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Synaptic, transcriptional and chromatin genes disrupted in autism

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The genetic architecture of autism spectrum disorder involves the interplay of common and rare variants and their impact on hundreds of genes. Using exome sequencing, here we show that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, plus a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic formation, transcriptional regulation and chromatin-remodelling pathways. These include voltage-gated ion channels regulating the propagation of action potentials, pacemaking and excitability–transcription coupling, as well as histone-modifying enzymes and chromatin remodellers—most prominently those that mediate post-translational lysine methylation/demethylation modifications of histones.

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Figure 1: ASD genes in synaptic networks.
Figure 2: ASD genes in neuronal networks.
Figure 3: ASD genes in chromatin remodelling.
Figure 4: Transcription regulation network of TADA genes.
Figure 5: Involvement in disease of ASD genes.

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Data deposits

New data included in this manuscript have been deposited at dbGAP merged with our published data under accession number phs000298.v1.p1 and is available for download at (

Change history

  • 12 November 2014

    A minor change was made to the author affiliations.


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This work was supported by National Institutes of Health (NIH) grants U01MH100233, U01MH100209, U01MH100229 and U01MH100239 to the Autism Sequencing Consortium. Sequencing at Broad Institute was supported by NIH grants R01MH089208 (M.J.D.) and new sequencing by U54 HG003067 (S. Gabriel, E. Lander). Other funding includes NIH R01 MH089482, R37 MH057881 (B.D. and K.R.), R01 MH061009 (J.S.S.), UL1TR000445 (NCAT to VUMC); P50 HD055751 (E.H.C.); MH089482 (J.S.S.), NIH RO1 MH083565 and RC2MH089952 (C.A.W.), NIMH MH095034 (P.S), MH077139 (P.F. Sullivan); 5UL1 RR024975 and P30 HD15052. The DDD Study is funded by HICF-1009-003 and WT098051. UK10K is funded by WT091310. We also acknowledge The National Children’s Research Foundation, Our Lady’s Children’s Hospital, Crumlin; The Meath Foundation; AMNCH, Tallaght; The Health Research Board, Ireland and Autism Speaks, U.S.A. C.A.W. is an Investigator of the Howard Hughes Medical Institute. S.D.R., A.P.G., C.S.P., Y.K. and S.-C.F. are Seaver fellows, supported by the Seaver foundation. A.P.G. is also supported by the Charles and Ann Schlaifer Memorial Fund. P.F.B. is supported by a UK National Institute for Health Research (NIHR) Senior Investigator award and the NIHR Biomedical Research Centre in Mental Health at the South London & Maudsley Hospital. A.C. is supported by María José Jove Foundation and the grant FIS PI13/01136 of the Strategic Action from Health Carlos III Institute (FEDER). 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. We acknowledge the assistance of D. Hall and his team at National Database for Autism Research. We thank Jian Feng for providing a list of targets of both RBFOX1 and H3K4me3. We thank M. Potter for data coordination; K. Moore and J. Reichert for technical assistance; and, S. Lindsay for helping with molecular validation. We acknowledge the clinicians and organizations that contributed to samples used in this study. Finally, we are grateful to the many families whose participation made this study possible.

Author information

Authors and Affiliations




Lists of participants appear in the Supplementary Information.

Study conception and design: J.D.B., D.J.C., M.J.D., S.D.R., B.D., M.F., A.P.G., X.H., T.L., C.S.P., K.Ro., M.W.S. and M.E.Z. Data analysis: J.C.B., P.F.B., J.D.B., J.C., A.E.C, D.J.C., M.J.D., S.D.R., B.D., M.F., S.-C.F., A.P.G., X.H., L.K., J.K., Y.K., L.L., A.M., C.S.P., S.P., K.Ro., K.S., C.S., T.S., C.St., S.W., L.W. and M.E.Z. Contribution of samples, WES data or analytical tools: B.A., J.C.B., M.B., P.F.B., J.D.B., J.C., N.G.C., A.C., M.H.C., A.G.C., A.E.C, H.C., E.L.C., L.C., S.R.C., D.J.C., M.J.D., G.D., S.D.R., B.D., E.D., B.A.F., C.M.F., M.F., L.G., E.G., M.G., A.P.G., S.J.G., X.H., R.H., C.M.H., I.I.-L., P.J.G., H.K., S.M.K., L.K., A.K., J.K., Y.K., I.L., J.L., T.Le., C.L., L.L., A.M., C.R.M., A.L.M., B.N., M.J.O., N.O., A.P., M.P., J.R.P., C.S.P., S.P., K.P., D.R., K.R., A.R., K.Ro., A.S., M.S., K.S., S.J.S., C.S., G.D.S., S.W.S., M.S.-R., T.S., P.S., D.S., M.W.S., C.St., J.S.S., P.Sz., K.T., O.V., A.V., S.W., C.A.W., L.W., L.A.W., J.A.W., T.W.Y., R.K.C.Y., M.E.Z. Writing of the paper: J.C.B., J.D.B., E.H.C., D.J.C., M.J.D., S.D.R., B.D., M.G., A.P.G., X.H., C.S.P., K.Ro., S.W.S., M.E.Z. Leads of ASC committees: J.D.B., E.H.C., M.J.D., B.D., M.G., K.Ro., M.W.S., J.S.S., M.E.Z. Administration of ASC: J.M.B.

Corresponding authors

Correspondence to Mark J. Daly or Joseph D. Buxbaum.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Workflow of the study.

The workflow began with 16 sample sets, as listed in Supplementary Table 1. DNA was obtained, and exomes were captured and sequenced. After variant calling, quality control was performed: duplicate subjects and incomplete families were removed and subjects with extreme genotyping, de novo, or variant rates were removed. Following cleaning, 3,871 subjects with ASD remained. Analysis proceeded separately for SNVs and indels, and CNVs. De novo and transmission/non-transmission variants were obtained for trio data (published de novo variants from 825 trios11,13,14,15 were incorporated). This led to the TADA analysis, which found 33 ASD risk genes with an FDR < 0.1; and 107 with an FDR < 0.3. CNVs were called in 2,305 ASD subjects. BAM, binary alignment/map; MAF, minor allele frequency.

Extended Data Figure 2 Expected number of ASD genes discovered as a function of sample size.

The multiple LoF test (red) is a restricted version of TADA that uses only the de novo LoF data. TADA (blue) models de novo LoF, de novo Mis3, LoF variants transmitted/not transmitted and LoF variants observed in case-control samples. The sample size (n) indicates either n trios for which we record de novo and transmitted variation (TADA), or n trios for which we record only de novo events (multiple LoF), plus n cases and n controls.

Extended Data Figure 3 Heat map of the numbers of variants used in TADA analysis from each data set in genes with an FDR < 0.3.

Left, variants in affected subjects; right, unaffected subjects. For the counts, we only included de novo LoF and Mis3 variants, transmitted/untransmitted and case-control LoF variants. These variant counts are normalized by the length of coding regions of each gene and sample size of each data set (|trio| + |case| for the left, |trio| + |control| for the right). Description of the samples can be found in Supplementary Table 1.

Extended Data Figure 4 Genome browser view of the CNV deletions identified in ASD-affected subjects.

The deletions are displayed in red if with unknown inheritance, in grey if inherited, and in black in unaffected subjects. Deletions in parents are not shown. For deletions within a single gene, all splicing isoforms are shown.

Extended Data Figure 5 Frequency of variants by gender.

Frequency of de novo (dn) and transmitted (Tr) variants per sample in males (black) and females (white) for genes with an FDR < 0.1 (top row), FDR < 0.3 (middle row), or all TADA genes (bottom row). The P values were determined by one-tailed permutation tests (*P < 0.05; **P < 0.01; ***P < 0.01).

Extended Data Figure 6 Enrichment terms for the four clusters identified by protein–protein interaction networks.

P values calculated using mouse-genome-informatics–mammalian-phenotype (MGI_Mammalian phenotype, blue), Kyoto encyclopaedia of genes and genomes (KEGG) pathways (red), and gene ontology biological processes (yellow) are indicated.

Extended Data Figure 7 De novo variants in SET lysine methyltransferases and jumonji lysine demethylases.

Mis3 variants are in black, LoF in red, and variants identified in other disorders in grey (Fig. 5). ARID, AT-rich interacting domain; AWS, associated with SET domain; BAH, bromo adjacent homology; bromo, bromodomain; FYR C, FY-rich C-terminal domain; FYR N, FY-rich N-terminal domain; HiMG, high mobility group box; JmjC, jumonji C domain; JmjN, jumonji N domain; PHD, plant homeodomain; PWWP, Pro-Trp-Trp-Pro domain; SET, Su(var)3-9, enhancer-of-zeste, trithorax domain.

Extended Data Figure 8 Transcription regulation network of TADA genes only.

Edges indicate transcription regulators (source nodes) and their gene targets (target nodes) based on the ChEA network.

Extended Data Table 1 CNVs hitting TADA genes

Supplementary information

Supplementary Information

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Supplementary Table 1

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Supplementary Table 2

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Supplementary Table 3

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Supplementary Table 4

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Supplementary Table 5

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Supplementary Table 6

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

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