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The contribution of de novo coding mutations to autism spectrum disorder


Whole exome sequencing has proven to be a powerful tool for understanding the genetic architecture of human disease. Here we apply it to more than 2,500 simplex families, each having a child with an autistic spectrum disorder. By comparing affected to unaffected siblings, we show that 13% of de novo missense mutations and 43% of de novo likely gene-disrupting (LGD) mutations contribute to 12% and 9% of diagnoses, respectively. Including copy number variants, coding de novo mutations contribute to about 30% of all simplex and 45% of female diagnoses. Almost all LGD mutations occur opposite wild-type alleles. LGD targets in affected females significantly overlap the targets in males of lower intelligence quotient (IQ), but neither overlaps significantly with targets in males of higher IQ. We estimate that LGD mutation in about 400 genes can contribute to the joint class of affected females and males of lower IQ, with an overlapping and similar number of genes vulnerable to contributory missense mutation. LGD targets in the joint class overlap with published targets for intellectual disability and schizophrenia, and are enriched for chromatin modifiers, FMRP-associated genes and embryonically expressed genes. Most of the significance for the latter comes from affected females.

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Figure 1: Rates of de novo events by mutational type in the SSC.
Figure 2: Recurrently hit genes and non-verbal IQ.
Figure 3: Number of vulnerable genes and class vulnerability.
Figure 4: Estimated contributions of CNVs, LGDs and missense DN mutations to simplex ASD.

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Simons Foundation Autism Research Initiative grants to E.E.E. (SF191889), M.W.S. (M144095 R11154) and M.W. (SF235988) supported this work. Additional support was provided by the Howard Hughes Medical Institute (International Student Research Fellowship to S.J.S.) and the Canadian Institutes of Health Research (Doctoral Foreign Study Award to A.J.W.). E.E.E. is an Investigator of the Howard Hughes Medical Institute. We thank all the families at the participating SSC sites, as well as the principal investigators (A. L. Beaudet, R. Bernier, J. Constantino, E. H. Cook Jr, E. Fombonne, D. Geschwind, D. E. Grice, A. Klin, D. H. Ledbetter, C. Lord, C. L. Martin, D. M. Martin, R. Maxim, J. Miles, O. Ousley, B. Peterson, J. Piggot, C. Saulnier, M. W. State, W. Stone, J. S. Sutcliffe, C. A. Walsh and E. Wijsman) and the coordinators and staff at the SSC sites for the recruitment and comprehensive assessment of simplex families; the SFARI staff for facilitating access to the SSC; and the Rutgers University Cell and DNA Repository (RUCDR) for accessing biomaterials. We would also like to thank the CSHL Woodbury Sequencing Center, the Genome Institute at the Washington University School of Medicine, and Yale Center for Genomic Analysis (in particular J. Overton) for generating sequencing data; E. Antoniou and E. Ghiban for their assistance in data production at CSHL; and T. Brooks-Boone, N. Wright-Davis and M. Wojciechowski for their help in administering the project at Yale. 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).

Author information

Authors and Affiliations



CSHL: I.I., M.R. and M.W. designed the study; I.I., D.L., B.Y., Y.L., E.G., E.D., P.A., A.L., J.K., G.N., S.Y., M.C.S., K.Y. and M.W. analysed the data; M.R., I.H., J.R., B.M., L.R., J.T. and W.R.M. generated the exome data at Cold Spring Harbor Laboratory; I.I., Z.W., S.M. and J.T. confirmed the variants; I.I., M.R. and M.W. wrote the paper. UCSF/Yale: S.J.S. and M.W.S. designed the study; S.J.S., S.D., L.W. and A.J.W. analysed the data; S.J.S., J.D., L.E.G., J.D.M., C.A.S., M.F.W. and Z.W. confirmed the variants; S.M.M. and M.T.M. generated the exome data at Yale Medical Center. UW: B.J.O., J.S. and E.E.E. designed the study; B.J.O. and N.K. analysed the data; B.J.O., H.A.S., K.T.W. and L.V. confirmed the variants; E.E.E. and J.S. revised the manuscript; K.E.P, J.D.S., B.P. and D.A.N. generated the exome data at the University of Washington.

Corresponding authors

Correspondence to Jay Shendure, Evan E. Eichler, Matthew W. State or Michael Wigler.

Ethics declarations

Competing interests

E.E.E. is on the scientific advisory board of DNAnexus, Inc. and was a scientific advisory board member of Pacific Biosciences, Inc. (2009–2013) and SynapDx Corp. (2011–2013). J.S. is a member of the scientific advisory board or serves as a consultant for Adaptive Biotechnologies, Ariosa Diagnostics, Stratos Genomics, GenePeeks, Gen9, Good Start Genetics, Ingenuity Systems and Rubicon Genomics. B.J.O. is an inventor on patent PCT/US2009/30620: Mutations in contactin-associated protein 2 are associated with increased risk for idiopathic autism.

Additional information

Sequence data used in these work are available from the National Database for Autism Research (, under study DOI:10.15154/1149697.

Extended data figures and tables

Extended Data Figure 1 Number of families sequenced by centre.

The numbers of families sequenced at the three centres are plotted as a Venn diagram. Families sequenced at more than one centre are indicated by the overlapping regions between circles. CSHL, Cold Spring Harbor Laboratory; UW, University of Washington, Seattle; YALE, Yale Medical Center.

Extended Data Figure 2 SSC sequencing by pedigree type and non-verbal IQ.

A summary of all SSC families sequenced is indicated across the ‘all’ row. Numbers of SSC families with complete exome sequencing data are displayed by centre in the following rows (see Extended Data Fig. 1 legend for centre designations). The top number in entries under the ‘families’ column indicates the total number of families sequenced, and the number in parentheses below indicates the total number of individuals. Family pedigree structures are shown across the top row with gender indicated by shape (square for male, circle for female) and affected status indicated by colour (white for unaffected, grey for affected). Distributions of non-verbal IQ within each cohort are shown for male probands (blue) and female probands (red).

Extended Data Figure 3 Rates of de novo LGD and missense mutations in the SSC by child status.

On the left we show the LGD rate per child in six types of children, labelled on the x axis, defined by their affected status, gender, and non-verbal IQ. We test for equal rates for every pair of child types and we show the ones with P > 0.05 with thin lines on the top of the figure. Although not significant, the rates in affected females and in affected males of lower non-verbal IQ are larger than the rate in males of higher non-verbal IQ. On the right, we show the missense rates per child for the same six groups of children.

Extended Data Figure 4 Paternal age and DN mutation rate at child birth.

Distribution of paternal age at birth of children (top) and rates of DN mutation in offspring as a function of paternal age are shown (bottom). Children were ordered by paternal age at birth and split into 20 groups of similar size, as shown in the bottom panel. The red curve shows the mean observed rates of de novo exomic substitutions in each of the 20 groups, with the x coordinate equal to the mean each of the fathers’ ages within each group. The blue line shows a linear fit to the observed rates. The dotted green line represents DN mutation rates from whole genome sequencing data15 scaled to rates per exome based on representation in the SeqCap EZ Human Exome Library v2.0 (Roche NimbleGen).

Extended Data Figure 5 Coding region size distribution for query sets of genes.

Probability density function (PDF) and cumulative distribution functions (CDF) (right bottom) of the distributions of the coding region length in base pairs of five sets of genes: a set of 1,200 genes picked uniformly from the set of exome-targeted genes (blue); a separate set of 1,200 genes picked with probabilities proportional to length of the coding region (green); the set of gene targets of neutral mutations, including synonymous mutations in probands and siblings, and missense mutation in siblings (red); genes with de novo missense mutations in probands (cyan); and genes with de novo LGDs in probands (magenta). Black within the histograms shows the distribution of lengths of the recurrently hit genes from each class. Coding region length distribution under a uniform model does not fit the lengths of the genes with observed mutations, and genes with LGD mutations are longer than predicted by a simple length-based model (bottom right).

Extended Data Figure 6 Distributions of sequencing depth.

Distributions of sequencing depth (number of sequence reads covering a given genomic position) per person per position for the three sequencing centres are plotted. Centre designations are as in Extended Data Fig. 1.

Extended Data Figure 7 Yield of DN LGD and missense mutations.

We plot the yield of DN LGD and missense mutations per sequencing centre (designations as in Extended Data Fig. 1). In each case we show the number of mutations we expect to see based on the estimated rates per child, indicated by the numbers above the bars. We also show what percentage of the expected number we have observed. Black refers to strong calls in the 40× target, grey refers to strong calls outside of 40× target, and magenta refers to weak (but valid) calls. The white region represents the difference between the expected and observed numbers of variants.

Extended Data Figure 8 Categorization of embryonically expressed genes.

We downloaded expression data18 from The data set provides normalized expression levels for 17,000 genes across brain regions from 36 individuals, 18 of which were from embryos. Each brain was further subdivided into 14 anatomical regions for a total of 508 regions. We computed correlation values for the 17,000 genes, and generated a graph by connecting genes that had correlations >0.85. We then identified connected components and averaged the expression of genes within these components as a function of the annotated age of the brain and by region. Each region is sorted first by age, then by type. The averaged normalized expression of the 1,912 genes in the first component decreases after birth, and hence we call this set embryonic. See Supplementary Table 7 for the list of embryonic genes.

Supplementary information

Supplementary Information

This file contains Supplementary Table 3 (Experimental validation in the 40X target), Supplementary Table 4 (Multiple de novo events), Supplementary Table 8 (Compound non-synonymous hits in targets), Supplementary Table 11 (Validation summary by centre) and Supplementary Table 13 (Median gene lengths) as well as legends for Supplementary Tables 1, 2, 5–7, 9, 10 and 12. (PDF 191 kb)

Supplementary Data

This zipped file contains Supplementary Tables 1-2, 5-7, 9, 10 and 12. (ZIP 1639 kb)

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Iossifov, I., O’Roak, B., Sanders, S. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

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