Skip to main content

Thank you for visiting 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.

Exome sequencing in schizophrenia-affected parent–offspring trios reveals risk conferred by protein-coding de novo mutations

This article has been updated


Protein-coding de novo mutations (DNMs) are significant risk factors in many neurodevelopmental disorders, whereas schizophrenia (SCZ) risk associated with DNMs has thus far been shown to be modest. We analyzed DNMs from 1,695 SCZ-affected trios and 1,077 published SCZ-affected trios to better understand the contribution to SCZ risk. Among 2,772 SCZ probands, exome-wide DNM burden remained modest. Gene set analyses revealed that SCZ DNMs were significantly concentrated in genes that were highly expressed in the brain, that were under strong evolutionary constraint and/or overlapped with genes identified in other neurodevelopmental disorders. No single gene surpassed exome-wide significance; however, 16 genes were recurrently hit by protein-truncating DNMs, corresponding to a 3.15-fold higher rate than the mutation model expectation (permuted 95% confidence interval: 1–10 genes; permuted P = 3 × 105). Overall, DNMs explain a small fraction of SCZ risk, and larger samples are needed to identify individual risk genes, as coding variation across many genes confers risk for SCZ in the population.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: DNM rates (dots), 95% confidence intervals (bars) and DNM model expectations (dashed lines) among SCZ and control probands.
Fig. 2: DNM rates (dots) and 95% CIs (bars) compared to DNM model expectations (dashed lines) for exome-sequenced trios with various mental disorders.
Fig. 3: Partitioning gene set enrichment among evolutionarily constrained and neurodevelopmental disorder gene sets.
Fig. 4: Partitioning of the genes expressed in neuronal cell types by interaction of their gene products with mRNAs highly active in the synapse (for example, FMRP, RBFOX and CELF4), with interacting genes listed as ‘potentially synaptic’.

Data availability

Data included in this manuscript have been deposited in the database of Genotypes and Phenotypes (dbGAP) under accession number phs001196.v1.

Data collection and analysis were not performed with blinding to the conditions of the experiments.

Code availability

Code used to identify coding DNMs and assess enrichment is publicly available at

Change history

  • 24 February 2020

    In the version of this article initially published, a string of nonsense characters appears in the second sentence of the section ‘Enrichment in highly brain-expressed and constrained genes’. This has been corrected in the PDF and HTML versions.


  1. 1.

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

    PubMed Central  Google Scholar 

  2. 2.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Lee, S. H. et al. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat. Genet. 44, 247–250 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    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).

    PubMed  Google Scholar 

  5. 5.

    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).

    CAS  PubMed  Google Scholar 

  6. 6.

    Malhotra, D. & Sebat, J. CNVs: harbingers of a rare variant revolution in psychiatric genetics. Cell 148, 1223–1241 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Rees, E. et al. Analysis of copy number variations at 15 schizophrenia-associated loci. Br. J. Psychiatry 204, 108–114 (2014).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

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

    PubMed  Google Scholar 

  9. 9.

    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  PubMed  Google Scholar 

  10. 10.

    Lelieveld, S. H. et al. Meta-analysis of 2,104 trios provides support for 10 new genes for intellectual disability. Nat. Neurosci. 19, 1194–1196 (2016).

    CAS  PubMed  Google Scholar 

  11. 11.

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

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    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 

  13. 13.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

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

    PubMed  PubMed Central  Google Scholar 

  16. 16.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

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

    Google Scholar 

  18. 18.

    Epi, K. C. De novo mutations in SLC1A2 and CACNA1A are important causes of epileptic encephalopathies. Am. J. Hum. Genet. 99, 287–298 (2016).

    Google Scholar 

  19. 19.

    Deciphering Developmental Disorders Study. Large-scale discovery of novel genetic causes of developmental disorders. Nature 519, 223–228 (2015).

    Google Scholar 

  20. 20.

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

    Google Scholar 

  21. 21.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Singh, T. et al. The contribution of rare variants to risk of schizophrenia in individuals with and without intellectual disability. Nat. Genet. 49, 1167–1173 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

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

    CAS  PubMed  Google Scholar 

  27. 27.

    Guipponi, M. et al. Exome sequencing in 53 sporadic cases of schizophrenia identifies 18 putative candidate genes. PLoS One 9, e112745 (2014).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    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 

  29. 29.

    McCarthy, S. E. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Xu, B. et al. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat. Genet. 43, 864–868 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Karczewski, K. J. et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. Preprint at bioRxiv (2019).

  35. 35.

    Kosmicki, J. A. et al. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples. Nat. Genet. 49, 504–510 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Takata, A. et al. De novo synonymous mutations in regulatory elements contribute to the genetic etiology of autism and schizophrenia. Neuron 89, 940–947 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Petrovski, S. et al. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 9, e1003709 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Robinson, E. B. 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).

    CAS  PubMed  Google Scholar 

  39. 39.

    Ganna, A. et al. Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nat. Neurosci. 19, 1563–1565 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Untergasser, A. et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 40, e115 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Robinson, J. T. et al. Integrative Genomics Viewer. Nat. Biotechnol. 29, 24–26 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Liu, X., Jian, X. & Boerwinkle, E. dbNSFPv2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum. Mutat. 34, E2393–E2402 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Ng, S. B. et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461, 272–276 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Fairbrother, W. G. et al. Predictive identification of exonic splicing enhancers in human genes. Science 297, 1007–1013 (2002).

    CAS  PubMed  Google Scholar 

  49. 49.

    Ke, S. et al. Quantitative evaluation of all hexamers as exonic splicing elements. Genome Res. 21, 1360–1374 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Wang, Z. et al. Systematic identification and analysis of exonic splicing silencers. Cell 119, 831–845 (2004).

    CAS  PubMed  Google Scholar 

Download references


This study was supported by grants from the National Human Genome Research Institute (U54 HG003067 and R01 HG006855), the Stanley Center for Psychiatric Research and the National Institute of Mental Health (R01 MH077139, R01 MH085521 and RC2 MH089905).

Author information




N.L., J.L.M., S.V.F., S.J.G., S.A.M., M.T. and B.M.N. initiated the project. H.-G.H. and W.J.C. led sample recruitment in Taiwan. H.-G.H., W.J.C., S.-H.W., S.V.F., S.J.G., N.L. and M.T. provided the sample and phenotype collection. F.C., K.C., S.D.C., A.D., J.L.M. and C.R. managed the sample collection and processing. D.P.H., K.E.S. and M.F. processed sequence data and generated DNM calls. S.A.R., F.C. and S.A.M. undertook validation of mutations and additional lab work. D.P.H. undertook the main bioinformatics and statistical analyses in close coordination with K.E.S., M.F., G.G., J.A.K., T.S. and B.M.N. The main findings were interpreted by K.E.S., M.F., M.J.D., G.G., J.K., T.S., S.V.F., S.J.G. and B.M.N. D.P.H. drafted the manuscript in close coordination with B.M.N. and C.C., with editing assistance from S.A.R., K.E.S., G.G., J.A.K., S.V.F. and S.J.G.

Corresponding authors

Correspondence to Daniel P. Howrigan or Benjamin M. Neale.

Ethics declarations

Competing interests

B.M.N. is on the Scientific Advisory Board of Deep Genomics and Camp4 Therapeutics Corporation and is on the Biogen Genomics Advisory Panel. M.F. is an employee of Verily Life Sciences.

Additional information

Peer review information Nature Neuroscience thanks Ryan Yuen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Notes 1–14, Supplementary Tables 1–5 and Supplementary Figs. 1–36.

Reporting Summary

Supplementary Data 1

Data file descriptions.

Supplementary Data 2

Taiwanese cohort sample list.

Supplementary Data 3

Taiwanese cohort trio list.

Supplementary Data 4

Taiwanese cohort DNM list.

Supplementary Data 5

DNM studies.

Supplementary Data 6

Combined cohorts DNM list.

Supplementary Data 7

SCZ DNM calls.

Supplementary Data 8

Candidate gene sets.

Supplementary Data 9

Candidate gene set results. Summary statistics from candidate gene set enrichment analysis.

Supplementary Data 10

GO + SynaptomeDB enrichment.

Supplementary Data 11

Recurrent PTV genes.

Supplementary Data 12

Recurrent PTV + missense genes.

Supplementary Data 13

Gene recurrence by gene set.

Supplementary Data 14

Predicted SCZ risk genes.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Howrigan, D.P., Rose, S.A., Samocha, K.E. et al. Exome sequencing in schizophrenia-affected parent–offspring trios reveals risk conferred by protein-coding de novo mutations. Nat Neurosci 23, 185–193 (2020).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing