Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome

Abstract

Risk variants for schizophrenia affect more than 100 genomic loci, yet cell- and tissue-specific roles underlying disease liability remain poorly characterized. We have generated for two cortical areas implicated in psychosis, the dorsolateral prefrontal cortex and anterior cingulate cortex, 157 reference maps from neuronal, neuron-depleted and bulk tissue chromatin for two histone marks associated with active promoters and enhancers, H3-trimethyl-Lys4 (H3K4me3) and H3-acetyl-Lys27 (H3K27ac). Differences between neuronal and neuron-depleted chromatin states were the major axis of variation in histone modification profiles, followed by substantial variability across subjects and cortical areas. Thousands of significant histone quantitative trait loci were identified in neuronal and neuron-depleted samples. Risk variants for schizophrenia, depressive symptoms and neuroticism were significantly over-represented in neuronal H3K4me3 and H3K27ac landscapes. Our Resource, sponsored by PsychENCODE and CommonMind, highlights the critical role of cell-type-specific signatures at regulatory and disease-associated noncoding sequences in the human frontal lobe.

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Fig. 1: Cell- and region-specific histone modification profiling in the human frontal lobe.
Fig. 2: Functional enrichment of non-overlapping cell- and tissue-specific histone peaks.
Fig. 3: Enrichment of heritability for brain- and non-brain-related phenotypes within cell- and tissue-specific histone peaks.
Fig. 4: Decomposing multiples sources of epigenetic variation.
Fig. 5: Overlap of cell-specific and homogenate hQTLs with genome-wide significant loci in schizophrenia.
Fig. 6: Regions differentially modified in neuronal and non-neuronal cell types.
Fig. 7: Cell-type-specific histone acetylation and methylation profiles are associated with differential enrichment for neuronal and glial transcripts.

References

  1. 1.

    Geschwind, D. H. & Flint, J. Genetics and genomics of psychiatric disease. Science 349, 1489–1494 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. 2.

    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).

    Article  PubMed  CAS  Google Scholar 

  3. 3.

    Bernstein, B. E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. 4.

    Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    Article  PubMed  CAS  Google Scholar 

  5. 5.

    Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. 6.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. 7.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. 8.

    Zhou, V. W., Goren, A. & Bernstein, B. E. Charting histone modifications and the functional organization of mammalian genomes. Nat. Rev. Genet. 12, 7–18 (2011).

    Article  PubMed  CAS  Google Scholar 

  9. 9.

    Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci. 18, 199–209 (2015).

    Article  CAS  Google Scholar 

  10. 10.

    Sun, W. et al. Histone acetylome-wide association study of autism spectrum disorder. Cell 167, 1385–1397.e1311 (2016).

    Article  PubMed  CAS  Google Scholar 

  11. 11.

    Ng, B. et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat. Neurosci. 20, 1418–1426 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. 12.

    Cheung, I. et al. Developmental regulation and individual differences of neuronal H3K4me3 epigenomes in the prefrontal cortex. Proc. Natl. Acad. Sci. USA 107, 8824–8829 (2010).

    Article  PubMed  CAS  Google Scholar 

  13. 13.

    Shulha, H. P., Cheung, I., Guo, Y., Akbarian, S. & Weng, Z. Coordinated cell type-specific epigenetic remodeling in prefrontal cortex begins before birth and continues into early adulthood. PLoS Genet. 9, e1003433 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. 14.

    Charney, D. S., Sklar, P. B., Buxbaum, J. D. & Nestler, E. J. Charney & Nestler’s Neurobiology of Mental Illness (Oxford Univ. Press, New York, 2018).

    Google Scholar 

  15. 15.

    Mancarci, B. O. et al. Cross-laboratory analysis of brain cell type transcriptomes with applications to interpretation of bulk tissue data. eNeuro 4, ENEURO.0212-17.2017 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Huttner, H. B. et al. The age and genomic integrity of neurons after cortical stroke in humans. Nat. Neurosci. 17, 801–803 (2014).

    Article  PubMed  CAS  Google Scholar 

  17. 17.

    Habib, N. et al. Div-Seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353, 925–928 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. 18.

    Sherwood, C. C. et al. Evolution of increased glia-neuron ratios in the human frontal cortex. Proc. Natl. Acad. Sci. USA 103, 13606–13611 (2006).

    Article  PubMed  CAS  Google Scholar 

  19. 19.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. 20.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. 21.

    Huang, K. L. et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat. Neurosci. 20, 1052–1061 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. 22.

    Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518, 365–369 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. 23.

    Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. 24.

    Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Grubert, F. et al. Genetic control of chromatin states in humans involves local and distal chromosomal interactions. Cell 162, 1051–1065 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. 26.

    Waszak, S. M. et al. Population variation and genetic control of modular chromatin architecture in humans. Cell 162, 1039–1050 (2015).

    Article  PubMed  CAS  Google Scholar 

  27. 27.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. 28.

    Kumasaka, N., Knights, A. J. & Gaffney, D. J. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat. Genet. 48, 206–213 (2016).

    Article  PubMed  CAS  Google Scholar 

  29. 29.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  PubMed Central  CAS  Google Scholar 

  30. 30.

    Posner, M. I., Rothbart, M. K., Sheese, B. E. & Tang, Y. The anterior cingulate gyrus and the mechanism of self-regulation. Cogn. Affect. Behav. Neurosci. 7, 391–395 (2007).

    Article  PubMed  Google Scholar 

  31. 31.

    Moghaddam,B . & Homayoun,H . Divergent plasticity of prefrontal cortex networks. Neuropsychopharmacology. 33, 42–55 (2008).

    Article  PubMed  Google Scholar 

  32. 32.

    Le Fevre, A. K. et al. FOXP1 mutations cause intellectual disability and a recognizable phenotype. Am. J. Med. Genet. A 161A, 3166–3175 (2013).

    Article  PubMed  CAS  Google Scholar 

  33. 33.

    Sadakata, T. et al. Reduced axonal localization of a Caps2 splice variant impairs axonal release of BDNF and causes autistic-like behavior in mice. Proc. Natl. Acad. Sci. USA 109, 21104–21109 (2012).

    Article  PubMed  Google Scholar 

  34. 34.

    Griswold, A. J. et al. Evaluation of copy number variations reveals novel candidate genes in autism spectrum disorder-associated pathways. Hum. Mol. Genet. 21, 3513–3523 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. 35.

    Kawaguchi, D. M. & Glatt, S. J. GRIK4 polymorphism and its association with antidepressant response in depressed patients: a meta-analysis. Pharmacogenomics 15, 1451–1459 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. 36.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. 37.

    Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. 38.

    Zeisel, A. et al. Molecular architecture of the mouse nervous system. Preprint at bioRxiv https://doi.org/10.1101/294918 (2018).

  39. 39.

    Sullivan, J. M. et al. Autism-like syndrome is induced by pharmacological suppression of BET proteins in young mice. J. Exp. Med. 212, 1771–1781 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Penney, J. & Tsai, L. H. Histone deacetylases in memory and cognition. Sci. Signal. 7, re12 (2014).

    Article  PubMed  CAS  Google Scholar 

  41. 41.

    Jakovcevski, M. & Akbarian, S. Epigenetic mechanisms in neurological disease. Nat. Med. 18, 1194–1204 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. 42.

    Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. 43.

    Yang, J. et al. Association of DNA methylation in the brain with age in older persons is confounded by common neuropathologies. Int. J. Biochem. Cell Biol. 67, 58–64 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. 44.

    Kundakovic, M. et al. Practical guidelines for high-resolution epigenomic profiling of nucleosomal histones in postmortem human brain tissue. Biol. Psychiatry 81, 162–170 (2017).

    Article  PubMed  CAS  Google Scholar 

  45. 45.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. 46.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. 47.

    Wysoker, A., Tibbetts, K. & Fennell, T. Picard tools version 1.90 (2013).

  48. 48.

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  CAS  Google Scholar 

  49. 49.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. 50.

    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. 51.

    Li, Q., Brown, J. B., Huang, H. & Bickel, P. J. Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat 5, 1752–1779 (2011).

    Article  Google Scholar 

  52. 52.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. 53.

    Kharchenko, P. V., Tolstorukov, M. Y. & Park, P. J. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat. Biotechnol. 26, 1351–1359 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. 54.

    Rozowsky, J. et al. PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nat. Biotechnol. 27, 66–75 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. 55.

    Shen, L., Shao, N., Liu, X. & Nestler, E. ngs.plot: quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genom. 15, 284 (2014).

    Article  CAS  Google Scholar 

  56. 56.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  57. 57.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. 58.

    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).

    Article  PubMed  CAS  Google Scholar 

  59. 59.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  60. 60.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. 61.

    Gaujoux, R. & Seoighe, C. CellMix: a comprehensive toolbox for gene expression deconvolution. Bioinformatics 29, 2211–2212 (2013).

    Article  PubMed  CAS  Google Scholar 

  62. 62.

    Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    Article  PubMed  CAS  Google Scholar 

  63. 63.

    Gel, B. et al. regioneR: an R/Bioconductor package for the association analysis of genomic regions based on permutation tests. Bioinformatics 32, 289–291 (2016).

    PubMed  CAS  Google Scholar 

  64. 64.

    Lambert, J.-C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. 65.

    Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. 66.

    Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. 67.

    Jones, S. E. et al. Genome-wide association analyses in 128,266 individuals identifies new morningness and sleep duration loci. PLoS Genet. 12, e1006125 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. 68.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. 69.

    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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. 70.

    Nikpay, M. et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. 71.

    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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. 72.

    Wetterstrand, K. DNA sequencing costs: data from the NHGRI Genome Sequencing Program (GSP). http://www.genome.gov/sequencingcosts (2016).

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Acknowledgements

We thank M. Fromer, E. Stahl, L. Huckins, L. Shen, G. Senthil and T. Lehner for discussion. This paper is dedicated to the memory of Pamela Sklar. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. We are extremely grateful to J. Ochando, C. Bare and other personnel of the Icahn School of Medicine at Mount Sinai’s Flow Cytometry Core for providing and teaching cell sorting expertise. Data were generated as part of the PsychENCODE Consortium, supported by U01MH103339, U01MH103365, U01MH103392, U01MH103340, U01MH103346, R01MH105472, R01MH094714, R01MH105898, R21MH102791, R21MH105881, R21MH103877 and P50MH106934 awarded to S.A. (Icahn School of Medicine at Mount Sinai), G. Crawford (Duke), S. Dracheva (Icahn School of Medicine at Mount Sinai), P. Farnham (USC), M. Gerstein (Yale), D. Geschwind (UCLA), T. M. Hyde (LIBD), A. Jaffe (LIBD), J. A. Knowles (USC), C. Liu (UIC), D. Pinto (Icahn School of Medicine at Mount Sinai), N. Sestan (Yale), P.S. (Icahn School of Medicine at Mount Sinai), M. State (UCSF), P. Sullivan (UNC), F. Vaccarino (Yale), S. Weissman (Yale), K. White (UChicago) and P. Zandi (JHU). Data were generated as part of the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1-MH-075916, P50M096891, P50MH084053S1, R37MH057881, R37MH057881S1, HHSN271201300031C, AG02219, AG05138 and MH06692. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer’s Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories and the NIMH Human Brain Collection Core. CMC Leadership: P.S., J. Buxbaum (Icahn School of Medicine at Mount Sinai), B. Devlin, D. Lewis (University of Pittsburgh), R. Gur, C.-G. Hahn (University of Pennsylvania), K. Hirai, H. Toyoshiba (Takeda Pharmaceuticals Company Limited), E. Domenici, L. Essioux (F. Hoffman-La Roche Ltd), L. Mangravite, M.A.P. (Sage Bionetworks), T. Lehner and B.K.L. (NIMH). Data on coronary artery disease and myocardial infarction have been contributed by CARDIoGRAMplusC4D investigators. We also thank the International Genomics of Alzheimer’s Project (IGAP) for providing summary results data for these analyses. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families. The i–Select chips were funded by the French National Foundation on Alzheimer’s disease and related disorders. EADI was supported by the LABEX (Laboratory of Excellence Program Investment for the Future) DISTALZ grant, Inserm, Institut Pasteur de Lille, Université de Lille 2 and the Lille University Hospital. GERAD was supported by the Medical Research Council (grant no. 503480), Alzheimer’s Research UK (grant no. 503176), the Wellcome Trust (grant no. 082604/2/07/Z) and German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) grant no. 01GI0102, 01GI0711, 01GI0420. CHARGE was partly supported by NIH NIA grant R01 AG033193 and NIA AG081220 and AGES contract N01–AG–12100, NHLBI grant R01 HL105756, the Icelandic Heart Association, and the Erasmus Medical Center and Erasmus University. ADGC was supported by NIH NIA grants U01 AG032984, U24 AG021886 and U01 AG016976, and Alzheimer’s Association grant ADGC–10–196728.

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Contributions

Wet lab work including tissue processing, sorting of nuclei and ChIP-seq library generation: Y.J., L.B., M.K., E.Z., R.J., J.R.W., R.P., B.S.K. Data processing and coordination: Y.J., M.K., D.H.K., J.S.J., L.S., S.K.S., M.A.P., Y.-c.W., H.S. Bioinformatics and computational genomics: K.G., G.E.H., M.E.H., N.J.F., E.M., Z.W. Provision of brain tissue and resources: B.T.H., B.K.L. Conception of study design (including wet lab and/or bioinformatic analyses pipelines): Y.J., K.G., G.E.H., P.R., P.S., S.A. Writing of the paper: K.G., G.E.H., P.S., P.R., S.A.

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Correspondence to Gabriel E. Hoffman or Panos Roussos or Schahram Akbarian.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–19

Reporting Summary

Supplementary Table 1

Postmortem brain metadata

Supplementary Table 2

Quality control measurements for sorted nuclei and homogenate samples

Supplementary Table 3

Contribution matrix

Supplementary Table 4

Similarity (Jaccard) of consolidated datasets with REP and Sun et al. and Ng et al. data

Supplementary Table 5

QC measurements of all consolidated datasets for both marks

Supplementary Table 6

Cell composition of neuronal, neuronal depleted and bulk tissue samples

Supplementary Table 7

GREAT pathways enrichment of non-overlapping regions

Supplementary Table 8

LDSR score regression P values

Supplementary Table 9

Cell-specific and bulk tissue hQTLs

Supplementary Table 10

hQTLs overlap with GWAS SCZ loci

Supplementary Table 11

Cell-type-specific peaks

Supplementary Table 12

Brain region (ACC, PFC)-specific peaks in neurons and non-neurons

Supplementary Table 13

GREAT pathways enrichment of cell-specific peaks

Supplementary Table 14

GREAT pathways enrichment of brain region (ACC, PFC) peaks

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Girdhar, K., Hoffman, G.E., Jiang, Y. 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). https://doi.org/10.1038/s41593-018-0187-0

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