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Exons as units of phenotypic impact for truncating mutations in autism

Molecular Psychiatry volume 26, pages 1685–1695 (2021)Cite this article

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Abstract

Autism spectrum disorders (ASD) are a group of related neurodevelopmental diseases displaying significant genetic and phenotypic heterogeneity. Despite recent progress in understanding ASD genetics, the nature of phenotypic heterogeneity across probands remains unclear. Notably, likely gene-disrupting (LGD) de novo mutations affecting the same gene often result in substantially different ASD phenotypes. Nevertheless, we find that truncating mutations affecting the same exon frequently lead to strikingly similar intellectual phenotypes in unrelated ASD probands. Analogous patterns are observed for two independent proband cohorts and several other important ASD-associated phenotypes. We find that exons biased toward prenatal and postnatal expression preferentially contribute to ASD cases with lower and higher IQ phenotypes, respectively. These results suggest that exons, rather than genes, often represent a unit of effective phenotypic impact for truncating mutations in autism. The observed phenotypic patterns are likely mediated by nonsense-mediated decay (NMD) of splicing isoforms, with autism phenotypes usually triggered by relatively mild (15–30%) decreases in overall gene dosage. We find that each ASD gene with recurrent mutations can be characterized by a parameter, phenotype dosage sensitivity (PDS), which quantifies the relationship between changes in a gene’s dosage and changes in a given disease phenotype. We further demonstrate analogous relationships between exon LGDs and gene expression changes in multiple human tissues. Therefore, similar phenotypic patterns may be also observed in other human genetic disorders.

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Fig. 1: Average difference in IQ scores between SSC probands.
Fig. 2: The relationship between the position of de novo LGD mutations in protein sequence and probands’ IQ scores.
Fig. 3: Gene expression changes across human tissues induced by LGD variants in the same exon and in the same gene but in different exons.
Fig. 4: Relationship between the relative expression of exons harboring LGD mutations and the corresponding decreases in probands’ intellectual phenotypes.
Fig. 5: Relationship between the developmental expression profiles of exons with LGD mutations and intellectual ASD phenotypes.
Fig. 6: Validation of the observed phenotypic patterns in independent cohorts using Vineland Adaptive Behavior Scales (VABS) scores.

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References

  1. Gilman SR, Chang J, Xu B, Bawa TS, Gogos JA, Karayiorgou M, et al. Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nat Neurosci. 2012;15:1723–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Ayalew M, Le-Niculescu H, Levey DF, Jain N, Changala B, Patel SD, et al. Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction. Mol Psychiatry. 2012;17:887–905.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Fromer M, Pocklington AJ, Kavanagh DH, Williams HJ, Dwyer S, Gormley P, et al. De novo mutations in schizophrenia implicate synaptic networks. Nature. 2014;506:179–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Parikshak NN, Gandal MJ, Geschwind DH. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat Rev Genet. 2015;16:441–58.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Chang J, Gilman SR, Chiang AH, Sanders SJ, Vitkup D. Genotype to phenotype relationships in autism spectrum disorders. Nat Neurosci. 2015;18:191–8.

    CAS  PubMed  Google Scholar 

  6. Gilman SR, Iossifov I, Levy D, Ronemus M, Wigler M, Vitkup D. Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron. 2011;70:898–907.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Sanders SJ, Ercan-Sencicek AG, Hus V, Luo R, Murtha MT, Moreno-De-Luca D, et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron. 2011;70:863–85.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 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;515:216–21.

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 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;485:246–50.

    PubMed  PubMed Central  Google Scholar 

  11. American Psychiatric Association (DSM-5 Task Force). Diagnostic and Statistical Manual of Mental Disorders: DSM-5. 5th ed. Washington, DC: American Psychiatric Association; 2013.

  12. Krumm N, O’Roak BJ, Shendure J, Eichler EE. A de novo convergence of autism genetics and molecular neuroscience. Trends Neurosci. 2014;37:95–105.

    CAS  PubMed  Google Scholar 

  13. Ronemus M, Iossifov I, Levy D, Wigler M. The role of de novo mutations in the genetics of autism spectrum disorders. Nat Rev Genet. 2014;15:133–41.

    CAS  PubMed  Google Scholar 

  14. de la Torre-Ubieta L, Won H, Stein JL, Geschwind DH. Advancing the understanding of autism disease mechanisms through genetics. Nat Med. 2016;22:345–61.

    PubMed  PubMed Central  Google Scholar 

  15. Jeste SS, Geschwind DH. Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nat Rev Neurol. 2014;10:74–81.

    PubMed  PubMed Central  Google Scholar 

  16. Talkowski ME, Minikel EV, Gusella JF. Autism spectrum disorder genetics: diverse genes with diverse clinical outcomes. Harv Rev Psychiatry. 2014;22:65–75.

    PubMed  Google Scholar 

  17. Gaugler T, Klei L, Sanders SJ, Bodea CA, Goldberg AP, Lee AB, et al. Most genetic risk for autism resides with common variation. Nat Genet. 2014;46:881–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Gratten J, Wray NR, Keller MC, Visscher PM. Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat Neurosci. 2014;17:782–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Anney R, Klei L, Pinto D, Almeida J, Bacchelli E, Baird G, et al. Individual common variants exert weak effects on the risk for autism spectrum disorders. Hum Mol Genet. 2012;21:4781–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Krumm N, Turner TN, Baker C, Vives L, Mohajeri K, Witherspoon K, et al. Excess of rare, inherited truncating mutations in autism. Nat Genet. 2015;47:582–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Turner TN, Coe BP, Dickel DE, Hoekzema K, Nelson BJ, Zody MC, et al. Genomic patterns of de novo mutation in simplex autism. Cell. 2017;171:710–.e712.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Robinson EB, Samocha KE, Kosmicki JA, McGrath L, Neale BM, Perlis RH, et al. Autism spectrum disorder severity reflects the average contribution of de novo and familial influences. Proc Natl Acad Sci. 2014;111:15161–5.

    CAS  PubMed  Google Scholar 

  23. Levy D, Ronemus M, Yamrom B, Lee YH, Leotta A, Kendall J, et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron. 2011;70:886–97.

    CAS  PubMed  Google Scholar 

  24. Buja A, Volfovsky N, Krieger AM, Lord C, Lash AE, Wigler M, et al. Damaging de novo mutations diminish motor skills in children on the autism spectrum. Proc Natl Acad Sci. 2018;115:E1859–66.

    CAS  PubMed  Google Scholar 

  25. Taylor LJ, Maybery MT, Wray J, Ravine D, Hunt A, Whitehouse AJO. Are there differences in the behavioural phenotypes of Autism Spectrum Disorder probands from simplex and multiplex families? Res Autism Spectr Disord. 2015;11:56–62.

    Google Scholar 

  26. Dissanayake C, Searles J, Barbaro J, Sadka N, Lawson LP. Cognitive and behavioral differences in toddlers with autism spectrum disorder from multiplex and simplex families. Autism Res. 2019;12:682–93.

    PubMed  Google Scholar 

  27. Berends D, Dissanayake C, Lawson LP. Differences in cognition and behaviour in multiplex and simplex autism: does prior experience raising a child with autism matter? J Autism Developmental Disord. 2019;49:3401–11.

    Google Scholar 

  28. Fischbach GD, Lord C. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron. 2010;68:192–5.

    CAS  PubMed  Google Scholar 

  29. Simons VIP. Consortium. Simons Variation in Individuals Project (Simons VIP): a genetics-first approach to studying autism spectrum and related neurodevelopmental disorders. Neuron. 2012;73:1063–7.

    Google Scholar 

  30. Fombonne E. Epidemiology of pervasive developmental disorders. Pediatr Res. 2009;65:591–8.

    PubMed  Google Scholar 

  31. Robinson EB, Lichtenstein P, Anckarsäter H, Happé F, Ronald A. Examining and interpreting the female protective effect against autistic behavior. Proc Natl Acad Sci. 2013;110:5258–62.

    CAS  PubMed  Google Scholar 

  32. El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2018;47:D427–32.

    PubMed Central  Google Scholar 

  33. Chang YF, Imam JS, Wilkinson MF. The nonsense-mediated decay RNA surveillance pathway. Annu Rev Biochem. 2007;76:51–74.

    CAS  PubMed  Google Scholar 

  34. GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–60.

    PubMed Central  Google Scholar 

  35. Mele M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, et al. Human genomics. The human transcriptome across tissues and individuals. Science. 2015;348:660–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Rivas MA, Pirinen M, Conrad DF, Lek M, Tsang EK, Karczewski KJ, et al. Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome. Science. 2015;348:666–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Keren H, Lev-Maor G, Ast G. Alternative splicing and evolution: diversification, exon definition and function. Nat Rev Genet. 2010;11:345–55.

    CAS  PubMed  Google Scholar 

  38. Yang X, Coulombe-Huntington J, Kang S, Sheynkman GM, Hao T, Richardson A, et al. Widespread expansion of protein interaction capabilities by alternative splicing. Cell. 2016;164:805–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, et al. Spatio-temporal transcriptome of the human brain. Nature. 2011;478:483–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Nagy E, Maquat LE. A rule for termination-codon position within intron-containing genes: when nonsense affects RNA abundance. Trends Biochemical Sci. 1998;23:198–9.

    CAS  Google Scholar 

  41. Haworth CMA, Wright MJ, Luciano M, Martin NG, de Geus EJC, van Beijsterveldt CEM, et al. The heritability of general cognitive ability increases linearly from childhood to young adulthood. Mol Psychiatry. 2009;15:1112–20.

    PubMed  PubMed Central  Google Scholar 

  42. Weyn-Vanhentenryck SM, Feng H, Ustianenko D, Duffie R, Yan Q, Jacko M, et al. Precise temporal regulation of alternative splicing during neural development. Nat Commun. 2018;9:2189.

    PubMed  PubMed Central  Google Scholar 

  43. Bishop SL, Farmer C, Bal V, Robinson E, Willsey AJ, Werling DM, et al. Identification of developmental and behavioral markers associated with genetic abnormalities in autism spectrum disorder. Am J Psychiatry. 2017;174:576–85.

    PubMed  PubMed Central  Google Scholar 

  44. Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res. 2017;46:D754–61.

    PubMed Central  Google Scholar 

  45. Sztainberg Y, Zoghbi HY. Lessons learned from studying syndromic autism spectrum disorders. Nat Neurosci. 2016;19:1408–17.

    CAS  PubMed  Google Scholar 

  46. Bernier R, Golzio C, Xiong B, Stessman HA, Coe BP, Penn O, et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell. 2014;158:263–76.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Helsmoortel C, Vulto-van Silfhout AT, Coe BP, Vandeweyer G, Rooms L, van den Ende J, et al. A SWI/SNF-related autism syndrome caused by de novo mutations in ADNP. Nat Genet. 2014;46:380–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Van Bon B, Coe B, Bernier R, Green C, Gerdts J, Witherspoon K, et al. Disruptive de novo mutations of DYRK1A lead to a syndromic form of autism and ID. Mol Psychiatry. 2016;21:126–32.

    PubMed  Google Scholar 

  49. Ben-Shalom R, Keeshen CM, Berrios KN, An JY, Sanders SJ, Bender KJ. Opposing effects on NaV1.2 function underlie differences between SCN2A variants observed in individuals with autism spectrum disorder or infantile seizures. Biol Psychiatry. 2017;82:224–32.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Keren L, Hausser J, Lotan-Pompan M, Vainberg Slutskin I, Alisar H, Kaminski S, et al. Massively parallel interrogation of the effects of gene expression levels on fitness. Cell. 2016;166:1282–.e1218.

    CAS  PubMed  Google Scholar 

  51. Gandal MJ, Leppa V, Won H, Parikshak NN, Geschwind DH. The road to precision psychiatry: translating genetics into disease mechanisms. Nat Neurosci. 2016;19:1397–407.

    CAS  PubMed  Google Scholar 

  52. Robinson EB, St Pourcain B, Anttila V, Kosmicki JA, Bulik-Sullivan B, Grove J, et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat Genet. 2016;48:552–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Qureshi AY, Mueller S, Snyder AZ, Mukherjee P, Berman JI, Roberts TP, et al. Opposing brain differences in 16p11. 2 deletion and duplication carriers. J Neurosci. 2014;34:11199–211.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Hanson E, Bernier R, Porche K, Jackson FI, Goin-Kochel RP, Snyder LG, et al. The cognitive and behavioral phenotype of the 16p11.2 deletion in a clinically ascertained population. Biol Psychiatry. 2015;77:785–93.

    CAS  PubMed  Google Scholar 

  55. D’Angelo D, Lebon S, Chen Q, Martin-Brevet S, Snyder LG, Hippolyte L, et al. Defining the effect of the 16p11.2 duplication on cognition, behavior, and medical comorbidities. JAMA Psychiatry. 2016;73:20–30.

    PubMed  PubMed Central  Google Scholar 

  56. Zhao X, Leotta A, Kustanovich V, Lajonchere C, Geschwind DH, Law K, et al. A unified genetic theory for sporadic and inherited autism. Proc Natl Acad Sci. 2007;104:12831–6.

    CAS  PubMed  Google Scholar 

  57. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Geschwind DH, State MW. Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol. 2015;14:1109–20.

    PubMed  PubMed Central  Google Scholar 

  59. Guy J, Gan J, Selfridge J, Cobb S, Bird A. Reversal of neurological defects in a mouse model of Rett syndrome. Science. 2007;315:1143–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Mei Y, Monteiro P, Zhou Y, Kim J-A, Gao X, Fu Z, et al. Adult restoration of Shank3 expression rescues selective autistic-like phenotypes. Nature. 2016;530:481–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Ehninger D, Han S, Shilyansky C, Zhou Y, Li W, Kwiatkowski DJ, et al. Reversal of learning deficits in a Tsc2+/− mouse model of tuberous sclerosis. Nat Med. 2008;14:843–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Matharu N, Rattanasopha S, Tamura S, Maliskova L, Wang Y, Bernard A, et al. CRISPR-mediated activation of a promoter or enhancer rescues obesity caused by haploinsufficiency. Science. 2019;363:eaau0629.

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Drs. W.K. Chung, I. Pe’er, A. Packer, and members of the Vitkup lab for helpful scientific discussions. DV acknowledges funding from the Simons Foundation (SFARI #308962). This work was supported in part by NIH grant no. T15LM007079 (AHC, JC, JW) and Ruth L. Kirschstein National Research Service Award Institutional Research Training grant no. T32GM082797 (AHC).

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  1. These authors contributed equally: Andrew H. Chiang, Jonathan Chang

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  1. Department of Biomedical Informatics, Columbia University, New York, NY, USA

    Andrew H. Chiang, Jonathan Chang, Jiayao Wang & Dennis Vitkup

  2. Department of Systems Biology, Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA

    Andrew H. Chiang, Jonathan Chang, Jiayao Wang & Dennis Vitkup

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Chiang, A.H., Chang, J., Wang, J. et al. Exons as units of phenotypic impact for truncating mutations in autism. Mol Psychiatry 26, 1685–1695 (2021). https://doi.org/10.1038/s41380-020-00876-3

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