Patterns and rates of exonic de novo mutations in autism spectrum disorders

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Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified1, 2. To identify further genetic risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes3 as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case–control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.

At a glance


  1. Protein-protein interaction for genes with an observed functional de novo event.
    Figure 1: Protein–protein interaction for genes with an observed functional de novo event.

    Direct protein connections from InWeb, restricting to genes harbouring de novo mutations for DAPPLE analysis. Two extensive networks are identified: the first is centred on SMARCC2 with 12 connections across 11 genes; the second is centred on FN1 with 7 connections across 6 genes. The P value for each gene having as many connections as those observed is indicated by node colour.

  2. Direct and indirect protein-protein interaction for genes with a functional de novo event and previous ASD genes.
    Figure 2: Direct and indirect protein–protein interaction for genes with a functional de novo event and previous ASD genes.

    PPI network analysis for de novo variants and 31 previous synaptic ASD genes (see Supplementary Information). Nodes are sized based on connectivity. Genes harbouring de novo variants (left) and previous ASD genes (right) are coloured blue, with dark blue nodes representing genes that belong to one of these lists and are also intermediate proteins. Intermediate proteins (centre) are coloured in shades of orange based on a P value computed using a proportion test, where a darker colour represents a lower P value. Green edges represent direct connections between genes harbouring de novo variants (left) and previous ASD genes. All other edges, connecting to intermediate proteins, are shown in grey.


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Author information


  1. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA

    • Benjamin M. Neale,
    • Kaitlin E. Samocha,
    • Elaine Lim,
    • Elizabeth Rossin,
    • Andrew Kirby,
    • Menachem Fromer &
    • Mark J. Daly
  2. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA

    • Benjamin M. Neale,
    • Kaitlin E. Samocha,
    • Christine Stevens,
    • Paz Polak,
    • Jared Maguire,
    • Benjamin F. Voight,
    • Elaine Lim,
    • Elizabeth Rossin,
    • Andrew Kirby,
    • Jason Flannick,
    • Menachem Fromer,
    • Khalid Shakir,
    • Tim Fennell,
    • Kiran Garimella,
    • Eric Banks,
    • Ryan Poplin,
    • Stacey Gabriel,
    • Mark DePristo,
    • Shamil Sunyaev &
    • Mark J. Daly
  3. Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, New York 10029, USA

    • Yan Kou,
    • Avi Ma’ayan &
    • Ruth Dannenfelser
  4. Seaver Autism Center for Research and Treatment, Mount Sinai School of Medicine, New York, New York 10029, USA

    • Yan Kou,
    • Vladimir Makarov,
    • Seungtai Yoon,
    • Guiqing Cai,
    • Jayon Lihm &
    • Joseph D. Buxbaum
  5. Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15232, USA

    • Li Liu,
    • Chad Schafer &
    • Kathryn Roeder
  6. Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA

    • Aniko Sabo,
    • Uma Nagaswamy,
    • Donna Muzny,
    • Jeffrey G. Reid,
    • Irene Newsham,
    • Yuanqing Wu,
    • Lora Lewis,
    • Yi Han,
    • Eric Boerwinkle &
    • Richard A. Gibbs
  7. Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Chiao-Feng Lin,
    • Li-San Wang,
    • Evan T. Geller,
    • Otto Valladares &
    • Gerard D. Schellenberg
  8. Department of Psychiatry, Mount Sinai School of Medicine, New York, New York 10029, USA

    • Vladimir Makarov,
    • Seungtai Yoon,
    • Guiqing Cai,
    • Jayon Lihm &
    • Joseph D. Buxbaum
  9. Division of Genetics, Department of Medicine Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA

    • Paz Polak &
    • Shamil Sunyaev
  10. Vanderbilt Brain Institute, Departments of Molecular Physiology & Biophysics and Psychiatry, Vanderbilt University, Nashville, Tennessee 37232, USA

    • Emily L. Crawford,
    • Nicholas G. Campbell &
    • James S. Sutcliffe
  11. Biostatistics Department and Computer Science Department, Johns Hopkins University, Baltimore, Maryland 21205, USA

    • Han Liu &
    • Tuo Zhao
  12. Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, New York 10029, USA

    • Omar Jabado,
    • Zuleyma Peralta &
    • Joseph D. Buxbaum
  13. Department of Pharmacology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA

    • Benjamin F. Voight
  14. HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA

    • Jack R. Wimbish,
    • Braden E. Boone &
    • Shawn E. Levy
  15. INSERM U952 and CNRS UMR 7224 and UPMC Univ Paris 06, 75005 Paris, France

    • Catalina Betancur
  16. Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas 77030, USA

    • Eric Boerwinkle
  17. Friedman Brain Institute, Mount Sinai School of Medicine, New York, New York 10029, USA

    • Joseph D. Buxbaum
  18. Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois 60608, USA

    • Edwin H. Cook Jr
  19. Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213, USA

    • Bernie Devlin


Laboratory work: A.S., C.St., G.C., O.J., Z.P., J.D.B., D.M., I.N., Y.W., L.L., Y.H., S.G., E.L.C., N.G.C. and E.T.G. Data processing: B.M.N., K.E.S., E.L., A.K., J.F., M.F., K.S., T.F., K.G., E.Ba., R.P., M.DeP., S.G., S.Y., V.M., J.L., J.D.B., A.S., C.St., U.N., J.G.R., J.R.W., B.E.B., S.E.L., C.F.L., L.S.W. and O.V. Statistical analysis: B.M.N., L.L., K.E.S., C.Sh., B.F.V., J.M., E.R., S.S., P.P., Y.K., A.M., R.D., C.-F.L., L.-S.W., H.L., T.Z., E.Bo., R.A.G., J.D.B., C.B., E.H.C., J.S.S., G.D.S., B.D., K.R. and M.J.D. Principal Investigators/study design: E.Bo., R.A.G., E.H.C., J.D.B., K.R., B.D., G.D.S., J.S.S. and M.J.D. Y.K., L.L., A.M., K.E.S., A.S. and C.-F.L. contributed equally to this work. E.Bo., J.D.B., E.H.C., B.D., R.A.G., K.R., G.D.S., J.S.S. and M.J.D. are lead investigators of the ARRA Autism Sequencing Collaboration.

Competing financial interests

The authors declare no competing financial interests.

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Data included in this manuscript have been deposited at dbGaP under accession number phs000298.v1.p1 and is available for download at

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  1. Supplementary Information (1.1M)

    This file contains Supplementary Text and Data, Supplementary Figures 1-4, Supplementary Tables 1-10 and additional references.

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