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Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder

A Publisher Correction to this article was published on 14 July 2020

This article has been updated


We systematically analyzed postzygotic mutations (PZMs) in whole-exome sequences from the largest collection of trios (5,947) with autism spectrum disorder (ASD) available, including 282 unpublished trios, and performed resequencing using multiple independent technologies. We identified 7.5% of de novo mutations as PZMs, 83.3% of which were not described in previous studies. Damaging, nonsynonymous PZMs within critical exons of prenatally expressed genes were more common in ASD probands than controls (P < 1 × 10−6), and genes carrying these PZMs were enriched for expression in the amygdala (P = 5.4 × 10−3). Two genes (KLF16 and MSANTD2) were significantly enriched for PZMs genome-wide, and other PZMs involved genes (SCN2A, HNRNPU and SMARCA4) whose mutation is known to cause ASD or other neurodevelopmental disorders. PZMs constitute a significant proportion of de novo mutations and contribute importantly to ASD risk.

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Figure 1: De novo mutations in ASD show an excess of low AAFs, consistent with postzygotic mosaicism.
Figure 2: Postzygotic mutations in ASD show excess deleterious mutations in critical exons of genes expressed during early brain development.
Figure 3: Postzygotic mutations implicate the prenatal amygdala in ASD.
Figure 4: Recurrent nonsynonymous postzygotic mosaic mutations implicate previously uncharacterized genes with more mutations than expected false calls.

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  1. 1

    Gaugler, T. et al. Most genetic risk for autism resides with common variation. Nat. Genet. 46, 881–885 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

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

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

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

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

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

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    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 

  6. 6

    Yu, T.W. et al. Using whole-exome sequencing to identify inherited causes of autism. Neuron 77, 259–273 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Lim, E.T. et al. Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron 77, 235–242 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    Jamuar, S.S. et al. Somatic mutations in cerebral cortical malformations. N. Engl. J. Med. 371, 733–743 (2014).

    PubMed  PubMed Central  Google Scholar 

  9. 9

    Poduri, A., Evrony, G.D., Cai, X. & Walsh, C.A. Somatic mutation, genomic variation, and neurological disease. Science 341, 1237758 (2013).

    PubMed  PubMed Central  Google Scholar 

  10. 10

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

    PubMed  PubMed Central  Google Scholar 

  11. 11

    Pleasance, E.D. et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2010).

    CAS  PubMed  Google Scholar 

  12. 12

    Polak, P. et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518, 360–364 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Prendergast, J.G. & Semple, C.A. Widespread signatures of recent selection linked to nucleosome positioning in the human lineage. Genome Res. 21, 1777–1787 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Cotney, J. et al. The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment. Nat. Commun. 6, 6404 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Koren, A. et al. Differential relationship of DNA replication timing to different forms of human mutation and variation. Am. J. Hum. Genet. 91, 1033–1040 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Woo, Y.H. & Li, W.H. DNA replication timing and selection shape the landscape of nucleotide variation in cancer genomes. Nat. Commun. 3, 1004 (2012).

    PubMed  Google Scholar 

  17. 17

    Adzhubei, I., Jordan, D.M. & Sunyaev, S.R. Predicting functional effect of human missense mutations using PolyPhen-2. Current Protocols in Human Genetics (eds.Haines, J.L. et al.,) Chapter 7, Unit 7.20 (Wiley, 2013).

  18. 18

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

    CAS  PubMed  Google Scholar 

  19. 19

    Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Uddin, M. et al. Brain-expressed exons under purifying selection are enriched for de novo mutations in autism spectrum disorder. Nat. Genet. 46, 742–747 (2014).

    CAS  PubMed  Google Scholar 

  21. 21

    Carvill, G.L. et al. Targeted resequencing in epileptic encephalopathies identifies de novo mutations in CHD2 and SYNGAP1. Nat. Genet. 45, 825–830 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Petrovski, S., Wang, Q., Heinzen, E.L., Allen, A.S. & Goldstein, D.B. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 9, e1003709 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Jelinic, P. et al. Recurrent SMARCA4 mutations in small cell carcinoma of the ovary. Nat. Genet. 46, 424–426 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Kosho, T., Okamoto, N. & Coffin-Siris Syndrome International Collaborators Genotype-phenotype correlation of Coffin-Siris syndrome caused by mutations in SMARCB1, SMARCA4, SMARCE1, and ARID1A. Am. J. Med. Genet. C. Semin. Med. Genet. 166C, 262–275 (2014).

    PubMed  Google Scholar 

  25. 25

    Forbes, S.A. et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–D811 (2015).

    CAS  PubMed  Google Scholar 

  26. 26

    Pugh, T.J. et al. Medulloblastoma exome sequencing uncovers subtype-specific somatic mutations. Nature 488, 106–110 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Qiu, Z. & Ghosh, A. A calcium-dependent switch in a CREST-BRG1 complex regulates activity-dependent gene expression. Neuron 60, 775–787 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Acuna-Hidalgo, R. et al. Post-zygotic point mutations are an underrecognized source of de novo genomic variation. Am. J. Hum. Genet. 97, 67–74 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Freed, D. & Pevsner, J. The contribution of mosaic variants to autism spectrum disorder. PLoS Genet. 12, e1006245 (2016).

    PubMed  PubMed Central  Google Scholar 

  30. 30

    Morris, J.S., Ohman, A. & Dolan, R.J. Conscious and unconscious emotional learning in the human amygdala. Nature 393, 467–470 (1998).

    CAS  PubMed  Google Scholar 

  31. 31

    Adolphs, R., Tranel, D. & Damasio, A.R. The human amygdala in social judgment. Nature 393, 470–474 (1998).

    CAS  PubMed  Google Scholar 

  32. 32

    Baron-Cohen, S. et al. The amygdala theory of autism. Neurosci. Biobehav. Rev. 24, 355–364 (2000).

    CAS  PubMed  Google Scholar 

  33. 33

    Rutishauser, U. et al. Single-neuron correlates of atypical face processing in autism. Neuron 80, 887–899 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Xu, X. et al. Modular genetic control of sexually dimorphic behaviors. Cell 148, 596–607 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Gkogkas, C.G. et al. Autism-related deficits via dysregulated eIF4E-dependent translational control. Nature 493, 371–377 (2013).

    CAS  PubMed  Google Scholar 

  36. 36

    Campbell, I.M. et al. Parental somatic mosaicism is underrecognized and influences recurrence risk of genomic disorders. Am. J. Hum. Genet. 95, 173–182 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    O'Roak, B.J. et al. Recurrent de novo mutations implicate novel genes underlying simplex autism risk. Nat. Commun. 5, 5595 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Lindhurst, M.J. et al. A mosaic activating mutation in AKT1 associated with the Proteus syndrome. N. Engl. J. Med. 365, 611–619 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Weiss, L.A. et al. Association between microdeletion and microduplication at 16p11.2 and autism. N. Engl. J. Med. 358, 667–675 (2008).

    CAS  PubMed  Google Scholar 

  40. 40

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

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

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

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Cingolani, P. 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).

    CAS  Google Scholar 

  43. 43

    Abecasis, G.R. et al. Genomes Project. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

    PubMed  Google Scholar 

  44. 44

    Kang, H.J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    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 

  46. 46

    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 

  47. 47

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

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Ramos, P. et al. Small cell carcinoma of the ovary, hypercalcemic type, displays frequent inactivating germline and somatic mutations in SMARCA4. Nat. Genet. 46, 427–429 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Le Loarer, F. et al. SMARCA4 inactivation defines a group of undifferentiated thoracic malignancies transcriptionally related to BAF-deficient sarcomas. Nat. Genet. 47, 1200–1205 (2015).

    CAS  PubMed  Google Scholar 

  50. 50

    Tsurusaki, Y. et al. Mutations affecting components of the SWI/SNF complex cause Coffin-Siris syndrome. Nat. Genet. 44, 376–378 (2012).

    CAS  PubMed  Google Scholar 

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We are grateful to all the families who participated in the research, including the Simons Foundation Autism Research Initiative (SFARI) Simplex Collection (SSC), the Autism Sequencing Consortium (ASC) and Autism Speaks. We acknowledge the clinicians and organizations that contributed to samples used in this study, including the ASC and SSC principal investigators; the coordinators and staff at the ASC and SSC sites for the recruitment and comprehensive assessment of simplex families; and the ASC, SFARI and NDAR staff for facilitating access to the data sets. This work was supported by a grant from the Simons Foundation (178093, C.A.W.); the National Institutes of Health (NIH) grants R01MH083565, RC2MH089952 and U01MH106883 to C.A.W.; grants R01MH097849, U01MH100233, U01MH100209, U01MH100229, U01MH100239, U01MH111661, U01MH111660, U01MH111658, U01MH111662 and R01MH097849 to the Autism Sequencing Consortium; grants from the Centre for Applied Genomics, the University of Toronto McLaughlin Centre, Genome Canada and Autism Speaks (S.W.S.); Simons Foundation grant (368485, G.M.C.); SRPBS and Brain/MINDS grants from AMED (I.K., B.A., N.O.); grants from the Spanish Ministry of Economy and Competitiveness (M.P.), Instituto de Salud Carlos III (M.P.), PI10/02989 (M.P.), CIBERSAM (M.P.) and ERA-NET NEURON (M.P., C.M.F.), Network of European Funding for Neuroscience Research (M.P.), and Fundación María José Jove and The Institute of Health Carlos III-Fondo de Investigaciones Sanitarias grant project PI13/01136 (A.C.) and the Seaver Foundation. We thank A. Hossain and N. Hatem for their help with sample preparation; F. Zhao and C. Stevens for their help with reprocessing the BAM files; and M. Daly, S. McCarroll, G. Genovese and J. Hirschhorn for comments and suggestions. Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD018522. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work was supported in part through the computational resources and staff expertise provided by the Department of Scientific Computing at the Icahn School of Medicine at Mount Sinai. Additional computing support was provided by the Harvard Medical School's Orchestra High-Performance Computing Group, which is partially supported by NIH grant NCRR 1S10RR028832-01. The NHLBI GO Exome Sequencing Project and its ongoing studies produced and provided exome variant calls for comparison: the Lung GO Sequencing Project (HL-102923), the WHI Sequencing Project (HL-102924), the Broad GO Sequencing Project (HL-102925), the Seattle GO Sequencing Project (HL-102926) and the Heart GO Sequencing Project (HL-103010). C.A.W. is an Investigator of the Howard Hughes Medical Institute; S.W.S. is funded by the GlaxoSmithKline-Canadian Institutes of Heath Research Chair in Genome Sciences at the Hospital for Sick Children and University of Toronto; A.M.D. is supported by the NIGMS (T32GM007753) and NRSA (5T32 GM007226-39); S.D.R. is supported by the Seaver Foundation.

Author information





E.T.L. and C.A.W. conceived the project and wrote the manuscript. E.T.L. and M.U. performed the spatiotemporal analyses. E.T.L., S.D.R., Y.C., A.S.K., A.M.D. and S.N.K. performed the resequencing and Sanger sequencing experiments. E.T.L., S.D.R. and X.Z. performed the mutagenesis, overexpression and qPCR experiments. E.T.L. and Y.C. performed the permutations and modeling of background rates. R.S.H., A.P.G. and C.P. performed the data processing and annotation of the variant call files. N.J.M., I.K., B.A., N.O., M.P., C.A., M.J.P., A.C., A.K., C.M.H., L.A.W., A.G.C. and C.M.F. provided additional trio exome sequence data and blood samples for resequencing experiments. E.T.L. and M.F. performed the phasing of mutations. G.M.C., S.W.S., J.D.B. and C.A.W. supervised the project, provided critical comments and edited the manuscript. All authors critically reviewed the manuscript for content.

Corresponding authors

Correspondence to Elaine T Lim or Christopher A Walsh.

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Competing interests

S.W.S., M.U. and the Hospital for Sick Children hold intellectual property used in this analysis, which is also licensed by Lineagen, Inc. M.P. has received educational honoraria from Otsuka, research grants from Fundación Alicia Koplowitz and Mutua Madrileña and travel grants from Otsuka and Janssen. C.A. has been a consultant to or has received honoraria or grants from Abbot, Amgen, AstraZeneca, Bristol-Myers-Squibb, Caja Navarra, CIBERSAM, Fundación Alicia Koplowitz, Instituto de Salud Carlos III, Janssen Cilag, Lundbeck, Merck, Ministerio de Ciencia e Innovación, Ministerio de Sanidad, Ministerio de Economía y Competitividad, Mutua Madrileña, Otsuka, Pfizer, Roche, Servier, Shire, Takeda and Schering-Plough.

Additional information

A list of members and affiliations appears in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 RVIS scores for genes with recurrent nonsynonymous PZMs in probands

(a) Graph showing the –log10(P-value) calculated from the genes with recurrent non-synonymous PZMs in probands versus RVIS scores of the genes, and (b) genes sorted by increasing order of p-values based on the recurrent non-synonymous PZMs in probands, with the RVIS score for each sorted gene shown on the y-axis.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1, Supplementary Tables 1, 6–12, 14, 15, and Supplementary Note (PDF 1026 kb)

Supplementary Methods Checklist (PDF 399 kb)

Supplementary Table 2

Rates of PZMs across ASC datasets, with the median depth and mode allele fraction of the de novos (Group A) in each dataset (XLSX 147 kb)

Supplementary Table 3

List of all de novo mutations found in the probands and unaffected siblings (XLSX 601 kb)

Supplementary Table 4

List of all de novo mutations that were validated using the different resequencing approaches (XLSX 145 kb)

Supplementary Table 5

Quantitative RT-PCR results for assaying copy number variants (XLSX 71 kb)

Supplementary Table 13

Gene-specific mutation rates for post-zygotic mutations estimated from the rare inherited variants (XLSX 660 kb)

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Lim, E., Uddin, M., De Rubeis, S. et al. Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder. Nat Neurosci 20, 1217–1224 (2017).

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