Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder

Molecular Psychiatry volume 27, pages 3075–3084 (2022)Cite this article

Subjects

Abstract

Genome-wide association studies (GWAS) have identified several risk loci for post-traumatic stress disorder (PTSD); however, how they confer PTSD risk remains unclear. We aimed to identify genes that confer PTSD risk through their effects on brain protein abundance to provide new insights into PTSD pathogenesis. To that end, we integrated human brain proteomes with PTSD GWAS results to perform a proteome-wide association study (PWAS) of PTSD, followed by Mendelian randomization, using a discovery and confirmatory study design. Brain proteomes (N = 525) were profiled from the dorsolateral prefrontal cortex using mass spectrometry. The Million Veteran Program (MVP) PTSD GWAS (n = 186,689) was used for the discovery PWAS, and the Psychiatric Genomics Consortium PTSD GWAS (n = 174,659) was used for the confirmatory PWAS. To understand whether genes identified at the protein-level were also evident at the transcript-level, we performed a transcriptome-wide association study (TWAS) using human brain transcriptomes (N = 888) and the MVP PTSD GWAS results. We identified 11 genes that contribute to PTSD pathogenesis via their respective cis-regulated brain protein abundance. Seven of 11 genes (64%) replicated in the confirmatory PWAS and 4 of 11 also had their cis-regulated brain mRNA levels associated with PTSD. High confidence level was assigned to 9 of 11 genes after considering evidence from the confirmatory PWAS and TWAS. Most of the identified genes are expressed in other PTSD-relevant brain regions and several are preferentially expressed in excitatory neurons, astrocytes, and oligodendrocyte precursor cells. These genes are novel, promising targets for mechanistic and therapeutic studies to find new treatments for PTSD.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2: Results of the discovery PWAS of PTSD.
Fig. 3: Bar graph of single-cell-type enrichment for the 11 potential PTSD-causal genes.
Fig. 4: Expression of the 11 potential PTSD-causal genes in different PTSD-relevant brain regions.

Similar content being viewed by others

Data availability

—MVP PTSD GWAS summary statistics: dbGAP (phs001672.v6.p1)

—PGC-PTSD GWAS summary statistics: publicly available on the PGC website (https://www.med.unc.edu/pgc/download-results/) under for the benefit of the wider biomedical community.

—Proteomic datasets: genotypes, protein expression levels, expression covariates: https://www.synapse.org/#!Synapse:syn24872746—AMP-AD datasets: genotypes, transcript expression levels, expression covariates https://adknowledgeportal.synapse.org/Explore/Studies/DetailsPage?Study=syn22313785—CMC datasets: We downloaded the transcript weights from http://gusevlab.org/projects/fusion/, https://data.broadinstitute.org/alkesgroup/FUSION/WGT/CMC.BRAIN.RNASEQ.tar.bz2—Sc-RNAseq datasets: https://www.synapse.org/#!Synapse:syn21589957—Allen brain atlas https://human.brain-map.org/microarray/search

References

  1. Bromet E, Sonnega A, Kessler RC. Risk factors for DSM-III-R posttraumatic stress disorder: findings from the National Comorbidity Survey. Am J Epidemiol. 1998;147:353–61.

    Article  CAS  PubMed  Google Scholar 

  2. Richardson LK, Frueh BC, Acierno R. Prevalence estimates of combat-related post-traumatic stress disorder: critical review. Aust NZ J Psychiatry. 2010;44:4–19.

    Article  Google Scholar 

  3. Boe HJ, Holgersen KH, Holen A. Mental health outcomes and predictors of chronic disorders after the North Sea oil rig disaster: 27-year longitudinal follow-up study. J Nerv Ment Dis. 2011;199:49–54.

    Article  PubMed  Google Scholar 

  4. Hull AM, Alexander DA, Klein S. Survivors of the Piper Alpha oil platform disaster: long-term follow-up study. Br J Psychiatry. 2002;181:433–8.

    Article  PubMed  Google Scholar 

  5. Steenkamp MM, Litz BT, Hoge CW, Marmar CR. Psychotherapy for military-related PTSD: a review of randomized clinical trials. Jama. 2015;314:489–500.

    Article  CAS  PubMed  Google Scholar 

  6. Hoskins M, Pearce J, Bethell A, Dankova L, Barbui C, Tol WA, et al. Pharmacotherapy for post-traumatic stress disorder: systematic review and meta-analysis. Br J Psychiatry. 2015;206:93–100.

    Article  PubMed  Google Scholar 

  7. Shiner B, Westgate CL, Gui J, Maguen S, Young-Xu Y, Schnurr PP, et al. A Retrospective Comparative Effectiveness Study of Medications for Posttraumatic Stress Disorder in Routine Practice. J Clin Psychiatry. 2018;79:18m12145.

  8. Lewis C, Roberts NP, Andrew M, Starling E, Bisson JI. Psychological therapies for post-traumatic stress disorder in adults: systematic review and meta-analysis. Eur J Psychotraumatol. 2020;11:1729633.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Steenkamp MM, Litz BT, Marmar CR. First-line psychotherapies for military-related PTSD. Jama. 2020;323:656–7.

    Article  PubMed  Google Scholar 

  10. Krystal JH, Davis LL, Neylan TC, Raskind MA, Schnurr PP, Stein MB, et al. It is time to address the crisis in the pharmacotherapy of posttraumatic stress disorder: a consensus statement of the PTSD psychopharmacology working group. Biol Psychiatry. 2017;82:e51–9.

    Article  PubMed  Google Scholar 

  11. Jovanovic T, Ressler KJ. How the neurocircuitry and genetics of fear inhibition may inform our understanding of PTSD. Am J Psychiatry. 2010;167:648–62.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Stein MB, Jang KL, Taylor S, Vernon PA, Livesley WJ. Genetic and environmental influences on trauma exposure and posttraumatic stress disorder symptoms: a twin study. Am J Psychiatry. 2002;159:1675–81.

    Article  PubMed  Google Scholar 

  13. Sartor CE, Grant JD, Lynskey MT, McCutcheon VV, Waldron M, Statham DJ, et al. Common heritable contributions to low-risk trauma, high-risk trauma, posttraumatic stress disorder, and major depression. Arch Gen Psychiatry. 2012;69:293–9.

    Article  PubMed  PubMed Central  Google Scholar 

  14. King EA, Davis JW, Degner JF. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 2019;15:e1008489.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Gelernter J, Sun N, Polimanti R, Pietrzak R, Levey DF, Bryois J, et al. Genome-wide association study of post-traumatic stress disorder reexperiencing symptoms in >165,000 US veterans. Nat Neurosci. 2019;22:1394–401.

  16. Nievergelt CM, Maihofer AX, Klengel T, Atkinson EG, Chen CY, Choi KW, et al. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nat Commun. 2019;10:4558.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Stein MB, Levey DF, Cheng Z, Wendt FR, Harrington K, Pathak GA, et al. Genome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the Million Veteran Program. Nat Genet. 2021;53:174–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.

    Article  CAS  PubMed  Google Scholar 

  19. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48:245–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wingo TS, Liu Y, Gerasimov ES, Gockley J, Logsdon BA, Duong DM, et al. Brain proteome-wide association study implicates novel proteins in depression pathogenesis. Nat Neurosci. 2021;24:810–7.

  21. Wingo AP, Liu Y, Gerasimov ES, Gockley J, Logsdon BA, Duong DM, et al. Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis. Nat Genet. 2021;53:143–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10:e1004383.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Gockley J, Montgomery KS, Poehlman WL, Wiley JC, Liu Y, Gerasimov E, et al. Multi-tissue neocortical transcriptome-wide association study implicates 8 genes across 6 genomic loci in Alzheimer’s disease. Genome Med. 2021;13:76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Gusev A, Mancuso N, Won H, Kousi M, Finucane HK, Reshef Y, et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet. 2018;50:538–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature. 2019;570:332–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fenster RJ, Lebois LAM, Ressler KJ, Suh J. Brain circuit dysfunction in post-traumatic stress disorder: from mouse to man. Nat Rev Neurosci. 2018;19:535–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489:391–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Li T, Kim A, Rosenbluh J, Horn H, Greenfeld L, An D, et al. GeNets: a unified web platform for network-based genomic analyses. Nat Methods. 2018;15:543–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Huckins LM, Dobbyn A, Ruderfer DM, Hoffman G, Wang W, Pardinas AF, et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nat Genet. 2019;51:659–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Raj T, Li YI, Wong G, Humphrey J, Wang M, Ramdhani S, et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat Genet. 2018;50:1584–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Rytwinski NK, Scur MD, Feeny NC, Youngstrom EA. The co-occurrence of major depressive disorder among individuals with posttraumatic stress disorder: a meta-analysis. J Trauma Stress. 2013;26:299–309.

    Article  PubMed  Google Scholar 

  33. Howard DM, Adams MJ, Clarke TK, 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sharma K, Schmitt S, Bergner CG, Tyanova S, Kannaiyan N, Manrique-Hoyos N, et al. Cell type- and brain region-resolved mouse brain proteome. Nat Neurosci. 2015;18:1819–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13:227–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Girgenti MJ, Wang J, Ji D, Cruz DA, Stein MB, Gelernter J, et al. Transcriptomic organization of the human brain in post-traumatic stress disorder. Nat Neurosci. 2021;24:24–33.

    Article  CAS  PubMed  Google Scholar 

  37. Huckins LM, Chatzinakos C, Breen MS, Hartmann J, Klengel T, da Silva Almeida AC, et al. Analysis of genetically regulated gene expression identifies a prefrontal PTSD gene, SNRNP35, specific to military cohorts. Cell Rep. 2020;31:107716.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinform. 2016;54:1.30.1–33.

    Article  Google Scholar 

  39. Chen D, Guo J, Miki T, Tachibana M, Gahl WA. Molecular cloning and characterization of rab27a and rab27b, novel human rab proteins shared by melanocytes and platelets. Biochemical Mol Med. 1997;60:27–37.

    Article  CAS  Google Scholar 

  40. Kim YK, Jeon SW. Neuroinflammation and the immune-kynurenine pathway in anxiety disorders. Curr Neuropharmacol. 2018;16:574–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kadriu B, Farmer CA, Yuan P, Park LT, Deng ZD, Moaddel R, et al. The kynurenine pathway and bipolar disorder: intersection of the monoaminergic and glutamatergic systems and immune response. Mol Psychiatry. 2021;26:4085–95.

  42. Lipinski M, Muñoz-Viana R, Del Blanco B, Marquez-Galera A, Medrano-Relinque J, Caramés JM, et al. KAT3-dependent acetylation of cell type-specific genes maintains neuronal identity in the adult mouse brain. Nat Commun. 2020;11:2588.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chaudhuri R, Khanna K, Koundinya D, Pattnaik B, Vatsa D, Agrawal A, et al. Novel nuclear translocation of inositol polyphosphate 4-phosphatase is associated with cell cycle, proliferation and survival. Biochim Biophys Acta Mol Cell Res. 2018:S0167-4889(18)30188-5. https://doi.org/10.1016/j.bbamcr.2018.07.013. Epub ahead of print.

  44. Cross-Disorder Group of the Psychiatric Genomics Consortium. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell. 2019;179:1469–82.e11.

  45. Muhie S, Gautam A, Chakraborty N, Hoke A, Meyerhoff J, Hammamieh R, et al. Molecular indicators of stress-induced neuroinflammation in a mouse model simulating features of post-traumatic stress disorder. Transl Psychiatry. 2017;7:e1135.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Carvalho-Silva D, Pierleoni A, Pignatelli M, Ong C, Fumis L, Karamanis N, et al. Open targets platform: new developments and updates two years on. Nucleic Acids Res. 2019;47:D1056–65.

    Article  CAS  PubMed  Google Scholar 

  47. Bennett DA, Buchman AS, Boyle PA, Barnes LL, Wilson RS, Schneider JA. Religious orders study and rush memory and aging project. J Alzheimers Dis. 2018;64:S161–89.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Wingo AP, Fan W, Duong DM, Gerasimov ES, Dammer EB, Liu Y, et al. Shared proteomic effects of cerebral atherosclerosis and Alzheimer’s disease on the human brain. Nat Neurosci. 2020;23:696–700.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Johnson ECB, Dammer EB, Duong DM, Ping L, Zhou M, Yin L, et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat Med. 2020;26:769–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mertins P, Tang LC, Krug K, Clark DJ, Gritsenko MA, Chen L, et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry. Nat Protoc. 2018;13:1632–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. De Jager PL, Shulman JM, Chibnik LB, Keenan BT, Raj T, Wilson RS, et al. A genome-wide scan for common variants affecting the rate of age-related cognitive decline. Neurobiol Aging. 2012;33:1017.e1–5.

    Article  CAS  Google Scholar 

  52. De Jager PL, Ma Y, McCabe C, Xu J, Vardarajan BN, Felsky D, et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci Data. 2018;5:180142.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M, Bender D, et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet. 2007;81:559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65.

    Article  PubMed  CAS  Google Scholar 

  55. Das S, Forer L, Schonherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies. Bioinformatics. 2010;26:2867–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Allen M, Carrasquillo MM, Funk C, Heavner BD, Zou F, Younkin CS, et al. Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases. Sci Data. 2016;3:160089.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang M, Beckmann ND, Roussos P, Wang E, Zhou X, Wang Q, et al. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease. Sci Data. 2018;5:180185.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Wan YW, Al-Ouran R, Mangleburg CG, Perumal TM, Lee TV, Allison K, et al. Meta-analysis of the Alzheimer’s disease human brain transcriptome and functional dissection in mouse models. Cell Rep. 2020;32:107908.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

    Article  CAS  PubMed  Google Scholar 

  61. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36:411–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Lage K, Karlberg EO, Storling ZM, Olason PI, Pedersen AG, Rigina O, et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol. 2007;25:309–16.

    Article  CAS  PubMed  Google Scholar 

  64. Clauset A, Newman ME, Moore C. Finding community structure in very large networks. Phys Rev E Stat Nonlinear Soft Matter Phys. 2004;70:066111.

    Article  CAS  Google Scholar 

  65. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful to the participants of the ROS, MAP, Mayo, Mount Sinai Brain Bank, and Banner Sun Health Research Institute Brain and Body Donation Program for their time and participation. We thank the Psychiatric Genomics Consortium PTSD working group for making the summary statistics from the published PTSD GWAS in Nievergelt et al. [16] available. We thank Jiaqi Liu for her assistance with creating the Fig. 3. The authors thank MVP staff, researchers, and volunteers, who have contributed to MVP, and especially participants who previously served their country in the military and now generously agreed to enroll in the study. (See https://www.research.va.gov/mvp/ for more details) The following NIH grants supported this work: P30 AG066511 (AIL), P50 AG025688 (AIL), R01 AG015819 (DAB), R01 AG017917 (DAB), R01 AG056533 (TSW, APW), VA 1IK4 BX005219 (APW), I01 BX005686 (APW). TSW is also supported by R56 AG060757, R56 AG062256, RF1 AG057470. NTS is also supported by R01 AG053960, R01 AG057911, R01 AG061800. DAB is also supported by RC2 AG036547, U01 AG046152, U01 AG061356. AIL is also supported by U01 AG046161, U01 AG061357. APW is also supported by U01 MH115484, VA I01 BX003853. CMN, MBS, KCK, KJR were supported by R01MH106595. The Brain and Body Donation Program has been supported by NIH, the Arizona Department of Health Services, the Arizona Biomedical Research Commission and the Michael J. Fox Foundation for Parkinson’s Research. The views expressed in this work do not necessarily represent the views of the Veterans Administration or the United States Government.

Author information

Authors and Affiliations

  1. Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA

    Thomas S. Wingo, Ekaterina S. Gerasimov, Yue Liu, Selina M. Vattathil & Allan I. Levey

  2. Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA

    Thomas S. Wingo

  3. Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA

    Duc M. Duong & Nicholas T. Seyfried

  4. Department of Psychiatry, Emory University School of Medicine, Atlanta, GA, USA

    Adriana Lori & Aliza P. Wingo

  5. Sage Bionetworks, Seattle, WA, USA

    Jake Gockley

  6. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Michael S. Breen

  7. Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Michael S. Breen

  8. Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Michael S. Breen

  9. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

    Adam X. Maihofer, Caroline M. Nievergelt & Murray B. Stein

  10. Veterans Affairs San Diego Health Care System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA

    Adam X. Maihofer & Caroline M. Nievergelt

  11. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA

    Karestan C. Koenen

  12. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Karestan C. Koenen

  13. Psychiatric Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Karestan C. Koenen

  14. Department of Psychiatry Yale, University School of Medicine, New Haven, CT, USA

    Daniel F. Levey & Joel Gelernter

  15. Veterans Affairs Connecticut Health Center System, New Haven, CT, USA

    Joel Gelernter

  16. School of Public Health, University of California San Diego, La Jolla, CA, USA

    Murray B. Stein

  17. McLean Hospital, Harvard Medical School, Belmont, MA, USA

    Kerry J. Ressler

  18. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

    David A. Bennett

  19. Veterans Affairs Atlanta Health Care System, Decatur, GA, USA

    Aliza P. Wingo

Authors
  1. Thomas S. Wingo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ekaterina S. Gerasimov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yue Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Duc M. Duong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Selina M. Vattathil
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Adriana Lori
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jake Gockley
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michael S. Breen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Adam X. Maihofer
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Caroline M. Nievergelt
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Karestan C. Koenen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Daniel F. Levey
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Joel Gelernter
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Murray B. Stein
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Kerry J. Ressler
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. David A. Bennett
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Allan I. Levey
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Nicholas T. Seyfried
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Aliza P. Wingo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

TSW and APW wrote the first draft of the paper. DAB, AIL, and NTS obtained the data. ESG, YL, SMV, JG, DMD, AL, TSW, and APW performed data analysis. MSB, AXM, CMN, KCK, DFL, JG, MBS, KJR, DAB, AIL contributed to data interpretation. All authors critically edited and commented on the paper.

Corresponding author

Correspondence to Aliza P. Wingo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wingo, T.S., Gerasimov, E.S., Liu, Y. et al. Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder. Mol Psychiatry 27, 3075–3084 (2022). https://doi.org/10.1038/s41380-022-01544-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-022-01544-4

This article is cited by

Search

Advanced search

Quick links