We quantified genome-wide patterns of lysine H3K27 acetylation (H3K27ac) in entorhinal cortex samples from Alzheimer’s disease (AD) cases and matched controls using chromatin immunoprecipitation and highly parallel sequencing. We observed widespread acetylomic variation associated with AD neuropathology, identifying 4,162 differential peaks (false discovery rate < 0.05) between AD cases and controls. Differentially acetylated peaks were enriched in disease-related biological pathways and included regions annotated to genes involved in the progression of amyloid-β and tau pathology (for example, APP, PSEN1, PSEN2, and MAPT), as well as regions containing variants associated with sporadic late-onset AD. Partitioned heritability analysis highlighted a highly significant enrichment of AD risk variants in entorhinal cortex H3K27ac peak regions. AD-associated variable H3K27ac was associated with transcriptional variation at proximal genes including CR1, GPR22, KMO, PIM3, PSEN1, and RGCC. In addition to identifying molecular pathways associated with AD neuropathology, we present a framework for genome-wide studies of histone modifications in complex disease.

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Data availability

Raw data has been deposited in GEO under accession number GSE102538. Browsable UCSC genome browser tracks of our processed H3K27ac ChIP-seq data are available as a resource at: https://epigenetics.essex.ac.uk/AD_H3K27ac/.

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The authors acknowledge the help of K. Paszkiewicz at the University of Exeter Sequencing Service for advice on ChIP-seq experiments. We also acknowledge the help of V. Teif at the University of Essex in generating the UCSC Genome Browser tracks. Analysis was facilitated by access to the Genome high performance computing cluster at the University of Essex School of Biological Sciences. We acknowledge the help of B. Lee from the University of Exeter Medical School for advice on gene expression assays. We acknowledge S. Prabhakar and W. Sun from the Genome Institute of Singapore for sharing their brain H3K27ac data for our comparative analyses. This work was funded by US National Institutes of Health grant R01 AG036039 to J.M. S.J.M. and T.R. were funded by the EU-FP7 Marie Curie ITN EpiTrain (REA grant agreement no. 316758). S.K.L. is funded by a Medical Research Council (MRC) CASE PhD studentship. Sequencing infrastructure was supported by a Wellcome Trust Multi User Equipment Award (WT101650MA) and Medical Research Council (MRC) Clinical Infrastructure Funding (MR/M008924/1). Generation of DNA hydroxymethylation data was funded by an Alzheimer’s Association US New Investigator Research Grant (grant number NIRG-14-320878) and a grant from BRACE (Bristol Research into Alzheimer’s and Care of the Elderly), both to K.L.

Author information

Author notes

  1. These authors contributed equally: Szi Kay Leung, Teodora Ribarska.

  2. These authors jointly supervised this work: Leonard C. Schalkwyk, Jonathan Mill.


  1. Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    • Sarah J. Marzi
    • , Claire Troakes
    •  & Safa Al-Sarraj
  2. The Blizard Institute, Queen Mary University of London, London, UK

    • Sarah J. Marzi
  3. University of Exeter Medical School, University of Exeter, Exeter, UK

    • Szi Kay Leung
    • , Eilis Hannon
    • , Adam R. Smith
    • , Ehsan Pishva
    • , Jeremie Poschmann
    • , Karen Moore
    • , Katie Lunnon
    •  & Jonathan Mill
  4. Oslo University Hospital, Oslo, Norway

    • Teodora Ribarska
  5. Department of Psychiatry and Neuropsychology, Maastricht University Medical Centre, Maastricht, The Netherlands

    • Ehsan Pishva
  6. Centre de Recherche en Transplantation et Immunologie, Inserm, Université de Nantes, Nantes, France

    • Jeremie Poschmann
  7. UCL Cancer Institute, University College London, London, UK

    • Stephan Beck
  8. University of Essex, Colchester, UK

    • Stuart Newman
    •  & Leonard C. Schalkwyk


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S.J.M., S.K.L., T.R., E.P., and K.M. conducted laboratory experiments. J.M., L.C.S., and S.J.M. designed the study. J.M. supervised the project and obtained funding. S.J.M. undertook primary data analyses and bioinformatics, with analytical and computational input from L.C.S., E.H., and S.N. E.H. undertook the LD score regression and GWAS enrichment analyses. C.T. and S.A.-S. provided brain tissue for analysis. K.L. and A.R.S. generated and preprocessed the DNA modification data. J.P. and S.B. provided advice for the ChIP-seq analyses. S.J.M. and J.M. drafted the manuscript. All of the authors read and approved the final submission.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Jonathan Mill.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–23 and Supplementary Tables 1–3, 6–10, 17, 18, 20, 21

  2. Reporting Summary

  3. Supplementary Table 4

    Hyperacetylated AD-associated H3K27ac peaks.

  4. Supplementary Table 5

    Hypoacetylated AD-associated H3K27ac peaks.

  5. Supplementary Table 11

    Enriched pathways (FDR < 0.05) for the ontology “Biological Process” amongst AD-hyperacetylated H3K27ac peaks.

  6. Supplementary Table 12

    Enriched pathways (FDR < 0.05) for the ontology “Molecular Function” amongst AD-hyperacetylated H3K27ac peaks.

  7. Supplementary Table 13

    Enriched pathways (FDR < 0.05) for the ontology “Disease Ontology” amongst AD-hyperacetylated H3K27ac peaks.

  8. Supplementary Table 14

    Enriched pathways (FDR < 0.05) for the ontology “Biological Process” amongst AD-hypoacetylated H3K27ac peaks.

  9. Supplementary Table 15

    Enriched pathways (FDR < 0.05) for the ontology “Molecular Function” amongst AD-hypoacetylated H3K27ac peaks.

  10. Supplementary Table 16

    Enriched pathways (FDR < 0.05) for the ontology “Disease Ontology” amongst AD-hypoacetylated H3K27ac peaks.

  11. Supplementary Table 19

    Correlation statistics for H3K27ac and DNA methylation at the 439 significantly correlated probe-peak pairs (FDR < 0.05).

  12. Supplementary Software

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