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The three-dimensional landscape of cortical chromatin accessibility in Alzheimer’s disease

Abstract

To characterize the dysregulation of chromatin accessibility in Alzheimer’s disease (AD), we generated 636 ATAC-seq libraries from neuronal and nonneuronal nuclei isolated from the superior temporal gyrus and entorhinal cortex of 153 AD cases and 56 controls. By analyzing a total of ~20 billion read pairs, we expanded the repertoire of known open chromatin regions (OCRs) in the human brain and identified cell-type-specific enhancer–promoter interactions. We show that interindividual variability in OCRs can be leveraged to identify cis-regulatory domains (CRDs) that capture the three-dimensional structure of the genome (3D genome). We identified AD-associated effects on chromatin accessibility, the 3D genome and transcription factor (TF) regulatory networks. For one of the most AD-perturbed TFs, USF2, we validated its regulatory effect on lysosomal genes. Overall, we applied a systematic approach to understanding the role of the 3D genome in AD. We provide all data as an online resource for widespread community-based analysis.

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Fig. 1: Large-scale chromatin accessibility analysis in the human brain.
Fig. 2: Variance component analysis of gene expression.
Fig. 3: Linking distal regulatory OCRs (OCRABC) to genes using the ABC method.
Fig. 4: Disease-associated chromatin changes.
Fig. 5: Gene set enrichment analysis using general gene sets.
Fig. 6: Mapping of TFs to cell types and AD-related phenotypes.
Fig. 7: Definition of neuronal and nonneuronal CRDs.
Fig. 8: Disease-associated perturbations in CRDs.

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Data availability

Raw data (FASTQ files) and processed data (BigWig files, peaks, and raw/normalized count matrices) are available via the AD Knowledge Portal (https://adknowledgeportal.org). The AD Knowledge Portal is a platform for accessing data, analyses and tools generated by the Accelerating Medicines Partnership (AMP-AD) Target Discovery Program and other National Institute on Aging (NIA)-supported programs to enable open-science practices and accelerate translational learning. The data, analyses and tools are shared early in the research cycle without a publication embargo on secondary use. Data are available for general research use according to the following requirements for data access and data attribution (https://adknowledgeportal.org/DataAccess/Instructions). For access to the content described in this manuscript, see https://doi.org/10.7303/syn21513145. Browsable UCSC genome browser tracks of processed data are available at http://icahn.mssm.edu/atacad.

External validation sets: MSBB RNA-seq of postmortem brains (Synapse ID: syn3157743), ATAC-seq on FANS-sorted NeuN+/− from postmortem brains (Synapse ID: syn20755767), H3K9ac ChIP–seq of postmortem brains (Synapse ID: syn4896408). ATAC-seq iPSC-derived neurons overexpressing MAPT gene (GEO: GSE97409), ROSMAP RNA-seq of postmortem brains (Synapse ID: syn3388564), fine-mapped eQTLs (https://alkesgroup.broadinstitute.org/LDSCORE/LDSC_QTL/, version ‘FE_META_TISSUE_GTEx_Brain_MaxCPP’), CTCF ChIP–seq peaks on human neural cell (GEO: GSE127577). OCRs (peaks) from The Cancer Genome Atlas (https://gdc.cancer.gov/about-data/publications/ATACseq-AWG), BOCA/BOCA2 brain epigenome atlas (https://icahn.mssm.edu/boca, https://icahn.mssm.edu/boca2), Dong. et al. 2021 (Synapse ID: syn25716684), Nott et al. 2019 (dbGaP ID: phs001373), and Meuleman et al. 2020 (ENCODE ID: ENCSR857UZV), fine-mapped eQTLs (https://alkesgroup.broadinstitute.org/LDSCORE/LDSC_QTL/, version ‘FE_META_TISSUE_GTEx_Brain_MaxCPP’), CTCF ChIP–seq on human neural cell (GEO: GSE127577), The Cancer Genome Atlas (https://gdc.cancer.gov/about-data/publications/ATACseq-AWG), REMC (http://www.roadmapepigenomics.org), mSigDB 7.0 (http://www.gsea-msigdb.org/), dbSNP v.151 (https://www.ncbi.nlm.nih.gov/snp/), PsychENCODE SNP-array: Capstone collection (https://psychencode.synapse.org/).

Code availability

The code used to perform the analysis described in this study is available at https://doi.org/10.7303/syn34034120.

References

  1. Andrews, S. J., Fulton-Howard, B. & Goate, A. Interpretation of risk loci from genome-wide association studies of Alzheimer’s disease. Lancet Neurol. 19, 326–335 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Marzi, S. J. et al. A histone acetylome-wide association study of Alzheimer’s disease identifies disease-associated H3K27ac differences in the entorhinal cortex. Nat. Neurosci. 21, 1618–1627 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Klein, H.-U. et al. Epigenome-wide study uncovers large-scale changes in histone acetylation driven by tau pathology in aging and Alzheimer’s human brains. Nat. Neurosci. 22, 37–46 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Gasparoni, G. et al. DNA methylation analysis on purified neurons and glia dissects age and Alzheimer’s disease-specific changes in the human cortex. Epigenet. Chromatin 11, 41 (2018).

    Article  Google Scholar 

  5. Li, P. et al. Epigenetic dysregulation of enhancers in neurons is associated with Alzheimer’s disease pathology and cognitive symptoms. Nat. Commun. 10, 2246 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 50, 1584–1592 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Nott, A. et al. Brain cell type-specific enhancer–promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kozlenkov, A. et al. Substantial DNA methylation differences between two major neuronal subtypes in human brain. Nucleic Acids Res. 44, 2593–2612 (2016).

    Article  PubMed  Google Scholar 

  9. Wang, M. et al. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease. Sci. Data 5, 180185 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Fillenbaum, G. G. et al. Consortium to Establish a Registry for Alzheimer’s Disease (CERAD): the first twenty years. Alzheimers Dement. 4, 96–109 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).

    Article  CAS  PubMed  Google Scholar 

  12. Braak, H., Alafuzoff, I., Arzberger, T., Kretzschmar, H. & Del Tredici, K. Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol. 112, 389–404 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Morris, J. C. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43, 2412–2414 (1993).

    Article  CAS  PubMed  Google Scholar 

  14. Nelson, P. T. et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J. Neuropathol. Exp. Neurol. 71, 362–381 (2012).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hauberg, M. E. et al. Common schizophrenia risk variants are enriched in open chromatin regions of human glutamatergic neurons. Nat. Commun. 11, 5581 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hu, B. et al. Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders. Nat. Commun. 12, 3968 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 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 

  19. Fulco, C. P. et al. Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47, 598–606 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hormozdiari, F. et al. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat. Genet. 50, 1041–1047 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pickar-Oliver, A. & Gersbach, C. A. The next generation of CRISPR–Cas technologies and applications. Nat. Rev. Mol. Cell Biol. 20, 490–507 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wang, M. et al. Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease. Genome Med. 8, 104 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Khan, U. A. et al. Molecular drivers and cortical spread of lateral entorhinal cortex dysfunction in preclinical Alzheimer’s disease. Nat. Neurosci. 17, 304–311 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Barrera, J. et al. Sex dependent glial-specific changes in the chromatin accessibility landscape in late-onset Alzheimer’s disease brains. Mol. Neurodegener. 16, 58 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bowles, K. et al. 17q21.31 sub-haplotypes underlying H1-associated risk for Parkinson’s disease are associated with LRRC37A/2 expression in astrocytes. Mol. Neurodegener. 16, 1–21 (2022).

    Google Scholar 

  28. Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Yokoyama, K. et al. NYAP: a phosphoprotein family that links PI3K to WAVE1 signalling in neurons. EMBO J. 30, 4739–4754 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chen, X. et al. Cholecystokinin release triggered by NMDA receptors produces LTP and sound–sound associative memory. Proc. Natl. Acad. Sci. U.S.A. 116, 6397–6406 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Li, X., Long, J., He, T., Belshaw, R. & Scott, J. Integrated genomic approaches identify major pathways and upstream regulators in late onset Alzheimer’s disease. Sci. Rep. 5, 12393 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Castillo, E. et al. Comparative profiling of cortical gene expression in Alzheimer’s disease patients and mouse models demonstrates a link between amyloidosis and neuroinflammation. Sci. Rep. 7, 17762 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Hokama, M. et al. Altered expression of diabetes-related genes in Alzheimer’s disease brains: the Hisayama study. Cereb. Cortex 24, 2476–2488 (2014).

    Article  PubMed  Google Scholar 

  36. Chan, P. M. & Manser, E. PAKs in human disease. Prog. Mol. Biol. Transl. Sci. 106, 171–187 (2012).

    Article  CAS  PubMed  Google Scholar 

  37. Bell, R. D. & Zlokovic, B. V. Neurovascular mechanisms and blood–brain barrier disorder in Alzheimer’s disease. Acta Neuropathol. 118, 103–113 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yuan, Z. et al. Regulation of neuronal cell death by MST1–FOXO1 signaling. J. Biol. Chem. 284, 11285–11292 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Greer, E. L. & Brunet, A. FOXO transcription factors at the interface between longevity and tumor suppression. Oncogene 24, 7410–7425 (2005).

    Article  CAS  PubMed  Google Scholar 

  40. Webb, A. E., Kundaje, A. & Brunet, A. Characterization of the direct targets of FOXO transcription factors throughout evolution. Aging Cell 15, 673–685 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Koopmans, F. et al. SynGO: an evidence-based, expert-curated knowledge base for the synapse. Neuron 103, 217–234 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bentsen, M. et al. ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation. Nat. Commun. 11, 4267 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Rahman, M. R. et al. Network-based approach to identify molecular signatures and therapeutic agents in Alzheimer’s disease. Comput. Biol. Chem. 78, 431–439 (2019).

    Article  CAS  PubMed  Google Scholar 

  44. Acquaah-Mensah, G. K. & Taylor, R. C. Brain in situ hybridization maps as a source for reverse-engineering transcriptional regulatory networks: Alzheimer’s disease insights. Gene 586, 77–86 (2016).

    Article  CAS  PubMed  Google Scholar 

  45. Qin, L. et al. Ethnicity-specific and overlapping alterations of brain hydroxymethylome in Alzheimer’s disease. Hum. Mol. Genet. 29, 149–158 (2020).

    Article  CAS  PubMed  Google Scholar 

  46. Malik, B. R., Maddison, D. C., Smith, G. A. & Peters, O. M. Autophagic and endo-lysosomal dysfunction in neurodegenerative disease. Mol. Brain 12, 100 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Yamanaka, T. et al. Genome-wide analyses in neuronal cells reveal that upstream transcription factors regulate lysosomal gene expression. FEBS J. 283, 1077–1087 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Johnson, D. E., Ostrowski, P., Jaumouillé, V. & Grinstein, S. The position of lysosomes within the cell determines their luminal pH. J. Cell Biol. 212, 677–692 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hayek, S. R., Rane, H. S. & Parra, K. J. Reciprocal regulation of v-ATPase and glycolytic pathway elements in health and disease. Front. Physiol. 10, 127 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Couoh-Cardel, S., Milgrom, E. & Wilkens, S. Affinity purification and structural features of the yeast vacuolar ATPase V0 membrane sector. J. Biol. Chem. 290, 27959–27971 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Delaneau, O. et al. Chromatin three-dimensional interactions mediate genetic effects on gene expression. Science 364, eaat8266 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Ambroise, C., Dehman, A., Neuvial, P., Rigaill, G. & Vialaneix, N. Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. Algorithms Mol. Biol. 14, 22 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Hoffman, G. E., Bendl, J., Girdhar, K. & Roussos, P. decorate: differential epigenetic correlation test. Bioinformatics 36, 2856–2861 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. De Jager, P. L. et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci. Data 5, 180142 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Meuleman, W. et al. Constitutive nuclear lamina–genome interactions are highly conserved and associated with A/T-rich sequence. Genome Res. 23, 270–280 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Roadmap Epigenomics Consortiumet al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  PubMed Central  Google Scholar 

  59. Corces, M.R. et al. The chromatin accessibility landscape of primary human cancers. Science 362, eaav1898 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Ishizuka, Y. & Hanamura, K. Drebrin in Alzheimer’s disease. Adv. Exp. Med. Biol. 1006, 203–223 (2017).

    Article  CAS  PubMed  Google Scholar 

  61. Turi, Z., Lacey, M., Mistrik, M. & Moudry, P. Impaired ribosome biogenesis: mechanisms and relevance to cancer and aging. Aging 11, 2512–2540 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Chambers, D. M., Peters, J. & Abbott, C. M. The lethal mutation of the mouse wasted (wst) is a deletion that abolishes expression of a tissue-specific isoform of translation elongation factor 1α, encoded by the Eef1a2 gene. Proc. Natl. Acad. Sci. U.S.A. 95, 4463–4468 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  PubMed  Google Scholar 

  64. Zhang, W. et al. Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits. Nat. Commun. 10, 3834 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Ernst, J. & Kellis, M. Chromatin-state discovery and genome annotation with ChromHMM. Nat. Protoc. 12, 2478–2492 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Meuleman, W. et al. Index and biological spectrum of human DNase I hypersensitive sites. Nature 584, 244–251 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Dong, P. et al. Population-level variation of enhancer expression identifies novel disease mechanisms in the human brain. Nat. Genet. https://doi.org/10.1038/s41588-022-01170-4 (2022).

  68. Ernst, J. & Kellis, M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat. Biotechnol. 33, 364–376 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Hoffman, G. E. & Roussos, P. Dream: powerful differential expression analysis for repeated measures designs. Bioinformatics 37, 192–201 (2021).

    Article  CAS  PubMed  Google Scholar 

  71. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Haroutunian, V., Katsel, P. & Schmeidler, J. Transcriptional vulnerability of brain regions in Alzheimer’s disease and dementia. Neurobiol. Aging 30, 561–573 (2009).

    Article  CAS  PubMed  Google Scholar 

  73. Morris, J. C. et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology 39, 1159–1165 (1989).

    Article  CAS  PubMed  Google Scholar 

  74. 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. 109, 21.29.1–21.29.9 (2015).

    Article  Google Scholar 

  75. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  77. 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  CAS  PubMed  PubMed Central  Google Scholar 

  78. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Shin, H. et al. TopDom: an efficient and deterministic method for identifying topological domains in genomes. Nucleic Acids Res. 44, e70 (2016).

    Article  PubMed  Google Scholar 

  83. 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  CAS  PubMed  Google Scholar 

  84. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Liberzon, A. et al. Molecular Signatures Database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Law, C.W. et al. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Res 5, 1408 (2016).

    Article  Google Scholar 

  89. Yoshida, H. et al. The cis-regulatory atlas of the mouse immune system. Cell 176, 897–912 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Anscombe, F. J. The transformation of Poisson, binomial and negative-binomial data. Biometrika 35, 246–254 (1948).

    Article  Google Scholar 

  91. Francis Harrison, P. Varistran: Anscombe’s variance stabilizing transformation for RNA-seq gene expression data. JOSS 2, 257 (2017).

    Article  Google Scholar 

  92. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Nagpal, S. et al. TIGAR: an improved Bayesian tool for transcriptomic data imputation enhances gene mapping of complex traits. Am. J. Hum. Genet. 105, 258–266 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Becker, M., Kuhse, J. & Kirsch, J. Effects of two elongation factor 1A isoforms on the formation of gephyrin clusters at inhibitory synapses in hippocampal neurons. Histochem. Cell Biol. 140, 603–609 (2013).

    Article  CAS  PubMed  Google Scholar 

  95. Shirao, T. et al. The role of drebrin in neurons. J. Neurochem. 141, 819–834 (2017).

    Article  CAS  PubMed  Google Scholar 

  96. Hetman, M. & Slomnicki, L. P. Ribosomal biogenesis as an emerging target of neurodevelopmental pathologies. J. Neurochem. 148, 325–347 (2019).

    Article  CAS  PubMed  Google Scholar 

  97. Schrode, N. et al. Synergistic effects of common schizophrenia risk variants. Nat. Genet. 51, 1475–1485 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Gupta, S., Stamatoyannopoulos, J. A., Bailey, T. L. & Noble, W. S. Quantifying similarity between motifs. Genome Biol. 8, R24 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Reyna, M. A., Leiserson, M. D. M. & Raphael, B. J. Hierarchical HotNet: identifying hierarchies of altered subnetworks. Bioinformatics 34, i972–i980 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the patients and families who donated material for these studies. We thank the members of the Roussos laboratory for thoughtful advice and critique and the computational resources and staff expertise provided by the Scientific Computing at the Icahn School of Medicine at Mount Sinai. This study was supported by grants from the National Institute on Aging, NIH grants R01-AG067025 (P.R. and V.H.), R01-AG065582 (P.R. and V.H.) and R01-AG050986 (P.R.) and by grants from the National Institute of Mental Health, NIH grants, R56-MH101454 (K.J.B.), R01-MH106056 (P.R. and K.J.B.), R01-MH109897 (P.R. and K.J.B.) and R01-MH121074 (K.J.B.). J.B. was supported in part by Alzheimer’s Association Research Fellowship AARF-21-722200. K.G. was supported in part by Alzheimer’s Association Research Fellowship AARF-21-722582. G.E.H. and P.D. were supported in part by NARSAD Young Investigator grants 26313 and 29683, respectively, from the Brain & Behavior Research Foundation. S.P.K. is a recipient of an NIH LRP award. Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award numbers S10OD018522 and S10OD026880. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

Authors

Contributions

R.A.N., V.H. and P.R. conceived of and designed the project. V.H. provided human brain tissue. J.F.F. and P.R. designed experimental strategies for epigenome profiling in human postmortem tissue. R.M., S.K., S.M.R. and J.F.F. performed ATAC-seq data generation. S.R. performed Hi-C data generation. P.A. performed ChIP–seq data generation. E.I., J.V. and R.A.N. performed USF2 in vitro validation studies. M.B.F., K.G.T., J.V., S.R. and K.J.B. performed the CRISPRi in vitro validation studies. J.B., M.E.H., K.G., G.E.H. and P.R. designed analytical strategies. J.B., M.E.H., K.G. and G.E.H. conducted initial bioinformatics sample processing and quality control. J.B., M.E.H., K.G. and G.E.H. developed and performed all downstream omics data analyses and interpreted results. B.Z. performed eQTL fine-mapping analysis. W.Z. and G.V. performed transcriptome imputation analysis. P.D. performed ChIP–seq and Hi-C data analysis. J.B., M.E.H., K.G., G.E.H., J.F.F. and P.R. wrote the manuscript with the help of all authors. G.E.H., J.F.F. and P.R. supervised overall data generation and analysis.

Corresponding author

Correspondence to Panos Roussos.

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The authors declare no competing interests.

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Bendl, J., Hauberg, M.E., Girdhar, K. et al. The three-dimensional landscape of cortical chromatin accessibility in Alzheimer’s disease. Nat Neurosci 25, 1366–1378 (2022). https://doi.org/10.1038/s41593-022-01166-7

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