Skip to main content

Identification of de novo Mutations in the Chinese Autism Spectrum Disorder Cohort via Whole-Exome Sequencing Unveils Brain Regions Implicated in Autism

Neuroscience Bulletin (2023)Cite this article

Abstract

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder characterized by deficits in social interactions and repetitive behaviors. Although hundreds of ASD risk genes, implicated in synaptic formation and transcriptional regulation, have been identified through human genetic studies, the East Asian ASD cohorts are still under-represented in genome-wide genetic studies. Here, we applied whole-exome sequencing to 369 ASD trios including probands and unaffected parents of Chinese origin. Using a joint-calling analytical pipeline based on GATK toolkits, we identified numerous de novo mutations including 55 high-impact variants and 165 moderate-impact variants, as well as de novo copy number variations containing known ASD-related genes. Importantly, combined with single-cell sequencing data from the developing human brain, we found that the expression of genes with de novo mutations was specifically enriched in the pre-, post-central gyrus (PRC, PC) and banks of the superior temporal (BST) regions in the human brain. By further analyzing the brain imaging data with ASD and healthy controls, we found that the gray volume of the right BST in ASD patients was significantly decreased compared to healthy controls, suggesting the potential structural deficits associated with ASD. Finally, we found a decrease in the seed-based functional connectivity between BST/PC/PRC and sensory areas, the insula, as well as the frontal lobes in ASD patients. This work indicated that combinatorial analysis with genome-wide screening, single-cell sequencing, and brain imaging data reveal the brain regions contributing to the etiology of ASD.

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

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Data and Materials Availability

The datasets used and/or analyzed during the current study are available from the lead contact on reasonable request.

References

  1. Shaw KA, Maenner MJ, Bakian AV, Bilder DA, Durkin MS, Furnier SM. Early identification of autism spectrum disorder among children aged 4 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2018. MMWR Surveill Summ 2021, 70: 1–14.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Zhou H, Xu X, Yan W, Zou X, Wu L, Luo X, et al. Prevalence of autism spectrum disorder in China: A nationwide multi-center population-based study among children aged 6 to 12 years. Neurosci Bull 2020, 36: 961–971.

    Article  PubMed  PubMed Central  Google Scholar 

  3. De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE, et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 2014, 515: 209–215.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Iossifov I, O’Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 2014, 515: 216–221.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, Willsey AJ, et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 2012, 485: 237–241.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An JY, et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 2020, 180: 568-584.e23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Abrahams BS, Arking DE, Campbell DB, Mefford HC, Morrow EM, Weiss LA, et al. SFARI Gene 2.0: A community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol Autism 2013, 4: 36.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Takata A, Miyake N, Tsurusaki Y, Fukai R, Miyatake S, Koshimizu E, et al. Integrative analyses of de novo mutations provide deeper biological insights into autism spectrum disorder. Cell Rep 2018, 22: 734–747.

    Article  CAS  PubMed  Google Scholar 

  9. Guo H, Wang T, Wu H, Long M, Coe BP, Li H, et al. Inherited and multiple de novo mutations in autism/developmental delay risk genes suggest a multifactorial model. Mol Autism 2018, 9: 64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wang T, Guo H, Xiong B, Stessman HA, Wu H, Coe BP, et al. De novo genic mutations among a Chinese autism spectrum disorder cohort. Nat Commun 2016, 7: 13316.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Jiang YH, Yuen RK, Jin X, Wang M, Chen N, Wu X, et al. Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing. Am J Hum Genet 2013, 93: 249–263.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wu J, Yu P, Jin X, Xu X, Li J, Li Z, et al. Genomic landscapes of Chinese sporadic autism spectrum disorders revealed by whole-genome sequencing. J Genet Genomics 2018, 45: 527–538.

    Article  PubMed  Google Scholar 

  13. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009, 25: 1754–1760.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Okonechnikov K, Conesa A, García-Alcalde F. Qualimap 2: Advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 2016, 32: 292–294.

    Article  CAS  PubMed  Google Scholar 

  15. Poplin R, Ruano-Rubio V, DePristo MA, Fennell TJ, Carneiro MO, Van der Auwera GA, et al. Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv 2018, https://doi.org/10.1101/201178.

    Article  Google Scholar 

  16. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. The ensembl variant effect predictor. Genome Biol 2016, 17: 122.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 2016, 536: 285–291.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 1000 Genomes Project Consortium, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1, 092 human genomes. Nature 2012, 491: 56–65.

    Article  Google Scholar 

  19. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 2015, 4: 7.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wei Q, Zhan X, Zhong X, Liu Y, Han Y, Chen W, et al. A Bayesian framework for de novo mutation calling in parents-offspring trios. Bioinformatics 2015, 31: 1375–1381.

    Article  CAS  PubMed  Google Scholar 

  21. Ramu A, Noordam MJ, Schwartz RS, Wuster A, Hurles ME, Cartwright RA, et al. DeNovoGear: de novo indel and point mutation discovery and phasing. Nat Methods 2013, 10: 985–987.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Vaser R, Adusumalli S, Leng SN, Sikic M, Ng PC. SIFT missense predictions for genomes. Nat Protoc 2016, 11: 1–9.

    Article  CAS  PubMed  Google Scholar 

  23. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods 2010, 7: 248–249.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Chun S, Fay JC. Identification of deleterious mutations within three human genomes. Genome Res 2009, 19: 1553–1561.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Schwarz JM, Rödelsperger C, Schuelke M, Seelow D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat Methods 2010, 7: 575–576.

    Article  CAS  PubMed  Google Scholar 

  26. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: Application to cancer genomics. Nucleic Acids Res 2011, 39: e118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Choi Y, Chan AP. PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 2015, 31: 2745–2747.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Liu X, Jian X, Boerwinkle E. dbNSFP: A lightweight database of human nonsynonymous SNPs and their functional predictions. Hum Mutat 2011, 32: 894–899.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Liu X, Wu C, Li C, Boerwinkle E. dbNSFP v3.0: A one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs. Hum Mutat 2016, 37: 235–241.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007, 81: 559–575.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jiang Y, Li Z, Liu Z, Chen D, Wu W, Du Y, et al. mirDNMR: A gene-centered database of background de novo mutation rates in human. Nucleic Acids Res 2017, 45: D796–D803.

    Article  CAS  PubMed  Google Scholar 

  32. He X, Sanders SJ, Liu L, de Rubeis S, Lim ET, Sutcliffe JS, et al. Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes. PLoS Genet 2013, 9: e1003671.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Feliciano P, Zhou X, Astrovskaya I, Turner TN, Wang T, Brueggeman L, et al. Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes. NPJ Genom Med 2019, 4: 19.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Neale BM, Kou Y, Liu L, Ma’ayan A, Samocha KE, Sabo A, et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 2012, 485: 242–245.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. O’Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, Coe BP, et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 2012, 485: 246–250.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Enomoto Y, Tsurusaki Y, Yokoi T, Abe-Hatano C, Ida K, Naruto T, et al. CNV analysis using whole exome sequencing identified biallelic CNVs of VPS13B in siblings with intellectual disability. Eur J Med Genet 2020, 63: 103610.

    Article  PubMed  Google Scholar 

  37. Geschwind DH, State MW. Gene hunting in autism spectrum disorder: On the path to precision medicine. Lancet Neurol 2015, 14: 1109–1120.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Fan X, Dong J, Zhong S, Wei Y, Wu Q, Yan L, et al. Spatial transcriptomic survey of human embryonic cerebral cortex by single-cell RNA-seq analysis. Cell Res 2018, 28: 730–745.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Fan X, Fu Y, Zhou X, Sun L, Yang M, Wang M, et al. Single-cell transcriptome analysis reveals cell lineage specification in temporal-spatial patterns in human cortical development. Sci Adv 2020, 6: eaaz2978.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ayub R, Sun KL, Flores RE, Lam VT, Jo B, Saggar M, et al. Thalamocortical connectivity is associated with autism symptoms in high-functioning adults with autism and typically developing adults. Transl Psychiatry 2021, 11: 93.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Mizuno Y, Kagitani-Shimono K, Jung M, Makita K, Takiguchi S, Fujisawa TX, et al. Structural brain abnormalities in children and adolescents with comorbid autism spectrum disorder and attention-deficit/hyperactivity disorder. Transl Psychiatry 2019, 9: 332.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Cai DC, Wang Z, Bo T, Yan S, Liu Y, Liu Z, et al. MECP2 duplication causes aberrant GABA pathways, circuits and behaviors in transgenic monkeys: Neural mappings to patients with autism. J Neurosci 2020, 40: 3799–3814.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Liu Z, Li X, Zhang JT, Cai YJ, Cheng TL, Cheng C, et al. Autism-like behaviours and germline transmission in transgenic monkeys overexpressing MeCP2. Nature 2016, 530: 98–102.

    Article  CAS  PubMed  Google Scholar 

  44. Zhan Y, Wei J, Liang J, Xu X, He R, Robbins TW, et al. Diagnostic classification for human autism and obsessive-compulsive disorder based on machine learning from a primate genetic model. Am J Psychiatry 2021, 178: 65–76.

    Article  PubMed  Google Scholar 

  45. Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, et al. The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 2014, 19: 659–667.

    Article  PubMed  Google Scholar 

  46. Belin P, Zatorre RJ, Lafaille P, Ahad P, Pike B. Voice-selective areas in human auditory cortex. Nature 2000, 403: 309–312.

    Article  CAS  PubMed  Google Scholar 

  47. Gervais H, Belin P, Boddaert N, Leboyer M, Coez A, Sfaello I, et al. Abnormal cortical voice processing in autism. Nat Neurosci 2004, 7: 801–802.

    Article  CAS  PubMed  Google Scholar 

  48. Shih P, Keehn B, Oram JK, Leyden KM, Keown CL, Müller RA. Functional differentiation of posterior superior temporal sulcus in autism: A functional connectivity magnetic resonance imaging study. Biol Psychiatry 2011, 70: 270–277.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Venkataraman A, Duncan JS, Yang DY, Pelphrey KA. An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism. Neuroimage Clin 2015, 8: 356–366.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Mensen VT, Wierenga LM, van Dijk S, Rijks Y, Oranje B, Mandl RC, et al. Development of cortical thickness and surface area in autism spectrum disorder. Neuroimage Clin 2017, 13: 215–222.

    Article  PubMed  Google Scholar 

  51. Braden BB, Riecken C. Thinning faster? Age-related cortical thickness differences in adults with autism spectrum disorder. Res Autism Spectr Disord 2019, 64: 31–38.

    Article  PubMed  PubMed Central  Google Scholar 

  52. von dem Hagen EA, Nummenmaa L, Yu R, Engell AD, Ewbank MP, Calder AJ. Autism spectrum traits in the typical population predict structure and function in the posterior superior temporal sulcus. Cereb Cortex 2011, 21: 493–500.

    Article  Google Scholar 

  53. Ozonoff S, Miller JN. An exploration of right-hemisphere contributions to the pragmatic impairments of autism. Brain Lang 1996, 52: 411–434.

    Article  CAS  PubMed  Google Scholar 

  54. Deen B, Koldewyn K, Kanwisher N, Saxe R. Functional organization of social perception and cognition in the superior temporal sulcus. Cereb Cortex 2015, 25: 4596–4609.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the families for their participation in this study. This work was supported by the National Natural Science Foundation of China (31625013, 81941015, 32000726, and 61973086), the Shanghai Brain-Intelligence Project from STCSM (16JC1420501), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDBS01060200), the Program of Shanghai Academic Research Leader, The Open Large Infrastructure Research of the Chinese Academy of Sciences, and the Shanghai Municipal Science and Technology Major Project (2018SHZDZX01).

Author information

Author notes

  1. Bo Yuan, Mengdi Wang and Xinran Wu contributed equally to this work.

Authors and Affiliations

  1. Songjiang Research Institute, Songjiang Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China

    Zilong Qiu

  2. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200032, China

    Peipei Cheng, Ran Zhang, Shunying Yu & Yasong Du

  3. Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200032, China

    Bo Yuan, Ran Zhang & Zilong Qiu

  4. Beijing Normal University, Beijing, 100875, China

    Xiaoqun Wang

  5. Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China

    Mengdi Wang

  6. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China

    Xinran Wu & Jie Zhang

Authors
  1. Bo Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mengdi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinran Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peipei Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ran Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ran Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shunying Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yasong Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xiaoqun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Zilong Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jie Zhang, Yasong Du, Xiaoqun Wang or Zilong Qiu.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yuan, B., Wang, M., Wu, X. et al. Identification of de novo Mutations in the Chinese Autism Spectrum Disorder Cohort via Whole-Exome Sequencing Unveils Brain Regions Implicated in Autism. Neurosci. Bull. (2023). https://doi.org/10.1007/s12264-023-01037-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12264-023-01037-6

Keywords

  • Autism
  • Cohorts
  • Whole-exome sequencing
  • Single-cell sequencing