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De novo mutations in schizophrenia implicate synaptic networks

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Inherited alleles account for most of the genetic risk for schizophrenia. However, new (de novo) mutations, in the form of large chromosomal copy number changes, occur in a small fraction of cases and disproportionally disrupt genes encoding postsynaptic proteins. Here we show that small de novo mutations, affecting one or a few nucleotides, are overrepresented among glutamatergic postsynaptic proteins comprising activity-regulated cytoskeleton-associated protein (ARC) and N-methyl-d-aspartate receptor (NMDAR) complexes. Mutations are additionally enriched in proteins that interact with these complexes to modulate synaptic strength, namely proteins regulating actin filament dynamics and those whose messenger RNAs are targets of fragile X mental retardation protein (FMRP). Genes affected by mutations in schizophrenia overlap those mutated in autism and intellectual disability, as do mutation-enriched synaptic pathways. Aligning our findings with a parallel case–control study, we demonstrate reproducible insights into aetiological mechanisms for schizophrenia and reveal pathophysiology shared with other neurodevelopmental disorders.

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Figure 1: De novo mutations from schizophrenia affect genes coding for synaptic proteins and genes affected in other neuropsychiatric diseases.

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Data deposits

Data included in this manuscript have been deposited at dbGaP under accession number phs000687.v1.p1 and is available for download at

Change history

  • 12 February 2014

    The link in reference 15 was incorrect and has been fixed.


  1. 1

    Sullivan, P. F., Daly, M. J. & O’Donovan, M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nature Rev. Genet. 13, 537–551 (2012)

    CAS  PubMed  Google Scholar 

  2. 2

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

    CAS  PubMed  Google Scholar 

  3. 3

    McClellan, J. M., Susser, E. & King, M.-C. Schizophrenia: a common disease caused by multiple rare alleles. Br. J. Psychiatry 190, 194–199 (2007)

    PubMed  Google Scholar 

  4. 4

    Malhotra, D. & Sebat, J. Genetics: Fish heads and human disease. Nature 485, 318–319 (2012)

    ADS  CAS  PubMed  Google Scholar 

  5. 5

    Rees, E., Moskvina, V., Owen, M. J., O’Donovan, M. C. & Kirov, G. De novo rates and selection of schizophrenia-associated copy number variants. Biol. Psychiatry 70, 1109–1114 (2011)

    PubMed  Google Scholar 

  6. 6

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

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

    ADS  PubMed  PubMed Central  Google Scholar 

  8. 8

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

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

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

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

    CAS  Google Scholar 

  11. 11

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

    ADS  CAS  PubMed  Google Scholar 

  12. 12

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

    CAS  PubMed  Google Scholar 

  13. 13

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

    CAS  PubMed  Google Scholar 

  14. 14

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

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature (22 January 2014)

  16. 16

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Mesholam-Gately, R. I., Giuliano, A. J., Goff, K. P. & Faraone, S. V & Seidman, L. J. Neurocognition in first-episode schizophrenia: a meta-analytic review. Neuropsychology 23, 315–336 (2009)

    PubMed  Google Scholar 

  18. 18

    Glessner, J. T. et al. Strong synaptic transmission impact by copy number variations in schizophrenia. Proc. Natl Acad. Sci. USA 107, 10584–10589 (2010)

    ADS  CAS  PubMed  Google Scholar 

  19. 19

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

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

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

    CAS  PubMed  Google Scholar 

  21. 21

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

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Malenka, R. C. & Nicoll, R. A. NMDA-receptor-dependent synaptic plasticity: multiple forms and mechanisms. Trends Neurosci. 16, 521–527 (1993)

    CAS  PubMed  Google Scholar 

  23. 23

    Bramham, C. R. et al. The Arc of synaptic memory. Exp. Brain Res. 200, 125–140 (2010)

    PubMed  Google Scholar 

  24. 24

    Korb, E. & Finkbeiner, S. Arc in synaptic plasticity: from gene to behavior. Trends Neurosci. 34, 591–598 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Shepherd, J. D. & Bear, M. F. New views of Arc, a master regulator of synaptic plasticity. Nature Neurosci. 14, 279–284 (2011)

    CAS  PubMed  Google Scholar 

  26. 26

    Mikuni, T. et al. Arc/Arg3.1 Is a postsynaptic mediator of activity-dependent synapse elimination in the developing cerebellum. Neuron 78, 1024–1035 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Feinberg, I. Schizophrenia: caused by a fault in programmed synaptic elimination during adolescence? J. Psychiatr. Res. 17, 319–334 (1982)

    Google Scholar 

  28. 28

    Darnell, J. C. & Klann, E. The translation of translational control by FMRP: therapeutic targets for FXS. Nature Neurosci. 16, 1530–1536 (2013)

    CAS  PubMed  Google Scholar 

  29. 29

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genet. 25 25–29 (2000)

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Gilman, S. R. 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)

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Golzio, C. et al. KCTD13 is a major driver of mirrored neuroanatomical phenotypes of the 16p11.2 copy number variant. Nature 485, 363–367 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Steinberg, S. et al. Common variants at VRK2 and TCF4 conferring risk of schizophrenia. Hum. Mol. Genet. 20, 4076–4081 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    von Eichborn, J. et al. SynSysNet: integration of experimental data on synaptic protein-protein interactions with drug-target relations. Nucleic Acids Res. 41, D834–D840 (2013)

    CAS  PubMed  Google Scholar 

  34. 34

    Aoto, J., Martinelli, D. C., Malenka, R. C., Tabuchi, K. & Südhof, T. C. Presynaptic neurexin-3 alternative splicing trans-synaptically controls postsynaptic AMPA receptor trafficking. Cell 154, 75–88 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Williams, N. M. et al. Rare chromosomal deletions and duplications in attention-deficit hyperactivity disorder: a genome-wide analysis. Lancet 376, 1401–1408 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Girirajan, S. et al. Phenotypic heterogeneity of genomic disorders and rare copy-number variants. N. Engl. J. Med. 367, 1321–1331 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Owen, M. J., O’Donovan, M. C., Thapar, A. & Craddock, N. Neurodevelopmental hypothesis of schizophrenia. Br. J. Psychiatry 198, 173–175 (2011)

    PubMed  PubMed Central  Google Scholar 

  38. 38

    Nozawa, R.-S. et al. Human POGZ modulates dissociation of HP1α from mitotic chromosome arms through Aurora B activation. Nature Cell Biol. 12, 719–727 (2010)

    CAS  PubMed  Google Scholar 

  39. 39

    Buxbaum, J. D. et al. The autism sequencing consortium: large-scale, high-throughput sequencing in autism spectrum disorders. Neuron 76, 1052–1056 (2012)

    CAS  PubMed  Google Scholar 

  40. 40

    Craddock, N. & Owen, M. J. The Kraepelinian dichotomy – going, going...but still not gone. Br. J. Psychiatry 196, 92–95 (2010)

    PubMed  PubMed Central  Google Scholar 

  41. 41

    Insel, T. R. Transforming diagnosis. NIMH Director’s Blog (29 April 2013)

  42. 42

    Pocklington, A. J., Armstrong, J. D. & Grant, S. G. N. Organization of brain complexity–synapse proteome form and function. Brief. Funct. Genomics Proteomics 5, 66–73 (2006)

    CAS  Google Scholar 

  43. 43

    Coba, M. P. et al. TNiK is required for postsynaptic and nuclear signaling pathways and cognitive function. J. Neurosci. 32, 13987–13999 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Ruderfer, D. M. et al. A family-based study of common polygenic variation and risk of schizophrenia. Mol. Psychiatry 16, 887–888 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

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Work in Cardiff was supported by Medical Research Council (MRC) Centre (G0800509) and Program Grants (G0801418), the European Community’s Seventh Framework Programme (HEALTH-F2-2010-241909 (Project EU-GEI)), and NIMH (2 P50 MH066392-05A1). Work at the Icahn School of Medicine at Mount Sinai was supported by the Friedman Brain Institute, the Institute for Genomics and Multiscale Biology (including computational resources and staff expertise provided by the Department of Scientific Computing), and National Institutes of Health grants R01HG005827 (S.M.P.), R01MH099126 (S.M.P.), and R01MH071681 (P.S.). Work at the Broad Institute was funded by Fidelity Foundations, the Sylvan Herman Foundation, philanthropic gifts from K. and E. Dauten, and the Stanley Medical Research Institute. Work at the Wellcome Trust Sanger Institute was supported by The Wellcome Trust (grant numbers WT089062 and WT098051) and also by the European Commission FP7 project gEUVADIS no. 261123 (P.P.). We would like to thank M. Daly, B. Neale and K. Samocha for discussions and providing unpublished autism data. We would also like to acknowledge M. DePristo, S. Gabriel, T. J. Fennel, K. Shakir, C. Tolonen and H. Shah for their help in generating and processing the various data sets.

Author information




The project was led in Cardiff by M.C.O.D. and M.J.O., in Mount Sinai by S.M.P. and P.S., at the Broad by S.A.M. and J.L.M., and at the Sanger by A.P.; H.J.W., J.L.M., K.C., J.S.J., D.D.B., M.M. and S.A.R. were responsible for sample processing and data management. M.F., H.J.W., P.G., D.M.R., D.H.K., G.K., E.R. and S.D. processed NGS data, annotated and validated mutations. L.G., N.C., I.H., S.D., H.J.W. and S.A.R. undertook validation of mutations and additional lab work. A.J.P., M.F., D.H.K., S.M.P. and P.H. co-ordinated/undertook the main bioinformatics/statistical analyses. E.R., D.M.R., E.B., P.P., E.H. and P.R. performed additional analyses. S.G.G. contributed additional insights into synaptic biology. Sample recruitment was led by G.K. and V.M.; The main findings were interpreted by M.C.O.D., M.F., M.J.O., P.H., G.K., E.M.S., S.A.M., D.H.K., A.J.P., A.P., S.M.P. and P.S. who drafted the manuscript.

Corresponding author

Correspondence to Michael J. Owen.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Comparison of sequencing metrics for putative de novo calls and parental singletons.

ae, Putative de novo calls (child heterozygous, both parents homozygous reference; N parent-proband trios = 623) were compared with variants observed in only a single parent (“singletons”), in terms of depth of all reads at the variant site (a), fraction of reads with the alternate allele (AB = allele balance) (b), mapping quality of the reads at the site (MQ) (c), the likelihood of the heterozygous genotype (PL = Phred-scaled likelihood) (d), and the number of other samples in the present study with a non-reference allele at that site (AAC = alternate allele count) (e). Distributions were calculated for putative de novo variants (red), or grouped by sites of putatively recurrent de novo mutations (orange) when relevant, transmitted singletons (green), and non-transmitted singletons (blue).

Extended Data Figure 2 Metrics for de novo variants across cohorts and trios.

a, Rates of recurrence of validated de novo mutations for tri-nucleotide sequences. For each of 96 possible tri-nucleotide base contexts of single-base mutations (accounting for strand symmetry by reverse complementarity), the number of observed de novo SNV is plotted (sorted by this count). Mutation counts are sub-divided into those not found in external data (red), those found in dbSNP (build 137, green), those found in controls (N = 2543) in the parallel exome sequencing study15 (cyan), and those found both in dbSNP and that study (purple). b, Comparison of on-target heterozygous SNV and indel call rate with putative de novo mutation calls. For each proband (N = 623), the number of heterozygous SNV and indel calls is compared with the number of putative de novo mutations (child heterozygous, both parents homozygous reference). Probands are coloured by sequencing centre (see Supplementary Information for differences in exome capture), and six trios are noticeable outliers from all others (marked by ‘×’) in terms of number of putative de novo mutations. c, Variation in sequencing coverage between and across trios and sequencing centres. For each trio (N = 623), the number of bases covered by 10 reads or more for each member (marked by ‘×’) and the joint coverage9 in all three members (marked by points) are plotted at corresponding horizontal points; trios are sorted in increasing order of joint coverage and coloured by sequencing centre (see Supplementary Information). The intersection of each exome capture with the RefSeq coding sequence is marked by respective dotted lines.

Extended Data Figure 3 De novo mutation counts and rates.

a, The observed distribution of number of validated RefSeq-coding (see Supplementary Information) de novo mutations found for each trio (N = 617) is compared with that expected from a Poisson distribution with a rate equal to the observed mean number of de novo mutations (λ = 1.032). b, Deleterious mutation rate inversely correlates with academic performance. Individuals were grouped according to their final school grade (3–6, corresponding to D, C, B, A in the US system,, and the proportion of individuals with one or more de novo loss-of-function mutations is plotted. N, number of individuals in each group. See Supplementary Information for details on linear regression performed to evaluate association; note that 19 samples were removed from this analysis for missing parental age or school grade information, leaving a total of 598 trios.

Extended Data Figure 4 Enrichment of de novo SNVs, indels and CNVs in genes encoding postsynaptic complexes at glutamatergic synapses.

a, Number of de novo mutations (N cases = 617) in postsynaptic complexes in current study (and genes affected) are shown alongside the most conservative estimate of de novo CNV enrichment from ref. 20 (N cases studied = 662). NS, nonsynonymous, LoF, loss-of-function, PSD, postsynaptic density. The NMDAR complex gene set was derived a priori from a published proteomics data set42. To avoid investigator bias, we did not add additional members post hoc, thus omitting genes with de novo mutations and important NMDAR functions; these include GRIN2A, which encodes a subunit of the NMDA receptor itself, and AKAP9, which directly anchors protein complexes involved in signalling at NMDA receptors43. P < 0.05 are marked in bold as are genes hit by mutations in the current study and by de novo CNVs in ref. 20. bg, 95% credible intervals (CI) for fold-enrichment statistics of de novo mutations in postsynaptic gene sets (corresponding to enrichments in a, above, and as marked) were calculated from the posterior distributions of fold-enrichment (O/E, observed to expected) statistic values for individuals in this study. Point estimates of O/E are given in Table 3, and correspond to the distribution modes here. The 95% CI is marked by red vertical lines, and a null effect size (value of 1) is marked by a grey line. Note that loss-of-function mutations in the large postsynaptic density set are not significantly enriched, and thus the corresponding CI includes an effect size of 1. All posterior distributions were calculated using dnenrich, as described in the Supplementary Information.

Extended Data Table 1 Stratification of de novo mutations based on polygenic burden, presence of a ‘pathogenic’ CNV, or poor scholastic achievement
Extended Data Table 2 Genes overlapped by two nonsynonymous de novo mutations in schizophrenia probands
Extended Data Table 3 Enrichment of de novo mutations in genes targeted by FMRP and conditional analysis of enrichment in postsynaptic density complexes
Extended Data Table 4 Brain expression biases of genes affected by de novo mutations
Extended Data Table 5 Comparison of genes hit by de novo mutations between this study and other disease studies and control individuals
Extended Data Table 6 Mammalian conservation at de novo mutation sites and of genes hit by de novo mutations

Supplementary information

Supplementary Information

This file contains Supplementary Text and additional references. (PDF 761 kb)

Supplementary Table 1

This file contains a list of validated coding de novo mutations discovered in subjects with schizophrenia. For each de novo mutation (single-nucleotide or insertion/deletion variant) discovered in a proband with schizophrenia in this study, listed are basic details, including genomic coordinates (hg19), reference and de novo alleles, functional impact in genes overlapped (see Supplementary Text), number of total alternate alleles called at that locus in this sample (N=623 trios, including parental genotypes), sequencing metrics for the genotypes (in the proband, father, and mother), the phased parent-of-origin when known, and family history (first-degree relatives). (XLSX 170 kb)

Supplementary Table 2

The file contains a compiled list of published de novo mutations in unaffected controls and individuals with neuropsychiatric illness. Sheet 1 shows de novo mutations analyzed alongside the schizophrenia mutations in this study, counts of individuals and RefSeq-coding mutations from published study sources, neuropsychiatric phenotype, and first author of study source are given. ASD = autism spectrum disorders, CONTROL = individual from unaffected family, ID = intellectual disability, SZ = schizophrenia, SIB = unaffected sibling of proband (from families with sequenced “quads” = father, mother, child with ASD or SZ, unaffected sibling). Sheet 2 shows that for the studies and sample sizes listed in the sheet 1, all published de novo mutations were collated and uniformly annotated. Note that only those annotated as RefSeq-coding by Plink/Seq (see Supplementary Text) are listed here. Columns are as described in Table S1. (XLSX 259 kb)

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Fromer, M., Pocklington, A., Kavanagh, D. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).

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