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.
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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.
Lim, G. Y. et al. Prevalence of depression in the community from 30 countries between 1994 and 2014. Sci. Rep. 8, 2861 (2018).
Friedrich, M. J. Depression is the leading cause of disability around the world. JAMA 317, 1517 (2017).
Rush, A. J. STAR*D: what have we learned? Am. J. Psychiatry 164, 201–204 (2007).
Thase, M. E. & Schwartz, T. L. Choosing medications for treatment-resistant depression based on mechanism of action. J. Clin. Psychiatry 76, 720–727 (2015).
Akil, H. et al. Treatment resistant depression: a multi-scale, systems biology approach. Neurosci. Biobehav. Rev. 84, 272–288 (2018).
Moya-García, A. et al. Structural and functional view of polypharmacology. Sci. Rep. 7, 10102 (2017).
Zheng, J. et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat. Genet. 52, 1122–1131 (2020).
Pena, C. J. & Nestler, E. J. Progress in epigenetics of depression. Prog. Mol. Biol. Transl. Sci. 157, 41–66 (2018).
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).
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).
Sharma, K. et al. Cell type- and brain region-resolved mouse brain proteome. Nat. Neurosci. 18, 1819–1831 (2015).
Vogel, C. & Marcotte, E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13, 227–232 (2012).
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).
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).
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).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
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).
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).
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).
Bennett, D. A. et al. Religious orders study and rush memory and aging project. J. Alzheimers Dis. 64, S161–S189 (2018).
Beach, T. G. et al. Arizona study of aging and neurodegenerative disorders and brain and body donation program. Neuropathology 35, 354–389 (2015).
Li, T. et al. GeNets: a unified web platform for network-based genomic analyses. Nat. Methods 15, 543–546 (2018).
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).
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).
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).
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).
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).
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).
Wu, Y. et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9, 918 (2018).
Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).
Huckins, L. M. et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nat. Genet. 51, 659–674 (2019).
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).
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).
Barres, B. A. The mystery and magic of glia: a perspective on their roles in health and disease. Neuron 60, 430–440 (2008).
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).
Jones, S. B. et al. Glutamate-induced δ-catenin redistribution and dissociation from postsynaptic receptor complexes. Neuroscience 115, 1009–1021 (2002).
Grunwald, I. C. et al. Kinase-independent requirement of EphB2 receptors in hippocampal synaptic plasticity. Neuron 32, 1027–1040 (2001).
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).
Kurcon, T. et al. miRNA proxy approach reveals hidden functions of glycosylation. Proc. Natl Acad. Sci. USA 112, 7327–7332 (2015).
Okada-Tsuchioka, M. et al. Electroconvulsive seizure induces thrombospondin-1 in the adult rat hippocampus. Prog. Neuropsychopharmacol. Biol. Psychiatry 48, 236–244 (2014).
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).
Arjona, F. J. et al. CNNM2 mutations cause impaired brain development and seizures in patients with hypomagnesemia. PLoS Genet. 10, e1004267 (2014).
Yamanaka, R., Shindo, Y. & Oka, K. Magnesium is a key player in neuronal maturation and neuropathology. Int. J. Mol. Sci. 20, 3439 (2019).
Thyme, S. B. et al. Phenotypic landscape of schizophrenia-associated genes defines candidates and their shared functions. Cell 177, 478–491 (2019).
Maeda, Y. & Kinoshita, T. Dolichol-phosphate mannose synthase: structure, function and regulation. Biochim. Biophys. Acta 1780, 861–868 (2008).
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).
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).
Carvalho-Silva, D. et al. Open Targets Platform: new developments and updates two years on. Nucleic Acids Res. 47, D1056–D1065 (2019).
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).
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).
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).
Purcell, S. et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).
Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
Allen, M. et al. Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases. Sci. Data 3, 160089 (2016).
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).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Sieberts, S. K. et al. Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions. Sci. Data 7, 340 (2020).
Lage, K. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007).
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).
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).
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.
The authors declare no competing interests.
Peer review information Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.
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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