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An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder

Nature Geneticsvolume 50pages727736 (2018) | Download Citation


Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.

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  1. 1.

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

  2. 2.

    Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429 (2016).

  3. 3.

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

  4. 4.

    Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).

  5. 5.

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

  6. 6.

    Marshall, C. R. et al. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat. Genet. 49, 27–35 (2017).

  7. 7.

    MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45 (D1), D896–D901 (2017).

  8. 8.

    Power, R. A. et al. Fecundity of patients with schizophrenia, autism, bipolar disorder, depression, anorexia nervosa, or substance abuse vs their unaffected siblings. JAMA Psychiatry 70, 22–30 (2013).

  9. 9.

    Jin, S. C. et al. Contribution of rare inherited and de novo variants in 2,871 congenital heart disease probands. Nat. Genet. 49, 1593–1601 (2017).

  10. 10.

    Visel, A. et al. ChIP–seq accurately predicts tissue-specific activity of enhancers. Nature 457, 854–858 (2009).

  11. 11.

    Shibata, M., Gulden, F. O. & Sestan, N. From trans to cis: transcriptional regulatory networks in neocortical development. Trends Genet. 31, 77–87 (2015).

  12. 12.

    Silbereis, J. C., Pochareddy, S., Zhu, Y., Li, M. & Sestan, N. The cellular and molecular landscapes of the developing human central nervous system. Neuron 89, 248–268 (2016).

  13. 13.

    Sanders, S. J. et al. Whole genome sequencing in psychiatric disorders: the WGSPD consortium. Nat. Neurosci. 20, 1661–1668 (2017).

  14. 14.

    Caskey, C. T., Tompkins, R., Scolnick, E., Caryk, T. & Nirenberg, M. Sequential translation of trinucleotide codons for the initiation and termination of protein synthesis. Science 162, 135–138 (1968).

  15. 15.

    Fischbach, G. D. & Lord, C. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron 68, 192–195 (2010).

  16. 16.

    Turner, T. N. et al. Genome sequencing of autism-affected families reveals disruption of putative noncoding regulatory DNA. Am. J. Hum. Genet. 98, 58–74 (2016).

  17. 17.

    Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015).

  18. 18.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  19. 19.

    O’Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012).

  20. 20.

    Kong, A. et al. Rate of de novo mutations and the importance of father’s age to disease risk. Nature 488, 471–475 (2012).

  21. 21.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

  22. 22.

    Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).

  23. 23.

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

  24. 24.

    Genovese, G. et al. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat. Neurosci. 19, 1433–1441 (2016).

  25. 25.

    Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).

  26. 26.

    Chaste, P. et al. A genome-wide association study of autism using the Simons Simplex Collection: does reducing phenotypic heterogeneity in autism increase genetic homogeneity? Biol. Psychiatry 77, 775–784 (2015).

  27. 27.

    Collins, R. L. et al. Defining the diverse spectrum of inversions, complex structural variation, and chromothripsis in the morbid human genome. Genome Biol. 18, 36 (2017).

  28. 28.

    Talkowski, M. E. et al. Sequencing chromosomal abnormalities reveals neurodevelopmental loci that confer risk across diagnostic boundaries. Cell 149, 525–537 (2012).

  29. 29.

    Redin, C. et al. The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies. Nat. Genet. 49, 36–45 (2017).

  30. 30.

    Brand, H. et al. Paired-duplication signatures mark cryptic inversions and other complex structural variation. Am. J. Hum. Genet. 97, 170–176 (2015).

  31. 31.

    Turner, T. N. et al. Genomic patterns of de novo mutation in simplex autism. Cell 171, 710–722 (2017).

  32. 32.

    Hirschhorn, J. N. & Daly, M. J. Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6, 95–108 (2005).

  33. 33.

    Dudbridge, F. & Gusnanto, A. Estimation of significance thresholds for genomewide association scans. Genet. Epidemiol. 32, 227–234 (2008).

  34. 34.

    Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012).

  35. 35.

    Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012).

  36. 36.

    Yuen, R. K. et al. Whole-genome sequencing of quartet families with autism spectrum disorder. Nat. Med. 21, 185–191 (2015).

  37. 37.

    Cummings, B. B. et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci. Transl. Med. 9, eaal5209 (2017).

  38. 38.

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

  39. 39.

    van Berkum, N. L. et al. Hi-C: a method to study the three-dimensional architecture of genomes. J. Vis. Exp. 39, e1869 (2010).

  40. 40.

    Melnikov, A. et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30, 271–277 (2012).

  41. 41.

    Johnson, E. C. et al. No evidence that schizophrenia candidate genes are more associated with schizophrenia than noncandidate genes. Biol. Psychiatry 82, 702–708 (2017).

  42. 42.

    Farrell, M. S. et al. Evaluating historical candidate genes for schizophrenia. Mol. Psychiatry 20, 555–562 (2015).

  43. 43.

    Munoz, A. et al. De novo indels within introns contribute to ASD incidence. Preprint at bioRxiv (2017).

  44. 44.

    Brandler, W. M. et al. Paternally inherited noncoding structural variants contribute to autism. Preprint at bioRxiv (2017).

  45. 45.

    Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

  46. 46.

    Ioannidis, J. P. Why most discovered true associations are inflated. Epidemiology 19, 640–648 (2008).

  47. 47.

    Li, H. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics 30, 2843–2851 (2014).

  48. 48.

    Zook, J. M. et al. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat. Biotechnol. 32, 246–251 (2014).

  49. 49.

    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

  50. 50.

    Wei, Q. et al. A Bayesian framework for de novo mutation calling in parents–offspring trios. Bioinformatics 31, 1375–1381 (2015).

  51. 51.

    Ramu, A. et al. DeNovoGear: de novo indel and point mutation discovery and phasing. Nat. Methods 10, 985–987 (2013).

  52. 52.

    Narzisi, G. et al. Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nat. Methods 11, 1033–1036 (2014).

  53. 53.

    Lai, Z. et al. VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res. 44, e108 (2016).

  54. 54.

    Yang, H. & Wang, K. Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat. Protoc. 10, 1556–1566 (2015).

  55. 55.

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

  56. 56.

    Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

  57. 57.

    Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).

  58. 58.

    Willsey, A. J. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155, 997–1007 (2013).

  59. 59.

    Wright, C. F. et al. Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data. Lancet 385, 1305–1314 (2015).

  60. 60.

    Cotney, J. et al. The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment. Nat. Commun. 6, 6404 (2015).

  61. 61.

    Sugathan, A. et al. CHD8 regulates neurodevelopmental pathways associated with autism spectrum disorder in neural progenitors. Proc. Natl. Acad. Sci. USA 111, E4468–E4477 (2014).

  62. 62.

    Bayés, A. et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat. Neurosci. 14, 19–21 (2011).

  63. 63.

    Visel, A., Minovitsky, S., Dubchak, I. & Pennacchio, L. A. VISTA Enhancer Browser—a database of tissue-specific human enhancers. Nucleic Acids Res. 35, D88–D92 (2007).

  64. 64.

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

  65. 65.

    Doan, R. N. et al. Mutations in human accelerated regions disrupt cognition and social behavior. Cell 167, 341–354 (2016).

  66. 66.

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

  67. 67.

    Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

  68. 68.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  69. 69.

    Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

  70. 70.

    Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).

  71. 71.

    Layer, R. M., Chiang, C., Quinlan, A. R. & Hall, I. M. LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 15, R84 (2014).

  72. 72.

    Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).

  73. 73.

    Kronenberg, Z. N. et al. Wham: identifying structural variants of biological consequence. PLoS Comput. Biol. 11, e1004572 (2015).

  74. 74.

    Handsaker, R. E. et al. Large multiallelic copy number variations in humans. Nat. Genet. 47, 296–303 (2015).

  75. 75.

    Abyzov, A., Urban, A. E., Snyder, M. & Gerstein, M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 21, 974–984 (2011).

  76. 76.

    Klambauer, G. et al. cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate. Nucleic Acids Res. 40, e69 (2012).

  77. 77.

    Gardner, E. J. et al. The Mobile Element Locator Tool (MELT): population-scale mobile element discovery and biology. Genome Res. 27, 1916–1929 (2017).

  78. 78.

    Pedersen, B. S., Collins, R. L., Talkowski, M. E. & Quinlan, A. R. Indexcov: fast coverage quality control for whole-genome sequencing. Gigascience 6, 1–6 (2017).

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We are grateful to the families participating in the Simons Foundation Autism Research Initiative (SFARI) Simplex Collection (SSC). This work was supported by grants from the Simons Foundation for Autism Research Initiative (SFARI 385110 to N.S., A.J.W., M.W.S., S.J.S.; 385027 to M.E.T., J.D.B., B.D., M.J.D., X.H., K.R.; 388196 to G.M., H.C., A.R.Q.; and 346042 to M.E.T.), the US National Institutes of Health (R37MH057881 and U01MH111658 to B.D. and K.R.; HD081256 and GM061354 to M.E.T.; U01MH105575 to M.W.S.; U01MH111662 to M.W.S. and S.J.S.; R01MH110928 and U01MH100239-03S1 to M.W.S., S.J.S., A.J.W.; U01MH111661 to J.D.B.; K99DE026824 to H.B.; U01MH100229 to M.J.D.), the Autism Science Foundation to D.M.W., and the March of Dimes to M.E.T. M.E.T. was also supported by the Desmond and Ann Heathwood MGH Research Scholars award. We thank the SSC principal investigators (A. L. Beaudet, R. Bernier, J. Constantino, E. H. Cook Jr, E. Fombonne, D. Geschwind, D. E. Grice, A. Klin, D. H. Ledbetter, C. Lord, C. L. Martin, D. M. Martin, R. Maxim, J. Miles, O. Ousley, B. Peterson, J. Piggot, C. Saulnier, M. W. State, W. Stone, J. S. Sutcliffe, C. A. Walsh, and E. Wijsman) and the coordinators and staff at the SSC clinical sites; the SFARI staff, in particular N. Volfovsky; D. B. Goldstein for contributing to the experimental design; the Rutgers University Cell and DNA repository for accessing biomaterials; and the New York Genome Center for generating the WGS data.

Author information

Author notes

  1. These authors contributed equally: Donna M. Werling, Harrison Brand, Joon-Yong An, Matthew R. Stone, Lingxue Zhu.


  1. Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA

    • Donna M. Werling
    • , Joon-Yong An
    • , Shan Dong
    • , Eirene Markenscoff-Papadimitriou
    • , Grace B. Schwartz
    • , Jeanselle Dea
    • , Clif Duhn
    • , Carolyn A. Erdman
    • , Michael C. Gilson
    • , Jeffrey D. Mandell
    • , Tomasz J. Nowakowski
    • , Louw Smith
    • , Michael F. Walker
    • , John L. Rubenstein
    • , A. Jeremy Willsey
    • , Matthew W. State
    •  & Stephan J. Sanders
  2. Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA

    • Harrison Brand
    • , Matthew R. Stone
    • , Joseph T. Glessner
    • , Ryan L. Collins
    • , Harold Z. Wang
    • , Benjamin B. Currall
    • , Xuefang Zhao
    • , Rachita Yadav
    •  & Michael E. Talkowski
  3. Department of Neurology, Harvard Medical School, Boston, MA, USA

    • Harrison Brand
    • , Joseph T. Glessner
    • , Ryan L. Collins
    • , Benjamin B. Currall
    • , Xuefang Zhao
    • , Rachita Yadav
    •  & Michael E. Talkowski
  4. Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA

    • Harrison Brand
    • , Joseph T. Glessner
    • , Benjamin B. Currall
    • , Xuefang Zhao
    • , Rachita Yadav
    • , Robert E. Handsaker
    • , Seva Kashin
    • , Steven A. McCarroll
    • , Benjamin M. Neale
    • , Mark J. Daly
    •  & Michael E. Talkowski
  5. Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, USA

    • Lingxue Zhu
    •  & Kathryn Roeder
  6. Program in Bioinformatics and Integrative Genomics, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA

    • Ryan L. Collins
  7. Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT, USA

    • Ryan M. Layer
    • , Andrew Farrell
    • , Aaron R. Quinlan
    •  & Gabor T. Marth
  8. USTAR Center for Genetic Discovery, University of Utah School of Medicine, Salt Lake City, UT, USA

    • Ryan M. Layer
    • , Andrew Farrell
    • , Aaron R. Quinlan
    •  & Gabor T. Marth
  9. Department of Genetics, Harvard Medical School, Boston, MA, USA

    • Robert E. Handsaker
    • , Seva Kashin
    •  & Steven A. McCarroll
  10. Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    • Lambertus Klei
    •  & Bernie Devlin
  11. Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA

    • Tomasz J. Nowakowski
  12. Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA

    • Tomasz J. Nowakowski
  13. Department of Human Genetics, University of Chicago, Chicago, IL, USA

    • Yuwen Liu
    •  & Xin He
  14. Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA

    • Sirisha Pochareddy
    •  & Nenad Sestan
  15. Department of Biology, Eastern Nazarene College, Quincy, MA, USA

    • Matthew J. Waterman
  16. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

    • Arnold R. Kriegstein
  17. Analytical and Translational Genetics Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    • Benjamin M. Neale
    •  & Mark J. Daly
  18. Department of Medicine, Harvard Medical School, Boston, MA, USA

    • Benjamin M. Neale
    •  & Mark J. Daly
  19. Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA

    • Hilary Coon
  20. Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA

    • Hilary Coon
    •  & Aaron R. Quinlan
  21. Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA

    • A. Jeremy Willsey
  22. Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • Joseph D. Buxbaum
  23. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • Joseph D. Buxbaum
  24. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • Joseph D. Buxbaum
  25. Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • Joseph D. Buxbaum
  26. Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA

    • Kathryn Roeder
  27. Departments of Pathology and Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    • Michael E. Talkowski


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Experimental design: D.M.W., H.B., J.-Y.A., M.R.S., J.T.G., M.J.W., X.H., N.S., B.M.N., H.C., A.J.W., J.D.B., M.J.D., M.W.S., A.R.Q., G.T.M., K.R., B.D., M.E.T., and S.J.S. Identification of de novo SNVs and indels: D.M.W., J.-Y.A., S.D., M.C.G., J.D.M., L.S., A.J.W., and S.J.S. Identification of structural variants: H.B., J.-Y.A., M.R.S., J.T.G., R.L.C., R.M.L., A.F., H.Z.W., X.Z., M.C.G., R.E.H., S.K., L.S., S.A.M., A.R.Q., G.T.M., and M.E.T. Confirmation of de novo variants: D.M.W., H.B., S.D., G.B.S., H.Z.W., B.B.C., J.D., C.D., C.A.E., R.Y., M.F.W., and M.J.W. Annotation of functional regions: D.M.W., J.-Y.A., S.D., E.M.-P., J.D.M., Y.L., S.P., J.L.R., N.S., M.E.T., and S.J.S. Generation of midfetal H3K27ac and ATAC–seq data: E.M.-P., T.J.N., A.R.K., and J.L.R. Development of genomic prediction score and de novo score: L.Z., L.K., K.R., and B.D. Analysis of SNVs and indels (Figs. 1–3): D.M.W., J.-Y.A., and S.J.S. Analysis of structural variants (Fig. 4): H.B., M.R.S., J.T.G., X.Z., and M.E.T. Assessment of P-value correlations, effective number of tests, and power analysis (Figs. 3 and 5): D.M.W., J.-Y.A., L.Z., G.B.S., K.R., B.D., and S.J.S. Manuscript preparation: D.M.W., H.B., J.-Y.A., M.R.S., L.Z., J.T.G., R.L.C., S.D., B.M.N., H.C., J.D.B., M.J.D., M.W.S., A.R.Q., G.T.M., K.R., B.D., M.E.T., and S.J.S.

Competing interests

J.L.R. is cofounder, stockholder, and currently on the scientific board of Neurona, a company studying the potential therapeutic use of interneuron transplantation. B.M.N. is an SAB member of Deep Genomics and serves as a consultant for Avanir Therapeutics. All other authors declare no competing interests.

Corresponding authors

Correspondence to Bernie Devlin or Michael E. Talkowski or Stephan J. Sanders.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–15 and Supplementary Note

  2. Reporting Summary

  3. Supplementary Tables

    Supplementary Tables 1–13

  4. Supplementary Data

    Visualization plots of de novo structural variants

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