Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Neuronal brain-region-specific DNA methylation and chromatin accessibility are associated with neuropsychiatric trait heritability

Abstract

Epigenetic modifications confer stable transcriptional patterns in the brain, and both normal and abnormal brain function involve specialized brain regions. We examined DNA methylation by whole-genome bisulfite sequencing in neuronal and non-neuronal populations from four brain regions (anterior cingulate gyrus, hippocampus, prefrontal cortex, and nucleus accumbens) as well as chromatin accessibility in the latter two. We find pronounced differences in both CpG and non-CpG methylation (CG-DMRs and CH-DMRs) only in neuronal cells across brain regions. Neuronal CH-DMRs were highly associated with differential gene expression, whereas CG-DMRs were consistent with chromatin accessibility and enriched for regulatory regions. These CG-DMRs comprise ~12 Mb of the genome that is highly enriched for genomic regions associated with heritability of neuropsychiatric traits including addictive behavior, schizophrenia, and neuroticism, thus suggesting a mechanistic link between pathology and differential neuron-specific epigenetic regulation in distinct brain regions.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Neuronal nuclei isolated from different brain regions display widespread differences in CpG methylation.
Fig. 2: Differential non-CpG methylation among neuronal nuclei from different brain regions.
Fig. 3: Chromatin accessibility landscape differs between neuronal nuclei isolated from the nucleus accumbens and prefrontal cortex.
Fig. 4: Differential gene expression is strongly associated with neuronal CA-DMRs.
Fig. 5: Neuronal CG-DMRs and DARs are highly enriched for explained heritability of multiple psychiatric, neurological, and behavioral-cognitive traits.

Data Availability

Raw and processed data generated are available through NCBI GEO under accession number GSE96615. Processed data is available through a UCSC hub, at http://genome.ucsc.edu/cgi-bin/hgTracks?db = hg19&hubUrl = https://s3.us-east-2.amazonaws.com/brainepigenome/hub.txt.

References

  1. Hoffmann, A., Sportelli, V., Ziller, M. & Spengler, D. Epigenomics of major depressive disorders and schizophrenia: Early life decides. Int. J. Mol. Sci. 18, E1711 (2017).

    Article  Google Scholar 

  2. Negi, S. K. & Guda, C. Global gene expression profiling of healthy human brain and its application in studying neurological disorders. Sci. Rep. 7, 897 (2017).

    Article  Google Scholar 

  3. Mo, A. et al. Epigenomic signatures of neuronal diversity in themammalian brain. Neuron 86, 1369–1384 (2015).

    CAS  Article  Google Scholar 

  4. Marques, S. et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352, 1326–1329 (2016).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  6. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  8. Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).

    CAS  Article  Google Scholar 

  9. Davies, M. N. et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 13, R43 (2012).

    CAS  Article  Google Scholar 

  10. Illingworth, R. S. et al. Inter-individual variability contrasts with regional homogeneity in the human brain DNA methylome. Nucleic Acids Res. 43, 732–744 (2015).

    CAS  Article  Google Scholar 

  11. Ladd-Acosta, C. et al. DNA methylation signatures within the human brain. Am. J. Hum. Genet. 81, 1304–1315 (2007).

    CAS  Article  Google Scholar 

  12. Viana, J. et al. Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions. Hum. Mol. Genet. 26, 210–225 (2017).

    CAS  PubMed  Google Scholar 

  13. Watson, C. T. et al. Genome-wide DNA methylation profiling in the superior temporal gyrus reveals epigenetic signatures associated with Alzheimer’s disease. Genome Med. 8, 5 (2016).

    Article  Google Scholar 

  14. Kaut, O. et al. Aberrant NMDA receptor DNA methylation detected by epigenome-wide analysis of hippocampus and prefrontal cortex in major depression. Eur. Arch. Psychiatry Clin. Neurosci. 265, 331–341 (2015).

    Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  16. Jaffe, A. E. et al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat. Neurosci. 19, 40–47 (2016).

    CAS  Article  Google Scholar 

  17. Ellis, S. E., Gupta, S., Moes, A., West, A. B. & Arking, D. E. Exaggerated CpH methylation in the autism-affected brain. Mol. Autism 8, 6 (2017).

    Article  Google Scholar 

  18. Mo, A. et al. Epigenomic landscapes of retinal rods and cones. eLife 5, e11613 (2016).

    Article  Google Scholar 

  19. Stroud, H. et al. Early-life gene expression in neurons modulates lasting epigenetic states. Cell 171, 1151–1164.e16 (2017).

    CAS  Article  Google Scholar 

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

    Article  Google Scholar 

  21. Kozlenkov, A. et al. Differences in DNA methylation between human neuronal and glial cells are concentrated in enhancers and non-CpG sites. Nucleic Acids Res. 42, 109–127 (2014).

    CAS  Article  Google Scholar 

  22. Lister, R. et al. Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905 (2013).

    Article  Google Scholar 

  23. Sanchez-Mut, J. V. et al. Human DNA methylomes of neurodegenerative diseases show common epigenomic patterns. Transl. Psychiatry 6, e718 (2016).

    CAS  Article  Google Scholar 

  24. Morel, L. et al. Molecular and functional properties of regional astrocytes in the adult brain. J. Neurosci. 37, 8706–8717 (2017).

    CAS  Article  Google Scholar 

  25. von Bartheld, C. S., Bahney, J. & Herculano-Houzel, S. The search for true numbers of neurons and glial cells in the human brain: A review of 150 years of cell counting. J. Comp. Neurol. 524, 3865–3895 (2016).

    Article  Google Scholar 

  26. Hansen, K. D., Langmead, B. & Irizarry, R. A. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome. Biol. 13, R83 (2012).

    Article  Google Scholar 

  27. Montaño, C. M. et al. Measuring cell-type specific differential methylation in human brain tissue. Genome. Biol. 14, R94 (2013).

    Article  Google Scholar 

  28. Vermunt, M. W. et al. Large-scale identification of coregulated enhancer networks in the adult human brain. Cell Rep. 9, 767–779 (2014).

    CAS  Article  Google Scholar 

  29. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    CAS  Article  Google Scholar 

  30. Hansen, K. D. et al. Increased methylation variation in epigenetic domains across cancer types. Nat. Genet. 43, 768–775 (2011).

    CAS  Article  Google Scholar 

  31. Hansen, K. D. et al. Large-scale hypomethylated blocks associated with Epstein-Barr virus-induced B-cell immortalization. Genome Res. 24, 177–184 (2014).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  33. Guo, J. U. et al. Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain. Nat. Neurosci. 17, 215–222 (2014).

    CAS  Article  Google Scholar 

  34. Ziller, M. J. et al. Genomic distribution and inter-sample variation of non-CpG methylation across human cell types. PLoS Genet. 7, e1002389 (2011).

    CAS  Article  Google Scholar 

  35. 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, 1–9 (2015).

    Google Scholar 

  36. Fullard, J. F. et al. Open chromatin profiling of human postmortem brain infers functional roles for non-coding schizophrenia loci. Hum. Mol. Genet. 26, 1942–1951 (2017).

    CAS  Article  Google Scholar 

  37. Pinello, L., Farouni, R. & Yuan, G. C. Haystack: systematic analysis of the variation of epigenetic states and cell-type specific regulatory elements. Bioinformatics 34, 1930–1933 (2018).

    Article  Google Scholar 

  38. Fukuchi, M. & Tsuda, M. Convergence of neurotransmissions at synapse on IEG regulation in nucleus. Front. Biosci. (LandmarkEd.) 1052–1072 (2017).

  39. Hu, T. M., Chen, C. H., Chuang, Y. A., Hsu, S. H. & Cheng, M. C. Resequencing of early growth response 2 (EGR2) gene revealed a recurrent patient-specific mutation in schizophrenia. Psychiatry Res. 228, 958–960 (2015).

    CAS  Article  Google Scholar 

  40. Pfaffenseller, B. et al. Differential expression of transcriptional regulatory units in the prefrontal cortex of patients with bipolar disorder: potential role of early growth response gene 3. Transl. Psychiatry 6, e805 (2016).

    CAS  Article  Google Scholar 

  41. Larson, E. B. et al. Striatal regulation of ΔFosB, FosB, and cFos during cocaine self-administration and withdrawal. J. Neurochem. 115, 112–122 (2010).

    CAS  Article  Google Scholar 

  42. Yin, Y. et al. Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science 356, eaaj2239 (2017).

    Article  Google Scholar 

  43. Durek, P. et al. Epigenomic profiling of human CD4+ T cells supports a linear differentiation model and highlights molecular regulators of memory development. Immunity 45, 1148–1161 (2016).

    CAS  Article  Google Scholar 

  44. Schmidt, F. et al. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction. Nucleic Acids Res. 45, 54–66 (2017).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    Article  Google Scholar 

  47. Okuno, H. Regulation and function of immediate-early genes in the brain: beyond neuronal activity markers. Neurosci. Res. 69, 175–186 (2011).

    CAS  Article  Google Scholar 

  48. Hertzberg, L., Katsel, P., Roussos, P., Haroutunian, V. & Domany, E. Integration of gene expression and GWAS results supports involvement of calcium signaling in schizophrenia. Schizophr. Res. 164, 92–99 (2015).

    CAS  Article  Google Scholar 

  49. Jaffe, A. E. et al. Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat. Neurosci. 21, 1117–1125 (2018).

    CAS  Article  Google Scholar 

  50. Feinberg, A. P. Phenotypic plasticity and the epigenetics of human disease. Nature 447, 433–440 (2007).

    CAS  Article  Google Scholar 

  51. Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    CAS  Article  Google Scholar 

  52. Gardiner-Garden, M. & Frommer, M. CpG islands in vertebrate genomes. J. Mol. Biol. 196, 261–282 (1987).

    CAS  Article  Google Scholar 

  53. Rosenbloom, K. R. et al. The UCSC Genome Browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2015).

    CAS  Article  Google Scholar 

  54. Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 1571–1572 (2011).

  55. Kuhn, R. M., Haussler, D. & Kent, W. J. The UCSC genome browser and associated tools. Brief. Bioinform. 14, 144–161 (2013).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  59. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  Google Scholar 

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

    Article  Google Scholar 

  61. 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).

    CAS  Article  Google Scholar 

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

    Article  Google Scholar 

  63. Leek, J. T. & Storey, J. D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, 1724–1735 (2007).

    CAS  Article  Google Scholar 

  64. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference. Nat. Methods 14, 417–419 (2017).

    CAS  Article  Google Scholar 

  65. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2015).

    Article  Google Scholar 

  66. R. Core Team. A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria, 2016).

  67. Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B 73, 3–36 (2011).

    Article  Google Scholar 

  68. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  Article  Google Scholar 

  69. Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).

    Google Scholar 

  70. Mathelier, A. et al. JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 44(D1), D110–D115 (2016).

    CAS  Article  Google Scholar 

  71. Tripathi, S. et al. Meta- and orthogonal integration of influenza “OMICs” data defines a role for UBR4 in virusbudding. Cell Host Microbe 18, 723–735 (2015).

    CAS  Article  Google Scholar 

  72. Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005).

    CAS  Article  Google Scholar 

  73. Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).

    CAS  Article  Google Scholar 

  74. Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome. Biol. 5, R80 (2004).

    Article  Google Scholar 

  75. Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).

    CAS  Article  Google Scholar 

  76. Wickham, H. ggplot2: Elegant Graphics for Data Analysis, (Springer, New York, NY, USA, 2016).

  77. Lawrence, M., Gentleman, R. & Carey, V. rtracklayer: an R package for interfacing with genome browsers. Bioinformatics 25, 1841–1842 (2009).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

This work was supported by funding awarded to A.P.F. (U01MH104393) through the enhanced Genotype-Tissue Expression (eGTEx) project supported by the Common Fund of the Office of the Director of NIH. This work was supported by National Cancer Institute under U24CA180996. We would like to thank H. Zhang from the Flow Cytometry Cell Sorting Core Facility at Johns Hopkins School of Public Health for flow sorting. The core facility is supported by CFAR: 5P30AI094189–04, 1S10OD016315–01, and 1S10RR13777001. Brain tissues were received from the NIH NeuroBioBank at the University of Maryland and University of Pittsburgh.

Author information

Authors and Affiliations

Authors

Contributions

L.F.R, K.D.H, and A.P.F designed the study; L.F.R. performed nuclei sorting, DNA and RNA extractions; V.R.D. performed ATAC-seq; R.T., A.I., C.M.C. performed WGBS and RNA-seq library preparation and sequencing; A.P.F. oversaw the experiments; K.D.H. oversaw the data analysis. L.F.R, P.F.H., K.D.H, and A.P.F. performed data analysis and interpreted the results; L.F.R, P.F.H., K.D.H, and A.P.F wrote manuscript.

Corresponding authors

Correspondence to Kasper D. Hansen or Andrew P. Feinberg.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Integrated supplementary information

Supplementary Figure 1 Isolation and global methylation analysis of neuronal (NeuN+) and non-neuronal (NeuN) nuclei from frozen brain tissues.

(a) Nuclei in this representative example were isolated by fluorescence activated nuclei sorting (FANS) from prefrontal cortex (BA9) and debris, doublets, and auto-fluorescent nuclei were gated out. The remaining nuclei were separated based on detection of AlexaFluor 488-conjugated anti-NeuN antibody. FANS was repeated a minimum of 8 times per brain region. (b) Example of nuclei stained with anti-NeuN and DAPI both pre- and post-sort; staining repeated twice with similar results. (c) Proportion of NeuN+ nuclei isolated via FANS from tissue samples from indicated brain regions; arrows indicate two punches from the same tissue sample. Tissues are from BA9, anterior cingulate cortex (BA24), hippocampus (HC) and nucleus accumbens (NAcc) from six individuals as indicated. (d) Proportion of NeuN+ nuclei isolated via FANS plotted against post-mortem interval from tissue samples from indicated brain regions (BA9, n = 11; BA24, n = 13; HC, n = 13; NAcc, n = 8 individuals). Overlaid is a line showing the conditional mean, and shaded 95% confidence interval, for this relationship as estimated using a linear model within each tissue. (e) Average autosomal CpG (mCG) and (f) non-CpG (mCH) methylation of NeuN+ (n = 6 individuals for BA9, HC, NAcc and n = 5 for BA24) and NeuN nuclei (n = 6 individuals for BA9, HC, NAcc and n = 4 for BA24); significant differences in global mC between tissues within dinucleotide contexts and NeuN status are indicated. P-values in (e,f) are adjusted for multiple comparisons using Tukey’s honest significant difference test.

Supplementary Figure 2 Differences in DNA methylation between brain regions are restricted to neuronal nuclei.

Principal component analyses (PCA) of distances derived from average autosomal CpG methylation levels in 1 kb intervals in (a) bulk tissues, (b) NeuN+ nuclei, (c) bulk tissues and sorted nuclei combined, and (d) NeuN nuclei. Tissues, sample type, and individuals are indicated as shown. (e) Correlation matrix between samples based on average methylation of CpGs shared across all samples. (a-e) Tissues are from anterior cingulate cortex (BA24; NeuN+ n = 5 individuals; NeuN n = 4, bulk n = 6), dorsolateral prefrontal cortex (BA9; NeuN+ n = 6, NeuN n = 6, bulk n = 6), hippocampus (HC; NeuN+ n = 6, NeuN n = 6, bulk n = 6), and nucleus accumbens (NAcc; NeuN+ n = 6, NeuN n = 6, bulk n = 6).

Supplementary Figure 3 Variability analysis across sample types.

(a) Pearson correlations for DNA methylation between samples from the same tissue within either a donor (bulk samples composed of both NeuN+ and NeuN; n = 27) or a cell type (NeuN+, n = 23; or NeuN, n = 22 samples). (b) Between sample variation within a brain region and sample type, as a function of methylation level. (c) Proportion of CpGs with an estimated absolute methylation difference greater than 10% (chosen because CG-DMRs all have an absolute methylation difference > 10%) between the indicated brain regions, stratified by sample type (NeuN+, NeuN, bulk).

Supplementary Figure 4 Methylation profiles are distinct between neuronal and non-neuronal nuclei and among neurons from distinct brain regions.

(a) Hierarchical clustering of samples based on the average per sample methylation of the top 20,000 cell type-specific CG-DMRs. Examples of (b) cell type-specific CG-DMRs and a cell type-specific block of differential methylation (NeuN+ is green; NeuN is purple) and (c) neuronal CG-DMRs. Methylation values are shown with CG-DMRs shaded pink and overlap with protein-coding genes depicted below each graph. (d) Average CpG methylation values for NeuN+ nuclei within each brain region over a 3 kb window centered on the CG-DMRs (n = 208) identified among BA9, BA24, and HC. For BA9 and HC, n = 6 individuals and n = 5 individuals for BA24. CG-DMRs were grouped by k-means clustering based on their methylation patterns. Metagene plots for each group are shown to the right with the number of CG-DMRs indicated. (e) As in (c) with normalized ATAC sequencing coverage for NeuN+ nuclei from NAcc and BA9 (individual means are transparent lines; tissue means are opaque lines). Regions of differential accessibility (DARs), CG-DMRs and CG-blocks are shaded pink.

Supplementary Figure 5 Non-CpG methylation is similar across strands and contexts.

Neuronal (NeuN+) non-CpG methylation was measured in 1 kb bins (n = 2,881,044) across the autosomes. Bins with little to no coverage of cytosines were removed. (a) Non-CpG methylation in the CA context on the positive strand compared across two biological replicates. (b) Non-CpG methylation in the CA context compared between the positive and negative strand within an individual sample. (c) Non-CpG methylation on the positive strand compared between the CA context and the CT context within an individual sample. Solid red line is y = x, dashed red line is the best regression line through the origin (correlation and equation shown on graph).

Supplementary Figure 6 Non-CpG methylation is consistent across strand and context and with CpG methylation.

Hierarchical clustering of samples based on the z-score of methylation over (a) CA(-) DMRs, (b) CT( + ) DMRs, and (c) CT(-) DMRs. (d) Hierarchical clustering of samples based on z-scores of methylation for CA( + ) DMRs that overlap CG-DMRs.

Supplementary Figure 7 Open chromatin regions clearly distinguish NeuN+ from NeuN samples.

(a) Sample correlation matrix of ATAC-seq read counts [log2(counts per million)] in open chromatin regions (green = NeuN+ nuclei, purple = NeuN nuclei, orange = nucleus accumbens (NAcc), blue = prefrontal cortex (BA9)). For NeuN+ and NeuN samples from BA9 and NAcc, n = 6 individuals each. (b) Mean-difference plot of peak accessibility data comparing: NeuN+ to NeuN nuclei. Differentially accessible regions (DARs) are shown in orange. Data were randomly sampled (100% of DARs, 10% of non-DARs).

Supplementary Figure 8 DNA methylation and chromatin accessibility are correlated with gene expression.

(a) Average methylation levels around protein coding genes (n = 19,823) with different expression levels in neuronal nuclei isolated from nucleus accumbens (NAcc) and prefrontal cortex (BA9). Each gene length is split into 100 bins and data extend two lengths up and downstream. Gene expression is split into quartiles based on RPKM. (b,c) Scatterplots showing Pearson correlation of differential gene expression [log2(fold change)] (n = 16,538) with: (b) differential mCA and mCG, and (c) differential accessibility [log2(fold change)] from NAcc vs BA9 neuronal nuclei. Differentially expressed protein coding genes (DEGs) are shown in orange. Correlation values (with 95% CI) are given below each graph. (d,e) Scatterplots showing Pearson correlations of differential gene expression [log2(fold change)] with differential epigenetic features from NAcc vs BA9 neuronal nuclei that overlap (d) promoters and (e) gene bodies. DEGs are shown in orange and the number of genes (n) with a feature(s) is indicated. (f) Relationships between expression of protein coding genes and chromatin accessibility, mCA, and mCG within the gene body in NAcc and BA9 neuronal nuclei. Contours show point densities; red line shows smoothed trend. (a-f) n = 6 individuals for both NAcc and BA9 neuronal samples.

Supplementary Figure 9 SLDSR analysis of each feature.

Each feature depicted in the figure was included separately in an SLDSR analysis including 53 baseline features. Results from our differential and the 3 non-differential epigenomic features (Brain H3K27ac30, CNS51, chromHMM15) are shown by trait; the same results are displayed in Fig. 5 without the ability to identify individual traits. (a) Coefficient z-scores. Enrichment is only reported for traits where at least one feature has a z-score significantly larger than 0 (one-sided z-test with alpha = 0.05, P-values corrected within each trait using Holm’s method). (b) Enrichment + /− 2 standard errors. GWAS sizes for each trait are reported in Supplementary Table 24.

Supplementary Figure 10 SLDSR analysis of differential epigenomic features, controlling for nondifferential epigenomic features.

Each of the differential epigenomic features (neuronal CG-DMRs, DARs and cell-type CG-DMRs and DARs) was included separately in a SLDSR analysis with the 3 non-differential epigenomic features (Brain H3K27ac30, CNS51, chromHMM15) included in the baseline (53 features). Results are shown by trait; the same results are displayed in Fig. 5 without the ability to identify individual traits. (a) Coefficient z-scores. Enrichment is only reported for traits where at least one feature has a z-score significantly larger than 0 (one-sided z-test with alpha = 0.05, P-values corrected within each trait using Holm’s method). (b) Enrichment + /− 2 standard errors. GWAS sizes for each trait are reported in Supplementary Table 24.

Supplementary Figure 11 SLDSR analysis of differential epigenomic features, controlling for nondifferential epigenomic features by excluding their overlap from baseline.

Each of the differential epigenomic features (neuronal CG-DMRs, DARs and cell-type CG-DMRs and DARs) was including separately in a SLDSR analysis including the 3 non-differential epigenomic features (Brain H3K27ac30, CNS51, chromHMM15) in the baseline. For the 3 non-differential epigenomic features included in the baseline, we removed any overlap with the differential epigenomic features. Results are shown by trait; the same results are displayed in Fig. 5 without the ability to identify individual traits. (a) Coefficient z-scores. Enrichment is only reported for traits where at least one feature has a z-score significantly larger than 0 (one-sided z-test with alpha = 0.05, P-values corrected within each trait using Holm’s method). (b) Enrichment + /− 2 standard errors. GWAS sizes for each trait are reported in Supplementary Table 24.

Supplementary information

Supplementary Figures 1–11

Reporting Summary

Supplementary Tables 1–4, 6–9, 11, 12, 14–16, 21, 23, 24, 26

Supplementary Table 5

Supplementary Table 10

GREAT Analysis of CG-DMRs where NAcc is hypomethylated compared to other three brain regions.

Supplementary Table 13

List of non-CpG DMRs (CA- and CT-DMRs).

Supplementary Table 17

GO analysis of differentially expressed genes in NAcc vs. BA9 in neurons.

Supplementary Table 18

Differentially accessible regions between NeuN-positive and NeuN-negative samples.

Supplementary Table 19

Differentially accessible regions between NAcc and BA9 neurons.

Supplementary Table 20

Differentially accessible regions between NAcc and BA9 in NeuN-negative samples (non-neurons).

Supplementary Table 22

Transcription factor motif enrichments with neuronal DARs overlapping hyper- or hypomethylated neuronal NAcc CG-DMRs.

Supplementary Table 25

SLDSR analysis results.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rizzardi, L.F., Hickey, P.F., Rodriguez DiBlasi, V. et al. Neuronal brain-region-specific DNA methylation and chromatin accessibility are associated with neuropsychiatric trait heritability. Nat Neurosci 22, 307–316 (2019). https://doi.org/10.1038/s41593-018-0297-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-018-0297-8

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing