Autism spectrum disorders (ASDs) are characterized by phenotypic and genetic heterogeneity. Our analysis of functional networks perturbed in ASD suggests that both truncating and nontruncating de novo mutations contribute to autism, with a bias against truncating mutations in early embryonic development. We find that functional mutations are preferentially observed in genes likely to be haploinsufficient. Multiple cell types and brain areas are affected, but the impact of ASD mutations appears to be strongest in cortical interneurons, pyramidal neurons and the medium spiny neurons of the striatum, implicating cortical and corticostriatal brain circuits. In females, truncating ASD mutations on average affect genes with 50–100% higher brain expression than in males. Our results also suggest that truncating de novo mutations play a smaller role in the etiology of high-functioning ASD cases. Overall, we find that stronger functional insults usually lead to more severe intellectual, social and behavioral ASD phenotypes.
This is a preview of subscription content
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Abrahams, B.S. & Geschwind, D.H. Advances in autism genetics: on the threshold of a new neurobiology. Nat. Rev. Genet. 9, 341–355 (2008).
Berg, J.M. & Geschwind, D.H. Autism genetics: searching for specificity and convergence. Genome Biol. 13, 247 (2012).
Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012).
Freitag, C.M. The genetics of autistic disorders and its clinical relevance: a review of the literature. Mol. Psychiatry 12, 2–22 (2007).
Geschwind, D.H. Genetics of autism spectrum disorders. Trends Cogn. Sci. 15, 409–416 (2011).
Levy, D. et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70, 886–897 (2011).
O'Roak, B.J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012).
Sanders, S.J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012).
Veltman, J.A. & Brunner, H.G. De novo mutations in human genetic disease. Nat. Rev. Genet. 13, 565–575 (2012).
McClellan, J. & King, M.C. Genetic heterogeneity in human disease. Cell 141, 210–217 (2010).
O'Roak, B.J. et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338, 1619–1622 (2012).
Gilman, S.R. et al. Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nat. Neurosci. 15, 1723–1728 (2012).
Gilman, S.R. et al. Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70, 898–907 (2011).
Kelleher, R.J. III & Bear, M.F. The autistic neuron: troubled translation? Cell 135, 401–406 (2008).
Zoghbi, H.Y. & Bear, M.F. Synaptic dysfunction in neurodevelopmental disorders associated with autism and intellectual disabilities. Cold Spring Harb. Perspect. Biol. 4, a009886 (2012).
Kang, H.J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).
Doyle, J.P. et al. Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell 135, 749–762 (2008).
Fischbach, G.D. & Lord, C. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron 68, 192–195 (2010).
Huang, D.W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
Krey, J.F. & Dolmetsch, R.E. Molecular mechanisms of autism: a possible role for Ca2+ signaling. Curr. Opin. Neurobiol. 17, 112–119 (2007).
Betancur, C., Sakurai, T. & Buxbaum, J.D. The emerging role of synaptic cell-adhesion pathways in the pathogenesis of autism spectrum disorders. Trends Neurosci. 32, 402–412 (2009).
Pinto, D. et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010).
Ronan, J.L., Wu, W. & Crabtree, G.R. From neural development to cognition: unexpected roles for chromatin. Nat. Rev. Genet. 14, 347–359 (2013).
Santini, E. et al. Exaggerated translation causes synaptic and behavioural aberrations associated with autism. Nature 493, 411–415 (2013).
Ziff, E.B. Enlightening the postsynaptic density. Neuron 19, 1163–1174 (1997).
Huang, N., Lee, I., Marcotte, E.M. & Hurles, M.E. Characterising and predicting haploinsufficiency in the human genome. PLoS Genet. 6, e1001154 (2010).
Darnell, J.C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).
Huber, K.M., Gallagher, S.M., Warren, S.T. & Bear, M.F. Altered synaptic plasticity in a mouse model of fragile X mental retardation. Proc. Natl. Acad. Sci. USA 99, 7746–7750 (2002).
Edbauer, D. et al. Regulation of synaptic structure and function by FMRP-associated microRNAs miR-125b and miR-132. Neuron 65, 373–384 (2010).
Fu, W. et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493, 216–220 (2013).
Ascano, M. Jr. et al. FMRP targets distinct mRNA sequence elements to regulate protein expression. Nature 492, 382–386 (2012).
Bayés, A. et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat. Neurosci. 14, 19–21 (2011).
Kennedy, M.B. The postsynaptic density at glutamatergic synapses. Trends Neurosci. 20, 264–268 (1997).
Kennedy, M.B. Signal-processing machines at the postsynaptic density. Science 290, 750–754 (2000).
Vogel-Ciernia, A. et al. The neuron-specific chromatin regulatory subunit BAF53b is necessary for synaptic plasticity and memory. Nat. Neurosci. 16, 552–561 (2013).
Newschaffer, C.J. et al. The epidemiology of autism spectrum disorders. Annu. Rev. Public Health 28, 235–258 (2007).
Fombonne, E. Epidemiology of pervasive developmental disorders. Pediatr. Res. 65, 591–598 (2009).
Zhao, X. et al. A unified genetic theory for sporadic and inherited autism. Proc. Natl. Acad. Sci. USA 104, 12831–12836 (2007).
Adolphs, R. What does the amygdala contribute to social cognition? Ann. NY Acad. Sci. 1191, 42–61 (2010).
Willsey, A.J. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155, 997–1007 (2013).
Samocha, K.E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014).
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).
Shepherd, G.M. Corticostriatal connectivity and its role in disease. Nat. Rev. Neurosci. 14, 278–291 (2013).
Langen, M., Durston, S., Kas, M.J., van Engeland, H. & Staal, W.G. The neurobiology of repetitive behavior: … and men. Neurosci. Biobehav. Rev. 35, 356–365 (2011).
Burguière, E., Monteiro, P., Feng, G. & Graybiel, A.M. Optogenetic stimulation of lateral orbitofronto-striatal pathway suppresses compulsive behaviors. Science 340, 1243–1246 (2013).
Rothwell, P.E. et al. Autism-associated neuroligin-3 mutations commonly impair striatal circuits to boost repetitive behaviors. Cell 158, 198–212 (2014).
Sanders, S.J. et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70, 863–885 (2011).
Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).
Cooper, G.M. et al. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 15, 901–913 (2005).
Chen, L. & Vitkup, D. Predicting genes for orphan metabolic activities using phylogenetic profiles. Genome Biol. 7, R17 (2006).
Feldman, I., Rzhetsky, A. & Vitkup, D. Network properties of genes harboring inherited disease mutations. Proc. Natl. Acad. Sci. USA 105, 4323–4328 (2008).
We would like to sincerely thank M. Wigler, M. State, D. Geschwind, A. Packer, G. Fischbach and all of the members of the Vitkup laboratory for helpful discussions. This work was supported by a grant from the Simons Foundation SFARI# 308962 to D.V. and US National Centers for Biomedical Computing (MAGNet) grant U54CA121852 to Columbia University. S.R.G. was supported in part by US NIGMS training grant T32 GM082797. S.J.S. was supported by a Howard Hughes Medical Institute International Student Research Fellowship.
The authors declare no competing financial interests.
Integrated supplementary information
Average linkage hierarchical clustering was used to divide the implicated network into functional clusters. The inverses of the phenotypic network likelihood scores were used as the clustering metric. In this way, gene pairs strongly connected in the phenotypic network were considered to be closer in distance. Clusters were assigned the following functions using DAVID: green represents the neuronal signaling and cytoskeleton cluster, red represents the chromatin modification and regulation cluster, blue represents the channel activity cluster, and cyan represents the postsynaptic density cluster. Grey nodes represent genes that are not members of any considered cluster.
Supplementary Figure 2 Temporal gene expression profiles in the human brain across developmental stages for genes in the implicating network (red) and random gene sets selected to match network genes based on various criteria.
Human expression data were obtained from the HBT database. Random sets shown in the figure are matched by connectivity in the phenotypic network (cyan), by protein sequence length (purple), and by protein length and connectivity (green). The vertical dashed line separates prenatal and postnatal developmental stages. Error bars represent s.e.m.
Supplementary Figure 3 Non-normalized (left) and normalized (right) temporal brain expression profiles for individual genes in the functional clusters of the implicated network.
Human expression data were obtained from the HBT database. The individual panels in the figure represent profiles for: (a) postsynaptic density cluster genes, (b) chromatin modification and regulation cluster genes, (c) signaling and cytoskeleton cluster genes, and (d) channel activity cluster genes. Non-normalized profiles are shown in the left column. Every gene profile shown in the right column was normalized by dividing by the gene’s average expression value across all developmental stages. The colored lines in the right column show the average gene profile in each functional cluster. Vertical dashed lines separate prenatal and postnatal developmental stages. Error bars represent s.e.m.
Supplementary Figures 1–3 and Supplementary Tables 3–9 (PDF 1677 kb)
Genetic SNV and CNV events from the Simons Simplex Collection used as input for NETBAG. (XLSX 95 kb)
Implicated ASD network genes and associated prioritization annotations (average log2 brain expression and truncating SNV status). (XLSX 49 kb)
About this article
Cite this article
Chang, J., Gilman, S., Chiang, A. et al. Genotype to phenotype relationships in autism spectrum disorders. Nat Neurosci 18, 191–198 (2015). https://doi.org/10.1038/nn.3907
Mind the translational gap: using iPS cell models to bridge from genetic discoveries to perturbed pathways and therapeutic targets
Molecular Autism (2021)
Molecular Psychiatry (2021)
Investigating cytosolic 5′-nucleotidase II family genes as candidates for neuropsychiatric disorders in Drosophila (114/150 chr)
Translational Psychiatry (2021)
Molecular Autism (2020)
Emerging proteomic approaches to identify the underlying pathophysiology of neurodevelopmental and neurodegenerative disorders
Molecular Autism (2020)