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De novo mutations identified by exome sequencing implicate rare missense variants in SLC6A1 in schizophrenia

Abstract

Schizophrenia is a highly polygenic disorder with important contributions from both common and rare risk alleles. We analyzed exome sequencing data for de novo variants (DNVs) in a new sample of 613 schizophrenia trios and combined this with published data to give a total of 3,444 trios. In this new data, loss-of-function (LoF) DNVs were significantly enriched among 3,471 LoF-intolerant genes, which supports previous findings. In the full dataset, genes associated with neurodevelopmental disorders (n = 159) were significantly enriched for LoF DNVs. Within these neurodevelopmental disorder genes, SLC6A1, which encodes a γ-aminobutyric acid transporter, was associated with missense-damaging DNVs. In 1,122 trios for which genome-wide common variant data were available, schizophrenia and bipolar disorder polygenic risk were significantly overtransmitted to probands. Probands carrying LoF or deletion DNVs in LoF-intolerant or neurodevelopmental disorder genes had significantly less overtransmission of schizophrenia polygenic risk than did non-carriers, which provides a second robust line of evidence that these DNVs increase liability to schizophrenia.

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Fig. 1: Gene set enrichment for loss-of-function de novo variants.
Fig. 2: Mean pTDT deviation and 95% confidence intervals for schizophrenia, bipolar disorder and height polygenic risk scores.
Fig. 3: Mean pTDT deviation and 95% confidence interval for schizophrenia PRS.

Data availability

DNVs discovered from the new trios are published in Supplementary Table 12. The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

A description of the R functions used for statistical analysis can be found in the Life Sciences Reporting Summary.

References

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  5. Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

    Article  Google Scholar 

  6. Rees, E., O’Donovan, M. C. & Owen, M. J. Genetics of schizophrenia. Curr. Opin. Behav. Sci. 2, 8–14 (2015).

    Article  Google Scholar 

  7. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  9. Satterstrom, F. K. et al. Novel genes for autism implicate both excitatory and inhibitory cell lineages in risk. Preprint at bioRxiv https://doi.org/10.1101/484113 (2018).

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  12. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. Steinberg, S. et al. Truncating mutations in RBM12 are associated with psychosis. Nat. Genet. 49, 1251–1254 (2017).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  16. Howrigan, D. et al. Schizophrenia risk conferred by protein-coding de novo mutations. Preprint at bioRxiv https://doi.org/10.1101/495036 (2018).

  17. Rees, E. et al. Analysis of intellectual disability copy number variants for association with schizophrenia. JAMA Psychiatry 73, 963–969 (2016).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  20. Bergen, S. E. et al. Joint contributions of rare copy number variants and common SNPs to risk for schizophrenia. Am. J. Psychiatry 176, 29–35 (2018).

    Article  Google Scholar 

  21. Tansey, K. E. et al. Common alleles contribute to schizophrenia in CNV carriers. Mol. Psychiatry 21, 1085–1089 (2015).

    Article  Google Scholar 

  22. Niemi, M. E. K. et al. Common genetic variants contribute to risk of rare severe neurodevelopmental disorders. Nature 562, 268–271 (2018).

    Article  CAS  Google Scholar 

  23. Weiner, D. J. et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat. Genet. 49, 978–985 (2017).

    Article  CAS  Google Scholar 

  24. Karczewski, K. 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 https://doi.org/10.1101/531210 (2019).

  25. Samocha, K. E. et al. Regional missense constraint improves variant deleteriousness prediction. Preprint at bioRxiv https://doi.org/10.1101/148353 (2017).

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

    Article  Google Scholar 

  27. 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).

    Article  CAS  Google Scholar 

  28. Gazal, S. et al. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat. Genet. 49, 1421–1427 (2017).

    Article  CAS  Google Scholar 

  29. Keller, M. C. Evolutionary perspectives on genetic and environmental risk factors for psychiatric disorders. Annu. Rev. Clin. Psychol. 14, 471–493 (2018).

    Article  Google Scholar 

  30. 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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  32. Pocklington, A. J. et al. Novel findings from CNVs implicate inhibitory and excitatory signaling complexes in schizophrenia. Neuron 86, 1203–1214 (2015).

    Article  CAS  Google Scholar 

  33. Gottesman, I. I. & Shields, J. A polygenic theory of schizophrenia. Proc. Natl Acad. Sci. USA 58, 199–205 (1967).

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  37. Jun, G. et al. Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am. J. Hum. Genet. 91, 839–848 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  39. Ware, J. S., Samocha, K. E., Homsy, J. & Daly, M. J. Interpreting de novo variation in human disease using denovolyzeR. Curr. Protoc. Hum. Genet. 87, 7.25.1–7.25.15 (2015).

    Google Scholar 

  40. McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    Article  Google Scholar 

  41. The Brainstorm Consortium et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

  42. Deelen, P. et al. Genotype harmonizer: automatic strand alignment and format conversion for genotype data integration. BMC Res. Notes 7, 901 (2014).

    Article  Google Scholar 

  43. Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: polygenic risk score software. Bioinformatics 31, 1466–1468 (2015).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The work at Cardiff University was supported by Medical Research Council Centre Grant no. MR/L010305/1 (M.J.O.) and Program Grant no. G0800509 (M.J.O, M.C.O’D., J.T.R.W., V.E.-P., P.H. and A.J.P.), European Community Seventh Framework Programme Grant no. HEALTH-F2-2010-241909 (Project EU-GEI, M.C.O’D.), and European Union Seventh Framework Programme for research, technological development, and demonstration Grant no. 279227 (CRESTAR Consortium, M.C.O’D. and J.T.R.W.). We acknowledge L. Bates and L. Hopkins, at Cardiff University, for laboratory sample management. We acknowledge M. Einon, at Cardiff University, for support with the use and setup of computational infrastructures.

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Contributions

M.C.O’D., M.J.O., J.T.R.W., P.H. and E.R. conceived and designed the research. E.R. analyzed the data. J.H., J.M. and N.C. performed and managed the sequencing experiments. J.H. and M.D. performed the Sanger sequencing validation experiment. V.E.-P., A.J.P., L.H., S.E.L., A.F.P. and A.L.R. contributed to the interpretation of the results. T.L., N.K., V.K., V.G., M.P., J.G.-P., C.A., GROUP Investigators, M.G., J.R., G.K., J.T.R.W., M.C.O’D. and M.J.O. led the acquisition of the clinical samples. E.R., M.C.O’D. and M.J.O. wrote the manuscript, which was read, edited and approved by all authors.

Corresponding authors

Correspondence to Michael C. O’Donovan or Michael J. Owen.

Ethics declarations

Competing interests

M.C.O’D., M.J.O., P.H., J.T.R.W. and A.J.P. are supported by a collaborative research grant from Takeda. Takeda played no part in the conception, design, implementation, funding or interpretation of this study. All other authors declare no competing interests.

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.

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Supplementary information

Supplementary Information

Ethics statement; Sample description; Supplementary Figures 1–4; CNV analysis; Principal component analysis; Sequencing coverage information; Alternative de novo enrichment tests; Supplementary Table legends.

Reporting Summary

Supplementary Table

Supplementary tables 1–14.

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Rees, E., Han, J., Morgan, J. et al. De novo mutations identified by exome sequencing implicate rare missense variants in SLC6A1 in schizophrenia. Nat Neurosci 23, 179–184 (2020). https://doi.org/10.1038/s41593-019-0565-2

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