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Multiomic profiling of transcription factor binding and function in human brain

Abstract

Transcription factors (TFs) orchestrate gene expression programs crucial for brain function, but we lack detailed information about TF binding in human brain tissue. We generated a multiomic resource (ChIP–seq, ATAC-seq, RNA-seq, DNA methylation) on bulk tissues and sorted nuclei from several postmortem brain regions, including binding maps for more than 100 TFs. We demonstrate improved measurements of TF activity, including motif recognition and gene expression modeling, upon identification and removal of high TF occupancy regions. Further, predictive TF binding models demonstrate a bias for these high-occupancy sites. Neuronal TFs SATB2 and TBR1 bind unique regions depleted for such sites and promote neuronal gene expression. Binding sites for TFs, including TBR1 and PKNOX1, are enriched for risk variants associated with neuropsychiatric disorders, predominantly in neurons. This work, titled BrainTF, is a powerful resource for future studies seeking to understand the roles of specific TFs in regulating gene expression in the human brain.

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Fig. 1: Overview of experimental design and profile of large brain regions tested.
Fig. 2: Comparison of ChIP–seq profiles of TFs from DLPFC-bulk.
Fig. 3: Identification and characterization of HOT sites.
Fig. 4: Motif recognition by TFs.
Fig. 5: Comparison of ChIP–seq results with predictive models.
Fig. 6: Correlating TF binding with gene expression and chromatin accessibility.
Fig. 7: Association of TFs with disease through GWAS traits.

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Data availability

All relevant data generated by this study have been made publicly available through the PsychENCODE Consortium and are available for download at the following link: https://doi.org/10.7303/syn51942384.1. This includes the raw sequencing data (fastq) and processed results that can be used for analyses or visualizations, including bed files of peak calls and bigwigs of signal tracks.

Code availability

The analysis scripts used to generate the images and statistical output in this paper are available on GitHub (https://github.com/aanderson54/Loupe_BrainTF). These scripts use openly accessible packages within the R (v.4.2.2) environment. The associated ‘read.me’ file also provides detailed instructions regarding how to use the provided code.

References

  1. Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).

    CAS  PubMed  Google Scholar 

  2. Hammonds, A. S. et al. Spatial expression of transcription factors in Drosophila embryonic organ development. Genome Biol. 14, R140 (2013).

    PubMed  PubMed Central  Google Scholar 

  3. Partridge, E. C. et al. Occupancy maps of 208 chromatin-associated proteins in one human cell type. Nature 583, 720–728 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Spitz, F. & Furlong, E. E. M. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).

    CAS  PubMed  Google Scholar 

  5. van der Lee, R., Correard, S. & Wasserman, W. W. Deregulated regulators: disease-causing cis variants in transcription factor genes. Trends Genet. 36, 523–539 (2020).

    PubMed  Google Scholar 

  6. Carrasco Pro, S., Bulekova, K., Gregor, B., Labadorf, A. & Fuxman Bass, J. I. Prediction of genome-wide effects of single nucleotide variants on transcription factor binding. Sci. Rep. 10, 17632 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Singh, T. et al. Rare coding variants in 10 genes confer substantial risk for schizophrenia. Nature 604, 509–516 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Ramaker, R. C. et al. Post-mortem molecular profiling of three psychiatric disorders. Genome Med. 9, 72 (2017).

    PubMed  PubMed Central  Google Scholar 

  9. Szczepankiewicz, A. et al. Glucocorticoid receptor polymorphism is associated with major depression and predominance of depression in the course of bipolar disorder. J. Affect. Disord. 134, 138–144 (2011).

    CAS  PubMed  Google Scholar 

  10. Forrest, M. P. et al. The psychiatric risk gene transcription factor 4 (TCF4) regulates neurodevelopmental pathways associated with schizophrenia, autism, and intellectual disability. Schizophr. Bull. 44, 1100–1110 (2018).

    PubMed  Google Scholar 

  11. Working, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Stefansson, H. et al. Common variants conferring risk of schizophrenia. Nature 460, 744–747 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Bryois, J. et al. Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia. Nat. Commun. 9, 3121 (2018).

    PubMed  PubMed Central  Google Scholar 

  16. Fullard, J. F. et al. An atlas of chromatin accessibility in the adult human brain. Genome Res. 28, 1243–1252 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Gandal, M. J., Leppa, V., Won, H., Parikshak, N. N. & Geschwind, D. H. The road to precision psychiatry: translating genetics into disease mechanisms. Nat. Neurosci. 19, 1397–1407 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. van de Geijn, B. et al. Annotations capturing cell type-specific TF binding explain a large fraction of disease heritability. Hum. Mol. Genet. 29, 1057–1067 (2020).

    PubMed  Google Scholar 

  20. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet Lond. Engl. 381, 1371–1379 (2013).

    Google Scholar 

  21. PsychENCODE Consortium. et al. The PsychENCODE project. Nat. Neurosci. 18, 1707–1712 (2015).

    Google Scholar 

  22. Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Girdhar, K. et al. Chromatin domain alterations linked to 3D genome organization in a large cohort of schizophrenia and bipolar disorder brains. Nat. Neurosci. 25, 474–483 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. The ENCODE Project Consortium. et al. Perspectives on ENCODE. Nature 583, 693–698 (2020).

    Google Scholar 

  26. Haenni, S. et al. Analysis of C. elegans intestinal gene expression and polyadenylation by fluorescence-activated nuclei sorting and 3′-end-seq. Nucleic Acids Res. 40, 6304–6318 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Dammer, E. B. et al. Neuron enriched nuclear proteome isolated from human brain. J. Proteome Res. 12, 3193–3206 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Dunham, I. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    CAS  Google Scholar 

  29. The ENCODE Project Consortium. et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).

    CAS  Google Scholar 

  30. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Gu, Z. & Hübschmann, D. rGREAT: an R/bioconductor package for functional enrichment on genomic regions. Bioinformatics 39, btac745 (2023).

    CAS  PubMed  Google Scholar 

  32. Datta, V., Siddharthan, R. & Krishna, S. Detection of cooperatively bound transcription factor pairs using ChIP–seq peak intensities and expectation maximization. PLoS ONE 13, e0199771 (2018).

    PubMed  PubMed Central  Google Scholar 

  33. Wei, B. et al. A protein activity assay to measure global transcription factor activity reveals determinants of chromatin accessibility. Nat. Biotechnol. 36, 521–529 (2018).

    CAS  PubMed  Google Scholar 

  34. Dobson, T. H. W. et al. Regulation of USP37 expression by REST-associated G9a-dependent histone methylation. Mol. Cancer Res. MCR 15, 1073–1084 (2017).

    CAS  PubMed  Google Scholar 

  35. Mulligan, P. et al. CDYL bridges REST and histone methyltransferases for gene repression and suppression of cellular transformation. Mol. Cell 32, 718–726 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Hevner, R. F. et al. Tbr1 regulates differentiation of the preplate and layer 6. Neuron 29, 353–366 (2001).

    CAS  PubMed  Google Scholar 

  37. Britanova, O. et al. Satb2 is a postmitotic determinant for upper-layer neuron specification in the neocortex. Neuron 57, 378–392 (2008).

    CAS  PubMed  Google Scholar 

  38. Moffat, J. J. et al. Differential roles of ARID1B in excitatory and inhibitory neural progenitors in the developing cortex. Sci. Rep. 11, 3856 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Wiegreffe, C. et al. Bcl11a (Ctip1) controls migration of cortical projection neurons through regulation of Sema3c. Neuron 87, 311–325 (2015).

    CAS  PubMed  Google Scholar 

  40. Hao, N., Bhakti, V. L. D., Peet, D. J. & Whitelaw, M. L. Reciprocal regulation of the basic helix-loop-helix/Per-Arnt-Sim partner proteins, Arnt and Arnt2, during neuronal differentiation. Nucleic Acids Res. 41, 5626–5638 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Latypova, X. et al. Haploinsufficiency of the Sin3/HDAC corepressor complex member SIN3B causes a syndromic intellectual disability/autism spectrum disorder. Am. J. Hum. Genet. 108, 929–941 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Wreczycka, K. et al. HOT or not: examining the basis of high-occupancy target regions. Nucleic Acids Res. 47, 5735–5745 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Kvon, E. Z., Stampfel, G., Yáñez-Cuna, J. O., Dickson, B. J. & Stark, A. HOT regions function as patterned developmental enhancers and have a distinct cis-regulatory signature. Genes Dev. 26, 908–913 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Gerstein, M. B. et al. Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project. Science 330, 1775–1787 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Moorman, C. et al. Hotspots of transcription factor colocalization in the genome of Drosophila melanogaster. Proc. Natl Acad. Sci. USA 103, 12027–12032 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Castro-Mondragon, J. A. et al. JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 50, D165–D173 (2022).

    CAS  PubMed  Google Scholar 

  47. Worsley Hunt, R. & Wasserman, W. W. Non-targeted transcription factors motifs are a systemic component of ChIP–seq datasets. Genome Biol. 15, 412 (2014).

    PubMed  PubMed Central  Google Scholar 

  48. Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME suite. Nucleic Acids Res. 43, W39–W49 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. McGann, J. C. et al. The genome-wide binding profile for human RE1 silencing transcription factor unveils a unique genetic circuitry in hippocampus. J. Neurosci. 41, 6582–6595 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Vierstra, J. et al. Global reference mapping of human transcription factor footprints. Nature 583, 729–736 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Long, Y.-S. et al. Human transcription factor genes involved in neuronal development tend to have high GC content and CpG elements in the proximal promoter region. J. Genet. Genomics Yi Chuan Xue Bao 38, 157–163 (2011).

    CAS  PubMed  Google Scholar 

  52. Mao, X., Yang, S.-H., Simpkins, J. W. & Barger, S. W. Glutamate receptor activation evokes calpain-mediated degradation of Sp3 and Sp4, the prominent Sp-family transcription factors in neurons. J. Neurochem. 100, 1300–1314 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Kiyama, T. et al. Essential roles of mitochondrial biogenesis regulator Nrf1 in retinal development and homeostasis. Mol. Neurodegener. 13, 56 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Dejosez, M. et al. Regulatory architecture of housekeeping genes is driven by promoter assemblies. Cell Rep. 42, 112505 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Karimzadeh, M. & Hoffman, M. M. Virtual ChIP–seq: predicting transcription factor binding by learning from the transcriptome. Genome Biol. 23, 126 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Xu, Q. et al. ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination. Nucleic Acids Res. 49, 7966–7985 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Anderson, A. G. et al. Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis-regulatory elements. Cell Genom. 3, 100263 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Chen, X. et al. Structural insights into preinitiation complex assembly on core promoters. Science 372, eaba8490 (2021).

    CAS  PubMed  Google Scholar 

  60. Vaquero, A. et al. Human SirT1 interacts with histone H1 and promotes formation of facultative heterochromatin. Mol. Cell 16, 93–105 (2004).

    CAS  PubMed  Google Scholar 

  61. Han, W. et al. TBR1 directly represses Fezf2 to control the laminar origin and development of the corticospinal tract. Proc. Natl Acad. Sci. USA 108, 3041–3046 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Wang, L. et al. The zinc finger transcription factor Zbtb7b represses CD8-lineage gene expression in peripheral CD4+ T cells. Immunity 29, 876–887 (2008).

    PubMed  PubMed Central  Google Scholar 

  63. Li, S. et al. Zbtb7b engages the long noncoding RNA Blnc1 to drive brown and beige fat development and thermogenesis. Proc. Natl Acad. Sci. USA 114, E7111–E7120 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Managò, F. & Papaleo, F. Schizophrenia: what’s Arc got to do with It? Front. Behav. Neurosci. 11, 181 (2017).

    PubMed  PubMed Central  Google Scholar 

  66. Zhang, W. et al. Structural basis of arc binding to synaptic proteins: implications for cognitive disease. Neuron 86, 490–500 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Lanoue, V. et al. The adhesion-GPCR BAI3, a gene linked to psychiatric disorders, regulates dendrite morphogenesis in neurons. Mol. Psychiatry 18, 943–950 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE Blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).

    PubMed  PubMed Central  Google Scholar 

  69. Bellenguez, C. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 54, 412–436 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Hass, J. et al. Associations between DNA methylation and schizophrenia-related intermediate phenotypes a gene set enrichment analysis. Prog. Neuropsychopharmacol. Biol. Psychiatry 59, 31–39 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Ohayon, S., Yitzhaky, A. & Hertzberg, L. Gene expression meta-analysis reveals the up-regulation of CREB1 and CREBBP in Brodmann Area 10 of patients with schizophrenia. Psychiatry Res. 292, 113311 (2020).

    CAS  PubMed  Google Scholar 

  72. Liu, S. et al. The early growth response protein 1-miR-30a-5p-neurogenic differentiation factor 1 axis as a novel biomarker for schizophrenia diagnosis and treatment monitoring. Transl. Psychiatry 7, e998 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. van Rheenan, W. et al. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology. Nat. Genet. 53, 1636–1648 (2021).

    Google Scholar 

  75. Ruderfer, D. M. et al. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715.e16 (2018).

    CAS  PubMed Central  Google Scholar 

  76. 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  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  78. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 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  Google Scholar 

  80. Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    CAS  PubMed  Google Scholar 

  82. Julià, A. et al. Genome-wide association study meta-analysis identifies five new loci for systemic lupus erythematosus. Arthritis Res. Ther. 20, 100 (2018).

    PubMed  PubMed Central  Google Scholar 

  83. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Savic, D., Gertz, J., Cooper, G. M. & Myers, R. M. Mapping genome-wide transcription factor binding sites in frozen tissues. Epigenetics Chromatin 6, 30 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Jiang, Y., Matevossian, A., Huang, H.-S., Straubhaar, J. & Akbarian, S. Isolation of neuronal chromatin from brain tissue. BMC Neurosci. 9, 42 (2008).

    PubMed  PubMed Central  Google Scholar 

  87. Reddy, T. E. et al. Genomic determination of the glucocorticoid response reveals unexpected mechanisms of gene regulation. Genome Res. 19, 2163–2171 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).

    Google Scholar 

  89. Landt, S. G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for bisulfite-seq applications. Bioinformatics 27, 1571–1572 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 2015, 21.29.1–21.29.9 (2015).

    Google Scholar 

  93. Corces, M. R. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 176, 139–148 (2017).

    Google Scholar 

  94. Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the brain donors and their families, without whom this research would not have been possible. This study was supported by National Institutes of Health grant 5R01MH110472 awarded to R.M.M. and G.M.C., the Memory and Mobility Fund from HudsonAlpha Institute for Biotechnology and support from the Pritzker Neuropsychiatric Research Consortium. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

Authors

Contributions

R.M.M., G.M.C. and J.M.L. conceptualized the study. J.M.L., L.F.R., I.R.-N., K.T.-L. and R.J. performed the investigation. J.M.L., L.F.R. and A.G.A. curated the data. J.M.L., A.G.A., B.M. and I.R.-N. performed the formal analysis. W.E.B., B.G.B., P.C., A.S., S.J.W. and H.A. provided brain tissue and resources. J.M.L. wrote the original draft of the manuscript. R.M.M. and G.M.C. acquired funding.

Corresponding authors

Correspondence to Gregory M. Cooper or Richard M. Myers.

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Nature Neuroscience thanks Stella Dracheva and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–14.

Reporting Summary

Supplementary Table 1

Postmortem metadata.

Supplementary Table 2

Experiment list and quality control.

Supplementary Table 3

Antibodies for ChIP–seq.

Supplementary Table 4

ChIP–seq peak unions.

Supplementary Table 5

GREAT pathways enrichment.

Supplementary Table 6

HOT skewness values.

Supplementary Table 7

motifmatchR results.

Supplementary Table 8

Motif calling MEME.

Supplementary Table 9

Virtual ChIP–seq.

Supplementary Table 10

Partitioned heritability.

Supplementary Table 11

Statistics.

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Loupe, J.M., Anderson, A.G., Rizzardi, L.F. et al. Multiomic profiling of transcription factor binding and function in human brain. Nat Neurosci 27, 1387–1399 (2024). https://doi.org/10.1038/s41593-024-01658-8

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