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

Nature Neuroscience volume 23, pages 185–193 (2020)Cite this article

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Abstract

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 × 10−5). 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.

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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’.

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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 https://github.com/howrigan/trio_sequence_analysis.

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.

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Acknowledgements

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).

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Authors and Affiliations

  1. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Daniel P. Howrigan, Kaitlin E. Samocha, Claire Churchhouse, Mark J. Daly, Jack A. Kosmicki, Tarjinder Singh & Benjamin M. Neale

  2. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Daniel P. Howrigan, Samuel A. Rose, Kaitlin E. Samocha, Menachem Fromer, Felecia Cerrato, Claire Churchhouse, Kimberly Chambert, Mark J. Daly, Ashley Dumont, Giulio Genovese, Jack A. Kosmicki, Jennifer L. Moran, Tarjinder Singh, Steven A. McCarroll & Benjamin M. Neale

  3. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Samuel A. Rose & Menachem Fromer

  4. National Taiwan University, Taipei, Taiwan

    Wei J. Chen & Hai-Gwo Hwu

  5. University of California, San Diego, La Jolla, CA, USA

    Sharon D. Chandler & Ming Tsuang

  6. Harvard School of Public Health, Boston, MA, USA

    Nan Laird

  7. SUNY Upstate Medical University, Syracuse, NY, USA

    Cheryl Roe, Stephen V. Faraone & Stephen J. Glatt

  8. China Medical University, Taichung, Taiwan

    Shi-Heng Wang

  9. Harvard University, Cambridge, MA, USA

    Steven A. McCarroll

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Contributions

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.

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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.

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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). https://doi.org/10.1038/s41593-019-0564-3

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