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.

Recent ultra-rare inherited variants implicate new autism candidate risk genes

Nature Genetics volume 53, pages 1125–1134 (2021)Cite this article

Subjects

Abstract

Autism is a highly heritable complex disorder in which de novo mutation (DNM) variation contributes significantly to risk. Using whole-genome sequencing data from 3,474 families, we investigate another source of large-effect risk variation, ultra-rare variants. We report and replicate a transmission disequilibrium of private, likely gene-disruptive (LGD) variants in probands but find that 95% of this burden resides outside of known DNM-enriched genes. This variant class more strongly affects multiplex family probands and supports a multi-hit model for autism. Candidate genes with private LGD variants preferentially transmitted to probands converge on the E3 ubiquitin–protein ligase complex, intracellular transport and Erb signaling protein networks. We estimate that these variants are approximately 2.5 generations old and significantly younger than other variants of similar type and frequency in siblings. Overall, private LGD variants are under strong purifying selection and appear to act on a distinct set of genes not yet associated with autism.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Fig. 1: Overview of private variants in the discovery cohort.
Fig. 2: Burden of private LGD variants in affected children.
Fig. 3: Genetic properties of inherited LGD variant burden.
Fig. 4: PPI network for autism candidate genes.
Fig. 5: Estimate of allele age.

Data availability

The WGS data used in this study are available from the following resources. The AGRE study is available at the Database of Genotypes and Phenotypes (dbGaP) under accession no. phs001766. Access to the AGRE WGS data is subject to approval by Autism Speaks and AGRE. All sequencing and phenotype data for the SSC are available through SFARI and are available to approved researchers at SFARI Base (accession nos. SFARI_SSC_WGS_p, SFARI_SSC_WGS_1 and SFARI_SSC_WGS_2). The genomic and phenotypic data for the SPARK study are available by request from SFARI Base (accession no. SFARI_SPARK_WES_1). Data from the SAGE study are available at the dbGaP under accession no. phs001740.v1.p1. Data from the TASC study are available at dbGaP under accession no. phs001741. Family-level FreeBayes and GATK VCF files for SAGE, SSC and TASC are available under dbGaP accession no. phs001874.v1.p1 and at SFARI Base under accession no. SFARI_SSC_WGS_2a.

Code availability

All software used in this study is publicly available. The code for the ultra-rare transmitted variant pipeline can be found at https://github.com/EichlerLab/ultra_rare_transmitted.git. The code for the figures and analyses are available upon request.

References

  1. Baio, J. et al. Prevalence of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014. MMWR Surveill. Summ. 67, 1–23 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Krumm, N. et al. Excess of rare, inherited truncating mutations in autism. Nat. Genet. 47, 582–588 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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 

  5. 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 

  6. Constantino, J. N. et al. Autism recurrence in half siblings: strong support for genetic mechanisms of transmission in ASD. Mol. Psychiatry 18, 137–138 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. Ganna, A. et al. Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum. Am. J. Hum. Genet. 102, 1204–1211 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 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 

  11. 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 

  12. 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 

  13. Schaaf, C. P. et al. Oligogenic heterozygosity in individuals with high-functioning autism spectrum disorders. Hum. Mol. Genet. 20, 3366–3375 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Girirajan, S. et al. A recurrent 16p12.1 microdeletion supports a two-hit model for severe developmental delay. Nat. Genet. 42, 203–209 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Du, Y. et al. Nonrandom occurrence of multiple de novo coding variants in a proband indicates the existence of an oligogenic model in autism. Genet. Med. 22, 170–180 (2020).

    Article  CAS  PubMed  Google Scholar 

  16. Jiang, Y.-H. et al. A mixed epigenetic/genetic model for oligogenic inheritance of autism with a limited role for UBE3A. Am. J. Med. Genet. A 131, 1–10 (2004).

    Article  PubMed  Google Scholar 

  17. 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 

  18. 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 

  19. Turner, T. N. et al. Genome sequencing of autism-affected families reveals disruption of putative noncoding regulatory DNA. Am. J. Hum. Genet. 98, 58–74 (2016).

    Article  CAS  PubMed  Google Scholar 

  20. Fischbach, G. D. & Lord, C. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron 68, 192–195 (2010).

    Article  CAS  PubMed  Google Scholar 

  21. 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 

  22. An, J.-Y. et al. Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science 362, eaat6576 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Buxbaum, J. D. et al. The Autism Simplex Collection: an international, expertly phenotyped autism sample for genetic and phenotypic analyses. Mol. Autism 5, 34 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Feliciano, P. et al. Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes. NPJ Genom. Med. 4, 19 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  26. Snijders Blok, L. et al. De novo mutations in MED13, a component of the Mediator complex, are associated with a novel neurodevelopmental disorder. Hum. Genet. 137, 375–388 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Shah, A. A. et al. Excess of RALGAPB de novo variants in neurodevelopmental disorders. Eur. J. Med. Genet. 63, 104041 (2020).

    Article  PubMed  Google Scholar 

  28. Sapio, M. R. et al. Novel carboxypeptidase A6 (CPA6) mutations identified in patients with juvenile myoclonic and generalized epilepsy. PLoS ONE 10, e0123180 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Li, Q. S., Parrado, A. R., Samtani, M. N., Narayan, V. A. & Alzheimer’s Disease Neuroimaging Initiative. Variations in the FRA10AC1 fragile site and 15q21 are associated with cerebrospinal fluid Aβ1-42 level. PLoS ONE 10, e0134000 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Siitonen, A. et al. Genetics of early-onset Parkinson’s disease in Finland: exome sequencing and genome-wide association study. Neurobiol. Aging 53, 195.e7–195.e10 (2017).

    Article  CAS  Google Scholar 

  31. Gravel, S. et al. Demographic history and rare allele sharing among human populations. Proc. Natl Acad. Sci. USA 108, 11983–11988 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Tennessen, J. A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Iossifov, I. et al. Low load for disruptive mutations in autism genes and their biased transmission. Proc. Natl Acad. Sci. USA 112, E5600–E5607 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Epi25 Collaborative. Ultra-rare genetic variation in the epilepsies: a whole-exome sequencing study of 17,606 individuals. Am. J. Hum. Genet. 105, 267–282 (2019).

    Article  CAS  Google Scholar 

  36. 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 

  37. 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 

  38. 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 

  39. 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 

  40. Maruyama, T. The age of a rare mutant gene in a large population. Am. J. Hum. Genet. 26, 669–673 (1974).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Speidel, L., Forest, M., Shi, S. & Myers, S. R. A method for genome-wide genealogy estimation for thousands of samples. Nat. Genet. 51, 1321–1329 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 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 

  43. Deardorff, M. A. et al. HDAC8 mutations in Cornelia de Lange syndrome affect the cohesin acetylation cycle. Nature 489, 313–317 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Williams, S. R. et al. Haploinsufficiency of HDAC4 causes brachydactyly mental retardation syndrome, with brachydactyly type E, developmental delays, and behavioral problems. Am. J. Hum. Genet. 87, 219–228 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Bernier, R. et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell 158, 263–276 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. King, I. F. et al. Topoisomerases facilitate transcription of long genes linked to autism. Nature 501, 58–62 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 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 

  48. 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 

  49. Fairless, R. et al. Polarized targeting of neurexins to synapses is regulated by their C-terminal sequences. J. Neurosci. 28, 12969–12981 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Gromova, K. V. et al. Neurobeachin and the kinesin KIF21B are critical for endocytic recycling of NMDA receptors and regulate social behavior. Cell Rep. 23, 2705–2717 (2018).

    Article  CAS  PubMed  Google Scholar 

  51. Tomaselli, P. J. et al. A de novo dominant mutation in KIF1A associated with axonal neuropathy, spasticity and autism spectrum disorder. J. Peripher. Nerv. Syst. 22, 460–463 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).

    Article  CAS  Google Scholar 

  53. Girirajan, S. et al. Phenotypic heterogeneity of genomic disorders and rare copy-number variants. N. Engl. J. Med. 367, 1321–1331 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Stessman, H. A., Bernier, R. & Eichler, E. E. A genotype-first approach to defining the subtypes of a complex disease. Cell 156, 872–877 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Epi4K consortium & Epilepsy Phenome/Genome Project. Ultra-rare genetic variation in common epilepsies: a case-control sequencing study. Lancet Neurol. 16, 135–143 (2017)..

  56. Regier, A. A. et al. Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. Nat. Commun. 9, 4038 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2018).

  61. Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://arxiv.org/abs/1207.3907 (2012).

  62. Rimmer, A. et al. Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications. Nat. Genet. 46, 912–918 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hsieh, P. et al. Adaptive archaic introgression of copy number variants and the discovery of previously unknown human genes. Science 366, eaax2083 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  PubMed  Google Scholar 

  69. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Cingolani, P. et al. Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift. Front. Genet. 3, 35 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).

    Article  CAS  Google Scholar 

  72. Quinlan, A. R. BEDTools: the Swiss-Army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 11.12.1-34 (2014).

    Article  Google Scholar 

  73. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Krumm, N. et al. Copy number variation detection and genotyping from exome sequence data. Genome Res. 22, 1525–1532 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Fromer, M. et al. Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am. J. Hum. Genet. 91, 597–607 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Wang, K. et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 17, 1665–1674 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Scharpf, R. B., Irizarry, R. A., Ritchie, M. E., Carvalho, B. & Ruczinski, I. Using the R package crlmm for genotyping and copy number estimation. J. Stat. Softw. 40, 1–32 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Hormozdiari, F. et al. Next-generation VariationHunter: combinatorial algorithms for transposon insertion discovery. Bioinformatics 26, i350–i357 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Kronenberg, Z. N. et al. Wham: identifying structural variants of biological consequence. PLoS Comput. Biol. 11, e1004572 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Layer, R. M., Chiang, C., Quinlan, A. R. & Hall, I. M. LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 15, R84 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Handsaker, R. E. et al. Large multiallelic copy number variations in humans. Nat. Genet. 47, 296–303 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Sudmant, P. H. et al. Global diversity, population stratification, and selection of human copy-number variation. Science 349, aab3761 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Abyzov, A., Urban, A. E., Snyder, M. & Gerstein, M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 21, 974–984 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Turner, T. N. et al. Sex-based analysis of de novo variants in neurodevelopmental disorders. Am. J. Hum. Genet. 105, 1274–1285 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Ware, J. S., Samocha, K. E., Homsy, J. & Daly, M. J. Interpreting de novo variation in human disease using denovolyzeR. Curr. Protoc. Hum. Genet. 87, 7.25.1–7.25.15 (2015).

    Google Scholar 

  90. He, Z. et al. Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data. Am. J. Hum. Genet. 94, 33–46 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Cole, P. & MacMahon, B. Attributable risk percent in case-control studies. Br. J. Prev. Soc. Med. 25, 242–244 (1971).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Dougherty, J. D., Schmidt, E. F., Nakajima, M. & Heintz, N. Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells. Nucleic Acids Res. 38, 4218–4230 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    Article  CAS  PubMed  Google Scholar 

  96. Ono, K., Muetze, T., Kolishovski, G., Shannon, P. & Demchak, B. CyREST: turbocharging Cytoscape access for external tools via a RESTful API. F1000Res. 4, 478 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank T. Brown for assistance in editing this manuscript and S. Stray, M. Eng, J. Moore, H. Kortbawi and A. Thornton from the laboratory of Mary-Claire King for the isolation of DNA from whole blood. We thank T. Maniatis and the New York Genome Center for conducting the sequencing and initial quality control. We thank all the families at the participating SSC sites, as well as the principal investigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, R. Goin-Kochel, E. Hanson, D. Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, K. Pelphrey, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, Z. Warren and E. Wijsman). We thank all the families in SPARK, the SPARK clinical sites and SPARK staff. We appreciate obtaining access to the phenotypic and genetic data on SFARI Base. Approved researchers can obtain the SSC population dataset described in this study (https://www.sfari.org/resource/simons-simplex-collection/) and the SPARK population dataset described in this study (https://www.sfari.org/resource/spark/) by applying at https://base.sfari.org. We gratefully acknowledge the resources provided by the AGRE Consortium and the participating AGRE families. Genomic data for the AGRE cohort was provided by iHART, an initiative led by the Hartwell Foundation and directed by D. Wall and D. Geschwind. This work was supported, in part, by grants from the National Institutes of Health (no. R01 MH101221 to E.E.E.; no. R01 MH100047 to R.A.B.; no. K99 MH117165 to T.N.T.; no. K99 HG011041 to P.H.; and no. UM1 HG008901 to M.C.Z.) and the Simons Foundation (no. SFARI 608045 to E.E.E.). The CCDG is funded by the National Human Genome Research Institute and the National Heart, Lung, and Blood Institute. The Genome Sequencing Program Coordinating Center (no. U24 HG008956) contributed to cross-program scientific initiatives and provided logistical and general study coordination. AGRE is a program of Autism Speaks and is supported in part by grant no. 1U24MH081810 from the National Institute of Mental Health to C. M. Lajonchere. E.E.E. is an investigator of the Howard Hughes Medical Institute.

Author information

Author notes

  1. Tychele N. Turner

    Present address: Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

Authors and Affiliations

  1. Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA

    Amy B. Wilfert, Tychele N. Turner, Shwetha C. Murali, PingHsun Hsieh, Arvis Sulovari, Tianyun Wang, Bradley P. Coe, Hui Guo, Kendra Hoekzema, William Harvey & Evan E. Eichler

  2. Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

    Shwetha C. Murali & Evan E. Eichler

  3. Center for Medical Genetic & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China

    Hui Guo

  4. Allen Institute for Brain Science, Seattle, WA, USA

    Trygve E. Bakken

  5. New York Genome Center, New York, NY, USA

    Lara H. Winterkorn, Uday S. Evani, Marta Byrska-Bishop & Michael C. Zody

  6. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA

    Rachel K. Earl & Raphael A. Bernier

  7. Department of Systems Biology, Columbia University, New York, NY, USA

    Xueya Zhou, Chang Shu, Joseph Obiajulu & Yufeng Shen

  8. Department of Pediatrics, Columbia Medical Center, New York, NY, USA

    Xueya Zhou, Chang Shu, Joseph Obiajulu & Wendy K. Chung

  9. Simons Foundation, New York, NY, USA

    Pamela Feliciano, Jacob Hall, Irina Astrovskaya, Simon Xu, Jessica Wright, Olena Marchenko, Chris Fleisch, LeeAnne Green Snyder, Sarah D. Barns, Bing Han, Natalia Volfovsky & Wendy K. Chung

  10. Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA

    Leo Brueggeman & Jacob Michaelson

  11. Program in Neurogenetics, David Geffen School of Medicine, Los Angeles, CA, USA

    Timothy S. Chang & Daniel Geschwind

  12. Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA

    Timothy S. Chang & Daniel Geschwind

  13. Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA

    Andrew Nishida & Brian J. O’Roak

  14. Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA, USA

    Ryan Doan, Aubrey Soucy & Timothy W. Yu

  15. Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    Ryan Doan, Aubrey Soucy & Timothy W. Yu

  16. The Broad Institute, Cambridge, MA, USA

    Timothy W. Yu

  17. Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA

    Daniel Geschwind

Authors
  1. Amy B. Wilfert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tychele N. Turner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shwetha C. Murali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. PingHsun Hsieh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arvis Sulovari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tianyun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bradley P. Coe
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hui Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kendra Hoekzema
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Trygve E. Bakken
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lara H. Winterkorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Uday S. Evani
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Marta Byrska-Bishop
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Rachel K. Earl
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Raphael A. Bernier
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Michael C. Zody
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Evan E. Eichler
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

The SPARK Consortium

  • Xueya Zhou
  • , Tianyun Wang
  • , Pamela Feliciano
  • , Jacob Hall
  • , Irina Astrovskaya
  • , Shwetha C. Murali
  • , Simon Xu
  • , Chang Shu
  • , Joseph Obiajulu
  • , Leo Brueggeman
  • , Jessica Wright
  • , Olena Marchenko
  • , Chris Fleisch
  • , Timothy S. Chang
  • , LeeAnne Green Snyder
  • , Sarah D. Barns
  • , Tychele N. Turner
  • , Bing Han
  • , William Harvey
  • , Andrew Nishida
  • , Ryan Doan
  • , Aubrey Soucy
  • , Brian J. O’Roak
  • , Timothy W. Yu
  • , Daniel Geschwind
  • , Jacob Michaelson
  • , Natalia Volfovsky
  • , Evan E. Eichler
  • , Yufeng Shen
  •  & Wendy K. Chung

Contributions

A.B.W. and E.E.E. designed and conceived the study. L.H.W. and M.C.Z. coordinated the sampling and sequencing for the CCDG cohorts. The SPARK Consortium coordinated the samples and sequencing for the SPARK cohort. A.B.W., T.N.T., S.C.M., A.S., T.W., B.P.C., U.S.E., M.B.-B. and H.G. called the variants and ran the quality control. K.H. performed the Sanger validations. A.B.W., T.N.T., S.C.M., P.H., A.S. and T.W. conducted the analyses and data interpretation. T.E.B. and A.B.W. performed the gene expression analyses. A.B.W., R.A.B. and R.K.E. performed the phenotypic analyses. A.B.W. and E.E.E. wrote the manuscript with input from coauthors.

Corresponding author

Correspondence to Evan E. Eichler.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Anders Børglum, Thomas Bourgeron and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–24

Reporting Summary

Supplementary Tables

Supplementary Tables 1–22

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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 (2021). https://doi.org/10.1038/s41588-021-00899-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-021-00899-8

This article is cited by

Search

Advanced 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