A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles


Most risk variants for brain disorders identified by genome-wide association studies reside in the noncoding genome, which makes deciphering biological mechanisms difficult. A commonly used tool, multimarker analysis of genomic annotation (MAGMA), addresses this issue by aggregating single nucleotide polymorphism associations to nearest genes. Here we developed a platform, Hi-C-coupled MAGMA (H-MAGMA), that advances MAGMA by incorporating chromatin interaction profiles from human brain tissue across two developmental epochs and two brain cell types. By analyzing gene regulatory relationships in the disease-relevant tissue, H-MAGMA identified neurobiologically relevant target genes. We applied H-MAGMA to five psychiatric disorders and four neurodegenerative disorders to interrogate biological pathways, developmental windows and cell types implicated for each disorder. Psychiatric-disorder risk genes tended to be expressed during mid-gestation and in excitatory neurons, whereas neurodegenerative-disorder risk genes showed increasing expression over time and more diverse cell-type specificities. H-MAGMA adds to existing analytic frameworks to help identify the neurobiological principles of brain disorders.

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Fig. 1: Schematics of the H-MAGMA approach.
Fig. 2: Spatiotemporal dynamics of brain-disorder risk genes.
Fig. 3: Cellular expression profiles of brain-disorder risk genes.
Fig. 4: Characteristics of brain-disorder risk genes.
Fig. 5: Pleiotropic genes reveal shared molecular mechanisms of psychiatric disorders.

Data availability

All GWAS summary statistics used in this study are publicly available. We deposited H-MAGMA input files derived from fetal brain and adult brain, and neuronal and astrocytic Hi-C data, and H-MAGMA output files for nine brain disorders in the GitHub repository at https://github.com/thewonlab/H-MAGMA.

Code availability

Codes used in this study are provided in the GitHub repository at https://github.com/thewonlab/H-MAGMA.


  1. 1.

    Edwards, S. L., Beesley, J., French, J. D. & Dunning, A. M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (2013).

  2. 2.

    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).

  3. 3.

    Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

    Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

  8. 8.

    Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).

  9. 9.

    Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

  10. 10.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

  11. 11.

    Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018).

  12. 12.

    Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

  13. 13.

    De La Torre-Ubieta, L., Won, H., Stein, J. L. L. & Geschwind, D. H. H. Advancing the understanding of autism disease mechanisms through genetics. Nat. Med. 22, 345–361 (2016).

  14. 14.

    Cross-Disorder Group of the Psychiatric Genomics Consortium. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482.e11 (2019).

  15. 15.

    Schork, A. J. et al. A genome-wide association study of shared risk across psychiatric disorders implicates gene regulation during fetal neurodevelopment. Nat. Neurosci. 22, 353–361 (2019).

  16. 16.

    Network Pathway Analysis Subgroup of the Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci. 18, 199–209 (2015).

  17. 17.

    Marín, O. Developmental timing and critical windows for the treatment of psychiatric disorders. Nat. Med. 22, 1229–1238 (2016).

  18. 18.

    Wegiel, J. et al. The neuropathology of autism: defects of neurogenesis and neuronal migration, and dysplastic changes. Acta Neuropathol. 119, 755–770 (2010).

  19. 19.

    Zoghbi, H. Y. & Bear, M. F. Synaptic dysfunction in neurodevelopmental disorders associated with autism and intellectual disabilities. Cold Spring Harb. Perspect. Biol. 4, a009886 (2012).

  20. 20.

    Deutsch, S. I., Urbano, M. R., Burket, J. A., Herndon, A. L. & Winebarger, E. E. Pharmacotherapeutic implications of the association between genomic instability at chromosome 15q13.3 and autism spectrum disorders. Clin. Neuropharmacol. 34, 203–205 (2011).

  21. 21.

    Muller, C. L., Anacker, A. M. J. & Veenstra-VanderWeele, J. The serotonin system in autism spectrum disorder: from biomarker to animal models. Neuroscience 321, 24–41 (2016).

  22. 22.

    Berman, J. A., Talmage, D. A. & Role, L. W. Cholinergic circuits and signaling in the pathophysiology of schizophrenia. Int. Rev. Neurobiol. 78, 193–223 (2007).

  23. 23.

    Marino, F. & Cosentino, M. Repurposing dopaminergic drugs for MS—the evidence mounts. Nat. Rev. Neurol. 12, 191–192 (2016).

  24. 24.

    Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

  25. 25.

    The Brainstorm Consortium et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

  26. 26.

    Iqbal, K., Liu, F. & Gong, C.-X. Tau and neurodegenerative disease: the story so far. Nat. Rev. Neurol. 12, 15–27 (2016).

  27. 27.

    Haass, C. & Selkoe, D. J. Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer’s amyloid β-peptide. Nat. Rev. Mol. Cell Biol. 8, 101–112 (2007).

  28. 28.

    Mulligan, K. A. & Cheyette, B. N. R. Neurodevelopmental perspectives on Wnt signaling in psychiatry. Mol. Neuropsychiatry 2, 219–246 (2017).

  29. 29.

    Edmunds, S. R., Kover, S. T. & Stone, W. L. The relation between parent verbal responsiveness and child communication in young children with or at risk for autism spectrum disorder: a systematic review and meta‐analysis. Autism Rev. 12, 715–731 (2019).

  30. 30.

    Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016).

  31. 31.

    Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).

  32. 32.

    Hansen, D. V., Hanson, J. E. & Sheng, M. Microglia in Alzheimer’s disease. J. Cell Biol. 217, 459–472 (2018).

  33. 33.

    Clement, A. M. et al. Wild-type nonneuronal cells extend survival of SOD1 mutant motor neurons in ALS mice. Science 302, 113–117 (2003).

  34. 34.

    Halliday, G. M. & Stevens, C. H. Glia: initiators and progressors of pathology in Parkinson’s disease. Mov. Disord 26, 6–17 (2011).

  35. 35.

    Ortiz, G. G. et al. Role of the blood–brain barrier in multiple sclerosis. Arch. Med. Res. 45, 687–697 (2014).

  36. 36.

    Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011).

  37. 37.

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

  38. 38.

    Clarke, L. E. et al. Normal aging induces A1-like astrocyte reactivity. Proc. Natl Acad. Sci. USA 115, E1896–E1905 (2018).

  39. 39.

    Brambilla, R. The contribution of astrocytes to the neuroinflammatory response in multiple sclerosis and experimental autoimmune encephalomyelitis. Acta Neuropathol. 137, 757–783 (2019).

  40. 40.

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

  41. 41.

    Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

  42. 42.

    Mah, W. & Won, H. The three-dimensional landscape of the genome in human brain tissue unveils regulatory mechanisms leading to schizophrenia risk. Schizophr. Res. https://doi.org/10.1016/j.schres.2019.03.007 (2019).

  43. 43.

    Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

  44. 44.

    Plaisier, S. B., Taschereau, R., Wong, J. A. & Graeber, T. G. Rank–rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures. Nucleic Acids Res. 38, e169 (2010).

  45. 45.

    Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

  46. 46.

    Gerring, Z. F., Gamazon, E. R. & Derks, E. M. for the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. A gene co-expression network-based analysis of multiple brain tissues reveals novel genes and molecular pathways underlying major depression. PLoS Genet. 15, e1008245 (2019).

  47. 47.

    Alasoo, K. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50, 424–431 (2018).

  48. 48.

    Gate, R. E. et al. Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat. Genet. 50, 1140–1150 (2018).

  49. 49.

    Mohammadi, S., Davila-Velderrain, J. & Kellis, M. Reconstruction of cell-type-specific interactomes at single-cell resolution. Cell Syst. 9, 559–568.e4 (2019).

  50. 50.

    Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

  51. 51.

    Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

  52. 52.

    Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

  53. 53.

    Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).

  54. 54.

    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).

  55. 55.

    Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).

  56. 56.

    Andlauer, T. F. M. et al. Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation. Sci. Adv. 2, e1501678 (2016).

  57. 57.

    van Rheenen, W. et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat. Genet. 48, 1043–1048 (2016).

  58. 58.

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

  59. 59.

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

  60. 60.

    Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).

  61. 61.

    Nowakowski, T. J. et al. Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. Science 358, 1318–1323 (2017).

  62. 62.

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

  63. 63.

    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).

  64. 64.

    Deciphering Developmental Disorders Study. Prevalence and architecture of de novo mutations in developmental disorders. Nature 542, 433–438 (2017).

  65. 65.

    Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584.e23 (2020).

  66. 66.

    Singh, T. et al. Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders. Nat. Neurosci. 19, 571–577 (2016).

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We thank members of the Won Lab and D. H. Geschwind for helpful discussions and comments on this paper. S. Espeso Gil helped transfer Hi-C datasets for neurons and astrocytes. This research was supported by National Institute of Mental Health grants (R00MH113823 and DP2MH122403 to H.W.; R56MH101454 to K.J.B.; and R0MH106056 to S.A. and K.J.B.), a NARSAD Young Investigator Award from the Brain and Behavior Research Foundation (to H.W.), a SPARK grant from the Simons Foundation Autism Research Initiative (to H.W.), training grants from the UNC Neuroscience Center (5T32NS007431 to N.Y.A.S. and H.F.), and a Helen Lyng White Fellowship (to W.M.).

Author information

H.W. designed the H-MAGMA framework. N.Y.A.S. and H.F. applied cMAGMA and H-MAGMA to nine brain disorders. J.C.M. compared H-MAGMA with cMAGMA. H.F. and W.M. performed LDSC and genetic correlation analyses. N.Y.A.S. conducted developmental trajectories analyses, RRHO and functional characterization of pleiotropic genes. B.H. analyzed astrocyte RNA sequencing data and compared H-MAGMA with eQTL-based tools. P.R., K.J.B. and S.A. contributed the Hi-C data from iPSC-derived neurons and astrocytes. N.Y.A.S. and H.W. wrote the manuscript.

Correspondence to Hyejung Won.

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The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Nicholas Bray, Andrew Jaffe, Mina Ryten, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison between H-MAGMA and cMAGMA.

a, The number and proportion of intronic and intergenic SNPs annotated to proximal and distal genes. SNPs mapped to proximal genes may also have distal associations, while SNPs mapped to distal genes do not have any association with proximal genes. b, The number of brain disorder risk genes (genes that are significantly associated with each brain disorder at a threshold of FDR<0.05) predicted by H-MAGMA and cMAGMA. % H-MAGMA denotes the percentage of H-MAGMA selective genes (genes that were identified by H-MAGMA but not by cMAGMA). c, The number of SNPs assigned to each gene for H-MAGMA and cMAGMA. Center, median; box=1st-3rd quartiles (Q); minima, Q1 - 1.5 x interquartile range (IQR); maxima, Q3 + 1.5 x IQR. d, The number and proportion of SNPs annotated to the cognate genes by H-MAGMA and cMAGMA. e, H-MAGMA selective SNPs (SNPs assigned to H-MAGMA selective genes in H-MAGMA – SNPs assigned to H-MAGMA selective genes in cMAGMA) explain a significant proportion of heritability. Top graph: Heritability enrichment ± standard error; enrichment denotes proportion of heritability/proportion of SNPs; red broken line, enrichment=1. Bottom graph: false discovery rate (FDR) of heritability enrichment: red broken line, FDR=0.05.

Extended Data Fig. 2 Heritability enrichment of brain disorders in active regulatory elements of multiple tissue/cell types.

(Top) Scaled enrichment values. (Bottom) Significance of heritability enrichment (P-values). ESC, embryonic stem cells. ESDR, embryonic stem cell derived cell lines.

Extended Data Fig. 3 Developmental expression trajectories of brain disorder risk genes derived from cMAGMA.

PCW, post-conception week; M, month; Y, year. (Left) N = 410 and 453 for prenatal and postnatal samples, respectively. Center, median; box=Q1-Q3; lower whisker, Q1 - 1.5 x IQR; upper whisker, Q3 + 1.5 x IQR. (Right) LOESS smooth curve with 95% confidence bands.

Extended Data Fig. 4 Cellular expression profiles of brain disorder risk genes.

a, Cellular expression profiles of brain disorder risk genes derived from H-MAGMA and cMAGMA. Psychiatric disorder-associated genes are highly expressed in neurons, while neurogenerative-disorder-associated genes show glial signatures. Astro, astrocytes; Micro, microglia; Endo, endothelial cells; Oligo, oligodendrocytes; OPC, oligodendrocytes progenitor cells. b, Psychiatric disorder-associated genes are highly expressed in radial glia and excitatory neurons in the developing cortex as well as excitatory neurons in the adult cortex. RG, radial glia, vRG; ventricular RG; oRG, outer RG; tRG, truncated RG; IPC, intermediate progenitor cells; Ex, excitatory neurons; In, inhibitory neurons; nEx/nIn, newly born excitatory/inhibitor neurons; PFC, prefrontal cortex; V1, visual cortex; CGE, caudal ganglionic eminence; MGE, medial ganglionic eminence.

Extended Data Fig. 5 Overlap between brain disorder risk genes derived from neuronal and astrocytic H-MAGMA.

Brain disorder risk genes (FDR<0.05) were compared between neuronal and astrocytic H-MAGMA results.

Extended Data Fig. 6 Psychiatric disorder risk genes predicted by H-MAGMA are dysregulated in postmortem brains of individuals with psychiatric disorders.

a, Overlap between common variation associated genes and genes differentially expressed (DEG) in postmortem brains with psychiatric disorders. b, Overlap between common variation associated genes and co-expression (co-exp) modules differentially regulated in psychiatric disorders. Down, modules are downregulated in disorders; Up, modules are upregulated in disorders.

Extended Data Fig. 7 Genetic relationships among brain disorders.

a, Psychiatric disorders show strong genetic relationships both at the level of genetic correlations (bottom left, rg) and gene-level overlaps (top right, RRHO). BY FDR, P-values adjusted by the Benjamini and Yekutieli procedure. b, Genetic correlations measured via LDSC (rg) are highly correlated with gene-level overlaps (RRHO Z), indicating that gene-level overlaps obtained by H-MAGMA recapitulate genetic relationships across brain disorders. Brain disorders that show strong genetic correlations (rg > 0.2) and gene-level overlaps (RRHO Z > 15) are marked in blue. Linear regression line with 95% confidence bands.

Supplementary information

Reporting Summary

Supplementary Data 1

Comparison of brain disorder risk genes identified by H-MAGMA and cMAGMA.

Supplementary Data 2

Brain disorder risk genes identified by H-MAGMA.

Supplementary Data 3

Statistical comparison between prenatal and postnatal expression values of brain disorder risk genes.

Supplementary Data 4

Gene ontologies of brain disorder risk genes based on fetal and adult brain H-MAGMA.

Supplementary Data 5

Gene ontologies of brain disorder risk genes based on cMAGMA.

Supplementary Data 6

Gene ontologies of brain disorder risk genes based on neuronal and astrocytic H-MAGMA.

Supplementary Data 7

A list of pleiotropic genes.

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Sey, N.Y.A., Hu, B., Mah, W. et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat Neurosci (2020). https://doi.org/10.1038/s41593-020-0603-0

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