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Mapping cis-regulatory chromatin contacts in neural cells links neuropsychiatric disorder risk variants to target genes

Nature Genetics volume 51, pages 1252–1262 (2019)Cite this article

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Abstract

Mutations in gene regulatory elements have been associated with a wide range of complex neuropsychiatric disorders. However, due to their cell-type specificity and difficulties in characterizing their regulatory targets, the ability to identify causal genetic variants has remained limited. To address these constraints, we perform an integrative analysis of chromatin interactions, open chromatin regions and transcriptomes using promoter capture Hi-C, assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and RNA sequencing, respectively, in four functionally distinct neural cell types: induced pluripotent stem cell (iPSC)-induced excitatory neurons and lower motor neurons, iPSC-derived hippocampal dentate gyrus-like neurons and primary astrocytes. We identify hundreds of thousands of long-range cis-interactions between promoters and distal promoter-interacting regions, enabling us to link regulatory elements to their target genes and reveal putative processes that are dysregulated in disease. Finally, we validate several promoter-interacting regions by using clustered regularly interspaced short palindromic repeats (CRISPR) techniques in human excitatory neurons, demonstrating that CDK5RAP3, STRAP and DRD2 are transcriptionally regulated by physically linked enhancers.

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Fig. 1: Genome-wide mapping of physical chromatin interactions in functionally distinct neural cell types.
Fig. 2: Integrative analysis of chromatin interactions, epigenomic features and gene expression.
Fig. 3: Cell-type-specific PIRs and TF motif enrichment analysis.
Fig. 4: Validation of PIRs in human neural cells.
Fig. 5: Genetic analysis of chromatin interactions with complex neuropsychiatric disorder-associated variants.
Fig. 6: Genetics variants contribute to chromatin interaction bias and alterations in gene expression.

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Data availability

All datasets used in this study (pcHi-C, ATAC-seq, RNA-seq, CUT&RUN, and chromosome-wide SNP phasing data) are available at the Gene Expression Omnibus under the accession number GSE113483. Open chromatin peaks and gene expression results for each cell type are also available on Zenodo through the following link: https://zenodo.org/record/3243977

Data can be visualized on the WashU Epigenome Browser using the session bundle ID (session ID in parentheses): 6e375740-8e71-11e9-be37-cb77c4bbb5fc (brain_pchic_nature_genetics_00).

Alternatively, the data can also be visualized on the legacy WashU Epigenome Browser (session ID in parentheses): http://epigenomegateway.wustl.edu/legacy/?genome=g19&session=8OCs2rkpEA (brain_pchic_nature_genetics_00).

Tracks include ATAC-seq signal, chromatin interactions with score ≥5 and RNA-seq plus and minus strand signal for each cell type. HindIII fragments, in vivo-validated enhancer elements, GENCODE 19 genes and GWAS SNPs are also displayed.

References

  1. Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).

    Article  CAS  Google Scholar 

  2. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    Article  CAS  Google Scholar 

  3. Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).

    Article  CAS  Google Scholar 

  4. Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).

    Article  CAS  Google Scholar 

  5. de la Torre-Ubieta, L. et al. The dynamic landscape of open chromatin during human cortical neurogenesis. Cell 172, 289–304 e218 (2018).

    Article  Google Scholar 

  6. Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).

    Article  Google Scholar 

  7. Miyaoka, Y. et al. Isolation of single-base genome-edited human iPS cells without antibiotic selection. Nat. Methods 11, 291–293 (2014).

    Article  CAS  Google Scholar 

  8. Wang, C. et al. Scalable production of iPSC-derived human neurons to identify Tau-lowering compounds by high-content screening. Stem Cell Rep. 9, 1221–1233 (2017).

    Article  CAS  Google Scholar 

  9. Mertens, J. et al. Differential responses to lithium in hyperexcitable neurons from patients with bipolar disorder. Nature 527, 95–99 (2015).

    Article  CAS  Google Scholar 

  10. Fernandopulle, M. S. et al. Transcription Factor-Mediated Differentiation of Human iPSCs into Neurons. Curr. Protoc. Cell Biol. 79, e51 (2018).

    Article  Google Scholar 

  11. Visel, A., Minovitsky, S., Dubchak, I. & Pennacchio, L. A. VISTA Enhancer Browser–a database of tissue-specific human enhancers. Nucleic Acids Res. 35, D88–92 (2007).

    Article  CAS  Google Scholar 

  12. Zhang, Y. et al. Rapid single-step induction of functional neurons from human pluripotent stem cells. Neuron 78, 785–798 (2013).

    Article  CAS  Google Scholar 

  13. Yu, D. X. et al. Modeling hippocampal neurogenesis using human pluripotent stem cells. Stem Cell Rep. 2, 295–310 (2014).

    Article  CAS  Google Scholar 

  14. Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

    Article  CAS  Google Scholar 

  15. Jung, I. et al. A compendium of promoter-centered long-range chromatin interactions in the human genome. Nat. Genet. (in the press).

  16. Yang, T. et al. HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Res. 27, 1939–1949 (2017).

    Article  CAS  Google Scholar 

  17. Cairns, J. et al. CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data. Genome Biol. 17, 127 (2016).

    Article  Google Scholar 

  18. Jackson, D. A., Hassan, A. B., Errington, R. J. & Cook, P. R. Visualization of focal sites of transcription within human nuclei. EMBO J. 12, 1059–1065 (1993).

    Article  CAS  Google Scholar 

  19. Zhang, Y. et al. Chromatin connectivity maps reveal dynamic promoter-enhancer long-range associations. Nature 504, 306–310 (2013).

    Article  CAS  Google Scholar 

  20. Diao, Y. et al. A tiling-deletion-based genetic screen for cis-regulatory element identification in mammalian cells. Nat. Methods 14, 629–635 (2017).

    Article  CAS  Google Scholar 

  21. Engreitz, J. M. et al. Local regulation of gene expression by lncRNA promoters, transcription and splicing. Nature 539, 452–455 (2016).

    Article  CAS  Google Scholar 

  22. Consortium, E. P. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  Google Scholar 

  23. Shen, Y. et al. A map of the cis-regulatory sequences in the mouse genome. Nature 488, 116–120 (2012).

    Article  CAS  Google Scholar 

  24. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  26. Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6, e21856 (2017).

    Article  Google Scholar 

  27. Davis, C. A. et al. The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–D801 (2018).

    Article  CAS  Google Scholar 

  28. Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–97 (2016).

    Article  CAS  Google Scholar 

  29. Nadif Kasri, N., Nakano-Kobayashi, A. & Van Aelst, L. Rapid synthesis of the X-linked mental retardation protein OPHN1 mediates mGluR-dependent LTD through interaction with the endocytic machinery. Neuron 72, 300–315 (2011).

    Article  CAS  Google Scholar 

  30. Shen, Y. et al. Postnatal activation of TLR4 in astrocytes promotes excitatory synaptogenesis in hippocampal neurons. J. Cell Biol. 215, 719–734 (2016).

    Article  CAS  Google Scholar 

  31. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  Google Scholar 

  32. Ren, G. et al. CTCF-mediated enhancer-promoter interaction is a critical regulator of cell-to-cell variation of gene expression. Mol. Cell 67, 1049–1058 e1046 (2017).

    Article  CAS  Google Scholar 

  33. Guo, Y. et al. CRISPR inversion of CTCF sites alters genome topology and enhancer/promoter function. Cell 162, 900–910 (2015).

    Article  CAS  Google Scholar 

  34. Handoko, L. et al. CTCF-mediated functional chromatin interactome in pluripotent cells. Nat. Genet. 43, 630–638 (2011).

    Article  CAS  Google Scholar 

  35. Hou, C., Dale, R. & Dean, A. Cell type specificity of chromatin organization mediated by CTCF and cohesin. Proc. Natl Acad. Sci. USA 107, 3651–3656 (2010).

    Article  CAS  Google Scholar 

  36. Chuang, H. C., Huang, T. N. & Hsueh, Y. P. Neuronal excitation upregulates Tbr1, a high-confidence risk gene of autism, mediating Grin2b expression in the adult brain. Front Cell Neurosci. 8, 280 (2014).

    Article  Google Scholar 

  37. Liddell, J. R. Are astrocytes the predominant cell type for activation of Nrf2 in aging and neurodegeneration? Antioxid. 6, E65 (2017).

    Article  Google Scholar 

  38. Yin, X., Warner, D. R., Roberts, E. A., Pisano, M. M. & Greene, R. M. Novel interaction between nuclear co-activator CBP and the CDK5 activator binding protein - C53. Int. J. Mol. Med. 16, 251–256 (2005).

    CAS  PubMed  Google Scholar 

  39. Xie, Y. H. et al. Cloning and characterization of human IC53-2, a novel CDK5 activator binding protein. Cell Res. 13, 83–91 (2003).

    Article  CAS  Google Scholar 

  40. Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    Article  Google Scholar 

  41. Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–934 (2012).

    Article  CAS  Google Scholar 

  42. Abdulla, S. et al. Hippocampal degeneration in patients with amyotrophic lateral sclerosis. Neurobiol. Aging 35, 2639–2645 (2014).

    Article  Google Scholar 

  43. Booth, H. D. E., Hirst, W. D. & Wade-Martins, R. The role of astrocyte dysfunction in Parkinson’s disease pathogenesis. Trends Neurosci. 40, 358–370 (2017).

    Article  CAS  Google Scholar 

  44. Tiwari, M., Lopez-Cruzan, M., Morgan, W. W. & Herman, B. Loss of caspase-2-dependent apoptosis induces autophagy after mitochondrial oxidative stress in primary cultures of young adult cortical neurons. J. Biol. Chem. 286, 8493–8506 (2011).

    Article  CAS  Google Scholar 

  45. Zhao, X. et al. Caspase-2 cleavage of tau reversibly impairs memory. Nat. Med. 22, 1268–1276 (2016).

    Article  CAS  Google Scholar 

  46. Huang, C. W. et al. Conditional knockout of breast carcinoma amplified sequence 2 (BCAS2) in mouse forebrain causes dendritic malformation via beta-catenin. Sci. Rep. 6, 34927 (2016).

    Article  CAS  Google Scholar 

  47. Grimmler, M. et al. Unrip, a factor implicated in cap-independent translation, associates with the cytosolic SMN complex and influences its intracellular localization. Hum. Mol. Genet 14, 3099–3111 (2005).

    Article  CAS  Google Scholar 

  48. Burghes, A. H. & Beattie, C. E. Spinal muscular atrophy: why do low levels of survival motor neuron protein make motor neurons sick? Nat. Rev. Neurosci. 10, 597–609 (2009).

    Article  CAS  Google Scholar 

  49. Zhang, J. P. et al. Association of a schizophrenia risk variant at the DRD2 locus with antipsychotic treatment response in first-episode psychosis. Schizophr. Bull. 41, 1248–1255 (2015).

    Article  Google Scholar 

  50. Eisenstein, S. A. et al. Prediction of striatal D2 receptor binding by DRD2/ANKK1 TaqIA allele status. Synapse 70, 418–431 (2016).

    Article  CAS  Google Scholar 

  51. Selvaraj, S., R Dixon, J., Bansal, V. & Ren, B. Whole-genome haplotype reconstruction using proximity-ligation and shotgun sequencing. Nat. Biotechnol. 31, 1111–1118 (2013).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  53. Lydall, G. J. et al. Confirmation of prior evidence of genetic susceptibility to alcoholism in a genome-wide association study of comorbid alcoholism and bipolar disorder. Psychiatr. Genet. 21, 294–306 (2011).

    Article  Google Scholar 

  54. Sudhof, T. C. Calcium control of neurotransmitter release. Cold Spring Harb. Perspect. Biol. 4, a011353 (2012).

    Article  Google Scholar 

  55. Consortium, G. T. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  Google Scholar 

  56. Rajarajan, P. et al. Neuron-specific signatures in the chromosomal connectome associated with schizophrenia risk. Science 362, eaat4311 (2018).

    Article  Google Scholar 

  57. Straws in a haystack. Nat. Genet. 50, 631 (2018).

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

    Article  CAS  Google Scholar 

  59. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article  Google Scholar 

  60. Wingett, S. et al. HiCUP: pipeline for mapping and processing Hi-C data. F1000Res 4, 1310 (2015).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  62. Bansal, V. & Bafna, V. HapCUT: an efficient and accurate algorithm for the haplotype assembly problem. Bioinformatics 24, i153–159 (2008).

    Article  Google Scholar 

  63. Li, N. & Stephens, M. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics 165, 2213–2233 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Delaneau, O., Zagury, J. F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

    Article  CAS  Google Scholar 

  65. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

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Acknowledgements

We thank A. Schmitt and B. Ren (Ludwig Institute for Cancer Research, University of California, San Diego) for sharing pcHi-C probes and the pcHi-C protocol. Genomic analysis of the WTC11 line in this study was made possible by the whole-genome sequencing data generated by the Allen Institute for Cell Science. We thank the Institute and its founder P.G. Allen for making this work possible. We thank G. Hon (University of Texas Southwestern Medical Center) and S. Henikoff (Fred Hutchinson Cancer Research Center, Howard Hughes Medical Institute) for providing reagents. We acknowledge the ENCODE Consortium and Bradley Bernstein’s laboratory for generating the ChIP-seq data for astrocytes used in this study. We thank N. Ahituv, Y. Guo, R.D. Hawkins, M. McManus and B. Ren for providing critical feedback on the manuscript. We thank Y. Qu for her contributions to the illustrations for Fig. 1. This work was made possible in part by the Core Grant for Vision Research and the Research to Prevent Blindness Unrestricted Grant no. NIH-NEI P30EY002162. This work was supported by the National Institutes of Health (NIH) grant nos. R01AG057497 (to Y.S., L.G. and H.S.) and R01EY027789 and UM1HG009402 (to Y.S.), the UCSF Weill Institute for Neuroscience Innovation Award, the Hillblom Foundation, and the American Federation for Aging Research New Investigator Award in Alzheimer’s Disease to Y.S., NIH grant nos. R01EY028249, R01HL130533, R01-HL135358 (to B.R.C.), P01NS097206 and U19MH106434 (to H.S.) and R01MH105128, R35NS097370, and U19AI131130 (to G.L.M.). M.S. is supported by NIH grant no. T32GM007175. F.J. is supported by NIH grant no. T32GM007309.

Author information

Authors and Affiliations

  1. Institute for Human Genetics, University of California, San Francisco, CA, USA

    Michael Song, Xiaoyu Yang, Xingjie Ren, Lenka Maliskova, Bingkun Li, Ian R. Jones, Tsz Wai Tam, Kirsty Jamieson, Lauren A. Weiss & Yin Shen

  2. Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, CA, USA

    Michael Song & Yin Shen

  3. Gladstone Institute of Neurological Disease, University of California, San Francisco, CA, USA

    Chao Wang & Li Gan

  4. Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Fadi Jacob, Guo-Li Ming & Hongjun Song

  5. Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Fadi Jacob, Guo-Li Ming & Hongjun Song

  6. Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Fadi Jacob

  7. Gladstone Institute of Cardiovascular Disease, University of California, San Francisco, CA, USA

    Kenneth Wu, Luke M. Judge & Bruce R. Conklin

  8. Department of Psychiatry, University of California, San Francisco, CA, USA

    Michela Traglia & Lauren A. Weiss

  9. Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China

    Si-Yao Lu & Jun Yao

  10. State Key Laboratory of Membrane Biology, Tsinghua University, Beijing, China

    Si-Yao Lu & Jun Yao

  11. IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China

    Si-Yao Lu & Jun Yao

  12. Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, USA

    Guo-Li Ming & Hongjun Song

  13. Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Guo-Li Ming & Hongjun Song

  14. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA

    Guo-Li Ming

  15. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

    Yun Li

  16. Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA

    Yun Li

  17. Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA

    Yun Li

  18. Salk Institute for Biological Studies, La Jolla, CA, USA

    Jesse R. Dixon

  19. Department of Pediatrics, University of California, San Francisco, CA, USA

    Luke M. Judge

  20. Department of Medicine, University of California, San Francisco, CA, USA

    Bruce R. Conklin

  21. Department of Ophthalmology, University of California, San Francisco, CA, USA

    Bruce R. Conklin

  22. Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA

    Hongjun Song

  23. Helen and Robert Appel Alzheimer’s Disease Research Institute, Weill Cornell Medicine, New York, NY, USA

    Li Gan

  24. Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA

    Li Gan

  25. Department of Neurology, University of California, San Francisco, CA, USA

    Li Gan & Yin Shen

Authors
  1. Michael Song
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  2. Xiaoyu Yang
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  3. Xingjie Ren
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  9. Kenneth Wu
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  15. Yun Li
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  21. Hongjun Song
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  22. Li Gan
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  23. Yin Shen
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Contributions

M.S. and Y.S. designed the study. M.S., X.Y., X.R., L.M., I.J., T.W.T. and K.J. performed the experiments. M.S., B.L., I.J., M.T., L.W. and Y.L. performed data analysis. J.D. contributed to genomic phasing using HaploSeq. S.L., J.Y., K.W., B.R.C., F.J., G.L.M., H.S., L.D., C.W. and L.G. provided cells. M.S. and Y.S. prepared the manuscript with assistance from all authors.

Corresponding author

Correspondence to Yin Shen.

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The authors declare no competing interests.

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Integrated supplementary information

Supplementary Figure 1 Characterization of the cell types used in the study.

(a) Immunofluorescence staining of key markers in excitatory neurons, hippocampal DG-like neurons, lower motor neurons, and astrocytes. Excitatory neurons were positively stained for CUX1, an upper cortical layer marker, and MAP2, a neuronal marker specifically expressed in dendrites. The yield of mature excitatory neurons is the number of CUX1 and MAP2 double positive cells divided by the total number of live cells. Hippocampal DG-like neurons were positively stained for PROX1, a transcription factor specifying granule cell identity in the DG. The yield of mature hippocampal DG-like neurons is the number of PROX1 and MAP2 double positive cells divided by the total number of live cells. Lower motor neurons were positively stained for HB9, a motor neuron marker, and the pan-neuronal neurofilament marker SMI32. The yield of mature lower motor neurons is the number of HB9 and SMI32 double positive cells divided by the total number of live cells. Astrocytes were positively stained for GFAP. The yield of GFAP-positive astrocytes is the number of GFAP positive cells divided by the total number of live cells. The number of staining experiments and the total number of cells are indicated, and error bars represent the s.e.m. Scale bars indicate 25 μm. (b) Heatmaps displaying gene expression results for key marker genes across the neural cell types. Astrocytes used in this study exhibit a gene expression profile consistent with APC identity. (c) Counts of protein coding (dark blue) and noncoding RNA (light blue) genes with interacting promoters in each cell type.

Supplementary Figure 2 Correlation between pcHi-C, ATAC-seq, and RNA-seq replicates.

(a) RNA-seq replicates were hierarchically clustered according to gene expression sample distances using DESeq2. (b) Heatmap with pairwise correlations and hierarchical clustering of read densities at the set of unified open chromatin peaks for the ATAC-seq replicates. (c) Heatmap with pairwise correlations based on the stratum-adjusted correlation coefficient (SCC) from HiCRep for the pcHi-C replicates. (d) Saturation of the SCC between pcHi-C replicates as a function of the total sequencing depth.

Supplementary Figure 3 Integrative analysis of chromatin interactions in individual cell types.

(a). Histograms of interaction distances for each cell type. The mean interaction distances for each cell type are indicated with red lines. (b) Bar plots showing counts of H3K27ac and CTCF binding sites overlapping significant (solid bars) versus randomly shuffled (striped bars) PIRs for excitatory neurons, lower motor neurons, and astrocytes. Means and the s.e.m. for the number of overlaps across n=100 sets of randomly shuffled PIRs are shown. (c) Comparative gene expression analysis for each cell type for expressed genes (normalized RPKM > 0.5) whose promoters interact exclusively with either enhancer-PIRs or repressive-PIRs (P=1.7*10-12, t=7.11, df=1497.2 and n=2,103, 625 genes for excitatory neurons, P=4.1*10-43, t=14.0, df=2666 and n=3,098, 1,060 genes for hippocampal DG-like neurons, and P=3.5*10-28, t=11.1, df=2515.5 and n=1,635, 927 genes for astrocytes, two-tailed two sample t-test). Violin plots show the distributions of gene expression values within each group, and boxplots indicate the median, IQR, Q1-1.5*IQR, and Q3+1.5*IQR. Means are indicated with dotted horizontal lines. (d) Distributions of gene expression values across each cell type for expressed genes (normalized RPKM > 0.5) grouped according to the numbers of interactions their promoters form with enhancer-PIRs. Boxplots indicate the median, IQR, Q1-1.5*IQR, and Q3+1.5*IQR. Linear regression was performed on the mean gene expression values for bins containing at least 10 genes (P=6.4*10-2, F1,5=5.65 and n=7 for excitatory neurons, P=6.6*10-4, F1,7=33.7 and n=9 for hippocampal DG-like neurons, and P=3.1*10-3, F1,6=22.8 and n=8 for astrocytes, F-test for linear regression).

Supplementary Figure 4 Cell-type-specific aspects of chromatin interactions.

(a) Venn diagram displaying counts of unique significant promoter-PIR interactions for excitatory neurons, hippocampal DG-like neurons, lower motor neurons, and astrocytes for each specificity pattern (groups 1-15 in Fig. 3a). (b) Examples of interactions between cell-type-specific PIRs (yellow) and the promoters of OPHN1, CHAT, and TLR4 (orange). ATAC-seq and RNA-seq signal are also shown for each cell type. (c) Significant downregulation of CDK5RAP5 expression was observed across three independent clones containing biallelic deletions for the PIR in excitatory neurons (P=1.6*10-2, t=4.98, df=3, two-tailed two sample t-test). Error bars represent the s.e.m.

Supplementary Figure 5 Using chromatin interactions to elucidate the functions of GWAS variants.

(a) Counts of GWAS SNPs for each disease with at least one linked SNP interacting exclusively with their nearest genes (blue), more distal genes (pink), or both (orange), and the number of target genes for each scenario. (b) Significant promoter-PIR interactions in hippocampal DG-like neurons and astrocytes recapitulate a previously reported interaction between the FOXG1 promoter and a distal open chromatin peak containing rs1191551:G>T, a SCZ-associated variant6. (c) CRISPRi silencing of a PIR for STRAP results in significant downregulation of STRAP expression in excitatory neurons (P=7.4*10-3, t=3.34, df=10, two-tailed two sample t-test). No significant downregulation was detected for the neighboring genes MGST1 and WBP1, though the expression of PTPRO was affected (P=3.0*10-1, t=1.09, df=10, P=5.8x10-1, t=0.570, df=10, and P=2.2*10-3, t=4.07, df=10, respectively, two-tailed two sample t-test). Three independent replicates per condition and two sgRNAs per replicate were used for each experiment. Boxplots indicate the median, IQR, minimum, and maximum. (d) Schematic of detected genotypes in the DRD2 gene and its PIR in two wild type clones and three monoallelic deletion clones. Genotyping and qPCR sequencing for WTC11 variants in the DRD2 gene reveal allele-specific imbalances in DRD2 expression, consistent with the monoallelic deletion of the PIR.

Supplementary Figure 6 Top enriched GO terms for genes targeted by complex neuropsychiatric disorder-associated variants.

Top enriched GO terms from Enrichr for genes whose promoters are targeted by variants for each disease. EP and FTD are omitted due to their low numbers of reported variants and target genes identified by significant promoter-PIR interactions. Expanded lists of enriched GO terms are available in Supplementary Table 9.

Supplementary Figure 7 Examples of putative regulatory SNPs at cell-type-specific PIRs.

. In all examples, interacting PIRs are highlighted in yellow and the targeted promoters are highlighted in orange. (a) A PIR with AD SNPs interacts with the promoters of FAM131B and CASP2 in astrocytes, but it interacts instead with the ZYX promoter in hippocampal DG-like neurons and lower motor neurons. (b) PIRs with MP SNPs in an intron for PTPRO interact with the STRAP promoter across all four cell types. (c) A PIR with SCZ SNPs interacts with the TRIM33 promoter in astrocytes. Two additional PIRs with SCZ SNPs interact with the promoters of TRIM33 and BCAS2 in hippocampal DG-like neurons. (d) A PIR with BD SNPs interacts with the MSI2 promoter in hippocampal DG-like neurons, lower motor neurons, and astrocytes, while also interacting with the AKAP1 promoter in lower motor neurons and astrocytes. Another group of PIRs with SCZ SNPs interacts with the MSI2 promoter in astrocytes.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Supplementary Note

Reporting Summary

Supplementary Table 1

pcHi-C, ATAC-seq, and RNA-seq data processing metrics

Supplementary Table 2

Processed significant interactions called by CHiCAGO

Supplementary Table 3

GO enrichment results for genes interacting with cell-type-specific PIRs

Supplementary Table 4

Motif enrichment results at cell-type-specific PIRs

Supplementary Table 5

Putative target genes for in vivo-validated enhancer elements

Supplementary Table 6

GWAS Catalog SNP mining summary

Supplementary Table 7

Putative target genes for neuropsychiatric disorder-associated SNPs

Supplementary Table 8

Putative target genes for SNPs overlapping open chromatin peaks

Supplementary Table 9

GO enrichment results for disease-specific target genes

Supplementary Table 10

Interactions exhibiting significant allelic bias

Supplementary Table 11

sgRNA and primer sequences

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Song, M., Yang, X., Ren, X. et al. Mapping cis-regulatory chromatin contacts in neural cells links neuropsychiatric disorder risk variants to target genes. Nat Genet 51, 1252–1262 (2019). https://doi.org/10.1038/s41588-019-0472-1

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