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.

  • Review Article
  • Published:

Genomics, convergent neuroscience and progress in understanding autism spectrum disorder

Abstract

More than a hundred genes have been identified that, when disrupted, impart large risk for autism spectrum disorder (ASD). Current knowledge about the encoded proteins — although incomplete — points to a very wide range of developmentally dynamic and diverse biological processes. Moreover, the core symptoms of ASD involve distinctly human characteristics, presenting challenges to interpreting evolutionarily distant model systems. Indeed, despite a decade of striking progress in gene discovery, an actionable understanding of pathobiology remains elusive. Increasingly, convergent neuroscience approaches have been recognized as an important complement to traditional uses of genetics to illuminate the biology of human disorders. These methods seek to identify intersection among molecular-level, cellular-level and circuit-level functions across multiple risk genes and have highlighted developing excitatory neurons in the human mid-gestational prefrontal cortex as an important pathobiological nexus in ASD. In addition, neurogenesis, chromatin modification and synaptic function have emerged as key potential mediators of genetic vulnerability. The continued expansion of foundational ‘omics’ data sets, the application of higher-throughput model systems and incorporating developmental trajectories and sex differences into future analyses will refine and extend these results. Ultimately, a systems-level understanding of ASD genetic risk holds promise for clarifying pathobiology and advancing therapeutics.

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

Access options

Buy this article

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

Fig. 1: Relationship between effect size and allele frequency for loci discovered in autism spectrum disorder or schizophrenia.
Fig. 2: Emerging patterns of ASD convergence identified from gene expression data.

Similar content being viewed by others

References

  1. Lord, C. et al. Autism spectrum disorder. Nat. Rev. Dis. Prim. 6, 5 (2020).

    Article  PubMed  Google Scholar 

  2. Díaz-Caneja, C. M. et al. A white paper on a neurodevelopmental framework for drug discovery in autism and other neurodevelopmental disorders. Eur. Neuropsychopharmacol. 48, 49–88 (2021).

    Article  PubMed  CAS  Google Scholar 

  3. Folstein, S. & Rutter, M. Infantile autism: a genetic study of 21 twin pairs. J. Child. Psychol. Psychiatry 18, 297–321 (1977).

    Article  CAS  PubMed  Google Scholar 

  4. Gaugler, T. et al. Most genetic risk for autism resides with common variation. Nat. Genet. 46, 881–885 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Klei, L. et al. Common genetic variants, acting additively, are a major source of risk for autism. Mol. Autism 3, 9 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Cross-Disorder Group of the Psychiatric Genomics Consortium. et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).

    Article  PubMed Central  CAS  Google Scholar 

  7. Bailey, A. et al. Autism as a strongly genetic disorder: evidence from a British twin study. Psychol. Med. 25, 63–77 (1995).

    Article  CAS  PubMed  Google Scholar 

  8. Devlin, B. & Scherer, S. W. Genetic architecture in autism spectrum disorder. Curr. Opin. Genet. Dev. 22, 229–237 (2012).

    Article  CAS  PubMed  Google Scholar 

  9. Hallmayer, J. et al. Genetic heritability and shared environmental factors among twin pairs with autism. Arch. Gen. Psychiatry 68, 1095–1102 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Steffenburg, S. et al. A twin study of autism in Denmark, Finland, Iceland, Norway and Sweden. J. Child. Psychol. Psychiatry 30, 405–416 (1989).

    Article  CAS  PubMed  Google Scholar 

  11. Ozonoff, S. et al. Recurrence risk for autism spectrum disorders: a Baby Siblings Research Consortium study. Pediatrics 128, 488–495 (2011).

    Article  Google Scholar 

  12. Constantino, J. N., Zhang, Y., Frazier, T., Abbacchi, A. M. & Law, P. Sibling recurrence and the genetic epidemiology of autism. Am. J. Psychiatry 167, 1349–1356 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Rosenberg, R. E. et al. Characteristics and concordance of autism spectrum disorders among 277 twin pairs. Arch. Pediatr. Adolesc. Med. 163, 907–914 (2009).

    Article  PubMed  Google Scholar 

  14. Le Couteur, A. et al. A broader phenotype of autism: the clinical spectrum in twins. J. Child. Psychol. Psychiatry 37, 785–801 (1996).

    Article  PubMed  Google Scholar 

  15. Tick, B., Bolton, P., Happé, F., Rutter, M. & Rijsdijk, F. Heritability of autism spectrum disorders: a meta-analysis of twin studies. J. Child. Psychol. Psychiatry 57, 585–595 (2016).

    Article  PubMed  Google Scholar 

  16. Ritvo, E. R., Freeman, B. J., Mason-Brothers, A., Mo, A. & Ritvo, A. M. Concordance for the syndrome of autism in 40 pairs of afflicted twins. Am. J. Psychiatry 142, 74–77 (1985).

    Article  CAS  PubMed  Google Scholar 

  17. Taniai, H., Nishiyama, T., Miyachi, T., Imaeda, M. & Sumi, S. Genetic influences on the broad spectrum of autism: study of proband-ascertained twins. Am. J. Med. Genet. B Neuropsychiatr. Genet. 147B, 844–849 (2008).

    Article  PubMed  Google Scholar 

  18. Lichtenstein, P., Carlström, E., Råstam, M., Gillberg, C. & Anckarsäter, H. The genetics of autism spectrum disorders and related neuropsychiatric disorders in childhood. Am. J. Psychiatry 167, 1357–1363 (2010).

    Article  PubMed  Google Scholar 

  19. Nordenbæk, C., Jørgensen, M., Kyvik, K. O. & Bilenberg, N. A Danish population-based twin study on autism spectrum disorders. Eur. Child. Adolesc. Psychiatry 23, 35–43 (2014).

    Article  PubMed  Google Scholar 

  20. Colvert, E. et al. Heritability of autism spectrum disorder in a UK population-based twin sample. JAMA Psychiatry 72, 415–423 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sebat, J. et al. Strong association of de novo copy number mutations with autism. Science 316, 445–449 (2007). This study conducts comparative genomic hybridization in simplex and multiplex families with ASD and establishes that de novo CNVs significantly contribute to ASD risk.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Levy, D. et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70, 886–897 (2011).

    Article  CAS  PubMed  Google Scholar 

  23. Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584 (2020). This work presents the largest published ASD WES study to date, which identifies 102 high-confidence ASD risk genes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015). This large study combines WES data from De Rubeis et al. (2014) and Iossifov et al. (2014) as well as de novo CNV data to identify 65 ASD risk genes and 6 ASD risk loci.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Willsey, A. J. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155, 997–1007 (2013). One of the first studies to assess spatiotemporal convergence of ASD risk genes, this study performs a co-expression network analysis that integrates BrainSpan developmental expression data with genetic data from simplex families with ASD to identify deep layer cortical projection neurons in prefrontal and primary motor-somatosensory cortical regions during mid-gestational development as a critical nexus of ASD risk.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. O’Roak, B. J. et al. Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nat. Genet. 43, 585–589 (2011). One of the first WES studies of people with ASD and their families.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012). Together with refs. 28, 30 and 34, this paper cemented the contribution of de novo sequence variants to ASD risk.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014). This large ASD WES study, contemporaneous with ref. 44, confirms that de novo loss-of-function mutations contribute significantly to ASD, identifies 33 ASD risk genes and implicates ASD risk genes in chromatin remodelling, transcription and splicing, and synaptic function.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012). As well as contributing as noted above, this paper outlined a key paradigm for the statistical assessment of a gene’s association to ASD, based on the number and type of recurrent de novo variants.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jacquemont, S. et al. A higher mutational burden in females supports a ‘female protective model’ in neurodevelopmental disorders. Am. J. Hum. Genet. 94, 415–425 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Dong, S. et al. De novo insertions and deletions of predominantly paternal origin are associated with autism spectrum disorder. Cell Rep. 9, 16–23 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 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). One of several early studies associating de novo CNVs with ASD risk, this paper also generated a statistical framework for quantifying the significance of recurrent de novo CNVs. This framework was later adapted to early studies of de novo sequence variants.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. O’Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012). As well as contributing as noted above, this paper was one of the first to demonstrate that genes with damaging mutations in individuals with ASD are highly connected in protein–protein interaction modules, suggesting that they may converge on similar biological functions.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Sestan, N. & State, M. W. Lost in translation: traversing the complex path from genomics to therapeutics in autism spectrum disorder. Neuron 100, 406–423 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. State, M. W. & Sestan, N. The emerging biology of autism spectrum disorders. Science 337, 1301–1303 (2012). This review describes the rationale for and approaches to leveraging convergence among ASD genes to identify not only relevant molecules and pathways but also when and where they act in the developing human brain.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Willsey, A. J. et al. The psychiatric cell map initiative: a convergent systems biological approach to illuminating key molecular pathways in neuropsychiatric disorders. Cell 174, 505–520 (2018). This review describes convergent system biological approaches to study genes implicated in neuropsychiatric disorders such as ASD, emphasizing the importance of parallel investigations of multiple genes and mutations, in as unbiased a fashion as possible, to identify points of convergence and reveal high-order (molecular, cellular and circuit-level) phenotypes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Willsey, A. J. & State, M. W. Autism spectrum disorders: from genes to neurobiology. Curr. Opin. Neurobiol. 30, 92–99 (2015).

    Article  PubMed  CAS  Google Scholar 

  39. Jin, X. et al. In vivo Perturb-seq reveals neuronal and glial abnormalities associated with autism risk genes. Science 370, eaaz6063 (2020). This paper conducted in vivo transcriptional screening of a large number of ASD risk genes using single-cell transcriptomics and CRISPR technology.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Willsey, H. R. et al. Parallel in vivo analysis of large-effect autism genes implicates cortical neurogenesis and estrogen in risk and resilience. Neuron 109, 1409 (2021). This study conducts parallel in vivo analysis of the ten highest-confidence ASD risk genes in Xenopus, finding that the functional consequences of mutations in these genes converge upon impaired neurogenesis in the developing forebrain — a phenotype that could be rescued by exogenous oestrogen, suggesting oestrogen is a resilience factor that may mitigate various ASD genetic risks.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zoghbi, H. Y. Postnatal neurodevelopmental disorders: meeting at the synapse? Science 302, 826–830 (2003). This early commentary proposes that ASD genetic risk may mechanistically converge at the synapse.

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. 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). This study develops a framework (NETBAG) that integrates multiple levels of molecular data to demonstrate that genes affected by CNVs in ASD are functionally interconnected.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014). This large ASD WES study, contemporaneous with ref. 29, confirms that de novo missense and likely gene-disrupting mutations contribute significantly to ASD and identifies 27 ASD risk genes. It also carefully quantifies the contributions of different types of rare variants to ASD risk by comparing normalized rates in probands versus unaffected sibling controls.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hoffman, E. J. et al. Estrogens suppress a behavioral phenotype in zebrafish mutants of the autism risk gene, CNTNAP2. Neuron 89, 725–733 (2016). This is one of the first studies to identify a functional interaction between oestrogen and an ASD risk gene.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Jamain, S. et al. Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nat. Genet. 34, 27–29 (2003). This work uses linkage analysis and targeted DNA sequencing to identify two ASD-associated genes, NLGN3 and NLGN4, which are among the earliest individual genes with robust evidence of association with idiopathic (non-syndromic) ASD.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Laumonnier, F. et al. X-Linked mental retardation and autism are associated with a mutation in the NLGN4 gene, a member of the neuroligin family. Am. J. Hum. Genet. 74, 552–557 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kumar, R. A. et al. Recurrent 16p11.2 microdeletions in autism. Hum. Mol. Genet. 17, 628–638 (2008).

    Article  CAS  PubMed  Google Scholar 

  49. Marshall, C. R. et al. Structural variation of chromosomes in autism spectrum disorder. Am. J. Hum. Genet. 82, 477–488 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Weiss, L. A. et al. Association between microdeletion and microduplication at 16p11.2 and autism. N. Engl. J. Med. 358, 667–675 (2008).

    Article  CAS  PubMed  Google Scholar 

  51. Pinto, D. et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. SPARK Consortium. SPARK: a US cohort of 50,000 families to accelerate autism research. Neuron 97, 488–493 (2018).

    Article  CAS  Google Scholar 

  53. He, X. et al. Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes. PLoS Genet. 9, e1003671 (2013). This work develops a Bayesian model (transmission and de novo association (TADA)) that integrates information from multiple types of genetic variation from large-scale human gene sequencing studies to improve the power to detect novel risk genes. A variation of this model is still used to discover ASD risk genes. This model has also been applied to other psychiatric disorders, such as Tourette syndrome.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ruzzo, E. K. et al. Inherited and de novo genetic risk for autism impacts shared networks. Cell 178, 850–866.e26 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Satterstrom, F. K. et al. Autism spectrum disorder and attention deficit hyperactivity disorder have a similar burden of rare protein-truncating variants. Nat. Neurosci. 22, 1961–1965 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Rodin, R. E. et al. The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing. Nat. Neurosci. 24, 176–185 (2021). This is one of the largest studies to date assessing the frequency of somatic (mosaic) variants in brain tissue from people with ASD and controls.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Sherman, M. A. et al. Large mosaic copy number variations confer autism risk. Nat. Neurosci. 24, 197–203 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016). This study aggregates and analyses high-quality WES data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC), providing a key foundation for the field (which has since grown to include more than 100,000 individuals). Together with several other studies, this work develops a paradigm to prioritize genes on the basis of ‘tolerance’ to genetic variation. Intolerant genes tend to carry higher risk for ASD and other human disorders, so tolerance is a key metric for prioritizing functional mutations. The associated resource also facilitates the identification of ultra-rare genetic variants — a similarly critical metric for prioritizing mutations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Petrovski, S., Wang, Q., Heinzen, E. L., Allen, A. S. & Goldstein, D. B. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 9, e1003709 (2013). Together with several other studies, this work develops a paradigm to prioritize genes based on “tolerance” to genetic variation. Intolerant genes tend to carry higher risk for ASD and other human disorders, and is therefore a key metric for prioritizing functional mutations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Huang, N., Lee, I., Marcotte, E. M. & Hurles, M. E. Characterising and predicting haploinsufficiency in the human genome. PLoS Genet. 6, e1001154 (2010). This work develops a paradigm to prioritize genes based on likelihood of haploinsufficiency (i.e. likelihood that a heterozygous loss of function variant results in a clinical phenotype). Haploinsufficient genes tend to carry higher risk for ASD and other human disorders, and is therefore a key metric for prioritizing functional mutations.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).A longstanding and widely used resource for predicting the severity of missense mutations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kosmicki, J. A. et al. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples. Nat. Genet. 49, 504–510 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014). This study presents a frequentist framework to evaluate excesses of de novo mutations at the level of individual genes, for which simple case–control comparisons cannot achieve meaningful levels of significance due to the rarity of de novo events; when applied to ASD WES data, this method highlights several risk genes at a genome-wide level of statistical significance.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Samocha, K. E. et al. Regional missense constraint improves variant deleteriousness prediction. Preprint at bioRxiv https://doi.org/10.1101/148353 (2017).

    Article  Google Scholar 

  66. Singh, T. et al. Rare coding variants in ten genes confer substantial risk for schizophrenia. Nature https://doi.org/10.1038/s41586-022-04556-w (2022). The largest WES study published on schizophrenia to date, this paper identifies 10 exome-wide significant schizophrenia risk genes of large effect size.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Bilgüvar, K. et al. Whole-exome sequencing identifies recessive WDR62 mutations in severe brain malformations. Nature 467, 207–210 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Gilmore, E. C. & Walsh, C. A. Genetic causes of microcephaly and lessons for neuronal development. Wiley Interdiscip. Rev. Dev. Biol. 2, 461–478 (2013).

    Article  CAS  PubMed  Google Scholar 

  69. Dias, C. M. et al. Homozygous missense variants in NTNG2, encoding a presynaptic Netrin-G2 adhesion protein, lead to a distinct neurodevelopmental disorder. Am. J. Hum. Genet. 105, 1048–1056 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Strauss, K. A. et al. Recessive symptomatic focal epilepsy and mutant contactin-associated protein-like 2. N. Engl. J. Med. 354, 1370–1377 (2006).

    Article  CAS  PubMed  Google Scholar 

  71. Novarino, G. et al. Mutations in BCKD-kinase lead to a potentially treatable form of autism with epilepsy. Science 338, 394–397 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Morrow, E. M. et al. Identifying autism loci and genes by tracing recent shared ancestry. Science 321, 218–223 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Manzini, M. C. et al. CC2D1A regulates human intellectual and social function as well as NF-κB signaling homeostasis. Cell Rep. 8, 647–655 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Yu, T. W. et al. Using whole-exome sequencing to identify inherited causes of autism. Neuron 77, 259–273 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Doan, R. N. et al. Recessive gene disruptions in autism spectrum disorder. Nat. Genet. 51, 1092–1098 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Lim, E. T. et al. Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron 77, 235–242 (2013). This paper characterizes the rate of homozygous knockout mutations in ASD and is one of the first studies to characterize the contribution of rare mutations on chromosome X to ASD risk.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Schmitz-Abe, K. et al. Homozygous deletions implicate non-coding epigenetic marks in autism spectrum disorder. Sci. Rep. 10, 14045 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. D’Gama, A. M. et al. Targeted DNA sequencing from autism spectrum disorder brains implicates multiple genetic mechanisms. Neuron 88, 910–917 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Freed, D. & Pevsner, J. The contribution of mosaic variants to autism spectrum disorder. PLoS Genet. 12, e1006245 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Krupp, D. R. et al. Exonic mosaic mutations contribute risk for autism spectrum disorder. Am. J. Hum. Genet. 101, 369–390 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Lim, E. T. et al. Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder. Nat. Neurosci. 20, 1217–1224 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Dou, Y. et al. Postzygotic single-nucleotide mosaicisms contribute to the etiology of autism spectrum disorder and autistic traits and the origin of mutations. Hum. Mutat. 38, 1002–1013 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. D’Gama, A. M. & Walsh, C. A. Somatic mosaicism and neurodevelopmental disease. Nat. Neurosci. 21, 1504–1514 (2018).

    Article  PubMed  CAS  Google Scholar 

  84. Kong, A. et al. Rate of de novo mutations and the importance of father’s age to disease risk. Nature 488, 471–475 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Leppa, V. M. et al. Rare inherited and de novo CNVs reveal complex contributions to ASD risk in multiplex families. Am. J. Hum. Genet. 99, 540–554 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Jin, S. C. et al. Contribution of rare inherited and de novo variants in 2,871 congenital heart disease probands. Nat. Genet. 49, 1593–1601 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Capkova, Z. et al. Differences in the importance of microcephaly, dysmorphism, and epilepsy in the detection of pathogenic CNVs in ID and ASD patients. PeerJ 7, e7979 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Robinson, E. B. et al. Autism spectrum disorder severity reflects the average contribution of de novo and familial influences. Proc. Natl Acad. Sci. USA 111, 15161–15165 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Guo, H. et al. Genome sequencing identifies multiple deleterious variants in autism patients with more severe phenotypes. Genet. Med. 21, 1611–1620 (2019).

    Article  CAS  PubMed  Google Scholar 

  90. Werling, D. M. et al. An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder. Nat. Genet. 50, 727–736 (2018). This study develops an analytical framework for WGS termed a category-wide association study, which mirrors the statistical rigour of GWAS with annotation categories in place of SNPs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Sullivan, P. F. et al. Psychiatric genomics: an update and an agenda. Am. J. Psychiatry 175, 15–27 (2018).

    Article  PubMed  Google Scholar 

  92. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019). This work is the largest ASD GWAS published to date, including ~18,000 cases and ~28,000 controls, and identifies 5 genome-wide significant loci.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Antaki, D. et al. A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex. Preprint at medRxiv https://doi.org/10.1101/2021.03.30.21254657 (2021).

    Article  Google Scholar 

  94. Wigdor, E. M. et al. The female protective effect against autism spectrum disorder. Preprint at medRxiv https://doi.org/10.1101/2021.03.29.21253866 (2021).

    Article  Google Scholar 

  95. Werling, D. M. The role of sex-differential biology in risk for autism spectrum disorder. Biol. Sex. Differ. 7, 58 (2016). This review explores the magnitude of male bias in ASD prevalence, describes the FPE and highlights sex-differential pathways that may underlie sex bias in ASD.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013). A critical study that assesses the common variant risk shared across ASD, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder and schizophrenia.

    Article  PubMed Central  CAS  Google Scholar 

  97. Weiner, D. J. et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat. Genet. 49, 978–985 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Iakoucheva, L. M., Muotri, A. R. & Sebat, J. Getting to the cores of autism. Cell 178, 1287–1298 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. State, M. W. & Levitt, P. The conundrums of understanding genetic risks for autism spectrum disorders. Nat. Neurosci. 14, 1499–1506 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Davies, R. W. et al. Using common genetic variation to examine phenotypic expression and risk prediction in 22q11.2 deletion syndrome. Nat. Med. 26, 1912–1918 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Jannot, A.-S., Ehret, G. & Perneger, T. P < 5 × 10–8 has emerged as a standard of statistical significance for genome-wide association studies. J. Clin. Epidemiol. 68, 460–465 (2015).

    Article  PubMed  Google Scholar 

  102. Banerjee-Basu, S. & Packer, A. SFARI Gene: an evolving database for the autism research community. Dis. Model. Mech. 3, 133–135 (2010).

    Article  PubMed  Google Scholar 

  103. Abrahams, B. S. et al. SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol. Autism 4, 36 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Bleuler, E. in Dementia Praecox or the Group of Schizophrenias (ed. Zinkin, J.) 548 (International Universities Press, 1950).

  105. Evans, B. How autism became autism: the radical transformation of a central concept of child development in Britain. Hist. Hum. Sci. 26, 3–31 (2013).

    Article  Google Scholar 

  106. Spitzer, R. L., Williams, J. B. & Skodol, A. E. DSM-III: the major achievements and an overview. Am. J. Psychiatry 137, 151–164 (1980).

    Article  CAS  PubMed  Google Scholar 

  107. Thurm, A., Farmer, C., Salzman, E., Lord, C. & Bishop, S. State of the field: differentiating intellectual disability from autism spectrum disorder. Front. Psychiatry 10, 526 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Brainstorm Consortium. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

    Article  CAS  Google Scholar 

  109. Malhotra, D. & Sebat, J. CNVs: harbingers of a rare variant revolution in psychiatric genetics. Cell 148, 1223–1241 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Myers, S. M. et al. Insufficient evidence for ‘autism-specific’ genes. Am. J. Hum. Genet. 106, 587–595 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Buxbaum, J. D. et al. Not all autism genes are created equal: a response to Myers et al. Am. J. Hum. Genet. 107, 1000–1003 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Myers, S. M., Challman, T. D., Martin, C. L. & Ledbetter, D. H. Response to Buxbaum et al. Am. J. Hum. Genet. 107, 1004 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Gudmundsson, O. O. et al. Attention-deficit hyperactivity disorder shares copy number variant risk with schizophrenia and autism spectrum disorder. Transl. Psychiatry 9, 258 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  114. Martin, J. et al. Biological overlap of attention-deficit/hyperactivity disorder and autism spectrum disorder: evidence from copy number variants. J. Am. Acad. Child. Adolesc. Psychiatry 53, 761–70.e26 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Zarrei, M. et al. A large data resource of genomic copy number variation across neurodevelopmental disorders. NPJ Genom. Med. 4, 26 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Williams, N. M. et al. Rare chromosomal deletions and duplications in attention-deficit hyperactivity disorder: a genome-wide analysis. Lancet 376, 1401–1408 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Shi, J. et al. Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature 460, 753–757 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Stefansson, H. et al. Common variants conferring risk of schizophrenia. Nature 460, 744–747 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. International Schizophrenia Consortium. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

    Article  PubMed Central  CAS  Google Scholar 

  120. Ben-Shalom, R. et al. Opposing effects on Na1.2 function underlie differences between SCN2A variants observed in individuals with autism spectrum disorder or infantile seizures. Biol. Psychiatry 82, 224–232 (2017). This study identifies a link between putative loss-of-function and gain-of-function mutations in ASD and epilepsy, respectively, providing one of the first successful examples of understanding how a single gene (SCN2A) contributes risk to multiple disorders.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Skuse, D. H. Rethinking the nature of genetic vulnerability to autistic spectrum disorders. Trends Genet. 23, 387–395 (2007).

    Article  CAS  PubMed  Google Scholar 

  122. Skuse, D. H. et al. Social communication competence and functional adaptation in a general population of children: preliminary evidence for sex-by-verbal IQ differential risk. J. Am. Acad. Child. Adolesc. Psychiatry 48, 128–137 (2009).

    Article  PubMed  Google Scholar 

  123. Rees, E. et al. Schizophrenia, autism spectrum disorders and developmental disorders share specific disruptive coding mutations. Nat. Commun. 12, 5353 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Rosenthal, S. B. et al. A convergent molecular network underlying autism and congenital heart disease. Cell Syst. 12, 1094–1107 (2021).

    Article  CAS  PubMed  Google Scholar 

  125. Liu, L. et al. DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics. Mol. Autism 5, 22 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  126. Chang, J., Gilman, S. R., Chiang, A. H., Sanders, S. J. & Vitkup, D. Genotype to phenotype relationships in autism spectrum disorders. Nat. Neurosci. 18, 191–198 (2015).

    Article  CAS  PubMed  Google Scholar 

  127. Chaste, P. et al. A genome-wide association study of autism using the Simons Simplex Collection: does reducing phenotypic heterogeneity in autism increase genetic homogeneity? Biol. Psychiatry 77, 775–784 (2015).

    Article  PubMed  Google Scholar 

  128. Huang, J. K. et al. Systematic evaluation of molecular networks for discovery of disease genes. Cell Syst. 6, 484–495.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Ben-David, E. & Shifman, S. Networks of neuronal genes affected by common and rare variants in autism spectrum disorders. PLoS Genet. 8, e1002556 (2012). This is one of the first studies to assess spatiotemporal convergence of ASD risk genes, highlighting the prenatal period as well as chromatin regulation as putative factors in ASD pathogenesis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature https://doi.org/10.1038/s41586-022-04434-5 (2022).

    Article  PubMed  Google Scholar 

  131. Durand, C. M. et al. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are associated with autism spectrum disorders. Nat. Genet. 39, 25–27 (2007).

    Article  CAS  PubMed  Google Scholar 

  132. Kim, H.-G. et al. Disruption of neurexin 1 associated with autism spectrum disorder. Am. J. Hum. Genet. 82, 199–207 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Bourgeron, T. A synaptic trek to autism. Curr. Opin. Neurobiol. 19, 231–234 (2009).

    Article  CAS  PubMed  Google Scholar 

  134. Walsh, C. A., Morrow, E. M. & Rubenstein, J. L. R. Autism and brain development. Cell 135, 396–400 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000). A marker paper for the Gene Ontology Consortium, this manuscript outlines their goal to produce a structured, precisely defined, common, controlled vocabulary for describing the roles of genes and gene products in any organism. These ‘GO terms’ have formed the foundation of many systems biological analyses conducting GO enrichment or gene set enrichment analysis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Gene Ontology Consortium. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 49, D325–D334 (2021).

    Article  CAS  Google Scholar 

  138. Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019).

    Article  CAS  PubMed  Google Scholar 

  139. Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–W311 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  141. Dutkowski, J. et al. A Gene Ontology inferred from molecular networks. Nat. Biotechnol. 31, 38–45 (2013).

    Article  CAS  PubMed  Google Scholar 

  142. Schnoes, A. M., Ream, D. C., Thorman, A. W., Babbitt, P. C. & Friedberg, I. Biases in the experimental annotations of protein function and their effect on our understanding of protein function space. PLoS Comput. Biol. 9, e1003063 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Haynes, W. A., Tomczak, A. & Khatri, P. Gene annotation bias impedes biomedical research. Sci. Rep. 8, 1362 (2018). This paper analyses multiple annotation databases and finds significant inequalities across genes that have become more prominent over time, highlighting a self-perpetuating cycle that may be driven by the tendency of researchers to focus their efforts on richly annotated genes rather than those with the strongest molecular data.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Zhao, H. et al. Altered neurogenesis and disrupted expression of synaptic proteins in prefrontal cortex of SHANK3-deficient non-human primate. Cell Res. 27, 1293–1297 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011). This work presents a large gene expression database that includes human brain samples from before birth to late adulthood in 16 brain regions, which allows spatially and temporally informed analyses. This key resource has been widely used to generate critical insights into convergence of genetic risk for ASD and other disorders of the human brain.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013). One of the first studies to assess spatiotemporal convergence of ASD risk genes, this study conducts a weighted gene co-expression network analysis using BrainSpan developmental expression data and assesses modules for enrichment of a broad range of ASD-associated genes, implicating the early and late foetal periods and upper layer glutamatergic neurons in ASD.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Ben-David, E. & Shifman, S. Combined analysis of exome sequencing points toward a major role for transcription regulation during brain development in autism. Mol. Psychiatry 18, 1054–1056 (2013).

    Article  CAS  PubMed  Google Scholar 

  148. Xu, X., Wells, A. B., O’Brien, D. R., Nehorai, A. & Dougherty, J. D. Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders. J. Neurosci. 34, 1420–1431 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Uddin, M. et al. Brain-expressed exons under purifying selection are enriched for de novo mutations in autism spectrum disorder. Nat. Genet. 46, 742–747 (2014).

    Article  CAS  PubMed  Google Scholar 

  150. Lin, G. N. et al. Spatiotemporal 16p11.2 protein network implicates cortical late mid-fetal brain development and KCTD13–Cul3–RhoA pathway in psychiatric diseases. Neuron 85, 742–754 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Walker, R. L. et al. Genetic control of expression and splicing in developing human brain informs disease mechanisms. Cell 179, 750–771.e22 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Werling, D. M. et al. Whole-genome and RNA sequencing reveal variation and transcriptomic coordination in the developing human prefrontal cortex. Cell Rep. 31, 107489 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014). This study presents gene expression data from laser-microdissected human mid-gestational brain tissue. These data have been central to increasing the resolution of systems biological analyses of ASD genetic risk.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Pollen, A. A. et al. Molecular identity of human outer radial glia during cortical development. Cell 163, 55–67 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Nowakowski, T. J. et al. Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. Science 358, 1318–1323 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Polioudakis, D. et al. A single-cell transcriptomic atlas of human neocortical development during mid-gestation. Neuron 103, 785–801.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Eze, U. C., Bhaduri, A., Haeussler, M., Nowakowski, T. J. & Kriegstein, A. R. Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia. Nat. Neurosci. 24, 584–594 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Fan, X. et al. Spatial transcriptomic survey of human embryonic cerebral cortex by single-cell RNA-seq analysis. Cell Res. 28, 730–745 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Bernard, A. et al. Transcriptional architecture of the primate neocortex. Neuron 73, 1083–1099 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Doyle, J. P. et al. Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell 135, 749–762 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011). One of the first papers comparing gene expression profiles in postmortem brain tissue of neurotypical controls versus ASD patients, this study identifies several molecular signatures that have now been well replicated (e.g. downregulation of neuron- and synapse-related genes and upregulation of microglia-related genes).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Gupta, S. et al. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat. Commun. 5, 5748 (2014).

    Article  CAS  PubMed  Google Scholar 

  165. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Velmeshev, D. et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science 364, 685–689 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018). This study describes molecular data resources generated by the PsychENCODE Consortium from adult human brain tissue, and conducts a deconvolution analysis of bulk and single-cell expression data that finds that the majority of expression variation across bulk brain tissue samples is attributable to varying proportions of basic cell types.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Jaffe, A. E. et al. qSVA framework for RNA quality correction in differential expression analysis. Proc. Natl Acad. Sci. USA 114, 7130–7135 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Zhu, Y., Wang, L., Yin, Y. & Yang, E. Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Sci. Rep. 7, 5435 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  170. Li, J. Z. et al. Systematic changes in gene expression in postmortem human brains associated with tissue pH and terminal medical conditions. Hum. Mol. Genet. 13, 609–616 (2004).

    Article  CAS  PubMed  Google Scholar 

  171. Chiaradia, I. & Lancaster, M. A. Brain organoids for the study of human neurobiology at the interface of in vitro and in vivo. Nat. Neurosci. 23, 1496–1508 (2020).

    Article  CAS  PubMed  Google Scholar 

  172. Mariani, J. et al. FOXG1-dependent dysregulation of GABA/glutamate neuron differentiation in autism spectrum disorders. Cell 162, 375–390 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Schafer, S. T. et al. Pathological priming causes developmental gene network heterochronicity in autistic subject-derived neurons. Nat. Neurosci. 22, 243–255 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Marchetto, M. C. et al. Altered proliferation and networks in neural cells derived from idiopathic autistic individuals. Mol. Psychiatry 22, 820–835 (2017). One of the first studies comparing iPSC-derived neural cells from neurotypical controls to those from people with ASD and macrocephaly.

    Article  CAS  PubMed  Google Scholar 

  175. Adhya, D. et al. Atypical neurogenesis in induced pluripotent stem cells from autistic individuals. Biol. Psychiatry 89, 486–496 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. DeRosa, B. A. et al. Convergent pathways in idiopathic autism revealed by time course transcriptomic analysis of patient-derived neurons. Sci. Rep. 8, 8423 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  177. Griesi-Oliveira, K. et al. Transcriptome of iPSC-derived neuronal cells reveals a module of co-expressed genes consistently associated with autism spectrum disorder. Mol. Psychiatry 26, 1589–1605 (2021).

    Article  CAS  PubMed  Google Scholar 

  178. Amiri, A. et al. Transcriptome and epigenome landscape of human cortical development modeled in organoids. Science 362, eaat6720 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Trevino, A. E. et al. Chromatin accessibility dynamics in a model of human forebrain development. Science 367, eaay1645 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Pollen, A. A. et al. Establishing cerebral organoids as models of human-specific brain evolution. Cell 176, 743–756.e17 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Andrews, M. G. & Nowakowski, T. J. Human brain development through the lens of cerebral organoid models. Brain Res. 1725, 146470 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Bhaduri, A. et al. Cell stress in cortical organoids impairs molecular subtype specification. Nature 578, 142–148 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  183. Andersen, J. et al. Generation of functional human 3D cortico-motor assembloids. Cell 183, 1913–1929.e26 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Miura, Y. et al. Generation of human striatal organoids and cortico-striatal assembloids from human pluripotent stem cells. Nat. Biotechnol. 38, 1421–1430 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. Birey, F. et al. Assembly of functionally integrated human forebrain spheroids. Nature 545, 54–59 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  186. Deneault, E. et al. Complete disruption of autism-susceptibility genes by gene editing predominantly reduces functional connectivity of isogenic human neurons. Stem Cell Rep. 11, 1211–1225 (2018).

    Article  CAS  Google Scholar 

  187. Lalli, M. A., Avey, D., Dougherty, J. D., Milbrandt, J. & Mitra, R. D. High-throughput single-cell functional elucidation of neurodevelopmental disease-associated genes reveals convergent mechanisms altering neuronal differentiation. Genome Res. 30, 1317–1331 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  188. Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  190. Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896.e15 (2016).

    Article  CAS  PubMed  Google Scholar 

  191. Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Rubin, A. J. et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176, 361–376.e17 (2019).

    Article  CAS  PubMed  Google Scholar 

  193. Tian, R. et al. CRISPR interference-based platform for multimodal genetic screens in human iPSC-derived neurons. Neuron 104, 239–255.e12 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  194. Kampmann, M. CRISPR-based functional genomics for neurological disease. Nat. Rev. Neurol. 16, 465–480 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  195. Tian, R. et al. Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis. Nat. Neurosci. 24, 1020–1034 (2021).

    Article  CAS  PubMed  Google Scholar 

  196. Volpato, V. & Webber, C. Addressing variability in iPSC-derived models of human disease: guidelines to promote reproducibility. Dis. Model. Mech. 13, dmm042317 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  197. Genç, Ö. et al. Homeostatic plasticity fails at the intersection of autism-gene mutations and a novel class of common genetic modifiers. eLife 9, e55775 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  198. Packer, A. Neocortical neurogenesis and the etiology of autism spectrum disorder. Neurosci. Biobehav. Rev. 64, 185–195 (2016). This comprehensive review details the potential convergence of ASD genetic risk around neurogenesis.

    Article  PubMed  Google Scholar 

  199. Exner, C. R. T. & Willsey, H. R. Xenopus leads the way: frogs as a pioneering model to understand the human brain. Genesis 59, e23405 (2021).

    Article  PubMed  Google Scholar 

  200. Werling, D. M. & Geschwind, D. H. Sex differences in autism spectrum disorders. Curr. Opin. Neurol. 26, 146–153 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Loomes, R., Hull, L. & Mandy, W. P. L. What is the male-to-female ratio in autism spectrum disorder? A systematic review and meta-analysis. J. Am. Acad. Child. Adolesc. Psychiatry 56, 466–474 (2017).

    Article  PubMed  Google Scholar 

  202. Kim, Y. S. et al. Prevalence of autism spectrum disorders in a total population sample. Am. J. Psychiatry 168, 904–912 (2011).

    Article  PubMed  Google Scholar 

  203. Bai, D. et al. Inherited risk for autism through maternal and paternal lineage. Biol. Psychiatry 88, 480–487 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  204. Palmer, N. et al. Association of sex with recurrence of autism spectrum disorder among siblings. JAMA Pediatr. 171, 1107–1112 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  205. Gockley, J. et al. The female protective effect in autism spectrum disorder is not mediated by a single genetic locus. Mol. Autism 6, 25 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  206. Martin, H. C. et al. The contribution of X-linked coding variation to severe developmental disorders. Nat. Commun. 12, 627 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. McCarthy, M. M. Multifaceted origins of sex differences in the brain. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 20150106 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  208. Baron-Cohen, S. The extreme male brain theory of autism. Trends Cogn. Sci. 6, 248–254 (2002).

    Article  PubMed  Google Scholar 

  209. Baron-Cohen, S. et al. Elevated fetal steroidogenic activity in autism. Mol. Psychiatry 20, 369–376 (2015).

    Article  CAS  PubMed  Google Scholar 

  210. Manoli, D. S. & Tollkuhn, J. Gene regulatory mechanisms underlying sex differences in brain development and psychiatric disease. Ann. N. Y. Acad. Sci. 1420, 26–45 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  211. Ingudomnukul, E., Baron-Cohen, S., Wheelwright, S. & Knickmeyer, R. Elevated rates of testosterone-related disorders in women with autism spectrum conditions. Horm. Behav. 51, 597–604 (2007).

    Article  CAS  PubMed  Google Scholar 

  212. Auyeung, B. et al. Fetal testosterone and autistic traits. Br. J. Psychol. 100, 1–22 (2009).

    Article  PubMed  Google Scholar 

  213. Komada, M. et al. Hedgehog signaling is involved in development of the neocortex. Development 135, 2717–2727 (2008).

    Article  CAS  PubMed  Google Scholar 

  214. Britto, J., Tannahill, D. & Keynes, R. A critical role for sonic hedgehog signaling in the early expansion of the developing brain. Nat. Neurosci. 5, 103–110 (2002).

    Article  CAS  PubMed  Google Scholar 

  215. Echelard, Y. et al. Sonic hedgehog, a member of a family of putative signaling molecules, is implicated in the regulation of CNS polarity. Cell 75, 1417–1430 (1993).

    Article  CAS  PubMed  Google Scholar 

  216. Finkel, R. S. et al. Nusinersen versus sham control in infantile-onset spinal muscular atrophy. N. Engl. J. Med. 377, 1723–1732 (2017).

    Article  CAS  PubMed  Google Scholar 

  217. Mendell, J. R. et al. Single-dose gene-replacement therapy for spinal muscular atrophy. N. Engl. J. Med. 377, 1713–1722 (2017).

    Article  CAS  PubMed  Google Scholar 

  218. Colantuoni, C. et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519–523 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  219. O’Brien, H. E. et al. Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders. Genome Biol. 19, 194 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  220. Zhong, S. et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555, 524–528 (2018).

    Article  CAS  PubMed  Google Scholar 

  221. Trevino, A. E. et al. Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell 184, 5053–5069.e23 (2021).

    Article  CAS  PubMed  Google Scholar 

  222. Marshall, C. R. et al. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat. Genet. 49, 27–35 (2017).

    Article  CAS  PubMed  Google Scholar 

  223. Searles Quick, V. B., Wang, B. & State, M. W. Leveraging large genomic datasets to illuminate the pathobiology of autism spectrum disorders. Neuropsychopharmacology 46, 55–69 (2021).

    Article  PubMed  Google Scholar 

  224. MacDonald, J. R., Ziman, R., Yuen, R. K. C., Feuk, L. & Scherer, S. W. The Database of Genomic Variants: a curated collection of structural variation in the human genome. Nucleic Acids Res. 42, D986–D992 (2014).

    Article  CAS  PubMed  Google Scholar 

  225. Collins, R. L. et al. A structural variation reference for medical and population genetics. Nature 581, 444–451 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  226. Purcell, A. E., Jeon, O. H., Zimmerman, A. W., Blue, M. E. & Pevsner, J. Postmortem brain abnormalities of the glutamate neurotransmitter system in autism. Neurology 57, 1618–1628 (2001).

    Article  CAS  PubMed  Google Scholar 

  227. Sutcliffe, J. S. et al. The E6-Ap ubiquitin-protein ligase (UBE3A) gene is localized within a narrowed Angelman syndrome critical region. Genome Res. 7, 368–377 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  228. Greenberg, F. & Ledbetter, D. H. Deletions of proximal 15q without Prader–Willi syndrome. Am. J. Med. Genet. 28, 813–820 (1987).

    Article  CAS  PubMed  Google Scholar 

  229. Kaplan, L. C. et al. Clinical heterogeneity associated with deletions in the long arm of chromosome 15: report of 3 new cases and their possible genetic significance. Am. J. Med. Genet. 28, 45–53 (1987).

    Article  CAS  PubMed  Google Scholar 

  230. Magenis, R. E., Brown, M. G., Lacy, D. A., Budden, S. & LaFranchi, S. Is Angelman syndrome an alternate result of del(15)(q11q13)? Am. J. Med. Genet. 28, 829–838 (1987).

    Article  CAS  PubMed  Google Scholar 

  231. Williams, C. A., Gray, B. A., Hendrickson, J. E., Stone, J. W. & Cantú, E. S. Incidence of 15q deletions in the Angelman syndrome: a survey of twelve affected persons. Am. J. Med. Genet. 32, 339–345 (1989).

    Article  CAS  PubMed  Google Scholar 

  232. Vilain, A., Apiou, F., Vogt, N., Dutrillaux, B. & Malfoy, B. Assignment of the gene for methyl-CpG-binding protein 2 (MECP2) to human chromosome band Xq28 by in situ hybridization. Cytogenet. Cell Genet. 74, 293–294 (1996).

    Article  CAS  PubMed  Google Scholar 

  233. D’Esposito, M. et al. Isolation, physical mapping, and northern analysis of the X-linked human gene encoding methyl CpG-binding protein, MECP2. Mamm. Genome 7, 533–535 (1996).

    Article  PubMed  Google Scholar 

  234. Kremer, E. J. et al. Mapping of DNA instability at the fragile X to a trinucleotide repeat sequence p(CCG)n. Science 252, 1711–1714 (1991).

    Article  CAS  PubMed  Google Scholar 

  235. Verkerk, A. J. et al. Identification of a gene (FMR-1) containing a CGG repeat coincident with a breakpoint cluster region exhibiting length variation in fragile X syndrome. Cell 65, 905–914 (1991).

    Article  CAS  PubMed  Google Scholar 

  236. Pieretti, M. et al. Absence of expression of the FMR-1 gene in fragile X syndrome. Cell 66, 817–822 (1991).

    Article  CAS  PubMed  Google Scholar 

  237. Li, J. et al. PTEN, a putative protein tyrosine phosphatase gene mutated in human brain, breast, and prostate cancer. Science 275, 1943–1947 (1997).

    Article  CAS  PubMed  Google Scholar 

  238. Li, D. M. & Sun, H. TEP1, encoded by a candidate tumor suppressor locus, is a novel protein tyrosine phosphatase regulated by transforming growth factor β. Cancer Res. 57, 2124–2129 (1997).

    CAS  PubMed  Google Scholar 

  239. Steck, P. A. et al. Identification of a candidate tumour suppressor gene, MMAC1, at chromosome 10q23.3 that is mutated in multiple advanced cancers. Nat. Genet. 15, 356–362 (1997).

    Article  CAS  PubMed  Google Scholar 

  240. van Slegtenhorst, M. et al. Identification of the tuberous sclerosis gene TSC1 on chromosome 9q34. Science 277, 805–808 (1997).

    Article  PubMed  Google Scholar 

  241. European Chromosome 16 Tuberous Sclerosis Consortium. Identification and characterization of the tuberous sclerosis gene on chromosome 16. Cell 75, 1305–1315 (1993).

    Article  Google Scholar 

  242. Marchuk, D. A. et al. cDNA cloning of the type 1 neurofibromatosis gene: complete sequence of the NF1 gene product. Genomics 11, 931–940 (1991).

    Article  CAS  PubMed  Google Scholar 

  243. Cawthon, R. M. et al. A major segment of the neurofibromatosis type 1 gene: cDNA sequence, genomic structure, and point mutations. Cell 62, 193–201 (1990).

    Article  CAS  PubMed  Google Scholar 

  244. Viskochil, D. et al. Deletions and a translocation interrupt a cloned gene at the neurofibromatosis type 1 locus. Cell 62, 187–192 (1990).

    Article  CAS  PubMed  Google Scholar 

  245. Wallace, M. R. et al. Type 1 neurofibromatosis gene: identification of a large transcript disrupted in three NF1 patients. Science 249, 181–186 (1990).

    Article  CAS  PubMed  Google Scholar 

  246. Rouleau, G. A. et al. Alteration in a new gene encoding a putative membrane-organizing protein causes neuro-fibromatosis type 2. Nature 363, 515–521 (1993).

    Article  CAS  PubMed  Google Scholar 

  247. Trofatter, J. A. et al. A novel moesin-, ezrin-, radixin-like gene is a candidate for the neurofibromatosis 2 tumor suppressor. Cell 72, 791–800 (1993).

    Article  CAS  PubMed  Google Scholar 

  248. Guo, H. et al. NCKAP1 disruptive variants lead to a neurodevelopmental disorder with core features of autism. Am. J. Hum. Genet. 107, 963–976 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  249. Mirzaa, G. M. et al. De novo and inherited variants in ZNF292 underlie a neurodevelopmental disorder with features of autism spectrum disorder. Genet. Med. 22, 538–546 (2020).

    Article  CAS  PubMed  Google Scholar 

  250. Guo, H. et al. Disruptive mutations in TANC2 define a neurodevelopmental syndrome associated with psychiatric disorders. Nat. Commun. 10, 4679 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  251. Van Dijck, A. et al. Clinical presentation of a complex neurodevelopmental disorder caused by mutations in ADNP. Biol. Psychiatry 85, 287–297 (2019).

    Article  PubMed  Google Scholar 

  252. Blackburn, A. T. M. et al. DYRK1A-related intellectual disability: a syndrome associated with congenital anomalies of the kidney and urinary tract. Genet. Med. 21, 2755–2764 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  253. Ji, J. et al. DYRK1A haploinsufficiency causes a new recognizable syndrome with microcephaly, intellectual disability, speech impairment, and distinct facies. Eur. J. Hum. Genet. 23, 1473–1481 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  254. Bernier, R. et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell 158, 263–276 (2014). This is one of the first studies to recognize distinctive physical features and co-morbidities shared across patients with rare mutations in the same ‘idiopathic’ ASD risk gene — opening the door to the presence of previously unappreciated syndromes within idiopathic ASD.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank S. Wang for data analysis and graphical representation for Fig. 1 and S. Pyle for graphic design. This work was supported by a gift from the Overlook International Foundation and grant support from the National Institutes of Mental Health (NIMH) (U01MH116487, R01MH115963, 1U01MH115747, R25MH06048) and the Psychiatric Cell Map Initiative (pcmi.ucsf.edu).

Author information

Authors and Affiliations

Authors

Contributions

The authors all contributed to all aspects of preparing the article.

Corresponding authors

Correspondence to A. Jeremy Willsey or Matthew W. State.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Neuroscience thanks R. Anney, S. Shifman and F. Volkmar for their contribution to the peer review of this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

BrainSpan Atlas of the Developing Human Brain: https://brainspan.org

Supplementary information

Glossary

Autism spectrum disorder

(ASD). A group of developmental disorders characterized by deficits in social communication and social interaction, and restrictive and repetitive patterns of behaviour, interests or activities.

Intellectual disability

A developmental disorder characterized by deficits in intellectual functioning (including reasoning, problem-solving and academic learning) and adaptive functioning (including communication and independent living).

Genetic architecture

The characteristics of genetic variation that contribute to a specific phenotype, including variant frequency, effect size and their interaction with each other and the environment.

Locus heterogeneity

Variants at different gene loci result in a similar phenotype, individually (for monogenic diseases) or in combination (for complex traits).

Pleiotropy

A phenomenon in which a single gene contributes to multiple processes or phenotypic traits.

Convergent neuroscience

Studies that address the overlap, or intersection, of genetic risk for a psychiatric disorder with respect to molecular-level, cellular-level and circuit-level function as well as across multiple dimensions of analysis, including anatomical localization and developmental timing.

Gene Ontology terms

A hierarchical set of terms that aim to define the universe of possible descriptors a gene can have, including properties such as molecular function, cellular component and biological process.

Penetrance

The probability that an individual with a given genotype will exhibit the associated phenotype.

Resilience

The capacity of an individual to have a ‘better than expected outcome’ (for example, being unaffected despite having multiple autism spectrum disorder-associated genetic variants).

Protein-truncating variants

(Also referred to as loss-of-function or likely gene disrupting variants). Sequence variants that are predicted to shorten the protein-coding sequence of a gene — usually to the point of resulting in a non-functional protein or no protein at all due to nonsense-mediated decay — including nonsense, frameshift and essential splice site variants.

Somatic mutations

(Also known as mosaic mutations). Variants that are present in fewer than 100% of the cells of an individual (for example, present in brain cells but not present in germ-line cells), generally because the mutation occurred after fertilization.

Compound heterozygous variants

Variants similar to recessive variants, in that both alleles of a gene are mutated; however, in this case, the two alleles are different.

Neurodevelopmental disorders

(NDDs). A group of conditions (including autism spectrum disorder, attention deficit hyperactivity disorder, tic disorders and intellectual disability) characterized by developmental onset, atypical brain development and resultant impairments in cognition, communication, behaviour and/or motor skills.

Polygenic risk scores

Estimates of an individual’s genetic predisposition for a disorder or disease based on the collective effects of many genetic variants.

SFARI Gene

An evolving database compiled by the Simons Foundation Autism Research Initiative (SFARI) research program that includes a list of genes and copy number variants (CNVs) associated with autism spectrum disorder curated from human studies and animal models.

Missense variants

Single base-pair coding variants that result in altered amino acids, for which several metrics have been developed to predict the functional consequence, including the PolyPhen-2 (Polymorphism Phenotyping v2) and MPC (missense badness, PolyPhen-2 and constraint) metrics, for which a categorization of PolyPhen-2 missense 3 (Mis3) or an MPC score ≥2 reflects a probably damaging variant.

Gene Ontology enrichment analysis

A statistical approach that assesses whether specific Gene Ontology terms are statistically over-represented in a (generally large) set of genes or proteins (for example, among autism spectrum disorder risk genes).

Gene set enrichment analysis

Like Gene Ontology enrichment analysis, this statistical approach assesses whether specific Gene Ontology terms are statistically over-represented in a large set of genes or proteins; however, unlike Gene Ontology enrichment analysis, gene set enrichment analysis also incorporates the rank of genes within the set into the statistical test (for example, differentially expressed genes ranked based on the fold-change between cases and controls).

Pathway databases

Databases that annotate genes with an ontological approach to capture functional relationships, including molecular interactions, regulation and phenotype associations; common pathway databases include Gene Ontologies (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG).

Neurogenesis

The process by which new neurons are generated, in humans most active during gestational weeks 10–25.

BrainSpan Atlas of the Developing Human Brain

A foundational resource that includes bulk transcriptome profiling of up to 16 cortical and subcortical structures across human brain development (prenatal to adult).

Neurotypical

Description of individuals with apparently typical intellectual and cognitive development (for example, not affected by a neurodevelopmental or psychiatric disorder).

PsychENCODE

A consortium-based project that aims to produce multidimensional genomic data from human post-mortem brain tissue from neurotypical and patient donors to begin to functionally characterize risk variants in model systems, with an initial focus on autism spectrum disorder, bipolar disorder and schizophrenia.

Induced pluripotent stem cell

(iPSC). A pluripotent cell obtained by reprogramming somatic cells through ectopic expression of defined pluripotency factors and/or treatment with small molecules.

Organoids

(Also known as spheroids) 3D structures, derived in vitro from primary tissue, embryonic stem cells or induced pluripotent stem cells (iPSCs), that self-organize and recapitulate aspects of organ development, anatomy, cellular composition, physiology and function.

Gene modules

Typically, a set of genes with similar expression profiles that are inferred to be functionally related and co-regulated, and that can be identified through various module-detection methods (for example, gene co-expression).

Functional convergence

A situation in which seemingly disparate entities are associated with an overlapping function, which can occur at various levels of investigation, including molecular, cell taxonomic, morphological, neural circuit or phenotype level.

Sex differences

Differences between individuals of different sex in the same species, often including secondary sex characteristics, size and behavioural or cognitive traits.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Willsey, H.R., Willsey, A.J., Wang, B. et al. Genomics, convergent neuroscience and progress in understanding autism spectrum disorder. Nat Rev Neurosci 23, 323–341 (2022). https://doi.org/10.1038/s41583-022-00576-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41583-022-00576-7

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