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Rare coding variation provides insight into the genetic architecture and phenotypic context of autism

Abstract

Some individuals with autism spectrum disorder (ASD) carry functional mutations rarely observed in the general population. We explored the genes disrupted by these variants from joint analysis of protein-truncating variants (PTVs), missense variants and copy number variants (CNVs) in a cohort of 63,237 individuals. We discovered 72 genes associated with ASD at false discovery rate (FDR) ≤ 0.001 (185 at FDR ≤ 0.05). De novo PTVs, damaging missense variants and CNVs represented 57.5%, 21.1% and 8.44% of association evidence, while CNVs conferred greatest relative risk. Meta-analysis with cohorts ascertained for developmental delay (DD) (n = 91,605) yielded 373 genes associated with ASD/DD at FDR ≤ 0.001 (664 at FDR ≤ 0.05), some of which differed in relative frequency of mutation between ASD and DD cohorts. The DD-associated genes were enriched in transcriptomes of progenitor and immature neuronal cells, whereas genes showing stronger evidence in ASD were more enriched in maturing neurons and overlapped with schizophrenia-associated genes, emphasizing that these neuropsychiatric disorders may share common pathways to risk.

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Fig. 1: Overview of SNV/indel and CNV rates in ASD by mode of inheritance and constraint.
Fig. 2: Contribution of CNVs to ASD by mechanism and genomic location.
Fig. 3: Integrating variant types and inheritance classes improves association power and reveals mutational biases within candidate genes.
Fig. 4: Relative contribution of evidence types in ASD risk genes.
Fig. 5: Integration of ASD and DD datasets.
Fig. 6: Single-cell data reveals differential neuronal layers impacted by ASD and DD genes.

Data availability

The data used in this study are available at: Repository/DataBank Accession: NHGRI AnVIL; accession ID: phs000298; Databank URL: https://anvilproject.org/data; Repository/DataBank Accession: Simons Foundation for Autism Research Initiative SFARIbase; accession ID: SPARK/Regeneron/SPARK_WES_2/; Databank URL: https://www.sfari.org/resource/spark/; de novo variant data used analyses reported in Supplementary Table 9 (CNVs) and Supplementary Table 20 (SNV/indels). Other candidate de novo CNVs that were either too small (spanning two exons or less) or did not meet quality score threshold (quality score < 200) to be included in our statistical analyses are reported in Supplementary Table 21. Aggregated rare variant counts (inherited, case/control) are released in Supplementary Tables 57. To access all individual variants, please see above repositories. GRCh38 reference genome: gs://gcp-public-data–broad-references/hg38/v0/Homo_sapiens_assembly38.fasta; Access to UK Biobank data will be provided by the UK Biobank.

Code availability

The R code used to generate TADA association results is available under the MIT license at https://github.com/talkowski-lab/TADA_2022; https://doi.org/10.5281/zenodo.6496480; analyses executed in R 3.5.3: limma_3.38.3, stringr_1.4.0, GenomicRanges_1.34.0, GenomeInfoDb_1.18.1, IRanges_2.16.0, S4Vectors_0.20.1 and BiocGenerics_0.28.0.

References

  1. Maenner, M. J. et al. Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018. MMWR Surveill. Summ. 70, 1–16 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sandin, S. et al. The heritability of autism spectrum disorder. JAMA 318, 1182–1184 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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.e23 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kaplanis, J. et al. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature 586, 757–762 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Coe, B. P. et al. Neurodevelopmental disease genes implicated by de novo mutation and copy number variation morbidity. Nat. Genet. 51, 106–116 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Singh, T. et al. Rare coding variants in ten genes confer substantial risk for schizophrenia. Nature 604, 509–516 (2022).

    Article  CAS  PubMed  Google Scholar 

  8. Wilfert, A.B., Turner, T.N., Murali, S.C. et al. Recent ultra-rare inherited variants implicate new autism candidate risk genes. Nat. Genet. 53, 1125–1134 https://doi.org/10.1038/s41588-021-00899-8 (2021).

  9. Zhou, X. et al. Integrating de novo and inherited variants in over 42,607 autism cases identifies mutations in new moderate risk genes. Preprint at bioRxiv https://doi.org/10.1101/2021.10.08.21264256 (2021).

  10. Lowther, C. et al. Systematic evaluation of genome sequencing as a first-tier diagnostic test for prenatal and pediatric disorders. Preprint at bioRxiv https://doi.org/10.1101/2020.08.12.248526 (2020).

  11. Lord, J. et al. Prenatal exome sequencing analysis in fetal structural anomalies detected by ultrasonography (PAGE): a cohort study. Lancet 393, 747–757 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Turner, T. N. & Eichler, E. E. The role of de novo noncoding regulatory mutations in neurodevelopmental disorders. Trends Neurosci. 42, 115–127 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Moyses-Oliveira, M., Yadav, R., Erdin, S. & Talkowski, M. E. New gene discoveries highlight functional convergence in autism and related neurodevelopmental disorders. Curr. Opin. Genet. Dev. 65, 195–206 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Sebat, J. et al. Strong association of de novo copy number mutations with autism. Science 316, 445–449 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Talkowski, M. E. et al. Sequencing chromosomal abnormalities reveals neurodevelopmental loci that confer risk across diagnostic boundaries. Cell 149, 525–537 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cooper, G. M. et al. A copy number variation morbidity map of developmental delay. Nat. Genet. 43, 838–846 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 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 

  19. Pinto, D. et al. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am. J. Hum. Genet. 94, 677–694 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Iafrate, A. J. et al. Detection of large-scale variation in the human genome. Nat. Genet. 36, 949–951 (2004).

    Article  CAS  PubMed  Google Scholar 

  21. Lupski, J. R. Genomic disorders ten years on. Genome Med. 1, 42 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Collins, R. L. et al. A cross-disorder dosage sensitivity map of the human genome. Preprint at medRxiv https://doi.org/10.1101/2021.01.26.21250098 (2021).

  23. Byrska-Bishop, M. et al. High coverage whole genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios. Preprint at bioRxiv https://doi.org/10.1101/2021.02.06.430068 (2021).

  24. Mills, R. E. et al. Mapping copy number variation by population-scale genome sequencing. Nature 470, 59–65 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 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 

  26. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Brandler, W. M. et al. Paternally inherited cis-regulatory structural variants are associated with autism. Science 360, 327–331 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Trost, B. et al. Genome-wide detection of tandem DNA repeats that are expanded in autism. Nature 586, 80–86 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Turner, T. N. et al. Genomic patterns of de novo mutation in simplex autism. Cell 171, 710–722.e12 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 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 

  31. Chaisson, M. J. P. et al. Multi-platform discovery of haplotype-resolved structural variation in human genomes. Nat. Commun. 10, 1784 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Ebert, P. et al. Haplotype-resolved diverse human genomes and integrated analysis of structural variation. Science 372, eabf7117 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zhao, X. et al. Expectations and blind spots for structural variation detection from long-read assemblies and short-read genome sequencing technologies. Am. J. Hum. Genet. 108, 919–928 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 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 

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

  36. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. O’Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Glessner, J. T. et al. Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459, 569–573 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Pinto, D. et al. Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants. Nat. Biotechnol. 29, 512–520 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2017).

  42. Belyeu, J. R. et al. De novo structural mutation rates and gamete-of-origin biases revealed through genome sequencing of 2,396 families. Am. J. Hum. Genet. 108, 597–607 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Robinson, E. B., Lichtenstein, P., Anckarsäter, H., Happé, F. & Ronald, A. Examining and interpreting the female protective effect against autistic behavior. Proc. Natl. Acad. Sci. USA 110, 5258–5262 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Szustakowski, J. D. et al. Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Nat. Genet. 53, 942–948 (2021).

    Article  CAS  PubMed  Google Scholar 

  45. 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 

  46. Jónsson, H. et al. Parental influence on human germline de novo mutations in 1,548 trios from Iceland. Nature 549, 519–522 (2017).

    Article  PubMed  CAS  Google Scholar 

  47. Duyzend, M. H. et al. Maternal modifiers and parent-of-origin bias of the autism-associated 16p11.2 CNV. Am. J. Hum. Genet. 98, 45–57 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Simons Vip Consortium. Simons variation in individuals project (Simons VIP): a genetics-first approach to studying autism spectrum and related neurodevelopmental disorders. Neuron 73, 1063–1067 (2012).

    Article  CAS  Google Scholar 

  49. 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 

  50. Russell, G., Steer, C. & Golding, J. Social and demographic factors that influence the diagnosis of autistic spectrum disorders. Soc. Psychiatry Psychiatr. Epidemiol. 46, 1283–1293 (2011).

    Article  PubMed  Google Scholar 

  51. Doshi-Velez, F., Ge, Y. & Kohane, I. Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis. Pediatrics 133, e54–e63 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Sanders, S. J. et al. A framework for the investigation of rare genetic disorders in neuropsychiatry. Nat. Med. 25, 1477–1487 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 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 

  54. Carroll, L. S. & Owen, M. J. Genetic overlap between autism, schizophrenia and bipolar disorder. Genome Med. 1, 102 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Peng, M., Li, Y., Wamsley, B., Wei, Y. & Roeder, K. Integration and transfer learning of single-cell transcriptomes via cFIT. Proc. Natl. Acad. Sci. USA 118, e2024383118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Peng, M. et al. Cell type hierarchy reconstruction via reconciliation of multi-resolution cluster tree. Nucleic Acids Res. 49, e91 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 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 

  58. van der Sluijs, P. J. et al. The ARID1B spectrum in 143 patients: from nonsyndromic intellectual disability to Coffin-Siris syndrome. Genet. Med. 21, 1295–1307 (2019).

    Article  PubMed  CAS  Google Scholar 

  59. Antaki, D. et al. A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex. Nat. Genet. https://doi.org/10.1038/s41588-022-01064-5 (2022).

  60. Wang, T. et al. Integrated gene analyses of de novo mutations from 46,612 trios with autism and developmental disorders. Preprint at bioRxiv https://doi.org/10.1101/2021.09.15.460398 (2021).

  61. Buxbaum, J. D. et al. The autism sequencing consortium: large-scale, high-throughput sequencing in autism spectrum disorders. Neuron 76, 1052–1056 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

  64. Van der Auwera, G. A. & O’Connor, B. D. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra (‘O’Reilly Media, Inc.’, 2020).

  65. 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 

  66. Tsirgiotis, J. M., Young, R. L. & Weber, N. A mixed-methods investigation of diagnostician sex/gender-bias and challenges in assessing females for autism spectrum disorder. Preprint at J. Autism Dev. Disord. https://doi.org/10.1007/s10803-021-05300-5 (2021).

  67. 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 

  68. Jiang, H. & Doerge, R. W. Estimating the proportion of true null hypotheses for multiple comparisons. Cancer Inform. 6, 25–32 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Benaglia, T., Chauveau, D., Hunter, D.R. & Young, D. mixtools: an R package for analyzing finite mixture models. J. Stat. Softw. 32, 1–29 (2009).

    Article  Google Scholar 

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Acknowledgements

We thank all of the individuals who participated in this research. We also thank all contributing investigators to the consortia datasets used here from the Autism Sequencing Consortium (ASC), the Simons Simplex Collection (SSC), Simons Powering Autism Research for Knowledge (SPARK) project, the iPSYCH project, the Deciphering Developmental Disorders (DDD) study and Schizophrenia Exome Meta-Analysis (SCHEMA). This work was supported by grants from the Simons Foundation for Autism Research Initiative (SSC-ASC Genomics Consortium 574598 to S.J.S., 575097 to B.D. and K.R., 573206 to M.E.T. and M.J.D., 571009 to J.D.); the SPARK project and SPARK analysis projects (606362 and 608540 to M.E.T., M.J.D., J.D.B., B.D., K.R. and S.J.S.); SFARI (736613 and 647371 to S.J.S.), NHGRI (HG008895 to M.J.D., S.G. and M.E.T.), NIMH (MH115957 and MH123155 to M.E.T., MH111658 and MH057881 to B.D., MH097849, MH111661 and MH100233 to J.D.B., MH109900 and MH123184 to K.R., MH111660 and MH129722 to M.J.D. and MH111662 and MH100027 to S.J.S.), NICHD (HD081256 and HD096326 to M.E.T.), AMED (JP21WM0425007 to N.O.) and the Beatrice and Samuel Seaver Foundation. J.M.F. was supported by an Autism Speaks Postdoctoral Fellowship and R.L.C. was supported by NSF GRFP 2017240332. E.D. was supported by Fondazione Italiana Autismo (FIA-2018/53).

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M.E.T., S.J.S, K.R., B.D., M.J.D, J.D.B. and S.B.G. designed the study. M.E.T., M.J.D., J.D.B., S.D.R., S.B.G., S.D., C.C., C.R.S., M.B, A.B, B.H.Y.C., M.L.C., E.D, G.B.F., J.J.G., G.E.H., I.H.-P., P.M., D.S.M., M.R.P.-B., A.M.P., A.R., F.T., E.T., G.C., M.C.Y.C., C.F., E.G., A.C.G., E.H.-K., S.L.L., C.L., Y.L., R.N., L.P., M.P-V., I.N.P., R.J.S., M.S., C.I.S.C., S.T., J.Y.T.W., M.H.C.Y., J.S.S., E.H.C. and C.B. contributed samples and generated data. M.E.T., S.J.S., M.J.D., J.D.B., S.D.R., L.S., B.M., C.R.S. and B.C. coordinated project management. M.E.T., S.J.S., K.R., B.D., M.J.D., D.J.C., E.B., A.N.S., M.B., S.K.L., L.G., B.W., L.K., L.W., S.P.H. S.D., R.L.C., H.B., M.P., F.K.S. and J.M.F. developed methodology and performed analysis. M.E.T., S.J.S., K.R., B.D., M.J.D., J.D.B., H.B., M.P., F.K.S. and J.M.F. wrote the paper.

Corresponding authors

Correspondence to Joseph D. Buxbaum, Mark J. Daly, Bernie Devlin, Kathryn Roeder, Stephan J. Sanders or Michael E. Talkowski.

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Competing interests

C.M.F. has been a consultant to Desitin and Roche and receives royalties for books on ASD, ADHD, and MDD. S.J.S. has been a consultant for, and receives funding for research from, BioMarin. J.D.B. and M.E.T. consult for BrigeBio Pharma. M.E.T. receives research funding and/or reagents from Illumina Inc., Levo Therapeutics, and Microsoft Inc. All other authors had no competing interests.

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Fu, J.M., Satterstrom, F.K., Peng, M. et al. Rare coding variation provides insight into the genetic architecture and phenotypic context of autism. Nat Genet 54, 1320–1331 (2022). https://doi.org/10.1038/s41588-022-01104-0

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