMed  Google Scholar 

  7. Goldstein RZ, Volkow ND. Dysfunction of the prefrontal cortex in addiction: Neuroimaging findings and clinical implications. Nat Rev Neurosci. 2011;12:652–69.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 2012;337:1190–5. https://doi.org/10.1126/science.1222794.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Trynka G, Sandor C, Han B, Xu H, Stranger BE, Liu XS, et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet. 2013;45:124–30.

    CAS  PubMed  Google Scholar 

  10. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh P-R, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet. 2015;47:1228–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Jung I, Schmitt A, Diao Y, Lee AJ, Liu T, Yang D, et al. A compendium of promoter-centered long-range chromatin interactions in the human genome. Nat Genet. 2019;51:1442–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Akhtar-Zaidi B, Cowper-Sal-lari R, Corradin O, Saiakhova A, Bartels CF, Balasubramanian D, et al. Epigenomic enhancer profiling defines a signature of colon cancer. Science 2012;336:736–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Cohen AJ, Saiakhova A, Corradin O, Luppino JM, Lovrenert K, Bartels CF, et al. Hotspots of aberrant enhancer activity punctuate the colorectal cancer epigenome. Nat Commun. 2017;8:1–13.

    CAS  Google Scholar 

  14. Morrow JJ, Bayles I, Funnell APW, Miller TE, Saiakhova A, Lizardo MM, et al. Positively selected enhancer elements endow osteosarcoma cells with metastatic competence. Nat Med. 2018;24:176–85.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Blackwood CA, McCoy MT, Ladenheim B, Cadet JL. Oxycodone self-administration activates the mitogen-activated protein kinase/ mitogen- and stress-activated protein kinase (MAPK-MSK) signaling pathway in the rat dorsal striatum. Sci Rep. 2021;11:2567.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Qiao X, Zhu Y, Dang W, Wang R, Sun M, Chen Y, et al. Dual-specificity phosphatase 15 (DUSP15) in the nucleus accumbens is a novel negative regulator of morphine-associated contextual memory. Addict Biol. 2021;26:e12884.

    CAS  PubMed  Google Scholar 

  17. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44:W90–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinforma. 2013;14:128.

    Google Scholar 

  19. Al-Hasani R, Bruchas MR. Molecular mechanisms of opioid receptor-dependent signaling and behavior. Anesthesiology 2011;115:1363–81.

    CAS  PubMed  Google Scholar 

  20. Brynildsen JK, Mace KD, Cornblath EJ, Weidler C, Pasqualetti F, Bassett DS, et al. Gene coexpression patterns predict opiate-induced brain-state transitions. Proc Natl Acad Sci USA. 2020;117:19556–65.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: Association for Computing Machinery; 2016. 785–94. (KDD ’16).

  22. McInnes L, Healy J, Melville J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv [stat.ML]. 2018. Available from: http://arxiv.org/abs/1802.03426.

  23. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8:1826.

    PubMed  PubMed Central  Google Scholar 

  24. Sallari RC, Sinnott-Armstrong NA, French JD, Kron KJ, Ho J, Moore JH, et al. Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer. biorxrv. 2017; Available from: https://doi.org/10.1101/097451.

  25. Bailey SD, Desai K, Kron KJ, Mazrooei P, Sinnott-Armstrong NA, Treloar AE, et al. Noncoding somatic and inherited single-nucleotide variants converge to promote ESR1 expression in breast cancer. Nat Genet. 2016;48:1260–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Lutz P-E, Almeida D, Filliol D, Jollant F, Kieffer BL, Turecki G. Increased functional coupling of the mu opioid receptor in the anterior insula of depressed individuals. Neuropsychopharmacology 2021;46:920–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Mendez EF, Wei H, Hu R, Stertz L, Fries GR, Wu X, et al. Angiogenic gene networks are dysregulated in opioid use disorder: evidence from multi-omics and imaging of postmortem human brain. Mol Psychiatry. 2021; Available from: https://doi.org/10.1038/s41380-021-01259-y.

  28. Martins SS, Fenton MC, Keyes KM, Blanco C, Zhu H, Storr CL. Mood and anxiety disorders and their association with non-medical prescription opioid use and prescription opioid-use disorder: longitudinal evidence from the National Epidemiologic Study on Alcohol and Related Conditions. Vol. 42, Psychological Med. 2012. 1261–72. Available from: https://doi.org/10.1017/s0033291711002145.

  29. Polimanti R, Walters RK, Johnson EC, McClintick JN, Adkins AE, Adkins DE, et al. Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium. Available from: https://doi.org/10.1101/765065.

  30. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34:267–73.

    CAS  PubMed  Google Scholar 

  32. Bettinger JC, Davies AG. The role of the BK channel in ethanol response behaviors: Evidence from model organism and human studies. Front Physiol. 2014;5:346.

    PubMed  PubMed Central  Google Scholar 

  33. Wolen AR, Phillips CA, Langston MA, Putman AH, Vorster PJ, Bruce NA, et al. Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex: Functional and mechanistic implications. PLoS One. 2012;7:e33575.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Abdul Rahman NZ, Greenwood SM, Brett RR, Tossell K, Ungless MA, Plevin R, et al. Mitogen-activated protein kinase Phosphatase-2 deletion impairs synaptic plasticity and hippocampal-dependent memory. J Neurosci. 2016;36:2348–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Cui W-Y, Seneviratne C, Gu J, Li MD. Genetics of GABAergic signaling in nicotine and alcohol dependence. Hum Genet. 2012;131:843–55.

    CAS  PubMed  Google Scholar 

  36. Ribeiro AF, Correia D, Torres AA, Boas GRV, Rueda AVL, Camarini R, et al. A transcriptional study in mice with different ethanol-drinking profiles: Possible involvement of the GABA(B) receptor. Pharm Biochem Behav. 2012;102:224–32.

    CAS  Google Scholar 

  37. Behesti H, Fore TR, Wu P, Horn Z, Leppert M, Hull C, et al. ASTN2 modulates synaptic strength by trafficking and degradation of surface proteins. Proc Natl Acad Sci USA. 2018;115:E9717–26.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Inoue R, Nishizawa D, Hasegawa J, Nakayama K, Fukuda K-I, Ichinohe T, et al. Effects of rs958804 and rs7858836 single-nucleotide polymorphisms of the ASTN2 gene on pain-related phenotypes in patients who underwent laparoscopic colectomy and mandibular sagittal split ramus osteotomy. Neuropsychopharmacol Rep. 2021;41:82–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Hillis SD, Anda RF, Felitti VJ, Marchbanks PA. Adverse childhood experiences and sexual risk behaviors in women: A retrospective cohort study. Fam Plann Perspect. 2001;33:206–11.

    CAS  PubMed  Google Scholar 

  40. Montez JK, Hayward MD. Cumulative childhood adversity, educational attainment, and active life expectancy among U.S. adults. Demography 2014;51:413–35.

    PubMed  Google Scholar 

  41. Murthy S, Gould E. How early life adversity influences defensive circuitry. Trends Neurosci. 2020;43:200–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Levis SC, Bentzley BS, Molet J, Bolton JL, Perrone CR, Baram TZ, et al. On the early life origins of vulnerability to opioid addiction. Mol Psychiatry. 2019; Available from: https://doi.org/10.1038/s41380-019-0628-5.

  43. Enoch M-A. The role of early life stress as a predictor for alcohol and drug dependence. Psychopharmacology 2011;214:17–31.

    CAS  PubMed  Google Scholar 

  44. Widom CS, Marmorstein NR, White HR. Childhood victimization and illicit drug use in middle adulthood. Psychol Addict Behav. 2006;20:394–403.

    PubMed  Google Scholar 

  45. Vaiserman AM, Koliada AK. Early-life adversity and long-term neurobehavioral outcomes: Epigenome as a bridge? Hum Genomics 2017;11:34.

    PubMed  PubMed Central  Google Scholar 

  46. Barnett Burns S, Almeida D, Turecki G. The Epigenetics of Early Life Adversity: Current Limitations and Possible Solutions. In: Grayson DR, editor. Progress in Molecular Biology and Translational Science. Academic Press; 2018. 343–425.

  47. Leza L, Siria S, López-Goñi JJ, Fernández-Montalvo J. Adverse childhood experiences (ACEs) and substance use disorder (SUD): A scoping review. Drug Alcohol Depend. 2021;221:108563.

    PubMed  Google Scholar 

  48. Dasgupta N, Beletsky L, Ciccarone D. Opioid crisis: No easy fix to its social and economic determinants. Am J Public Health. 2018;108:182–6.

    PubMed  PubMed Central  Google Scholar 

  49. Altekruse SF, Cosgrove CM, Altekruse WC, Jenkins RA, Blanco C. Socioeconomic risk factors for fatal opioid overdoses in the United States: Findings from the Mortality Disparities in American Communities Study (MDAC). PLoS One 2020;15:e0227966.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Case A, Deaton A, Stone AA. Decoding the mystery of American pain reveals a warning for the future. Proc Natl Acad Sci USA. 2020;117:24785–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Kundakovic M, Jiang Y, Kavanagh DH, Dincer A, Brown L, Pothula V, et al. Practical guidelines for high-resolution epigenomic profiling of nucleosomal histones in postmortem human brain tissue. Biol. Psychiatry 2017;81:162–70.

  52. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.

    Google Scholar 

  53. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv [q-bio.GN]. 2013. Available from: http://arxiv.org/abs/1303.3997.

  54. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics 2009;25:2078–9.

    PubMed  PubMed Central  Google Scholar 

  55. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9:R137.

    PubMed  PubMed Central  Google Scholar 

  56. Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, et al. deepTools2: A next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 2016;44:W160–5.

    PubMed  PubMed Central  Google Scholar 

  57. Thorvaldsdóttir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): High-performance genomics data visualization and exploration. Brief Bioinform. 2013;14:178–92.

    PubMed  Google Scholar 

  58. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al. Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol. 2004;5:R80.

    PubMed  PubMed Central  Google Scholar 

  59. Carroll TS, Liang Z, Salama R, Stark R, de Santiago I. Impact of artifact removal on ChIP quality metrics in ChIP-seq and ChIP-exo data. Front Genet. 2014;5:75.

    PubMed  PubMed Central  Google Scholar 

  60. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 2017;14:417–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Srivastava A, Malik L, Sarkar H, Zakeri M, Almodaresi F, Soneson C, et al. Alignment and mapping methodology influence transcript abundance estimation. Genome Biol. 2020;21:239.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Love MI, Hogenesch JB, Irizarry RA. Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. Nat Biotechnol. 2016;34:1287–91.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Res. 2015;4. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/pmc4712774/.

  65. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    PubMed  PubMed Central  Google Scholar 

  66. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012;28:882–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. From local explanations to global understanding with explainable AI for Trees. Nat Mach Intell 2020;2:56–67.

    PubMed  PubMed Central  Google Scholar 

  68. Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J. Schizophrenia working group of the psychiatric genomics consortium, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature 2015;526:68–74.

    Google Scholar 

  70. Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51:63–75.

    CAS  PubMed  Google Scholar 

  71. Zhou H, Sealock JM, Sanchez-Roige S, Clarke T-K, Levey DF, Cheng Z, et al. Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat Neurosci. 2020;23:809–18.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Boraska V, Franklin CS, Floyd JAB, Thornton LM, Huckins LM, Southam L, et al. A genome-wide association study of anorexia nervosa. Mol Psychiatry 2014;19:1085–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Stahl EA, Breen G, Forstner AJ, McQuillin A, Ripke S, Trubetskoy V, et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet. 2019;51:793–803.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42:937–48.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Dubois PCA, Trynka G, Franke L, Hunt KA, Romanos J, Curtotti A, et al. Multiple common variants for celiac disease influencing immune gene expression. Nat Genet. 2010;42:295–302.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 2012;491:119–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22:343–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Manning AK, Hivert M-F, Scott RA, Grimsby JL, Bouatia-Naji N, Chen H, et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet. 2012;44:659–69.

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Levey DF, Gelernter J, Polimanti R, Zhou H, Cheng Z, Aslan M, et al. Reproducible genetic risk loci for anxiety: Results from ∼200,000 participants in the Million Veteran Program. AJP. 2020;177:223–32.

    Google Scholar 

  80. Allen HL, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 2010;467:832–8.

    Google Scholar 

  81. Okbay A, Baselmans BML, De Neve J-E, Turley P, Nivard MG, Fontana MA, et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet. 2016;48:624–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014;511:421–7.

    PubMed Central  Google Scholar 

  83. 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  PubMed  PubMed Central  Google Scholar 

  84. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 2010;466:707–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Bradfield JP, Qu H-Q, Wang K, Zhang H, Sleiman PM, Kim CE, et al. A genome-wide meta-analysis of six type 1 diabetes cohorts identifies multiple associated Loci. PLoS Genet. 2011;7:e1002293.

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Rietveld CA, Medland SE, Derringer J, Yang J, Esko T, Martin NW, et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 2013;340:1467–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 2016;533:539–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Linnér RK, Biroli P, Kong E, Meddens SFW, Wedow R, Fontana MA, et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat Genet 2019;51:245–57.

    PubMed Central  Google Scholar 

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Acknowledgements

We would like to express our gratitude to the families of the subjects used in this study. We would also like to acknowledge the teams of technicians at the Miami Brain Endowment Bank. This work was supported by NIH National Institute of Drug Abuse R01 DA043980.

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Authors and Affiliations

  1. Whitehead Institute Biomedical Research, Cambridge, USA

    Olivia Corradin, An T. Hoang & Yanwei Song

  2. Department of Biology, Massachusetts Institute of Technology, Cambridge, USA

    Olivia Corradin

  3. Axiotl Inc, Cleveland, OH, USA

    Richard Sallari

  4. Department of Psychiatry and Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA

    Bibi S. Kassim, Gabriella Ben Hutta, Lizette Cuoto, Marina Iskhakova, Hannah Cates & Schahram Akbarian

  5. GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, 27709, USA

    Bryan C. Quach, Dana B. Hancock & Eric O. Johnson

  6. Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, USA

    Katreya Lovrenert, Berkley E. Gryder, Cynthia F. Bartels & Peter C. Scacheri

  7. College of Health and Human Services, Western Michigan University, Kalamazoo, USA

    Cameron Hays

  8. Dr. Kiran C. Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA

    Deborah C. Mash

  9. Fellow Program, RTI International, Research Triangle Park, NC, 27709, USA

    Eric O. Johnson

  10. Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, USA

    Peter C. Scacheri

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Contributions

This work was conceptualized and written by PCS, SA, EOJ, DCM, DBH, RS, and OC. ChIP-seq and RNA-seq experiments were completed by SA, BSK, GBH, LC, KL, MI, HC, and CFB. Data analysis was performed by, OC, RS, ATH, BCQ, KL, and YS. Discussion and feedback were provided by CH and BEG.

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Correspondence to Peter C. Scacheri.

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Peter Scacheri received compensation as a consultant for Kronos Bio.

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Corradin, O., Sallari, R., Hoang, A.T. et al. Convergence of case-specific epigenetic alterations identify a confluence of genetic vulnerabilities tied to opioid overdose. Mol Psychiatry 27, 2158–2170 (2022). https://doi.org/10.1038/s41380-022-01477-y

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  • DOI: https://doi.org/10.1038/s41380-022-01477-y

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