Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

References

  1. Diagnostic and Statistical Manual of Mental Disorders 5th edn. (American Psychiatric Association, 2013).

  2. Posthuma, D. & Polderman, T.J. What have we learned from recent twin studies about the etiology of neurodevelopmental disorders? Curr. Opin. Neurol. 26, 111–121 (2013).

    Article  PubMed  Google Scholar 

  3. Torres, F., Barbosa, M. & Maciel, P. Recurrent copy number variations as risk factors for neurodevelopmental disorders: critical overview and analysis of clinical implications. J. Med. Genet. 53, 73–90 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Matson, J.L. & Shoemaker, M. Intellectual disability and its relationship to autism spectrum disorders. Res. Dev. Disabil. 30, 1107–1114 (2009).

    Article  PubMed  Google Scholar 

  5. Stessman, H.A., Bernier, R. & Eichler, E.E. A genotype-first approach to defining the subtypes of a complex disease. Cell 156, 872–877 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. van Bon, B.W. et al. Disruptive de novo mutations of DYRK1A lead to a syndromic form of autism and ID. Mol. Psychiatry 21, 126–132 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Helsmoortel, C. et al. A SWI/SNF-related autism syndrome caused by de novo mutations in ADNP. Nat. Genet. 46, 380–384 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. O'Roak, B.J. et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338, 1619–1622 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hiatt, J.B., Pritchard, C.C., Salipante, S.J., O'Roak, B.J. & Shendure, J. Single molecule molecular inversion probes for targeted, high-accuracy detection of low-frequency variation. Genome Res. 23, 843–854 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. O'Roak, B.J. et al. Recurrent de novo mutations implicate novel genes underlying simplex autism risk. Nat. Commun. 5, 5595 (2014).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Krumm, N. et al. Excess of rare, inherited truncating mutations in autism. Nat. Genet. 47, 582–588 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. de Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367, 1921–1929 (2012).

    Article  CAS  PubMed  Google Scholar 

  16. Rauch, A. et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380, 1674–1682 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Deciphering Developmental Disorders Study. Large-scale discovery of novel genetic causes of developmental disorders. Nature 519, 223–228 (2015).

  18. Turner, T.N. et al. denovo-db: a compendium of human de novo variants. Nucleic Acids Res. 45, D804–D811 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. Coe, B.P. et al. Refining analyses of copy number variation identifies specific genes associated with developmental delay. Nat. Genet. 46, 1063–1071 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hormozdiari, F., Penn, O., Borenstein, E. & Eichler, E.E. The discovery of integrated gene networks for autism and related disorders. Genome Res. 25, 142–154 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang, T. et al. De novo genic mutations among a Chinese autism spectrum disorder cohort. Nat. Commun. 7, 13316 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Turner, T.N. et al. Genome sequencing of autism-affected families reveals disruption of putative noncoding regulatory DNA. Am. J. Hum. Genet. 98, 58–74 (2016).

    Article  CAS  PubMed  Google Scholar 

  23. Hamdan, F.F. et al. De novo mutations in FOXP1 in cases with intellectual disability, autism, and language impairment. Am. J. Hum. Genet. 87, 671–678 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ba, W. et al. TRIO loss of function is associated with mild intellectual disability and affects dendritic branching and synapse function. Hum. Mol. Genet. 25, 892–902 (2016).

    Article  CAS  PubMed  Google Scholar 

  25. Han, S. et al. Autistic-like behaviour in Scn1a+/− mice and rescue by enhanced GABA-mediated neurotransmission. Nature 489, 385–390 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Witteveen, J.S. et al. Haploinsufficiency of MeCP2-interacting transcriptional co-repressor SIN3A causes mild intellectual disability by affecting the development of cortical integrity. Nat. Genet. 48, 877–887 (2016).

    Article  CAS  PubMed  Google Scholar 

  27. Shoubridge, C. et al. Mutations in the guanine nucleotide exchange factor gene IQSEC2 cause nonsyndromic intellectual disability. Nat. Genet. 42, 486–488 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chan, C.B. et al. PIKE is essential for oligodendroglia development and CNS myelination. Proc. Natl. Acad. Sci. USA 111, 1993–1998 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. McNeill, E.M. et al. Nav2 hypomorphic mutant mice are ataxic and exhibit abnormalities in cerebellar development. Dev. Biol. 353, 331–343 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Stray-Pedersen, A. et al. Biallelic mutations in UNC80 cause persistent hypotonia, encephalopathy, growth retardation, and severe intellectual disability. Am. J. Hum. Genet. 98, 202–209 (2016).

    Article  CAS  PubMed  Google Scholar 

  31. Turner, T.N. et al. Loss of δ-catenin function in severe autism. Nature 520, 51–56 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sanders, S.J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Rope, A.F. et al. Using VAAST to identify an X-linked disorder resulting in lethality in male infants due to N-terminal acetyltransferase deficiency. Am. J. Hum. Genet. 89, 28–43 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Liszczak, G. et al. Molecular basis for N-terminal acetylation by the heterodimeric NatA complex. Nat. Struct. Mol. Biol. 20, 1098–1105 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Baird, P.A., Anderson, T.W., Newcombe, H.B. & Lowry, R.B. Genetic disorders in children and young adults: a population study. Am. J. Hum. Genet. 42, 677–693 (1988).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Rosenfeld, J.A., Coe, B.P., Eichler, E.E., Cuckle, H. & Shaffer, L.G. Estimates of penetrance for recurrent pathogenic copy-number variations. Genet. Med. 15, 478–481 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. Chen, E.Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Stessman, H.A. et al. Disruption of POGZ is associated with intellectual disability and autism spectrum disorders. Am. J. Hum. Genet. 98, 541–552 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Esmaeeli-Nieh, S. et al. BOD1 is required for cognitive function in humans and Drosophila. PLoS Genet. 12, e1006022 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Lugtenberg, D. et al. De novo loss-of-function mutations in WAC cause a recognizable intellectual disability syndrome and learning deficits in Drosophila. Eur. J. Hum. Genet. 24, 1145–1153 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kleefstra, T. et al. Disruption of an EHMT1-associated chromatin-modification module causes intellectual disability. Am. J. Hum. Genet. 91, 73–82 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. van Bon, B.W. et al. CEP89 is required for mitochondrial metabolism and neuronal function in man and fly. Hum. Mol. Genet. 22, 3138–3151 (2013).

    Article  CAS  PubMed  Google Scholar 

  43. Willemsen, M.H. et al. GATAD2B loss-of-function mutations cause a recognisable syndrome with intellectual disability and are associated with learning deficits and synaptic undergrowth in Drosophila. J. Med. Genet. 50, 507–514 (2013).

    Article  CAS  PubMed  Google Scholar 

  44. Schmid, S., Wilson, D.A. & Rankin, C.H. Habituation mechanisms and their importance for cognitive function. Front. Integr. Nuerosci. 8, 97 (2015).

    Article  Google Scholar 

  45. Kleinhans, N.M. et al. Reduced neural habituation in the amygdala and social impairments in autism spectrum disorders. Am. J. Psychiatry 166, 467–475 (2009).

    Article  PubMed  Google Scholar 

  46. Dinstein, I. et al. Unreliable evoked responses in autism. Neuron 75, 981–991 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Pellicano, E., Rhodes, G. & Calder, A.J. Reduced gaze aftereffects are related to difficulties categorising gaze direction in children with autism. Neuropsychologia 51, 1504–1509 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Ethridge, L.E. et al. Reduced habituation of auditory evoked potentials indicate cortical hyper-excitability in Fragile X Syndrome. Transl. Psychiatry 6, e787 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cascio, C.J., Woynaroski, T., Baranek, G.T. & Wallace, M.T. Toward an interdisciplinary approach to understanding sensory function in autism spectrum disorder. Autism Res. 9, 920–925 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ramaswami, M. Network plasticity in adaptive filtering and behavioral habituation. Neuron 82, 1216–1229 (2014).

    Article  CAS  PubMed  Google Scholar 

  51. Tartaglia, M. et al. Mutations in PTPN11, encoding the protein tyrosine phosphatase SHP-2, cause Noonan syndrome. Nat. Genet. 29, 465–468 (2001).

    Article  CAS  PubMed  Google Scholar 

  52. Iossifov, I. et al. Low load for disruptive mutations in autism genes and their biased transmission. Proc. Natl. Acad. Sci. USA 112, E5600–E5607 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Sugiura, N., Patel, R.G. & Corriveau, R.A. N-methyl-D-aspartate receptors regulate a group of transiently expressed genes in the developing brain. J. Biol. Chem. 276, 14257–14263 (2001).

    Article  CAS  PubMed  Google Scholar 

  54. Myklebust, L.M. et al. Biochemical and cellular analysis of Ogden syndrome reveals downstream Nt-acetylation defects. Hum. Mol. Genet. 24, 1956–1976 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Homsy, J. et al. De novo mutations in congenital heart disease with neurodevelopmental and other congenital anomalies. Science 350, 1262–1266 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. van Bokhoven, H. Genetic and epigenetic networks in intellectual disabilities. Annu. Rev. Genet. 45, 81–104 (2011).

    Article  CAS  PubMed  Google Scholar 

  57. Zhu, T. et al. Histone methyltransferase Ash1L mediates activity-dependent repression of neurexin-1α. Sci. Rep. 6, 26597 (2016).

    Article  CAS  Google Scholar 

  58. Griswold, A.J. et al. Targeted massively parallel sequencing of autism spectrum disorder-associated genes in a case control cohort reveals rare loss-of-function risk variants. Mol. Autism 6, 43 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Rhodes, C.T. et al. Cross-species analyses unravel the complexity of H3K27me3 and H4K20me3 in the context of neural stem progenitor cells. Neuroepigenetics 6, 10–25 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Courchesne, E. et al. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology 57, 245–254 (2001).

    Article  CAS  PubMed  Google Scholar 

  61. Shen, M.D. et al. Early brain enlargement and elevated extra-axial fluid in infants who develop autism spectrum disorder. Brain 136, 2825–2835 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Schumann, C.M. et al. Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. J. Neurosci. 30, 4419–4427 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Redcay, E. & Courchesne, E. When is the brain enlarged in autism? A meta-analysis of all brain size reports. Biol. Psychiatry 58, 1–9 (2005).

    Article  PubMed  Google Scholar 

  64. Marchetto, M.C. et al. Altered proliferation and networks in neural cells derived from idiopathic autistic individuals. Mol. Psychiatry http://dx.doi.org/10.1038/mp.2016.95 (2016).

  65. Sugathan, A. et al. CHD8 regulates neurodevelopmental pathways associated with autism spectrum disorder in neural progenitors. Proc. Natl. Acad. Sci. USA 111, E4468–E4477 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Cotney, J. et al. The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment. Nat. Commun. 6, 6404 (2015).

    Article  CAS  PubMed  Google Scholar 

  67. Courchesne, E. et al. Neuron number and size in prefrontal cortex of children with autism. J. Am. Med. Assoc. 306, 2001–2010 (2011).

    Article  CAS  Google Scholar 

  68. Stoner, R. et al. Patches of disorganization in the neocortex of children with autism. N. Engl. J. Med. 370, 1209–1219 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Chow, M.L. et al. Age-dependent brain gene expression and copy number anomalies in autism suggest distinct pathological processes at young versus mature ages. PLoS Genet. 8, e1002592 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Pramparo, T. et al. Cell cycle networks link gene expression dysregulation, mutation, and brain maldevelopment in autistic toddlers. Mol. Syst. Biol. 11, 841 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Geschwind, D.H. et al. The autism genetic resource exchange: a resource for the study of autism and related neuropsychiatric conditions. Am. J. Hum. Genet. 69, 463–466 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Buxbaum, J.D. et al. The Autism Simplex Collection: an international, expertly phenotyped autism sample for genetic and phenotypic analyses. Mol. Autism 5, 34 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Ardlie, K.G. et al.; GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Feenstra, I. et al. Balanced into array: genome-wide array analysis in 54 patients with an apparently balanced de novo chromosome rearrangement and a meta-analysis. Eur. J. Hum. Genet. 19, 1152–1160 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Vulto-van Silfhout, A.T. et al. Clinical significance of de novo and inherited copy-number variation. Hum. Mutat. 34, 1679–1687 (2013).

    Article  CAS  PubMed  Google Scholar 

  78. de Vries, B.B. et al. Clinical studies on submicroscopic subtelomeric rearrangements: a checklist. J. Med. Genet. 38, 145–150 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Lord, C., Rutter, M., DiLavore, P.C. & Risi, S. Autism Diagnostic Observation Schedule (Western Psychological Services, 2001).

  80. Lord, C., Rutter, M. & Le Couteur, A. Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J. Autism Dev. Disord. 24, 659–685 (1994).

    Article  CAS  PubMed  Google Scholar 

  81. Elliott, C.D. Differential Ability Scales: Introductory and Technical Manual 2nd edn. (Harcourt Assessment, 2007).

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

    Article  CAS  PubMed  Google Scholar 

  83. Pescosolido, M.F. et al. Expansion of the clinical phenotype associated with mutations in activity-dependent neuroprotective protein. J. Med. Genet. 51, 587–589 (2014).

    Article  CAS  PubMed  Google Scholar 

  84. Hoyer, J. et al. Haploinsufficiency of ARID1B, a member of the SWI/SNF-a chromatin-remodeling complex, is a frequent cause of intellectual disability. Am. J. Hum. Genet. 90, 565–572 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Epi4K Consortium. et al. De novo mutations in epileptic encephalopathies. Nature 501, 217–221 (2013).

  86. Merner, N. et al. A de novo frameshift mutation in chromodomain helicase DNA-binding domain 8 (CHD8): A case report and literature review. Am. J. Med. Genet. A. 170A, 1225–1235 (2016).

    Article  CAS  PubMed  Google Scholar 

  87. Kuechler, A. et al. De novo mutations in beta-catenin (CTNNB1) appear to be a frequent cause of intellectual disability: expanding the mutational and clinical spectrum. Hum. Genet. 134, 97–109 (2015).

    Article  CAS  PubMed  Google Scholar 

  88. Tucci, V. et al. Dominant β-catenin mutations cause intellectual disability with recognizable syndromic features. J. Clin. Invest. 124, 1468–1482 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Winczewska-Wiktor, A. et al. A de novo CTNNB1 nonsense mutation associated with syndromic atypical hyperekplexia, microcephaly and intellectual disability: a case report. BMC Neurol. 16, 35 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Lozano, R., Vino, A., Lozano, C., Fisher, S.E. & Deriziotis, P. A de novo FOXP1 variant in a patient with autism, intellectual disability and severe speech and language impairment. Eur. J. Hum. Genet. 23, 1702–1707 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Sollis, E. et al. Identification and functional characterization of de novo FOXP1 variants provides novel insights into the etiology of neurodevelopmental disorder. Hum. Mol. Genet. 25, 546–557 (2016).

    Article  CAS  PubMed  Google Scholar 

  92. Adams, D.R. et al. Three rare diseases in one Sib pair: RAI1, PCK1, GRIN2B mutations associated with Smith-Magenis Syndrome, cytosolic PEPCK deficiency and NMDA receptor glutamate insensitivity. Mol. Genet. Metab. 113, 161–170 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Endele, S. et al. Mutations in GRIN2A and GRIN2B encoding regulatory subunits of NMDA receptors cause variable neurodevelopmental phenotypes. Nat. Genet. 42, 1021–1026 (2010).

    Article  CAS  PubMed  Google Scholar 

  94. Freunscht, I. et al. Behavioral phenotype in five individuals with de novo mutations within the GRIN2B gene. Behav. Brain Funct. 9, 20 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Lemke, J.R. et al. GRIN2B mutations in West syndrome and intellectual disability with focal epilepsy. Ann. Neurol. 75, 147–154 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Cafiero, C. et al. Novel de novo heterozygous loss-of-function variants in MED13L and further delineation of the MED13L haploinsufficiency syndrome. Eur. J. Hum. Genet. 23, 1499–1504 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. van Haelst, M.M. et al. Further confirmation of the MED13L haploinsufficiency syndrome. Eur. J. Hum. Genet. 23, 135–138 (2015).

    Article  CAS  PubMed  Google Scholar 

  98. Fukai, R. et al. A case of autism spectrum disorder arising from a de novo missense mutation in POGZ. J. Hum. Genet. 60, 277–279 (2015).

    Article  CAS  PubMed  Google Scholar 

  99. White, J. et al. POGZ truncating alleles cause syndromic intellectual disability. Genome Med. 8, 3 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Busa, T. et al. Clinical presentation of PTEN mutations in childhood in the absence of family history of Cowden syndrome. Eur. J. Paediatr. Neurol. 19, 188–192 (2015).

    Article  CAS  PubMed  Google Scholar 

  101. Buxbaum, J.D. et al. Mutation screening of the PTEN gene in patients with autism spectrum disorders and macrocephaly. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 144B, 484–491 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Baasch, A.L. et al. Exome sequencing identifies a de novo SCN2A mutation in a patient with intractable seizures, severe intellectual disability, optic atrophy, muscular hypotonia, and brain abnormalities. Epilepsia 55, e25–e29 (2014).

    Article  CAS  PubMed  Google Scholar 

  103. Dhamija, R., Wirrell, E., Falcao, G., Kirmani, S. & Wong-Kisiel, L.C. Novel de novo SCN2A mutation in a child with migrating focal seizures of infancy. Pediatr. Neurol. 49, 486–488 (2013).

    Article  PubMed  Google Scholar 

  104. Dimassi, S. et al. Whole-exome sequencing improves the diagnosis yield in sporadic infantile spasm syndrome. Clin. Genet. 89, 198–204 (2016).

    Article  CAS  PubMed  Google Scholar 

  105. Nakamura, K. et al. Clinical spectrum of SCN2A mutations expanding to Ohtahara syndrome. Neurology 81, 992–998 (2013).

    Article  CAS  PubMed  Google Scholar 

  106. Tavassoli, T. et al. De novo SCN2A splice site mutation in a boy with Autism spectrum disorder. BMC Med. Genet. 15, 35 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Herenger, Y. et al. Long term follow up of two independent patients with Schinzel-Giedion carrying SETBP1 mutations. Eur. J. Med. Genet. 58, 479–487 (2015).

    Article  PubMed  Google Scholar 

  108. Miyake, F. et al. West syndrome in a patient with Schinzel-Giedion syndrome. J. Child Neurol. 30, 932–936 (2015).

    Article  PubMed  Google Scholar 

  109. Takeuchi, A. et al. Progressive brain atrophy in Schinzel-Giedion syndrome with a SETBP1 mutation. Eur. J. Med. Genet. 58, 369–371 (2015).

    Article  PubMed  Google Scholar 

  110. Stamberger, H. et al. STXBP1 encephalopathy: a neurodevelopmental disorder including epilepsy. Neurology 86, 954–962 (2016).

    Article  CAS  PubMed  Google Scholar 

  111. Heinen, C.A. et al. A specific mutation in TBL1XR1 causes Pierpont syndrome. J. Med. Genet. 53, 330–337 (2016).

    Article  CAS  PubMed  Google Scholar 

  112. Keshava Prasad, T.S. et al. Human Protein Reference Database: 2009 update. Nucleic Acids Res. 37, D767–D772 (2009).

    Article  CAS  PubMed  Google Scholar 

  113. Szklarczyk, D. et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39, D561–D568 (2011).

    Article  CAS  PubMed  Google Scholar 

  114. Wheeler, D.L. et al. Database resources of the National Center for Biotechnology. Nucleic Acids Res. 31, 28–33 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Attrill, H. et al. FlyBase: establishing a Gene Group resource for Drosophila melanogaster. Nucleic Acids Res. 44, D786–D792 (2016).

    Article  CAS  PubMed  Google Scholar 

  116. Brand, A.H. & Perrimon, N. Targeted gene expression as a means of altering cell fates and generating dominant phenotypes. Development 118, 401–415 (1993).

    CAS  PubMed  Google Scholar 

  117. Dietzl, G. et al. A genome-wide transgenic RNAi library for conditional gene inactivation in Drosophila. Nature 448, 151–156 (2007).

    Article  CAS  PubMed  Google Scholar 

  118. Oortveld, M.A. et al. Human intellectual disability genes form conserved functional modules in Drosophila. PLoS Genet. 9, e1003911 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Green, E.W., Fedele, G., Giorgini, F. & Kyriacou, C.P. A Drosophila RNAi collection is subject to dominant phenotypic effects. Nat. Methods 11, 222–223 (2014).

    Article  CAS  PubMed  Google Scholar 

  120. Vissers, J.H., Manning, S.A., Kulkarni, A. & Harvey, K.F. A Drosophila RNAi library modulates Hippo pathway-dependent tissue growth. Nat. Commun. 7, 10368 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Kramer, J.M. et al. Epigenetic regulation of learning and memory by Drosophila EHMT/G9a. PLoS Biol. 9, e1000569 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  123. Nijhof, B. et al. A new Fiji-based algorithm that systematically quantifies nine synaptic parameters provides insights into Drosophila NMJ morphometry. PLoS Comput. Biol. 12, e1004823 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

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.

Ethics declarations

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)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3792

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing