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

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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.


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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.

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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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