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:

Identifying causal genes for depression via integration of the proteome and transcriptome from brain and blood

Molecular Psychiatry volume 27, pages 2849–2857 (2022)Cite this article

Subjects

Abstract

Genome-wide association studies (GWASs) have identified numerous risk genes for depression. Nevertheless, genes crucial for understanding the molecular mechanisms of depression and effective antidepressant drug targets are largely unknown. Addressing this, we aimed to highlight potentially causal genes by systematically integrating the brain and blood protein and expression quantitative trait loci (QTL) data with a depression GWAS dataset via a statistical framework including Mendelian randomization (MR), Bayesian colocalization, and Steiger filtering analysis. In summary, we identified three candidate genes (TMEM106B, RAB27B, and GMPPB) based on brain data and two genes (TMEM106B and NEGR1) based on blood data with consistent robust evidence at both the protein and transcriptional levels. Furthermore, the protein-protein interaction (PPI) network provided new insights into the interaction between brain and blood in depression. Collectively, four genes (TMEM106B, RAB27B, GMPPB, and NEGR1) affect depression by influencing protein and gene expression level, which could guide future researches on candidate genes investigations in animal studies as well as prioritize antidepressant drug targets.

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: Analytical workflow of the study.
Fig. 2: Volcano plots of MR analysis of brain and blood pQTLs/eQTLs and depression.
Fig. 3: Genes wielding consistently positive outcome among different methods.
Fig. 4: Main PPI network among the proteins identified by pQTL analysis.

Similar content being viewed by others

Data availability

Data of brain pQTL from the ROS/MAP study are available through https://doi.org/10.7303/syn23627957. The smaller pQTL data from the 144 cognitively healthy participants of the ROS/MAP study are available though https://doi.org/10.7303/syn24172458. Data are available for general research use according to the following requirements for data access and data attribution (https://adknowledgeportal.org/DataAccess/Instructions). Data of brain eQTL from the PsychENCODE Consortium are accessible in BESD format through https://cnsgenomics.com/software/smr/#eQTLsummarydata. Data from AGES Reykjavik study can be accessed at www.sciencemag.org/cgi/content/full/science.aaq1327/DC1. Data from the AGES Reykjavik study are available through collaboration (AGES_data_request@hjarta.is) under a data usage agreement with the IHA. GTEx can be accessed at https://gtexportal.org/home/datasets (GTEx Analysis V6) or in BESD format through https://cnsgenomics.com/software/smr/#eQTLsummarydata. Data of eQTLGen are available through https://www.eqtlgen.org/cis-eqtls.html. Summary statistics for the Howard’s meta-analysis of depression GWAS from UK Biobank and PGC_139k are available from https://doi.org/10.7488/ds/2458.

Materials availability

Correspondence and requests for materials should be addressed to JTY.

References

  1. Razzak HA, Harbi A, Ahli S. Depression: prevalence and associated risk factors in the United Arab Emirates. Oman Med J. 2019;34:274–82.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Collaborators GDaIIaP. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1545–602.

    Article  Google Scholar 

  3. Tomás CC, Oliveira E, Sousa D, Uba-Chupel M, Furtado G, Rocha C et al. Proceedings of the 3rd IPLeiria’s International Health Congress: Leiria, Portugal. BMC Health Serv Res. 2016;16:111–242.

  4. Akil H, Gordon J, Hen R, Javitch J, Mayberg H, McEwen B, et al. Treatment resistant depression: A multi-scale, systems biology approach. Neurosci Biobehav Rev. 2018;84:272–88.

    Article  PubMed  Google Scholar 

  5. Sullivan PF, Neale MC, Kendler KS. Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry. 2000;157:1552–62.

    Article  CAS  PubMed  Google Scholar 

  6. 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 

  7. Akbarian S, Liu C, Knowles JA, Vaccarino FM, Farnham PJ, Crawford GE, et al. The PsychENCODE project. Nat Neurosci. 2015;18:1707–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Baird DA, Liu JZ, Zheng J, Sieberts SK, Perumal T, Elsworth B, et al. Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome. PLoS Genet. 2021;17:e1009224.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Robinson JW, Martin RM, Tsavachidis S, Howell AE, Relton CL, Armstrong GN, et al. Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility. Sci Rep. 2021;11:2329.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Smith GD, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22.

    Article  PubMed  Google Scholar 

  11. Smith GD, Ebrahim S. Data dredging, bias, or confounding. Bmj. 2002;325:1437–8.

    Article  PubMed  PubMed Central  Google Scholar 

  12. 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 

  13. Dall’Aglio L, Lewis CM, Pain O. Delineating the genetic component of gene expression in major depression. Biol Psychiatry. 2021;89:627–36.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Fabbri C, Pain O, Hagenaars SP, Lewis CM, Serretti A. Transcriptome-wide association study of treatment-resistant depression and depression subtypes for drug repurposing. Neuropsychopharmacology. 2021;46:1821–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Li HJ, Qu N, Hui L, Cai X, Zhang CY, Zhong BL, et al. Further confirmation of netrin 1 receptor (DCC) as a depression risk gene via integrations of multi-omics data. Transl Psychiatry. 2020;10:98.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Wang X, Cheng W, Zhu J, Yin H, Chang S, Yue W, et al. Integrating genome-wide association study and expression quantitative trait loci data identifies NEGR1 as a causal risk gene of major depression disorde. J Affect Disord. 2020;265:679–86.

    Article  CAS  PubMed  Google Scholar 

  17. Buccitelli C, Selbach M. mRNAs, proteins and the emerging principles of gene expression control. Nat Rev Genet. 2020;21:630–44.

    Article  CAS  PubMed  Google Scholar 

  18. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18:83.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Vitrinel B, Koh HWL, Mujgan Kar F, Maity S, Rendleman J, Choi H, et al. Exploiting interdata relationships in next-generation proteomics analysis. Mol Cell Proteom. 2019;18:S5–s14.

    Article  CAS  Google Scholar 

  20. Liu J, Li X, Luo XJ. Proteome-wide association study provides insights into the genetic component of protein abundance in psychiatric disorders. Biol Psychiatry. 2021;90:781–9.

    Article  CAS  PubMed  Google Scholar 

  21. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Mehta D, Menke A, Binder EB. Gene expression studies in major depression. Curr Psychiatry Rep. 2010;12:135–44.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Iwamoto K, Kato T. Gene expression profiling in schizophrenia and related mental disorders. Neuroscientist. 2006;12:349–61.

    Article  CAS  PubMed  Google Scholar 

  24. Howard DM, Adams MJ, Shirali M, Clarke TK, Marioni RE, Davies G, et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat Commun. 2018;9:1470.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 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 

  27. 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 

  28. 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 

  29. 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 

  30. Emilsson V, Ilkov M, Lamb JR, Finkel N, Gudmundsson EF, Pitts R, et al. Co-regulatory networks of human serum proteins link genetics to disease. Science. 2018;361:769–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Battle A, Brown CD, Engelhardt BE, Montgomery SB. Genetic effects on gene expression across human tissues. Nature. 2017;550:204–13.

    Article  PubMed  Google Scholar 

  32. Võsa U, Claringbould A, Westra H-J, Bonder MJ, Deelen P, Zeng B et al. Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis. bioRxiv 2018: 447367.

  33. Riggs DS, Guarnieri JA, Addelman S. Fitting straight lines when both variables are subject to error. Life Sci. 1978;22:1305–60.

    Article  CAS  PubMed  Google Scholar 

  34. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 2018;7:e34408.

  36. Plagnol V, Smyth DJ, Todd JA, Clayton DG. Statistical independence of the colocalized association signals for type 1 diabetes and RPS26 gene expression on chromosome 12q13. Biostatistics. 2009;10:327–34.

    Article  PubMed  Google Scholar 

  37. Wallace C. Statistical testing of shared genetic control for potentially related traits. Genet Epidemiol. 2013;37:802–13.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 2019;4:186.

    Article  PubMed  Google Scholar 

  39. Kibinge NK, Relton CL, Gaunt TR, Richardson TG. Characterizing the causal pathway for genetic variants associated with neurological phenotypes using human brain-derived proteome data. Am J Hum Genet. 2020;106:885–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Malhi GS, Mann JJ. Depression. Lancet. 2018;392:2299–312.

    Article  PubMed  Google Scholar 

  41. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46:D1074–82.

    Article  CAS  PubMed  Google Scholar 

  42. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13.

    Article  CAS  PubMed  Google Scholar 

  43. Wang Q, Dwivedi Y. Advances in novel molecular targets for antidepressants. Prog Neuropsychopharmacol Biol Psychiatry. 2021;104:110041.

    Article  CAS  PubMed  Google Scholar 

  44. Wu W, Howard D, Sibille E, French L. Differential and spatial expression meta-analysis of genes identified in genome-wide association studies of depression. Transl Psychiatry. 2021;11:8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Li S, Li Y, Li X, Liu J, Huo Y, Wang J, et al. Regulatory mechanisms of major depressive disorder risk variants. Mol Psychiatry. 2020;25:1926–45.

    Article  PubMed  Google Scholar 

  46. Li X, Su X, Liu J, Li H, Li M, Li W, et al. Transcriptome-wide association study identifies new susceptibility genes and pathways for depression. Transl Psychiatry. 2021;11:306.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Cao C, Moult J. GWAS and drug targets. BMC Genomics. 2014;15:S5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. White CC, Yang HS, Yu L, Chibnik LB, Dawe RJ, Yang J, et al. Identification of genes associated with dissociation of cognitive performance and neuropathological burden: Multistep analysis of genetic, epigenetic, and transcriptional data. PLoS Med. 2017;14:e1002287.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Hernández I, Rosende-Roca M, Alegret M, Mauleón A, Espinosa A, Vargas L, et al. Association of TMEM106B rs1990622 marker and frontotemporal dementia: evidence for a recessive effect and meta-analysis. J Alzheimers Dis. 2015;43:325–34.

    Article  PubMed  CAS  Google Scholar 

  50. Nicholson AM, Rademakers R. What we know about TMEM106B in neurodegeneration. Acta Neuropathol Commun. 2016;132:639–51.

    Article  CAS  Google Scholar 

  51. Ichikawa T, Baba H, Maeshima H, Shimano T, Inoue M, Ishiguro M, et al. Serum levels of TDP-43 in late-life patients with depressive episode. J Affect Disord. 2019;250:284–8.

    Article  CAS  PubMed  Google Scholar 

  52. Wider C, Wszolek ZK. Rapidly progressive familial parkinsonism with central hypoventilation, depression and weight loss (Perry syndrome)-a literature review. Parkinsonism Relat Disord. 2008;14:1–7.

    Article  PubMed  Google Scholar 

  53. Koga S, Lin WL, Walton RL, Ross OA, Dickson DW. TDP-43 pathology in multiple system atrophy: colocalization of TDP-43 and α-synuclein in glial cytoplasmic inclusions. Neuropathol Appl Neurobiol. 2018;44:707–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Jeannotte AM, McCarthy JG, Redei EE, Sidhu A. Desipramine modulation of alpha-, gamma-synuclein, and the norepinephrine transporter in an animal model of depression. Neuropsychopharmacology. 2009;34:987–98.

    Article  CAS  PubMed  Google Scholar 

  55. Petretto E, Mangion J, Dickens NJ, Cook SA, Kumaran MK, Lu H, et al. Heritability and tissue specificity of expression quantitative trait loci. PLoS Genet. 2006;2:e172.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Zhou X, Sun L, Brady OA, Murphy KA, Hu F. Elevated TMEM106B levels exaggerate lipofuscin accumulation and lysosomal dysfunction in aged mice with progranulin deficiency. Acta Neuropathol Commun. 2017;5:9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Zhou X, Brooks M, Jiang P, Koga S, Zuberi AR, Baker MC, et al. Loss of Tmem106b exacerbates FTLD pathologies and causes motor deficits in progranulin-deficient mice. EMBO Rep. 2020;21:e50197.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kryuchkova-Mostacci N, Robinson-Rechavi M. A benchmark of gene expression tissue-specificity metrics. Brief Bioinform. 2017;18:205–14.

    CAS  PubMed  Google Scholar 

  59. Ni H, Xu M, Zhan GL, Fan Y, Zhou H, Jiang HY, et al. The GWAS risk genes for depression may be actively involved in alzheimer’s disease. J Alzheimers Dis. 2018;64:1149–61.

    Article  CAS  PubMed  Google Scholar 

  60. Carboni L, Pischedda F, Piccoli G, Lauria M, Musazzi L, Popoli M et al. Depression-associated gene Negr1-Fgfr2 pathway is altered by antidepressant treatment. Cells 2020;9:1818.

  61. Maccarrone G, Ditzen C, Yassouridis A, Rewerts C, Uhr M, Uhlen M, et al. Psychiatric patient stratification using biosignatures based on cerebrospinal fluid protein expression clusters. J Psychiatr Res. 2013;47:1572–80.

    Article  PubMed  Google Scholar 

  62. Arias-Hervert ER, Xu N, Njus M, Murphy GG, Hou Y, Williams JA, et al. Actions of Rab27B-GTPase on mammalian central excitatory synaptic transmission. Physiol Rep. 2020;8:e14428.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Skaper SD, Debetto P, Giusti P. The P2X7 purinergic receptor: from physiology to neurological disorders. Faseb J. 2010;24:337–45.

    Article  CAS  PubMed  Google Scholar 

  64. Basso AM, Bratcher NA, Harris RR, Jarvis MF, Decker MW, Rueter LE. Behavioral profile of P2X7 receptor knockout mice in animal models of depression and anxiety: relevance for neuropsychiatric disorders. Behav Brain Res. 2009;198:83–90.

    Article  CAS  PubMed  Google Scholar 

  65. Wong ML, Dong C, Maestre-Mesa J, Licinio J. Polymorphisms in inflammation-related genes are associated with susceptibility to major depression and antidepressant response. Mol Psychiatry. 2008;13:800–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Rollins B, Martin MV, Morgan L, Vawter MP. Analysis of whole genome biomarker expression in blood and brain. Am J Med Genet B Neuropsychiatr Genet. 2010;153b:919–36.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Liew CC, Ma J, Tang HC, Zheng R, Dempsey AA. The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool. J Lab Clin Med. 2006;147:126–32.

    Article  CAS  PubMed  Google Scholar 

  68. Qi T, Wu Y, Zeng J, Zhang F, Xue A, Jiang L, et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat Commun. 2018;9:2282.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Obermeier B, Daneman R, Ransohoff RM. Development, maintenance and disruption of the blood-brain barrier. Nat Med. 2013;19:1584–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Daneman R, Prat A. The blood-brain barrier. Cold Spring Harb Perspect Biol. 2015;7:a020412.

    Article  PubMed  PubMed Central  Google Scholar 

  71. 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 

  72. Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet. 2020;52:1122–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Robins C, Liu Y, Fan W, Duong DM, Meigs J, Harerimana NV, et al. Genetic control of the human brain proteome. Am J Hum Genet. 2021;108:400–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Korologou-Linden R, Leyden GM, Relton CL, Richmond RC, Richardson TG. Multi-omics analyses of cognitive traits and psychiatric disorders highlights brain-dependent mechanisms. Hum Mol Genet. 2021;ddab016. https://doi.org/10.1093/hmg/ddab1016

  75. Zhong J, Li S, Zeng W, Li X, Gu C, Liu J, et al. Integration of GWAS and brain eQTL identifies FLOT1 as a risk gene for major depressive disorder. Neuropsychopharmacology. 2019;44:1542–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Ho TC, Sacchet MD, Connolly CG, Margulies DS, Tymofiyeva O, Paulus MP, et al. Inflexible functional connectivity of the dorsal anterior cingulate cortex in adolescent major depressive disorder. Neuropsychopharmacology. 2017;42:2434–45.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Hiser J, Koenigs M. The multifaceted role of the ventromedial prefrontal cortex in emotion, decision making, social cognition, and psychopathology. Biol Psychiatry. 2018;83:638–47.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was made possible by the generous sharing of statistics from the public databases. The authors acknowledge the important contributions of the many publicly available datasets used in this report’s analysis, including the ROS and MAP, the PsychENCODE Consortium, the AGES Reykjavik study, the GTEx project and the eQTLGen Consortium for their kind dedication. We thank the Howard’s GWAS meta-analysis of depression. Analyses were made possible by their generous sharing of GWAS summary statistics. How to access the data is shown below.

Funding

This study was supported by grants from the National Natural Science Foundation of China (82071201, 81971032), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01), Research Start-up Fund of Huashan Hospital (2022QD002), Excellence 2025 Talent Cultivation Program at Fudan University (3030277001), ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University.

Author information

Authors and Affiliations

  1. Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China

    Yue-Ting Deng, Bang-Sheng Wu, Yu-Xiang Yang, Yu-Yuan Huang, Qiang Dong & Jin-Tai Yu

  2. Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China

    Ya-Nan Ou, Yi Liu & Lan Tan

  3. Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Fudan University, 200032, Shanghai, China

    Yan Jiang

  4. Department of Psychiatry, University of Cambridge, Cambridge, UK

    John Suckling

  5. Beijing Institute of Pharmacology and Toxicology, Beijing, China

    Fei Li

Authors
  1. Yue-Ting Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ya-Nan Ou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bang-Sheng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu-Xiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yu-Yuan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lan Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Qiang Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. John Suckling
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Fei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jin-Tai Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JTY and FL conceptualized the study and revised the manuscript. YTD, YNO, BSW, YYX, YYH, and YL analyzed and interpreted the data. YTD and YNO prepared all the figures and tables. YTD, YNO, YJ, and JS drafted the manuscript. All authors contributed to the writing and revisions of the paper and approved the final version.

Corresponding authors

Correspondence to Fei Li or Jin-Tai Yu.

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

Deng, YT., Ou, YN., Wu, BS. et al. Identifying causal genes for depression via integration of the proteome and transcriptome from brain and blood. Mol Psychiatry 27, 2849–2857 (2022). https://doi.org/10.1038/s41380-022-01507-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-022-01507-9

This article is cited by

Search

Advanced search

Quick links