• James, S. L. et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392, 1789–1858 (2018).


    Google Scholar
     

  • Cui, L. et al. Major depressive disorder: hypothesis, mechanism, prevention and treatment. Signal Transduct. Target. Ther. 9, 30 (2024).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Nagy, C. et al. Astrocytic abnormalities and global DNA methylation patterns in depression and suicide. Mol. Psychiatry 20, 320–328 (2015).

    PubMed 
    CAS 

    Google Scholar
     

  • Edgar, N. & Sibille, E. A putative functional role for oligodendrocytes in mood regulation. Transl. Psychiatry 2, e109 (2012).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Yirmiya, R., Rimmerman, N. & Reshef, R. Depression as a microglial disease. Trends Neurosci. 38, 637–658 (2015).

    PubMed 
    CAS 

    Google Scholar
     

  • Pantazatos, S. P. et al. Whole-transcriptome brain expression and exon-usage profiling in major depression and suicide: evidence for altered glial, endothelial and ATPase activity. Mol. Psychiatry 22, 760–773 (2017).

    PubMed 
    CAS 

    Google Scholar
     

  • 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).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Als, T. D. et al. Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses. Nat. Med. 29, 1832–1844 (2023).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Chen, Y. et al. The interaction of early life factors and depression-associated loci affecting the age at onset of the depression. Transl. Psychiatry 12, 294 (2022).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Ochi, S. & Dwivedi, Y. Dissecting early life stress-induced adolescent depression through epigenomic approach. Mol. Psychiatry 28, 141–153 (2023).

    PubMed 
    CAS 

    Google Scholar
     

  • Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Nagy, C. et al. Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons. Nat. Neurosci. 23, 771–781 (2020).

    PubMed 
    CAS 

    Google Scholar
     

  • Maitra, M. et al. Cell type specific transcriptomic differences in depression show similar patterns between males and females but implicate distinct cell types and genes. Nat. Commun. 14, 2912 (2023).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Chawla, A., Nagy, C. & Turecki, G. Chromatin profiling techniques: exploring the chromatin environment and its contributions to complex traits. Int. J. Mol. Sci. 22, 7612 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).


    Google Scholar
     

  • Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Hauberg, M. E. et al. Common schizophrenia risk variants are enriched in open chromatin regions of human glutamatergic neurons. Nat. Commun. 11, 5581 (2020).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Morabito, S. et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat. Genet. 53, 1143–1155 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Girdhar, K. et al. Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome. Nat. Neurosci. 21, 1126–1136 (2018).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Gutiérrez-Sacristán, A. et al. PsyGeNET: a knowledge platform on psychiatric disorders and their genes. Bioinformatics 31, 3075–3077 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Malik, A. N. et al. Genome-wide identification and characterization of functional neuronal activity-dependent enhancers. Nat. Neurosci. 17, 1330–1339 (2014).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lutz, P.-E. et al. Non-CG methylation and multiple histone profiles associate child abuse with immune and small GTPase dysregulation. Nat. Commun. 12, 1132 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Lutz, P.-E. et al. Association of a history of child abuse with impaired myelination in the anterior cingulate cortex: convergent epigenetic, transcriptional, and morphological evidence. Am. J. Psychiatry 174, 1185–1194 (2017).

    PubMed 

    Google Scholar
     

  • Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).

    PubMed 
    CAS 

    Google Scholar
     

  • Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • West, A. E. & Greenberg, M. E. Neuronal activity-regulated gene transcription in synapse development and cognitive function. Cold Spring Harb. Perspect. Biol. 3, a005744 (2011).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dennis, D. J., Han, S. & Schuurmans, C. bHLH transcription factors in neural development, disease, and reprogramming. Brain Res. 1705, 48–65 (2019).

    PubMed 
    CAS 

    Google Scholar
     

  • Harris, H. K. et al. Disruption of RFX family transcription factors causes autism, attention-deficit/hyperactivity disorder, intellectual disability, and dysregulated behavior. Genet. Med. 23, 1028–1040 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Holtman, I. R., Skola, D. & Glass, C. K. Transcriptional control of microglia phenotypes in health and disease. J. Clin. Invest. 127, 3220–3229 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wehrspaun, C. C., Haerty, W. & Ponting, C. P. Microglia recapitulate a hematopoietic master regulator network in the aging human frontal cortex. Neurobiol. Aging 36, 2443.e9–2443.e20 (2015).

    PubMed 
    CAS 

    Google Scholar
     

  • Kierdorf, K. & Prinz, M. Factors regulating microglia activation. Front. Cell. Neurosci. 7, 44 (2013).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • BRAIN Initiative Cell Census Network (BICCN). A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598, 86–102 (2021).

    PubMed Central 

    Google Scholar
     

  • Peng, H. et al. Morphological diversity of single neurons in molecularly defined cell types. Nature 598, 174–181 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Niu, M. et al. Claustrum mediates bidirectional and reversible control of stress-induced anxiety responses. Sci. Adv. 8, eabi6375 (2022).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Jankovic, J., Chen, S. & Le, W. D. The role of Nurr1 in the development of dopaminergic neurons and Parkinson’s disease. Prog. Neurobiol. 77, 128–138 (2005).

    PubMed 
    CAS 

    Google Scholar
     

  • Torretta, S. et al. NURR1 and ERR1 modulate the expression of genes of a DRD2 coexpression network enriched for schizophrenia risk. J. Neurosci. 40, 932–941 (2020).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Eells, J. B., Lipska, B. K., Yeung, S. K., Misler, J. A. & Nikodem, V. M. Nurr1-null heterozygous mice have reduced mesolimbic and mesocortical dopamine levels and increased stress-induced locomotor activity. Behav. Brain Res. 136, 267–275 (2002).

    PubMed 
    CAS 

    Google Scholar
     

  • Imura, T., Kobayashi, Y., Suzutani, K., Ichikawa‐Tomikawa, N. & Chiba, H. Differential expression of a stress‐regulated gene Nr4a2 characterizes early‐ and late‐born hippocampal granule cells. Hippocampus 29, 539–549 (2019).

    PubMed 
    CAS 

    Google Scholar
     

  • Campos-Melo, D., Galleguillos, D., Sánchez, N., Gysling, K. & Andrés, M. E. Nur transcription factors in stress and addiction. Front. Mol. Neurosci. 6, 44 (2013).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Helbling, J.-C., Minni, A. M., Pallet, V. & Moisan, M.-P. Stress and glucocorticoid regulation of NR4A genes in mice. J. Neurosci. Res. 92, 825–834 (2014).

    PubMed 
    CAS 

    Google Scholar
     

  • Carpentier, R., Sacchetti, P., Ségard, P., Staels, B. & Lefebvre, P. The glucocorticoid receptor is a co-regulator of the orphan nuclear receptor Nurr1. J. Neurochem. 104, 777–789 (2008).

    PubMed 
    CAS 

    Google Scholar
     

  • Rojas, P., Joodmardi, E., Perlmann, T. & Ögren, S. O. Rapid increase of Nurr1 mRNA expression in limbic and cortical brain structures related to coping with depression-like behavior in mice. J. Neurosci. Res. 88, 2284–2293 (2010).

    PubMed 
    CAS 

    Google Scholar
     

  • He, Y. et al. Protective effect of Nr4a2 (Nurr1) against LPS-induced depressive-like behaviors via regulating activity of microglia and CamkII neurons in anterior cingulate cortex. Pharmacol. Res. 191, 106717 (2023).

    PubMed 
    CAS 

    Google Scholar
     

  • Xing, G., Zhang, L., Russell, S. & Post, R. Reduction of dopamine-related transcription factors Nurr1 and NGFI-B in the prefrontal cortex in schizophrenia and bipolar disorders. Schizophr. Res. 84, 36–56 (2006).

    PubMed 

    Google Scholar
     

  • Gammie, S. C. Creation of a gene expression portrait of depression and its application for identifying potential treatments. Sci. Rep. 11, 3829 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Loupe, J. M. et al. Multiomic profiling of transcription factor binding and function in human brain. Nat. Neurosci. https://doi.org/10.1038/s41593-024-01658-8 (2024).

  • Van Der Poel, M. et al. Transcriptional profiling of human microglia reveals grey–white matter heterogeneity and multiple sclerosis-associated changes. Nat. Commun. 10, 1139 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Maynard, K. R. et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci. 24, 425–436 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Scheepstra, K. W. F. et al. Microglia transcriptional profiling in major depressive disorder shows inhibition of cortical gray matter microglia. Biol. Psychiatry https://doi.org/10.1016/j.biopsych.2023.04.020 (2023).

  • ReproGen Consortium et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).


    Google Scholar
     

  • Nguyen, T.-D. et al. Genetic heterogeneity and subtypes of major depression. Mol. Psychiatry 27, 1667–1675 (2022).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Levey, D. F. et al. Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions. Nat. Neurosci. 24, 954–963 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Lee, D. et al. A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet. 47, 955–961 (2015).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Shrikumar, A., Prakash, E. & Kundaje, A. GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs. Bioinformatics 35, i173–i182 (2019).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Lee, D. LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics 32, 2196–2198 (2016).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Hu, B. et al. Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders. Nat. Commun. 12, 3968 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Yao, X. et al. Integrative analysis of genome-wide association studies identifies novel loci associated with neuropsychiatric disorders. Transl. Psychiatry 11, 69 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Martins-de-Souza, D., Guest, P. C., Vanattou-Saifoudine, N., Rahmoune, H. & Bahn, S. Phosphoproteomic differences in major depressive disorder postmortem brains indicate effects on synaptic function. Eur. Arch. Psychiatry Clin. Neurosci. 262, 657–666 (2012).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Matt, L., Kim, K., Chowdhury, D. & Hell, J. W. Role of palmitoylation of postsynaptic proteins in promoting synaptic plasticity. Front. Mol. Neurosci. 12, 8 (2019).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Callejas-Marin, A. et al. Gli2-mediated Shh signaling is required for thalamocortical projection guidance. Front. Neuroanat. 16, 830758 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pappas, A. L. et al. Deficiency of Shank2 causes mania-like behavior that responds to mood stabilizers. JCI Insight 2, e92052 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hong, C. et al. Constitutive activation of LXR in macrophages regulates metabolic and inflammatory gene expression: identification of ARL7 as a direct target. J. Lipid Res. 52, 531–539 (2011).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • van de Geijn, B., McVicker, G., Gilad, Y. & Pritchard, J. K. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 1061–1063 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wilson, P. C. et al. Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression. Nat. Commun. 13, 5253 (2022).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Herring, C. A. et al. Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution. Cell 185, 4428–4447.e28 (2022).

    PubMed 
    CAS 

    Google Scholar
     

  • Eells, J. B., Misler, J. A. & Nikodem, V. M. Early postnatal isolation reduces dopamine levels, elevates dopamine turnover and specifically disrupts prepulse inhibition in Nurr1-null heterozygous mice. Neuroscience 140, 1117–1126 (2006).

    PubMed 
    CAS 

    Google Scholar
     

  • Ouimet, C. C., Miller, P. E., Hemmings, H. C., Walaas, S. I. & Greengard, P. DARPP-32, a dopamine- and adenosine 3′:5′-monophosphate-regulated phosphoprotein enriched in dopamine-innervated brain regions. III. Immunocytochemical localization. J. Neurosci. 4, 111–124 (1984).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Santana, N., Mengod, G. & Artigas, F. Quantitative analysis of the expression of dopamine D1 and D2 receptors in pyramidal and GABAergic neurons of the rat prefrontal cortex. Cereb. Cortex 19, 849–860 (2009).

    PubMed 

    Google Scholar
     

  • Baik, J.-H. Stress and the dopaminergic reward system. Exp. Mol. Med. 52, 1879–1890 (2020).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Fang, C., Wang, H. & Naumann, R. K. Developmental patterning and neurogenetic gradients of Nurr1 positive neurons in the rat claustrum and lateral cortex. Front. Neuroanat. 15, 786329 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Català-Solsona, J. et al. Activity-dependent Nr4a2 induction modulates synaptic expression of AMPA receptors and plasticity via a Ca2+/CRTC1/CREB pathway. J. Neurosci. 43, 3028–3041 (2023).

  • Bleakman, D., Alt, A. & Witkin, J. AMPA receptors in the therapeutic management of depression. CNS Neurol. Disord. Drug Targets 6, 117–126 (2007).

    PubMed 
    CAS 

    Google Scholar
     

  • Wang, Q., Roy, B., Turecki, G., Shelton, R. C. & Dwivedi, Y. Role of complex epigenetic switching in tumor necrosis factor-α upregulation in the prefrontal cortex of suicide subjects. Am. J. Psychiatry 175, 262–274 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • O’Connor, J. C. et al. Lipopolysaccharide-induced depressive-like behavior is mediated by indoleamine 2,3-dioxygenase activation in mice. Mol. Psychiatry 14, 511–522 (2009).

    PubMed 

    Google Scholar
     

  • Snijders, G. J. L. J. et al. Distinct non-inflammatory signature of microglia in post-mortem brain tissue of patients with major depressive disorder. Mol. Psychiatry 26, 3336–3349 (2021).

    PubMed 
    CAS 

    Google Scholar
     

  • Böttcher, C. et al. Single-cell mass cytometry of microglia in major depressive disorder reveals a non-inflammatory phenotype with increased homeostatic marker expression. Transl. Psychiatry 10, 310 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, J., Kaye, A. P., Wang, J. & Girgenti, M. J. Transcriptomics of the depressed and PTSD brain. Neurobiol. Stress 15, 100408 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Hannestad, J. et al. The neuroinflammation marker translocator protein is not elevated in individuals with mild-to-moderate depression: a [11C]PBR28 PET study. Brain. Behav. Immun. 33, 131–138 (2013).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Jaffe, A. E. et al. Decoding shared versus divergent transcriptomic signatures across cortico-amygdala circuitry in PTSD and depressive disorders. Am. J. Psychiatry 179, 673–686 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, Y. et al. Microglia-specific transcriptional repression of interferon-regulated genes after prolonged stress in mice. Neurobiol. Stress 21, 100495 (2022).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Bolton, J. L. et al. Early stress-induced impaired microglial pruning of excitatory synapses on immature CRH-expressing neurons provokes aberrant adult stress responses. Cell Rep. 38, 110600 (2022).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 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).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sey, N. Y. A. et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat. Neurosci. 23, 583–593 (2020).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Li, X. et al. Transcriptome-wide association study identifies new susceptibility genes and pathways for depression. Transl. Psychiatry 11, 306 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Dall’Aglio, L., Lewis, C. M. & Pain, O. Delineating the genetic component of gene expression in major depression. Biol. Psychiatry 89, 627–636 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Dumais, A. et al. Risk factors for suicide completion in major depression: a case-control study of impulsive and aggressive behaviors in men. Am. J. Psychiatry 162, 2116–2124 (2005).

    PubMed 
    CAS 

    Google Scholar
     

  • Maitra, M. et al. Extraction of nuclei from archived postmortem tissues for single-nucleus sequencing applications. Nat. Protoc. 16, 2788–2801 (2021).

    PubMed 
    CAS 

    Google Scholar
     

  • Lareau, C. A., Ma, S., Duarte, F. M. & Buenrostro, J. D. Inference and effects of barcode multiplets in droplet-based single-cell assays. Nat. Commun. 11, 866 (2020).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Huang, Y., McCarthy, D. J. & Stegle, O. Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference. Genome Biol. 20, 273 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hagenauer, M. H. et al. Inference of cell type content from human brain transcriptomic datasets illuminates the effects of age, manner of death, dissection, and psychiatric diagnosis. PLoS ONE 13, e0200003 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Crow, M., Paul, A., Ballouz, S., Huang, Z. J. & Gillis, J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat. Commun. 9, 884 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Crowell, H. L. et al. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11, 6077 (2020).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    PubMed 
    CAS 

    Google Scholar
     

  • Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 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).

    PubMed 
    CAS 

    Google Scholar
     

  • 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).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

    PubMed 
    CAS 

    Google Scholar
     

  • Watanabe, K. et al. Genome-wide meta-analysis of insomnia prioritizes genes associated with metabolic and psychiatric pathways. Nat. Genet. 54, 1125–1132 (2022).

    PubMed 
    CAS 

    Google Scholar
     

  • Mullins, N. et al. Dissecting the shared genetic architecture of suicide attempt, psychiatric disorders, and known risk factors. Biol. Psychiatry 91, 313–327 (2022).

    PubMed 

    Google Scholar
     

  • Kerimov, N. et al. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat. Genet. 53, 1290–1299 (2021).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Emani, P. S. et al. Single-cell genomics and regulatory networks for 388 human brains. Science 384, eadi5199 (2024).

    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).


    Google Scholar
     

  • Chawla, A., Cakmakci, D., Denniston, R. & MGSSdouglas. MGSSdouglas/snATAC_MDD: snATAC_MDD v0.2. Zenodo https://zenodo.org/records/15320132 (2025).