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Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia

Nature Neuroscience volume 19, pages 14331441 (2016) | Download Citation

Abstract

By analyzing the exomes of 12,332 unrelated Swedish individuals, including 4,877 individuals affected with schizophrenia, in ways informed by exome sequences from 45,376 other individuals, we identified 244,246 coding-sequence and splice-site ultra-rare variants (URVs) that were unique to individual Swedes. We found that gene-disruptive and putatively protein-damaging URVs (but not synonymous URVs) were more abundant among individuals with schizophrenia than among controls (P = 1.3 × 10−10). This elevation of protein-compromising URVs was several times larger than an analogously elevated rate for de novo mutations, suggesting that most rare-variant effects on schizophrenia risk are inherited. Among individuals with schizophrenia, the elevated frequency of protein-compromising URVs was concentrated in brain-expressed genes, particularly in neuronally expressed genes; most of this elevation arose from large sets of genes whose RNAs have been found to interact with synaptically localized proteins. Our results suggest that synaptic dysfunction may mediate a large fraction of strong, individually rare genetic influences on schizophrenia risk.

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References

  1. 1.

    , , & Schizophrenia: a concise overview of incidence, prevalence, and mortality. Epidemiol. Rev. 30, 67–76 (2008).

  2. 2.

    , & Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch. Gen. Psychiatry 60, 1187–1192 (2003).

  3. 3.

    et al. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet 373, 234–239 (2009).

  4. 4.

    , & A systematic review and meta-analysis of the fertility of patients with schizophrenia and their unaffected relatives. Acta Psychiatr. Scand. 123, 98–106 (2011).

  5. 5.

    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).

  6. 6.

    et al. Searching for missing heritability: designing rare variant association studies. Proc. Natl. Acad. Sci. USA 111, E455–E464 (2014).

  7. 7.

    et al. CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature 505, 361–366 (2014).

  8. 8.

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

  9. 9.

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

  10. 10.

    et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828 (2012).

  11. 11.

    Nonsense-mediated mRNA decay: splicing, translation and mRNP dynamics. Nat. Rev. Mol. Cell Biol. 5, 89–99 (2004).

  12. 12.

    et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).

  13. 13.

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

  14. 14.

    , , , & Sequence kernel association tests for the combined effect of rare and common variants. Am. J. Hum. Genet. 92, 841–853 (2013).

  15. 15.

    , & Genetic power calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19, 149–150 (2003).

  16. 16.

    et al. Loss-of-function variants in schizophrenia risk and SETD1A as a candidate susceptibility gene. Neuron 82, 773–780 (2014).

  17. 17.

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

  18. 18.

    et al. Disruption of the neurexin 1 gene is associated with schizophrenia. Hum. Mol. Genet. 18, 988–996 (2009).

  19. 19.

    et al. De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat. Genet. 44, 1365–1369 (2012).

  20. 20.

    et al. Disruption of two novel genes by a translocation co-segregating with schizophrenia. Hum. Mol. Genet. 9, 1415–1423 (2000).

  21. 21.

    et al. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am. J. Hum. Genet. 94, 677–694 (2014).

  22. 22.

    et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014).

  23. 23.

    et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol. Cell. Proteomics 13, 397–406 (2014).

  24. 24.

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

  25. 25.

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

  26. 26.

    et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008).

  27. 27.

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

  28. 28.

    et al. CELF4 regulates translation and local abundance of a vast set of mRNAs, including genes associated with regulation of synaptic function. PLoS Genet. 8, e1003067 (2012).

  29. 29.

    et al. Cytoplasmic Rbfox1 regulates the expression of synaptic and autism-related genes. Neuron 89, 113–128 (2016).

  30. 30.

    et al. Biochemical and morphological characterization of A2BP1 in neuronal tissue. J. Neurosci. Res. 91, 1303–1311 (2013).

  31. 31.

    et al. HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism. Cell Rep. 6, 1139–1152 (2014).

  32. 32.

    et al. SynaptomeDB: an ontology-based knowledgebase for synaptic genes. Bioinformatics 28, 897–899 (2012).

  33. 33.

    et al. Epigenomic signatures of neuronal diversity in the mammalian brain. Neuron 86, 1369–1384 (2015).

  34. 34.

    et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol. Psychiatry 17, 142–153 (2012).

  35. 35.

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

  36. 36.

    , , , & Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol. 11, R90 (2010).

  37. 37.

    Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci. Nat. Genet. 43, 969–976 (2011).

  38. 38.

    et al. Autism spectrum disorder severity reflects the average contribution of de novo and familial influences. Proc. Natl. Acad. Sci. USA 111, 15161–15165 (2014).

  39. 39.

    , & Genetic research in autism spectrum disorders. Curr. Opin. Pediatr. 27, 685–691 (2015).

  40. 40.

    et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat. Genet. 48, 552–555 (2016).

  41. 41.

    Genetic evaluation of intellectual disabilities. Semin. Pediatr. Neurol. 15, 2–9 (2008).

  42. 42.

    , & The genetic landscape of intellectual disability arising from chromosome X. Trends Genet. 25, 308–316 (2009).

  43. 43.

    et al. Prevalence, phenotype and architecture of developmental disorders caused by de novo mutation. bioRxiv (2016).

  44. 44.

    et al. Mutations in the JARID1C gene, which is involved in transcriptional regulation and chromatin remodeling, cause X-linked mental retardation. Am. J. Hum. Genet. 76, 227–236 (2005).

  45. 45.

    et al. JARID1B is a histone H3 lysine 4 demethylase up-regulated in prostate cancer. Proc. Natl. Acad. Sci. USA 104, 19226–19231 (2007).

  46. 46.

    et al. Ataxia and epileptic seizures in mice lacking type 1 inositol 1,4,5-trisphosphate receptor. Nature 379, 168–171 (1996).

  47. 47.

    et al. Exome arrays capture polygenic rare variant contributions to schizophrenia. Hum. Mol. Genet. 25, 1001–1007 (2016).

  48. 48.

    et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012).

  49. 49.

    et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).

  50. 50.

    et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat. Genet. 45, 1150–1159 (2013).

  51. 51.

    et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

  52. 52.

    et al. Genome-wide association study in a Swedish population yields support for greater CNV and MHC involvement in schizophrenia compared with bipolar disorder. Mol. Psychiatry 17, 880–886 (2012).

  53. 53.

    et al. Copy number variation in schizophrenia in Sweden. Mol. Psychiatry 19, 762–773 (2014).

  54. 54.

    et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).

  55. 55.

    & Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  56. 56.

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

  57. 57.

    et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

  58. 58.

    et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 1–33 (2013).

  59. 59.

    et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

  60. 60.

    et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

  61. 61.

    et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).

  62. 62.

    et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).

  63. 63.

    et al. Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift. Front. Genet. 3, 35 (2012).

  64. 64.

    , & dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Hum. Mutat. 32, 894–899 (2011).

  65. 65.

    , & dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum. Mutat. 34, E2393–E2402 (2013).

  66. 66.

    et al. The UCSC known genes. Bioinformatics 22, 1036–1046 (2006).

  67. 67.

    , & Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

  68. 68.

    et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

  69. 69.

    & Identification of deleterious mutations within three human genomes. Genome Res. 19, 1553–1561 (2009).

  70. 70.

    , , & MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7, 575–576 (2010).

  71. 71.

    , & Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39, e118 (2011).

  72. 72.

    , , , & Predicting the functional effect of amino acid substitutions and indels. PLoS One 7, e46688 (2012).

  73. 73.

    et al. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000).

  74. 74.

    et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum. Mutat. 34, 57–65 (2013).

  75. 75.

    et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  76. 76.

    , , & GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

  77. 77.

    et al. Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat. Genet. 39, 319–328 (2007).

  78. 78.

    et al. Strong association of de novo copy number mutations with autism. Science 316, 445–449 (2007).

  79. 79.

    et al. Structural variation of chromosomes in autism spectrum disorder. Am. J. Hum. Genet. 82, 477–488 (2008).

  80. 80.

    et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010).

  81. 81.

    et al. De novo rates and selection of large copy number variation. Genome Res. 20, 1469–1481 (2010).

  82. 82.

    et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70, 863–885 (2011).

  83. 83.

    et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70, 886–897 (2011).

  84. 84.

    et al. Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70, 898–907 (2011).

  85. 85.

    et al. Strong association of de novo copy number mutations with sporadic schizophrenia. Nat. Genet. 40, 880–885 (2008).

  86. 86.

    et al. High frequencies of de novo CNVs in bipolar disorder and schizophrenia. Neuron 72, 951–963 (2011).

  87. 87.

    et al. Copy number variant study of bipolar disorder in Canadian and UK populations implicates synaptic genes. Am. J. Med. Genet. 165B, 303–313 (2014).

  88. 88.

    et al. De novo CNVs in bipolar affective disorder and schizophrenia. Hum. Mol. Genet. 23, 6677–6683 (2014).

  89. 89.

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

  90. 90.

    et al. Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing. Am. J. Hum. Genet. 93, 249–263 (2013).

  91. 91.

    et al. De novo mutations in epileptic encephalopathies. Nature 501, 217–221 (2013).

  92. 92.

    EuroEPINOMICS-RES Consortium; Epilepsy Phenome/Genome Project; Epi4K Consortium. De novo mutations in synaptic transmission genes including DNM1 cause epileptic encephalopathies. Am. J. Hum. Genet. 95, 360–370 (2014).

  93. 93.

    et al. De novo mutations in histone-modifying genes in congenital heart disease. Nature 498, 220–223 (2013).

  94. 94.

    et al. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367, 1921–1929 (2012).

  95. 95.

    et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380, 1674–1682 (2012).

  96. 96.

    et al. Genome sequencing identifies major causes of severe intellectual disability. Nature 511, 344–347 (2014).

  97. 97.

    et al. De novo mutations in moderate or severe intellectual disability. PLoS Genet. 10, e1004772 (2014).

  98. 98.

    et al. Increased exonic de novo mutation rate in individuals with schizophrenia. Nat. Genet. 43, 860–863 (2011).

  99. 99.

    et al. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 154, 518–529 (2013).

  100. 100.

    et al. De novo mutations in schizophrenia implicate chromatin remodeling and support a genetic overlap with autism and intellectual disability. Mol. Psychiatry 19, 652–658 (2014).

  101. 101.

    , , , & Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, D514–D517 (2005).

  102. 102.

    , , & Atlas of X-linked Intellectual Disability Syndromes (Oxford University Press, 2012).

  103. 103.

    , & American Academy of Pediatrics Committee on Genetics. Clinical genetic evaluation of the child with mental retardation or developmental delays. Pediatrics 117, 2304–2316 (2006).

  104. 104.

    et al. Diagnostic yield of various genetic approaches in patients with unexplained developmental delay or mental retardation. Am. J. Med. Genet. A. 140, 2063–2074 (2006).

  105. 105.

    et al. Analysis of expressed SNPs identifies variable extents of expression from the human inactive X chromosome. Genome Biol. 14, R122 (2013).

  106. 106.

    The XY gene hypothesis of psychosis: origins and current status. Am. J. Med. Genet. 162B, 800–824 (2013).

  107. 107.

    , , & Over-expression of XIST, the master gene for X chromosome inactivation, in females with major affective disorders. EBioMedicine 2, 909–918 (2015).

  108. 108.

    Is psychosis a disorder of XY epigenetics? EBioMedicine 2, 794–795 (2015).

  109. 109.

    et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

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Acknowledgements

We thank C. Usher for comments on the manuscript and work on the figures. This study was supported by grants from the National Human Genome Research Institute (U54 HG003067, R01 HG006855 to S.A.M.), the National Institute of Mental Health (R01 MH077139 to P.F.S., R01 MH095034 to P.S., and RC2 MH089905 to S.M.P. and P.S.), the Stanley Center for Psychiatric Research, the Alexander and Margaret Stewart Trust, and the Sylvan C. Herman Foundation.

Author information

Affiliations

  1. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Giulio Genovese
    • , Kimberly Chambert
    • , Jennifer L Moran
    •  & Steven A McCarroll
  2. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Giulio Genovese
    •  & Steven A McCarroll
  3. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA.

    • Giulio Genovese
    •  & Steven A McCarroll
  4. Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Menachem Fromer
    • , Eli A Stahl
    • , Douglas M Ruderfer
    • , Shaun M Purcell
    •  & Pamela Sklar
  5. Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Menachem Fromer
    • , Eli A Stahl
    • , Douglas M Ruderfer
    • , Shaun M Purcell
    •  & Pamela Sklar
  6. Department of Psychiatry and Neurochemistry, Institute of Neuroscience a Physiology at Sahlgrenska Academy at University of Gothenburg, Göteborg, Sweden.

    • Mikael Landén
  7. Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA.

    • Patrick F Sullivan
  8. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

    • Patrick F Sullivan
    •  & Christina M Hultman

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Contributions

G.G. and S.A.M. designed the analyses and wrote early drafts of the manuscript. G.G. performed the analyses. M.F. contributed to analyses of de novo mutated genes, D.M.R. and E.A.S. contributed with the specific design of the analyses. K.C. contributed with sample processing and data management. M.L., J.L.M., S.M.P., P.S., P.F.S. and C.M.H. contributed with sample and phenotype collection. All of the authors contributed to interpretation of the findings and revisions of the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Giulio Genovese or Steven A McCarroll.

Integrated supplementary information

Supplementary information

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  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–15 and Supplementary Tables 1, 2 and 8

  2. 2.

    Supplementary Methods Checklist

Excel files

  1. 1.

    Supplementary Table 3

    List of dURVs identified across 4,877 schizophernia cases and 6,203 controls.

  2. 2.

    Supplementary Table 4

    List of studies to define genes hit by de novo CNVs (Fig. 5a).

  3. 3.

    Supplementary Table 5

    List of de novo CNVs previously found in individuals with schizophrenia, bipolar disorder, and autism (Fig. 5a).

  4. 4.

    Supplementary Table 6

    List of studies to define genes hit by de novo non-synonymous variants (Fig. 5b).

  5. 5.

    Supplementary Table 7

    List of de novo variants found in individuals with schizophrenia, autism, epilepsy, intellectual disability, congenital heart disease, and controls (Fig. 5b).

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DOI

https://doi.org/10.1038/nn.4402

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