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.

Brain proteome-wide association study implicates novel proteins in depression pathogenesis

Abstract

Depression is a common condition, but current treatments are only effective in a subset of individuals. To identify new treatment targets, we integrated depression genome-wide association study (GWAS) results (N = 500,199) with human brain proteomes (N = 376) to perform a proteome-wide association study of depression followed by Mendelian randomization. We identified 19 genes that were consistent with being causal in depression, acting via their respective cis-regulated brain protein abundance. We replicated nine of these genes using an independent depression GWAS (N = 307,353) and another human brain proteomic dataset (N = 152). Eleven of the 19 genes also had cis-regulated mRNA levels that were associated with depression, based on integration of the depression GWAS with human brain transcriptomes (N = 888). Meta-analysis of the discovery and replication proteome-wide association study analyses identified 25 brain proteins consistent with being causal in depression, 20 of which were not previously implicated in depression by GWAS. Together, these findings provide promising brain protein targets for further mechanistic and therapeutic studies.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Discovery PWAS identified 19 genes consistent with being causal in depression.
Fig. 2: PPI network and pathways among the 25 potentially depression-causal proteins identified from the PWAS meta-analysis.
Fig. 3: Single-cell-type expression of the potentially depression-causal genes.

Data availability

Phenotypic, proteomic and transcriptomic data used in this manuscript are available via the AD Knowledge Portal (https://adknowledgeportal.org). The AD Knowledge Portal is a platform for accessing data, analyses and tools generated by the Accelerating Medicines Partnership (AMP-AD) Target Discovery Program and other National Institute on Aging (NIA)-supported programs to enable open-science practices and accelerate translational learning. The data, analyses and tools are shared early in the research cycle without a publication embargo on secondary use. Data are available for general research use according to the following requirements for data access and data attribution (https://adknowledgeportal.org/DataAccess/Instructions). For access to results of the pQTL analysis, protein weights and transcript weights described in this manuscript, see https://doi.org/10.7303/syn24872746.

References

  1. 1.

    Lim, G. Y. et al. Prevalence of depression in the community from 30 countries between 1994 and 2014. Sci. Rep. 8, 2861 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  2. 2.

    Friedrich, M. J. Depression is the leading cause of disability around the world. JAMA 317, 1517 (2017).

    PubMed  Google Scholar 

  3. 3.

    Rush, A. J. STAR*D: what have we learned? Am. J. Psychiatry 164, 201–204 (2007).

    PubMed  Article  Google Scholar 

  4. 4.

    Thase, M. E. & Schwartz, T. L. Choosing medications for treatment-resistant depression based on mechanism of action. J. Clin. Psychiatry 76, 720–727 (2015).

    PubMed  Article  Google Scholar 

  5. 5.

    Akil, H. et al. Treatment resistant depression: a multi-scale, systems biology approach. Neurosci. Biobehav. Rev. 84, 272–288 (2018).

    PubMed  Article  Google Scholar 

  6. 6.

    Moya-García, A. et al. Structural and functional view of polypharmacology. Sci. Rep. 7, 10102 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    Zheng, J. et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat. Genet. 52, 1122–1131 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  8. 8.

    Pena, C. J. & Nestler, E. J. Progress in epigenetics of depression. Prog. Mol. Biol. Transl. Sci. 157, 41–66 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Arloth, J. et al. Genetic differences in the immediate transcriptome response to stress predict risk-related brain function and psychiatric disorders. Neuron 86, 1189–1202 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Sharma, K. et al. Cell type- and brain region-resolved mouse brain proteome. Nat. Neurosci. 18, 1819–1831 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Vogel, C. & Marcotte, E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13, 227–232 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Wingo, A. P. et al. Shared proteomic effects of cerebral atherosclerosis and Alzheimer’s disease on the human brain. Nat. Neurosci. 23, 696–700 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Wingo, A. P. et al. Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age. Nat. Commun. 10, 1619 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  15. 15.

    Johnson, E. C. B. 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. 26, 769–780 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Wu, L. et al. A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer. Nat. Genet. 50, 968–978 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Gusev, A. et al. A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants. Nat. Genet. 51, 815–823 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Bennett, D. A. et al. Religious orders study and rush memory and aging project. J. Alzheimers Dis. 64, S161–S189 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Beach, T. G. et al. Arizona study of aging and neurodegenerative disorders and brain and body donation program. Neuropathology 35, 354–389 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Li, T. et al. GeNets: a unified web platform for network-based genomic analyses. Nat. Methods 15, 543–546 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Nagel, M. et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat. Genet. 50, 920–927 (2018).

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ~700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Pulit, S. L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet. 28, 166–174 (2018).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  30. 30.

    Wan, Y.-W. et al. Meta-analysis of the Alzheimer’s disease human brain transcriptome and functional dissection in mouse models. Cell Rep. 32, 107908 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Wu, Y. et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9, 918 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  32. 32.

    Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Huckins, L. M. et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nat. Genet. 51, 659–674 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 50, 1584–1592 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Vasudevan, D., Takeuchi, H., Johar, S. S., Majerus, E. & Haltiwanger, R. S. Peters plus syndrome mutations disrupt a noncanonical ER quality-control mechanism. Curr. Biol. 25, 286–295 (2015).

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Barres, B. A. The mystery and magic of glia: a perspective on their roles in health and disease. Neuron 60, 430–440 (2008).

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Aho, S. et al. Specific sequences in p120ctn determine subcellular distribution of its multiple isoforms involved in cellular adhesion of normal and malignant epithelial cells. J. Cell Sci. 115, 1391–1402 (2002).

    CAS  PubMed  Google Scholar 

  38. 38.

    Jones, S. B. et al. Glutamate-induced δ-catenin redistribution and dissociation from postsynaptic receptor complexes. Neuroscience 115, 1009–1021 (2002).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Grunwald, I. C. et al. Kinase-independent requirement of EphB2 receptors in hippocampal synaptic plasticity. Neuron 32, 1027–1040 (2001).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Otrokocsi, L., Kittel, A. & Sperlagh, B. P2X7 receptors drive spine synapse plasticity in the learned helplessness model of depression. Int. J. Neuropsychopharmacol. 20, 813–822 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Kurcon, T. et al. miRNA proxy approach reveals hidden functions of glycosylation. Proc. Natl Acad. Sci. USA 112, 7327–7332 (2015).

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Okada-Tsuchioka, M. et al. Electroconvulsive seizure induces thrombospondin-1 in the adult rat hippocampus. Prog. Neuropsychopharmacol. Biol. Psychiatry 48, 236–244 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  43. 43.

    Zhen, L. et al. EphB2 deficiency induces depression-like behaviors and memory impairment: involvement of NMDA 2B receptor dependent signaling. Front. Pharmacol. 9, 862 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. 44.

    Arjona, F. J. et al. CNNM2 mutations cause impaired brain development and seizures in patients with hypomagnesemia. PLoS Genet. 10, e1004267 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  45. 45.

    Yamanaka, R., Shindo, Y. & Oka, K. Magnesium is a key player in neuronal maturation and neuropathology. Int. J. Mol. Sci. 20, 3439 (2019).

  46. 46.

    Thyme, S. B. et al. Phenotypic landscape of schizophrenia-associated genes defines candidates and their shared functions. Cell 177, 478–491 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Maeda, Y. & Kinoshita, T. Dolichol-phosphate mannose synthase: structure, function and regulation. Biochim. Biophys. Acta 1780, 861–868 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. 48.

    Shi, J. et al. Up-regulation of PSMB4 is associated with neuronal apoptosis after neuroinflammation induced by lipopolysaccharide. J. Mol. Histol. 46, 457–466 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  49. 49.

    Hawi, Z. et al. The role of cadherin genes in five major psychiatric disorders: a literature update. Am. J. Med. Genet. B Neuropsychiatr. Genet. 177, 168–180 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  50. 50.

    Carvalho-Silva, D. et al. Open Targets Platform: new developments and updates two years on. Nucleic Acids Res. 47, D1056–D1065 (2019).

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Mertins, P. et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry. Nat. Protoc. 13, 1632–1661 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    De Jager, P. L. et al. A genome-wide scan for common variants affecting the rate of age-related cognitive decline. Neurobiol. Aging 33, 1017.e1–1017.e15 (2012).

    Article  CAS  Google Scholar 

  53. 53.

    De Jager, P. L. et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci. Data 5, 180142 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Purcell, S. et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Article  CAS  Google Scholar 

  57. 57.

    Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Allen, M. et al. Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases. Sci. Data 3, 160089 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Wang, M. et al. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease. Sci. Data 5, 180185 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Sieberts, S. K. et al. Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions. Sci. Data 7, 340 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Lage, K. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007).

    CAS  PubMed  Article  Google Scholar 

  64. 64.

    Clauset, A., Newman, M. E. & Moore, C. Finding community structure in very large networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 70, 066111 (2004).

    PubMed  Article  CAS  Google Scholar 

  65. 65.

    Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank 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, and we thank the research participants and employees of 23andMe for making this work possible. The following NIH grants supported this work: P30 AG066511 (A.I.L.), P30 AG10161 (D.A.B.), P30 NS055077 (A.I.L.), P50 AG025688 (A.I.L.), R01 AG015819 (D.A.B.), R01 AG017917 (D.A.B.), R01 AG053960 (N.T.S.), R01 AG056533 (T.S.W., A.P.W.), R01 AG057911 (N.T.S.), R01 AG061800 (N.T.S.), R56 AG060757 (T.S.W.), R56 AG062256 (T.S.W.), RC2 AG036547 (D.A.B.), RF1 AG057470 (T.S.W.), U01 AG046152 (P.L.D.), U01 AG046161 (A.I.L.), U01 AG061356 (P.L.D.), U01 AG061357 (A.I.L.) and U01 MH115484 (A.P.W.). NIH grants include those that supported the Accelerating Medicine Partnership for AD, the NINDS Emory Neuroscience Core and Goizueta Alzheimer’s Disease Research Center (ADRC) at Emory University, the Rush University ADRC and Arizona State University ADRC that made this work possible. The following Veterans Administration grants supported this work: I01 BX003853 (A.P.W.) and IK4 BX005219 (A.P.W.). The Brain and Body Donation Program has been supported by the NIH, the Arizona Department of Health Services, the Arizona Biomedical Research Commission and the Michael J. Fox Foundation for Parkinson’s Research. Additional support includes grants from the Alzheimer’s Association (N.T.S.), Alzheimer’s Research UK (N.T.S.), The Michael J. Fox Foundation for Parkinson’s Research (N.T.S.) and the Weston Brain Institute Biomarkers Across Neurodegenerative Diseases Grant 11060 (N.T.S.). The views expressed in this work do not necessarily represent the views of the Veterans Administration or the US government.

Author information

Affiliations

Authors

Contributions

A.P.W. and T.S.W. conceptualized and designed the study. T.G.B., E.M.R., P.L.D., J.J.L., D.A.B., N.T.S. and A.I.L. acquired the data. T.S.W., Y.L., E.S.G., J.G., B.A.L., D.M.D., E.B.D. and A.P.W. conducted analyses. T.S.W., Y.L., E.S.G., J.G., B.A.L., A.L., P.J.K., K.J.R., M.P.E. and D.A.B. interpreted the data. A.P.W. and T.S.W. wrote the first draft of the manuscript. All authors critically reviewed and revised the manuscript.

Corresponding authors

Correspondence to Thomas S. Wingo or Aliza P. Wingo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

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

Verify currency and authenticity via CrossMark

Cite this article

Wingo, T.S., Liu, Y., Gerasimov, E.S. et al. Brain proteome-wide association study implicates novel proteins in depression pathogenesis. Nat Neurosci (2021). https://doi.org/10.1038/s41593-021-00832-6

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing