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Targeted sequencing identifies 91 neurodevelopmental-disorder risk genes with autism and developmental-disability biases

Abstract

Gene-disruptive mutations contribute to the biology of neurodevelopmental disorders (NDDs), but most of the related pathogenic genes are not known. We sequenced 208 candidate genes from >11,730 cases and >2,867 controls. We identified 91 genes, including 38 new NDD genes, with an excess of de novo mutations or private disruptive mutations in 5.7% of cases. Drosophila functional assays revealed a subset with increased involvement in NDDs. We identified 25 genes showing a bias for autism versus intellectual disability and highlighted a network associated with high-functioning autism (full-scale IQ >100). Clinical follow-up for NAA15, KMT5B, and ASH1L highlighted new syndromic and nonsyndromic forms of disease.

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Figure 1: ASID patient network.
Figure 2: Targeted sequencing highlights genes reaching significance for de novo mutations and private disruptive variant burden.
Figure 3: Protein locations of private disruptive variants in new candidate NDD risk genes.
Figure 4: ASD versus ID/DD genes.
Figure 5: Habituation deficits in Drosophila knockdown models.

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Acknowledgements

We thank the individuals and their families for participation in this study. We acknowledge the Vienna Drosophila Resource Center and Bloomington Drosophila Stock Center (NIH P40OD018537). This research was supported in part by the following: the Simons Foundation Autism Research Initiative (SFARI 303241) and NIH (R01MH101221) to E.E.E.; VIDI and TOP grants (917-96-346, 912-12-109) from the Netherlands Organization for Scientific Research and Horizon 2020 Marie Sklodowska–Curie European Training Network (MiND, 643051) to A.S.; an NHGRI Interdisciplinary Training in Genome Science grant (T32HG00035) to H.A.F.S. and T.N.T.; Australian NHMRC grants 1091593 and 1041920 and Channel 7 Children's Research Foundation support to J.G.; the National Basic Research Program of China (2012CB517900) and the National Natural Science Foundation of China (81330027, 81525007 and 31400919) to K.X.; the China Scholarship Council (201406370028) and the Fundamental Research Funds for the Central Universities (2012zzts110) to T.W.; National Health and Medical Research Council of Australia Project grants (556759 and 1044175) to I.E.S., P.J.L., and M.B.D., and a Practitioner Fellowship (1006110) to I.E.S.; grants from the Jack Brockhoff Foundation and Perpetual Trustees, the Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS, the Swedish Brain Foundation, the Swedish Research Council, and the Stockholm County Council; the University of California, San Diego Clinical and Translational Research Institute (KL2TR00099 and 1KL2TR001444) to T.P.; and the Research Fund–Flanders (FWO) to R.F.K. and G.V.D.W. We are grateful to all of the families at the participating 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 access to phenotypic data on SFARI Base. We gratefully acknowledge the resources provided by the Autism Genetic Resource Exchange (AGRE) Consortium and the participating AGRE families. AGRE is a program of Autism Speaks and is supported in part by grant 1U24MH081810 from the National Institute of Mental Health to C.M. Lajonchere. We thank N. Brown, K. Pereira, T. Vick, T. Desai, C. Green, A.L. Doebley, and L. Grillo for their valuable contributions as well as T. Brown for assistance in editing this manuscript. H.P. is supported as a Senior Clinical Investigator of FWO. E.E.E. is supported as an investigator of the Howard Hughes Medical Institute.

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Authors

Contributions

E.E.E., H.A.F.S., B.X., and B.P.C. designed the study. H.A.F.S., B.X., T.W., K.H., L.V., and J. Lin performed the experiments. B.P.C. assisted with smMIP design and data analysis. F.H. performed the gene network analysis. R.A.B., J. Gerdts, and S.T. analyzed the patient data. B.X., M.F., B.H., and A.C.-N. performed and analyzed the Drosophila experiments. Other authors participated in the sample collection and DNA extraction and/or preparation. E.E.E., H.A.F.S., B.P.C., B.X., A.S., M.F., and R.A.B. wrote the manuscript with input from all authors. B.P.C. and T.W. contributed equally to this effort and should be regarded as joint second authors.

Corresponding author

Correspondence to Evan E Eichler.

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Competing interests

E.E.E. is on the scientific advisory board of DNAnexus, Inc., and was a member of the scientific advisory boards of Pacific Biosciences, Inc. (2009–2013) and SynapDx Corp. (2011–2013); E.E.E. is a consultant for Kunming University of Science and Technology (KUST) as part of the 1000 China Talent Program.

Integrated supplementary information

Supplementary Figure 1 smMIP quality control for the Gold pool.

For 960 sibling control samples, the frequency of samples (y-axis) that reach at least 8X coverage for each individual smMIP (x-axis) is plotted as a boxplot by gene.

Supplementary Figure 2 smMIP quality control for the ASD4 pool.

For 960 sibling control samples, the frequency of samples (y-axis) that reach at least 8X coverage for each individual smMIP (x-axis) is plotted as a boxplot by gene.

Supplementary Figure 3 smMIP quality control for the ASD5 pool.

For 960 sibling control samples, the frequency of samples (y-axis) that reach at least 8X coverage for each individual smMIP (x-axis) is plotted as a boxplot by gene.

Supplementary Figure 4 smMIP quality control for the ASD6 pool.

For 960 sibling control samples, the frequency of samples (y-axis) that reach at least 8X coverage for each individual smMIP (x-axis) is plotted as a boxplot by gene.

Supplementary Figure 5 Summary of private events identified in the study.

(a) Private events identified split by LGD and MIS30 variants in probands (orange), unaffected siblings (gray), and discordant siblings (i.e., a proband and sibling in the same family both share the event; black). (b) Number of private events identified per individual. (c) Private events split by LGD and MIS30 variants found to be de novo (orange), inherited (blue), validated by Sanger with unknown inheritance (light gray), Sanger validation failed (dark gray), and false+ (black). (d) De novo private events split by LGD and MIS30 variants into probands (orange) and unaffected siblings (gray). Dark orange represents new events in the study and light orange published events (all found in probands). (e) Inherited private events split by LGD and MIS30 variants into paternal (blue), maternal (orange) and unknown parent (gray).

Supplementary Figure 6 De novo (DN) significance is correlated with the number of ultra-rare/private DN variants identified.

The total number of DN proband LGD mutations is plotted on the y-axis against the FDR-corrected DN LGD P value on the x-axis for each gene. New DN events identified in this study were considered in addition to published studies of ASD, ID, and DD (Supplementary Table 15). Dashed gray lines indicate an FDR cutoff of 5% (q = 0.1) and a DN LGD proband count = 2.

Supplementary Figure 7 Inheritance patterns by gene count.

Plot of paternal (y-axis) or maternal (x-axis) inheritance counts by gene where at least one inherited event was identified in the smMIP dataset combined with published private inherited events in the SSC. Gene labels identify genes with a frequency >0.75 for either paternal or maternal inheritance where at least four inherited events have been identified.

Supplementary Figure 8 Genes exhibiting ASD and ID specificity by mutation type.

(a,b) Shown are the combined counts of private LGD (a) and MIS30 (b) events for each gene in our panel from probands in our study, published de novo events from ASD, ID, and DD proband studies, and published private inherited events from the SSC. Probands were scored as having ASD or ID (including DD) based on the primary ascertainment diagnosis of the cohort from which the case was sampled (Fig. 1 and published reports). Genes were tested for a bias of LGD and MIS30 events to one phenotype (ASD or ID) by two one-tailed binomial tests (P < 0.025 for either bias). The solid line indicates equal proportions of mutations corrected for the screened population size. Significant genes are indicated in red and labeled with gene names while the significance threshold is indicated as a dashed line.

Supplementary Figure 9 NMJ morphology changes in Drosophila knockdown models.

NMJ morphology is affected in dom (fly ortholog of SRCAP, VDRC #7787) and da (ortholog of TCF4, VDRC #105258) pan-neuronal knockdown flies. Two further da RNAi lines (VDRC #51297, #51300) confirmed a significant increase of branches and branching points (not shown). Top: representative Dlg staining of L3 wandering larva NMJs, body wall muscle 4, segment 3 of dom (SRCAP) and da (TCF4) knockdown larvae and their genetic background controls, respectively. Bottom: quantifications of NMJ area, perimeter, length, branching, bouton numbers for over 30 NMJs per genotype. Dom knockdown data is shown in dark red on the left and da knockdown data in light red on the right. Error bars are standard error of the mean. *P < 0.05, **P < 0.01, ***P < 0.001 (two-tailed Student’s t-test). Exact statistical values: SRCAP (dom), NMJ area P = 0.0012 df = 60, length P = 0.0184 df = 65, boutons P = 0.0771 df = 73, perimeter P = 0.0001 df = 60; TCF4 (da), NMJ area P = 0.0003 df = 63, length P = 0,0128 df = 68, branches P = 0.0009 df = 68, branching points P = 0.0390 df = 68.

Supplementary Figure 10 Probands carrying three private events in the study.

(a-i) Pedigrees show individuals carrying three private LGD (red) or MIS30 (blue) events identified in this study. Where available, inheritance is indicated (de novo or inherited). *Genes that reach DN significance in the study. Genes that show private disruptive burden in the study.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10, Supplementary Tables 1, 8–10 and 19–23, and Supplementary Note (PDF 2263 kb)

Supplementary Tables

Supplementary Tables 2–7 and 11–18 (XLSX 7173 kb)

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Stessman, H., Xiong, B., Coe, B. et al. Targeted sequencing identifies 91 neurodevelopmental-disorder risk genes with autism and developmental-disability biases. Nat Genet 49, 515–526 (2017). https://doi.org/10.1038/ng.3792

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