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.

  • Article
  • Published:

Integrative analyses highlight functional regulatory variants associated with neuropsychiatric diseases

Nature Genetics volume 55, pages 1876–1891 (2023)Cite this article

Subjects

Abstract

Noncoding variants of presumed regulatory function contribute to the heritability of neuropsychiatric disease. A total of 2,221 noncoding variants connected to risk for ten neuropsychiatric disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, borderline personality disorder, major depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder and schizophrenia, were studied in developing human neural cells. Integrating epigenomic and transcriptomic data with massively parallel reporter assays identified differentially-active single-nucleotide variants (daSNVs) in specific neural cell types. Expression-gene mapping, network analyses and chromatin looping nominated candidate disease-relevant target genes modulated by these daSNVs. Follow-up integration of daSNV gene editing with clinical cohort analyses suggested that magnesium transport dysfunction may increase neuropsychiatric disease risk and indicated that common genetic pathomechanisms may mediate specific symptoms that are shared across multiple neuropsychiatric diseases.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: MPRA, transcriptomic and epigenomic integrative analyses for neuropsychiatric disorders.
Fig. 2: MPRA identifies 892 functional daSNVs across ten different neuropsychiatric diseases.
Fig. 3: Epigenetic profiling of neural cell system shows neuropsychiatric disease relevance.
Fig. 4: daSNV–eGene networks and their transcription regulatory effects.
Fig. 5: The CNNM2 magnesium transporter gene locus.
Fig. 6: Altered coding genes in CNS diseases inform risk in psychiatric disorders.
Fig. 7: daSNV–eGene symptom linkage in neuropsychiatric disorders.

Similar content being viewed by others

Data availability

All raw and processed sequencing data are available in GEO accession GSE182095. For ease of reference, processed TPM values for RNA-seq are provided in the data supplement (Supplementary Table 13). Tracks for ATAC and HiChIP were visualized on WashU Epigenome Browser. Raw and processed RNA, ATAC and HiChIP D0 and D2 Ngn2-derived H9 samples are referenced85. All MPRA summary statistics and raw count results are provided (Supplementary Data 5) and processed data is available at https://arvid-data.shinyapps.io/neuropsychiatry/ or Supplementary Data 3. Previously published GWAS study data used as a basis for this work is noted in each study published and is annotated in online GWAS data resources, including https://www.ebi.ac.uk/gwas/ and http://www.nealelab.is/uk-biobank/. For LDSC scoring, GWAS data used was available and preprocessed by https://alkesgroup.broadinstitute.org/LDSCORE/all_sumstats/. For colocalization studies, available summary statistics are provided: https://zenodo.org/record/3518299#.XbMgFNF7m90. Additional publicly available data sets used include GTEx v7, Haploreg v4, ENCODE hg19, StringDB (https://string-db.org/), OMIM (https://www.omim.org/), The Drug Repurposing Hub (http://www.broadinstitute.org/repurposing), PsychENCODE (http://resource.psychencode.org/), HOCOMOCO v11 (https://hocomoco11.autosome.org/), UCSC browser (https://genome.ucsc.edu/), Brainmap SMART-seq cortical data (http://portal.brain-map.org/atlases-and-data/rnaseq/human-multiple-cortical-areas-smart-seq), SCHEMA (https://schema.broadinstitute.org/) and SNVlocs.Hsapiens.dbSNV142.GRCh37. VA cohort data have restricted access due to privacy concerns.

Code availability

Analyses were done in custom Jupyter Notebook or Rmarkdown scripts in Python 3.7.4 and R 3.6.1, locally or on the Stanford Sherlock computing cluster. Code to analyze transcriptomics and epigenomics data is available on GitHub (https://github.com/mguo123/pan_omics_psych.git)86. MPRA-based analysis scripts are available here (https://github.com/mguo123/psych_mpra.git)87. Additional software used includes LDSC (LD Score) (v1.0.1), MPRAnalyze (v1.4.0), STAR aligner (v2.5.4b), RSEM (v1.3.0), ENCODE ATAC–seq pipeline (https://github.com/ENCODE-DCC/atac-seq-pipeline), Bowtie2 (2.3.4.1) and EnrichR (https://maayanlab.cloud/Enrichr/). ChIPSeeker (v1.22.0), motifBreakR (v2.10.2), rgt (https://github.com/CostaLab/reg-gen), ClusterProfileR (v3.14.0), RColorBrewer (v1.1.0) HiC-Pro (v2.11.1), Hichipper (v 0.7.7), FitHiChIP (v7.0.0), diffloop(v1.10.0), DESeq2 (v1.26.0), CytoScape v3.7.2, ABC-Enhancer-Gene-Prediction (https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction), gatk (v4.1.9.0), picard (v2.24.0), MACS2 (v2.1.1), enloc (https://github.com/xqwen/integrative), PhenomeXcan (https://github.com/hakyimlab/phenomexcan), gkmSVM (v0.82.0), DeepSea (http://deepsea.princeton.edu/job/analysis/create/), pheatmap (v1.0.12), biothings (v0.2.6) and GenomicRanges (v.1.48.0), and Rsubread (v2.0.0).

References

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

    Article  PubMed Central  Google Scholar 

  2. PsychENCODE Consortium et al.The PsychENCODE project. Nat. Neurosci. 18, 1707–1712 (2015).

    Article  Google Scholar 

  3. Ombrato, L. et al. Metastatic-niche labelling reveals parenchymal cells with stem features. Nature 572, 603–608 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Witt, S. H. et al. Genome-wide association study of borderline personality disorder reveals genetic overlap with bipolar disorder, major depression and schizophrenia. Transl. Psychiatry 7, e1155 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Meier, S. M. et al. Genetic variants associated with anxiety and stress-related disorders: a genome-wide association study and mouse-model study. JAMA Psychiatry 76, 924–932 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Tam, V. et al. Benefits and limitations of genome-wide association studies. Nat. Rev. Genet. 20, 467–484 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Zhang, F. & Lupski, J. R. Non-coding genetic variants in human disease. Hum. Mol. Genet. 24, R102–R110 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  9. Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Mulvey, B., Lagunas, T. & Dougherty, J. D. Massively parallel reporter assays: defining functional psychiatric genetic variants across biological contexts. Biol. Psychiatry 89, 76–89 (2021).

    Article  CAS  PubMed  Google Scholar 

  11. Tewhey, R. et al. Direct identification of hundreds of expression-modulating variants using a multiplexed reporter assay. Cell 165, 1519–1529 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ulirsch, J. C. et al. Systematic functional dissection of common genetic variation affecting red blood cell traits. Cell 165, 1530–1545 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lonsdale, J. et al. The genotype-tissue expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ashuach, T. et al. MPRAnalyze: statistical framework for massively parallel reporter assays. Genome Biol. 20, 183 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zhou, J. et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat. Genet. 50, 1171–1179 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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, 21.29.1–9 (2015).

    PubMed  Google Scholar 

  18. Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Song, M. et al. Mapping cis-regulatory chromatin contacts in neural cells links neuropsychiatric disorder risk variants to target genes. Nat. Genet. 51, 1252–1262 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhang, S. et al. Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants. Science 369, 561–565 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Inoue, F. et al. A systematic comparison reveals substantial differences in chromosomal versus episomal encoding of enhancer activity.Genome Res. 27, 38–52 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Herdegen, T. & Waetzig, V. AP-1 proteins in the adult brain: facts and fiction about effectors of neuroprotection and neurodegeneration. Oncogene 20, 2424–2437 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Chew, L. J. et al. Sox17 regulates a program of oligodendrocyte progenitor cell expansion and differentiation during development and repair. Cell Rep. 29, 3173–3186 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ruiz i Altaba, A., Palma, V. & Dahmane, N. Hedgehog–GLI signaling and the growth of the brain. Nat. Rev. Neurosci. 3, 24–33 (2002).

    Article  CAS  PubMed  Google Scholar 

  25. Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hook, P. W. & McCallion, A. S. Leveraging mouse chromatin data for heritability enrichment informs common disease architecture and reveals cortical layer contributions to schizophrenia. Genome Res. 30, 528–539 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kinney, D. K. et al. A unifying hypothesis of schizophrenia: abnormal immune system development may help explain roles of prenatal hazards, post-pubertal onset, stress, genes, climate, infections, and brain dysfunction. Med. Hypotheses 74, 555–563 (2010).

    Article  CAS  PubMed  Google Scholar 

  31. Shao, Z. et al. Dysregulated protocadherin-pathway activity as an intrinsic defect in induced pluripotent stem cell-derived cortical interneurons from subjects with schizophrenia. Nat. Neurosci. 22, 229–242 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hoseth, E. Z. et al. Attenuated Notch signaling in schizophrenia and bipolar disorder. Sci. Rep. 8, 5349 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Onwordi, E. C. et al. Synaptic density marker SV2A is reduced in schizophrenia patients and unaffected by antipsychotics in rats. Nat. Commun. 11, 246 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yabut, O. R., Fernandez, G., Huynh, T., Yoon, K. & Pleasure, S. J. Suppressor of fused is critical for maintenance of neuronal progenitor identity during corticogenesis. Cell Rep. 12, 2021–2034 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Nord, A. S., Pattabiraman, K., Visel, A. & Rubenstein, J. L. R. Genomic perspectives of transcriptional regulation in forebrain development. Neuron 85, 27–47 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhang, S. et al. OCT4 and PAX6 determine the dual function of SOX2 in human ESCs as a key pluripotent or neural factor. Stem Cell Res. Ther. 10, 122 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Nichol, H., Amilhon, B., Manseau, F., Badrinarayanan, S. & Williams, S. Electrophysiological and morphological characterization of Chrna2 cells in the subiculum and Ca1 of the hippocampus: an optogenetic investigation. Front. Cell. Neurosci. 12, 32 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Giralt, A. et al. Pyk2 modulates hippocampal excitatory synapses and contributes to cognitive deficits in a Huntington’s disease model. Nat. Commun. 8, 15592 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Arjona, F. J. et al. CNNM2 mutations cause impaired brain development and seizures in patients with hypomagnesemia. PLoS Genet. 10, 1004267 (2014).

    Article  Google Scholar 

  41. Li, M. et al. A human-specific AS3MT isoform and BORCS7 are molecular risk factors in the 10q24.32 schizophrenia-associated locus. Nat. Med. 22, 649–656 (2016).

    Article  CAS  PubMed  Google Scholar 

  42. Franken, G. A. C. et al. Cyclin M2 (CNNM2) knockout mice show mild hypomagnesaemia and developmental defects. Sci. Rep. 11, 8217 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Adam, M. P., Mirzaa, G. M., Pagon, R. A., Wallace, S.E., Bean, L. J. H., Gripp, K. W. & Amemiya, A. (eds.). GeneReviews (University of Washington, 1993).

  44. Katsuki, A. et al. A single-nucleotide polymorphism influences brain morphology in drug-näve patients with major depressive disorder. Neuropsychiatr. Dis. Treat. 15, 2425–2432 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Logan, T. T., Rusnak, M. & Symes, A. J. Runx1 promotes proliferation and neuronal differentiation in adult mouse neurosphere cultures. Stem Cell Res. 15, 554–564 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Singh, T., Neale, B. M., Daly, M. J. et al. Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia. Preprint at medRxiv https://doi.org/10.1101/2020.09.18.20192815 (2022).

  47. Andrade, A. et al. Genetic associations between voltage-gated calcium channels and psychiatric disorders. Int. J. Mol. Sci. 20, 3537 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Davis, K. A. S. et al. Mental health in UK Biobank: development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis. BJPsych Open 6, e18 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yaghoobi, M. M., Mowla, S. J. & Tiraihi, T. Nucleostemin, a coordinator of self-renewal, is expressed in rat marrow stromal cells and turns off after induction of neural differentiation. Neurosci. Lett. 390, 81–86 (2005).

    Article  CAS  PubMed  Google Scholar 

  51. Teng, X. et al. KCTD: a new gene family involved in neurodevelopmental and neuropsychiatric disorders. CNS Neurosci. Ther. 25, 887–902 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Imbrici, P., Camerino, D. C. & Tricarico, D. Major channels involved in neuropsychiatric disorders and therapeutic perspectives. Front. Genet. 4, 76 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Meng, Q. et al. Integrative analyses prioritize GNL3 as a risk gene for bipolar disorder. Mol. Psychiatry 25, 2672–2684 (2020).

    Article  CAS  PubMed  Google Scholar 

  54. Eby, G. A. & Eby, K. L. Rapid recovery from major depression using magnesium treatment. Med. Hypotheses 67, 362–370 (2006).

    Article  CAS  PubMed  Google Scholar 

  55. Myint, L. et al. Testing the regulatory consequences of 1,049 schizophrenia associated variants with a massively parallel reporter assay. Preprint at bioRxiv https://doi.org/10.1101/447557 (2019).

  56. Calakos, N. et al. Functional evidence implicating a novel TOR1A mutation in idiopathic, late-onset focal dystonia. J. Med. Genet. 47, 646–650 (2010).

    Article  CAS  PubMed  Google Scholar 

  57. 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  CAS  PubMed  Google Scholar 

  58. Landrum, M. J. et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46, D1062–D1067 (2018).

    Article  CAS  PubMed  Google Scholar 

  59. Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F. & Hamosh, A. OMIM.org: Online Mendelian Inheritance in Man (OMIM), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789–D798 (2015).

    Article  PubMed  Google Scholar 

  60. Ward, L. D. & Kellis, M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 44, D877–D881 (2016).

    Article  CAS  PubMed  Google Scholar 

  61. De Souza, N. The ENCODE project. Nat. Methods 9, 1046 (2012).

    Article  PubMed  Google Scholar 

  62. Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).

    Article  CAS  PubMed  Google Scholar 

  63. Du, Z. W. et al. Generation and expansion of highly pure motor neuron progenitors from human pluripotent stem cells. Nat. Commun. 6, 6626 (2015).

    Article  CAS  PubMed  Google Scholar 

  64. 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 

  65. Kim, D. et al. The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation. Nat. Genet. 53, 1564–1576 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kim, H. K. et al. Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nat. Biotechnol. 36, 239–241 (2018).

    Article  CAS  PubMed  Google Scholar 

  67. Concordet, J. & Haeussler, M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46, 242–245 (2018).

    Article  Google Scholar 

  68. Gasperini, M. et al. A genome-wide framework for mapping gene regulation via cellular genetic screens. Cell 176, 377–390 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Labun, K., Montague, T. G., Krause, M., Cleuren, Y. N. T. & Valen, E. CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome editing. Nucleic Acids Res. 47, 171–174 (2019).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  71. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 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 

  74. Lareau, C. A. & Aryee, M. J. Hichipper: a preprocessing pipeline for calling DNA loops from HiChIP data. Nat. Methods 15, 155–156 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Bhattacharyya, S., Chandra, V., Vijayanand, P. & Ay, F. FitHiChIP: identification of significant chromatin contacts from HiChIP data. Preprint at bioRxiv https://doi.org/10.1101/412833 (2018).

  76. Li, Z. et al. Identification of transcription factor binding sites using ATAC–seq. Genome Biol. 20, 45 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Donohue, L. K. H. et al. A cis-regulatory lexicon of DNA motif combinations mediating cell-type-specific gene regulation. Cell Genom. 2, 100191 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    Article  CAS  PubMed  Google Scholar 

  81. Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Singh, T. et al. Rare coding variants in ten genes confer substantial risk for schizophrenia. Nature 604, 509–516 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Ghandi, M., Lee, D., Mohammad-Noori, M. & Beer, M. A. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput. Biol. 10, 1003711 (2014).

    Article  Google Scholar 

  85. Ang, C. E. et al. The dynamic interplay between homeodomain transcription factors and chromatin environment regulates proneural factor outcomes. Preprint at bioRxiv https://doi.org/10.1101/2020.12.02.398677 (2020).

  86. mguo123. mguo123/pan_omics_psych: publication release. Zenodo. https://zenodo.org/record/8098407 (2023).

  87. mguo123. mugo123/psych_mpra: publication release. Zenodo. https://zenodo.org/record/8098409 (2023).

  88. Duncan L, Yilmaz Z, Gaspar H, et al. Significant Locus and Metabolic Genetic Correlations Revealed in Genome-Wide Association Study of Anorexia Nervosa. Am. J. Psychiatry 174, 850–858 (2017).

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

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

  91. Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Electronic address: douglas.ruderfer@vanderbilt.edu; Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes. Cell 173, 1705–1715 (2018).

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

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

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

  95. Howard, D.M., Adams, M.J., Clarke, TK. 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).

  96. Nagel, M., Jansen, P.R., Stringer, S. et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat. Genet. 50, 920–927 (2018).

  97. International League Against Epilepsy Consortium on Complex Epilepsies. Electronic address: epilepsy-austin@unimelb.edu.au. Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies. Lancet Neurol. 13, 893–903 (2014).

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

  99. Marioni, R.E., Harris, S.E., Zhang, Q. et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 8, 99 (2018).

  100. Corces, M.R., Shcherbina, A., Kundu, S. 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).

  101. Ferreira MAR, Mathur R, Vonk JM, et al. Genetic Architectures of Childhood- and Adult-Onset Asthma Are Partly Distinct. Am J Hum Genet. 104, 665–684 (2019).

  102. de Lange, K., Moutsianas, L., Lee, J. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat Genet 49, 256–261 (2017).

Download references

Acknowledgements

We thank S. Srinivasan for the creation of the web resource. We thank G. Rayant and K. Fields for their generous support and helpful discussions. We also thank M.P. Snyder, W.J. Greenleaf, D.F. Levinson and H.Y. Chang for the presubmission review and J. Engreitz and members of the Khavari and Altman laboratories for helpful discussions. This work was supported by the USVA Office of Research and Development and by the Atlas of Regulatory Variants in Disease (ARVID) project from NHGRI/NIH U24HG010856 and by NIAMS/NIH AR076965 (both to P.A.K.). S.B.M. was supported by R01AG066490 and R01MH125244. A.E.U. was supported by P50HG00773506. This work was supported by Bitscopic’s R&D budget and intramural funding from the Department of Veterans Affairs. The funders had no role in study design, data collection and analysis, the decision to publish or the preparation of the manuscript. We thank C.C. Lee, H. Parekh and J. Mewton for Praedico maintenance, assistance with data extraction and professional support.

Author information

Authors and Affiliations

  1. Stanford Program in Biomedical Informatics, Stanford University, Stanford, CA, USA

    Margaret G. Guo & Russ B. Altman

  2. Program in Epithelial Biology, Stanford University, Stanford, CA, USA

    Margaret G. Guo, David L. Reynolds, Yang Zhao, Laura K. H. Donohue, Zurab Siprashvili, Xue Yang, Smarajit Mondal, Audrey Hong, Tania Fabo, Ibtihal Elfaki, Laura N. Kellman & Paul A. Khavari

  3. Department of Pathology, Stanford University, Stanford, CA, USA

    Cheen E. Ang, Stephen B. Montgomery & Marius Wernig

  4. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Cheen E. Ang, Yash Pershad & Russ B. Altman

  5. Institute for Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, CA, USA

    Cheen E. Ang, Yingfei Liu, Yongjin Yoo & Marius Wernig

  6. Institute of Neurobiology, Xi’an Jiaotong University Health Science Center, Xi’an, China

    Yingfei Liu

  7. Department of Genetics, Stanford University, Stanford, CA, USA

    Laura K. H. Donohue, Jessica Kain, Tania Fabo, Ibtihal Elfaki, Nathan S. Abell, Stephen B. Montgomery, Alexander E. Urban & Russ B. Altman

  8. Stanford Program in Cancer Biology, Stanford University, Stanford, CA, USA

    Xue Yang, Laura N. Kellman & Paul A. Khavari

  9. Department of Biology, Stanford University, Stanford, CA, USA

    Lindsey Meservey

  10. Bitscopic Inc., Los Angeles, CA, USA

    Vafa Bayat & Payam Etminani

  11. Public Health Surveillance and Research, Department of Veterans Affairs, Washington, DC, USA

    Mark Holodniy

  12. Division of Infectious Disease & Geographic Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Mark Holodniy

  13. Program in Neurobehavioral Genetics, Semel Institute, UCLA, Los Angeles, CA, USA

    Daniel H. Geschwind

  14. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

    Laramie E. Duncan & Alexander E. Urban

  15. Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA

    Paul A. Khavari

Authors
  1. Margaret G. Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David L. Reynolds
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cheen E. Ang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yingfei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laura K. H. Donohue
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zurab Siprashvili
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xue Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yongjin Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Smarajit Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Audrey Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jessica Kain
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Lindsey Meservey
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Tania Fabo
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Ibtihal Elfaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Laura N. Kellman
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Nathan S. Abell
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Yash Pershad
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Vafa Bayat
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Payam Etminani
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Mark Holodniy
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Daniel H. Geschwind
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Stephen B. Montgomery
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Laramie E. Duncan
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Alexander E. Urban
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Russ B. Altman
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Marius Wernig
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Paul A. Khavari
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.G.G., M.W. and P.A.K. conceptualized the project. M.G.G., D.L.R., C.E.A., Y.L., Y.Y., Y.Z., L.N.K., L.K.H.D., X.Y., L.M., T.F., I.E., A.H., Z.S., Y.P., V.B. and N.A. performed experiments and analyzed the data. M.G.G., P.A.K., M.W., R.B.A., L.E.D., A.E.U., S.B.M., P.E., M.H. and D.H.G. guided methodology development, experiments and data analysis. M.G.G. and P.A.K. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Paul A. Khavari.

Ethics declarations

Competing interests

V.B. and P.E. are employees of Bitscopic. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Yin Shen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 MPRA QC statistics.

(a) Bar chart showing number of reads per MPRA sample in log scale. Replicates are the number following the “R” prefix. Cell type abbreviations are as follows: AST= astrocytes; ES=hESC or human embryonic stem cell; A-NPC = anterior neural progenitor cell; P-NSC = posterior neural progenitor cell; N-DX = induced neuron of day X. Histograms showing barcodes per sequence in the (b) plasmid (prior to lentiviral infection) and (c) RNA library (extract post infection). (d) Power analysis for different levels of barcodes power for at 4 different log2-fold change thresholds (1.2, 1.5, 2, 3), for a total of 20 variants, simulated 5 times with 5, 10, 20, 50, and 100 barcodes each. (e) QQ plots showing the -log10 empirical vs theoretical p-values derived from MPRAnalyze for HEK293T as a cell-type example. The red line is (y = x). (f) Histograms showing barcodes per sequence in the RNA library, by cell type. (g) Heatmap showing Pearson count correlation between replicates for all cell types, conditions, and replicates.

Extended Data Fig. 2 Epigenetics study of the role of transcription regulation in neuropsychiatric diseases.

(a) Heatmap showing TF footprints that are enriched in cell types; color scale is normalized count values. (b) GO biological process dotplot depicting enrichment terms for genes closest to ATAC accessible peaks found across ES-derived neuronal differentiation. The size of the dot is the number of genes in the GO geneset and the color indicates FDR-adjusted p-values. (c) Bar chart showing frequency of loop types in promoters and promoter interaction anchor loops (putative enhancers) derived from HiChIP data. Type 1: where an enhancer is linked to a distal gene and the nearest gene, Type 2: where an enhancer is linked only to a distal gene, Type 3: where an enhancer is looped to the closest gene. (d) % of P-P (promoter-promoter) and P-PIR (promoter to promoter interaction regions) loops per cell type found via HiChIP. (e) Cumulative distribution curves of distance between loop anchors for the different tissues. (f) Heatmap (left) showing normalized enrichment scores of motifs broken or gained by SNVs associated with different neuropsychiatric diseases derived from MotifBreakR, relative to a background of other neuropsychiatric diseases. The * refers to motifs that are significantly broken (p-value < 0.10, Fisher’s exact test) in daSNVs compared to non-daSNVs for a specific disease. The heatmap (right) shows the log TPM expression values of these transcription factors in different neuronal cell lines and cell lines. (g) Scatterplot comparing log-2 fold changes (n = 206 variants) for the MPRA dataset (y-axis) with an external20 allele specific open chromatin dataset (x-axis), with a Pearson correlation of 0.48, p-value 1.7×10−13.

Extended Data Fig. 3 eGene network analysis of additional diseases.

eGene networks for the additional neuropsychiatric diseases with at least 20 eGenes (from left to right, top to bottom): MDD, BPD, OCD, ADHD, and GAD.

Extended Data Fig. 4 POU5F1/OCT4 vignette.

(a) Tracks for the POU5F1/OCT4 TF gene, where the peak tracks show the logFC change from cell-type specific MPRA for the daSNVs, and the bottom loop track shows the looping data for N-D2 cell type. Boxplots depicting ratios of cDNA to plasmid counts for reference versus alternate allele for SNVS (b) rs28428768, (c) rs2442722, (d) rs35735140, and (e) rs3134944, where the center line is the median of each MPRA normalized ratio (n = 10 genomic instances each); box limits are the upper and lower quartiles, whiskers are the 1.5x interquartile range, and points shown are outliers. Ratios are normalized to the median reference value for each cell type. Significant associations found by MPRAnalyze (FDR < 0.05) are shown with an asterisk*.

Extended Data Fig. 5 Gene concordance for variant annotation approaches.

(a) Distribution of # daSNVs for a GTEx eGene annotations show eGenes are on average, linked to five daSNVs. (b) Density plot showing the distribution of daSNV-to-eGene distance with the mean depicted as a vertical red dotted line at 20kB. (c) Pie chart showing gene annotation concordance between the different annotation of daSNVs, indicating almost a half of GWAS gene annotations do not match expression or chromatin-based gene linkages. (d) Enrichment map made via ClusterProfiler showing GO Molecular Functions enriched in genes linked to daSNVs.

Extended Data Fig. 6 Association between serum magnesium levels and relative psychiatric disease incidence in a VA cohort.

(a) Relative disease prevalence for serum magnesium levels in the bottom 10th and upper 10th deciles. The 10th decile of serum magnesium are values < 1.6 mg/dL and the 90th decile of serum magnesium are values > 2.4 mg/dL. ** indicates significance between the two proportion based on a two-sided 2-proportion z-test FDR-corrected p < 0.05 for a given disease. (b) Relative prevalence of diseases by serum magnesium levels in the VA cohort. The above graph includes all patients age 45-85, n = 846795. The below graph removes all patients who were diagnosed with Alcohol Use Disorder, n = 618692. Cohort was partitioned by serum magnesium levels into 6 quantiles and the prevalence of each disease was calculated within the quantile. Relative prevalence is calculated as the prevalence normalized to the disease prevalence in the entire cohort. Significance is determined by linear regression with the null hypothesis beta = 0, with p-values < 0.10 shown in solid. Abbreviations of disease are as follows: ADHD = attention deficit hyperactivity disorder, PD = panic disorder, GAD = generalized anxiety disorder, BPD = bipolar disorder, MDD = major depressive disorder, OCD = obsessive compulsive disorder, SCZ = schizophrenia, AD = Alzheimer’s disease, CKD = chronic kidney disease.

Extended Data Fig. 7 RERE vignette.

(a) Tracks for gene RERE, where the MPRA peak tracks show the logFC change from cell-type specific MPRA for the daSNVs, and the bottom ATAC peak tracks show accessibility profiles for all cell types. Box-and-whiskers plots depicting ratios of cDNA to plasmid counts for reference versus alternate allele for daSNVs (b) rs301806, the SNV of interest and (c) rs301807, as comparison, where the center line is the median of each MPRA normalized ratio (each point is a genomic instance with at least one count), box limits are the upper and lower quartiles, whiskers are the 1.5x interquartile range, and points shown are outliers. Ratios are normalized to the median reference value for each cell type. Additionally, MotifBreakR results are shown for (d) rs301806 (above) and rs301807 (below), depicting loss of RUNX1 motif in rs301806, and no RUNX1 motif present at rs301807 loci. (e) ChIP PCR for the transcription factor RUNX1 with n= 4 replicates, * indicated significance of two-sided paired t-test p-value between the reference and alternate allele for the two SNPs.

Extended Data Fig. 8 CMAP drug perturbation analysis.

Drug-eGene networks for (a) SCZ, (b) BPD, and (c) MDD. Linkages between eGene to drug indicate that the drug significantly upregulated (red) or downregulates (blue) the expression of that gene in neuro-relevant cell lines in CMAP. Genes (diamonds) are outlined based on the MPRA log fold change direction (red: positive, blue: negative). Drugs (ellipses) are color coded by drug type. Drug-gene pairs towards the left side of the map indicate the MPRA and expression vectors point in the same direction (putatively side effect causing variants); drug-gene pairs towards the right side of the map indicate MPRA and expression vectors pointing in the opposite direction (putatively therapeutic effects).

Supplementary information

Supplementary Information

Supplementary Methods.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–15.

Supplementary Data 1

LDSC analysis for hereditability.

Supplementary Data 2

Literature-derived daSNV gene annotations.

Supplementary Data 3

daSNV summary statistics and annotations.

Supplementary Data 4

Networks of shared putative pathomechanisms in neuropsychiatric disorders.

Supplementary Data 5

MPRA cell condition-specific summary statistics.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, M.G., Reynolds, D.L., Ang, C.E. et al. Integrative analyses highlight functional regulatory variants associated with neuropsychiatric diseases. Nat Genet 55, 1876–1891 (2023). https://doi.org/10.1038/s41588-023-01533-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-023-01533-5

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research