To assess the relative impact of inherited and de novo variants on autism risk, we generated a comprehensive set of exonic single-nucleotide variants (SNVs) and copy number variants (CNVs) from 2,377 families with autism. We find that private, inherited truncating SNVs in conserved genes are enriched in probands (odds ratio = 1.14, P = 0.0002) in comparison to unaffected siblings, an effect involving significant maternal transmission bias to sons. We also observe a bias for inherited CNVs, specifically for small (<100 kb), maternally inherited events (P = 0.01) that are enriched in CHD8 target genes (P = 7.4 × 10−3). Using a logistic regression model, we show that private truncating SNVs and rare, inherited CNVs are statistically independent risk factors for autism, with odds ratios of 1.11 (P = 0.0002) and 1.23 (P = 0.01), respectively. This analysis identifies a second class of candidate genes (for example, RIMS1, CUL7 and LZTR1) where transmitted mutations may create a sensitized background but are unlikely to be completely penetrant.

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  1. 1.

    Prevalence of autism spectrum disorders—Autism and Developmental Disabilities Monitoring Network, 14 sites, United States, 2008. MMWR Surveill. Summ. 61, 1–19 (2012).

  2. 2.

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

  3. 3.

    et al. Genetic heritability and shared environmental factors among twin pairs with autism. Arch. Gen. Psychiatry 68, 1095–1102 (2011).

  4. 4.

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

  5. 5.

    et al. Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nat. Genet. 43, 585–589 (2011).

  6. 6.

    et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338, 1619–1622 (2012).

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 10.

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

  11. 11.

    et al. A unified genetic theory for sporadic and inherited autism. Proc. Natl. Acad. Sci. USA 104, 12831–12836 (2007).

  12. 12.

    et al. Transmission disequilibrium of small CNVs in simplex autism. Am. J. Hum. Genet. 93, 595–606 (2013).

  13. 13.

    et al. Identification of small exonic CNV from whole-exome sequence data and application to autism spectrum disorder. Am. J. Hum. Genet. 93, 607–619 (2013).

  14. 14.

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

  15. 15.

    et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010).

  16. 16.

    et al. A higher mutational burden in females supports a “female protective model” in neurodevelopmental disorders. Am. J. Hum. Genet. 94, 415–425 (2014).

  17. 17.

    , , & The role of de novo mutations in the genetics of autism spectrum disorders. Nat. Rev. Genet. 15, 133–141 (2014).

  18. 18.

    et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70, 863–885 (2011).

  19. 19.

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

  20. 20.

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

  21. 21.

    et al. Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data. Am. J. Hum. Genet. 94, 33–46 (2014).

  22. 22.

    et al. Validation of a brief quantitative measure of autistic traits: comparison of the social responsiveness scale with the autism diagnostic interview-revised. J. Autism Dev. Disord. 33, 427–433 (2003).

  23. 23.

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

  24. 24.

    et al. The chromatin remodeller CHD8 is required for E2F-dependent transcription activation of S-phase genes. Nucleic Acids Res. 42, 2185–2196 (2014).

  25. 25.

    et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell 158, 263–276 (2014).

  26. 26.

    Simple estimation of population attributable risk from case-control studies. Am. J. Epidemiol. 106, 260 (1977).

  27. 27.

    & Insulin-like growth factor and the etiology of autism. Med. Hypotheses 80, 475–480 (2013).

  28. 28.

    , & Insulin-like growth factor-1 rescues synaptic and motor deficits in a mouse model of autism and developmental delay. Mol. Autism 4, 9 (2013).

  29. 29.

    et al. De novo insertions and deletions of predominantly paternal origin are associated with autism spectrum disorder. Cell Rep. 9, 16–23 (2014).

  30. 30.

    et al. An OBSL1-Cul7Fbxw8 ubiquitin ligase signaling mechanism regulates Golgi morphology and dendrite patterning. PLoS Biol. 9, e1001060 (2011).

  31. 31.

    et al. The complement control–related genes CSMD1 and CSMD2 associate to schizophrenia. Biol. Psychiatry 70, 35–42 (2011).

  32. 32.

    et al. Exome sequencing of extended families with autism reveals genes shared across neurodevelopmental and neuropsychiatric disorders. Mol. Autism 5, 1 (2014).

  33. 33.

    et al. Isolation and characterization of a novel gene deleted in DiGeorge syndrome. Hum. Mol. Genet. 4, 541–549 (1995).

  34. 34.

    et al. Many roads lead to primary autosomal recessive microcephaly. Prog. Neurobiol. 90, 363–383 (2010).

  35. 35.

    et al. A recurrent 16p12.1 microdeletion supports a two-hit model for severe developmental delay. Nat. Genet. 42, 203–209 (2010).

  36. 36.

    et al. Molecular and clinical characterization of 25 individuals with exonic deletions of NRXN1 and comprehensive review of the literature. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 162B, 388–403 (2013).

  37. 37.

    Digenic inheritance and Mendelian disease. Nat. Genet. 44, 1291–1292 (2012).

  38. 38.

    et al. A novel 4EHP-GIGYF2 translational repressor complex is essential for mammalian development. Mol. Cell. Biol. 32, 3585–3593 (2012).

  39. 39.

    & The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron 68, 192–195 (2010).

  40. 40.

    , & A genotype-first approach to defining the subtypes of a complex disease. Cell 156, 872–877 (2014).

  41. 41.

    & Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

  42. 42.

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

  43. 43.

    et al. QPLOT: a quality assessment tool for next generation sequencing data. Biomed. Res. Int. 2013, 865181 (2013).

  44. 44.

    & Haplotype-based variant detection from short-read sequencing. arXiv (2012).

  45. 45.

    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).

  46. 46.

    , & dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum. Mutat. 34, E2393–E2402 (2013).

  47. 47.

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

  48. 48.

    et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

  49. 49.

    et al. Copy number variation detection and genotyping from exome sequence data. Genome Res. 22, 1525–1532 (2012).

  50. 50.

    et al. Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am. J. Hum. Genet. 91, 597–607 (2012).

  51. 51.

    et al. mrsFAST: a cache-oblivious algorithm for short-read mapping. Nat. Methods 7, 576–577 (2010).

  52. 52.

    & A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23, 657–663 (2007).

  53. 53.

    , , & A de novo convergence of autism genetics and molecular neuroscience. Trends Neurosci. 37, 95–105 (2014).

  54. 54.

    , , , & R/Bioconductor software for Illumina's Infinium whole-genome genotyping BeadChips. Bioinformatics 25, 2621–2623 (2009).

  55. 55.

    , , , & Using the R package crlmm for genotyping and copy number estimation. J. Stat. Softw. 40, 1–32 (2011).

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We thank D. Obenshain, D. Hall, B. Koser and S. Novikova for providing support for usage of the Amazon Cloud and for assistance in the deposition of SNV and CNV call sets into the National Database for Autism Research (NDAR). We are grateful to the laboratories of M. Wigler and M. State for providing early access to exome sequencing data as well as access to SNP microarray data. We also thank T. Brown for assistance in editing this manuscript. Funding for this study was provided, in part, by the US National Institutes of Health (1U01MH100233 to E.E.E.), by the National Institute for Mental Health (R01MH101221 to E.E.E. and R01MH100047 to R.B.) and by the Simons Foundation (SFARI 89368 to R.B. and SFARI 137578 to E.E.E.). E.E.E. is an investigator of the Howard Hughes Medical Institute. We are grateful to all of the families at the participating Simons Simplex Collection (SSC) sites, as well as the principal investigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, R. Goin-Kochel, E. Hanson, D. Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, K. Pelphrey, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, Z. Warren and E. Wijsman). We appreciate obtaining access to phenotypic data on Simons Foundation Autism Research Initiative (SFARI) Base. Approved researchers can obtain the SSC population data set described in this study by applying at https://base.sfari.org/.

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Author notes

    • Niklas Krumm
    •  & Tychele N Turner

    These authors contributed equally to this work.


  1. Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA.

    • Niklas Krumm
    • , Tychele N Turner
    • , Carl Baker
    • , Laura Vives
    • , Kiana Mohajeri
    • , Kali Witherspoon
    • , Archana Raja
    • , Bradley P Coe
    • , Holly A Stessman
    •  & Evan E Eichler
  2. Howard Hughes Medical Institute, University of Washington, Seattle, Washington, USA.

    • Archana Raja
    •  & Evan E Eichler
  3. Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.

    • Zong-Xiao He
    •  & Suzanne M Leal
  4. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA.

    • Raphael Bernier


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N.K., T.N.T. and E.E.E. designed experiments and wrote and edited the manuscript. N.K. performed sequence data reanalysis and created and analyzed the SNV call set. T.N.T. created and analyzed the CNV call set, analyzed SNP microarray data, performed statistical analyses for SNV and CNV quality control, and examined epidemiological features for the full data set. C.B., L.V., K.M., K.W. and H.A.S. performed validation experiments and sample handling. A.R. and B.P.C. provided additional computational support. Z.-X.H. and S.M.L. performed the TDT tests and statistical analyses. R.B. provided phenotype data and additional SSC variables where needed.

Competing interests

E.E.E. is on the scientific advisory board (SAB) of DNAnexus, Inc., and is a consultant for the Kunming University of Science and Technology (KUST) as part of the 1000 China Talent Program.

Corresponding author

Correspondence to Evan E Eichler.

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