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

Article metrics

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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).

  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).

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  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).

  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).

  10. 10.

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

  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).

  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).

  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).

  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).

  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).

  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).

  17. 17.

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

  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).

  19. 19.

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

  20. 20.

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

  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).

  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).

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

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

  27. 27.

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

  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).

  29. 29.

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

  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).

  31. 31.

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

  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).

  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).

  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).

  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).

  36. 36.

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

  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).

  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).

  40. 40.

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

  41. 41.

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

  42. 42.

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

  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).

  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).

  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).

  46. 46.

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

  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).

  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).

  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).

  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).

  52. 52.

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

  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).

  54. 54.

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

  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).

  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).

  57. 57.

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

  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).

  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).

  60. 60.

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

  61. 61.

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

  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).

  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).

  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).

  65. 65.

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

  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).

  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).

  68. 68.

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

  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).

  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).

  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).

  72. 72.

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

Download references

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.

Author information

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.

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

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Further reading