Recessive gene disruptions in autism spectrum disorder

Abstract

Autism spectrum disorder (ASD) affects up to 1 in 59 individuals1. Genome-wide association and large-scale sequencing studies strongly implicate both common variants2,3,4 and rare de novo variants5,6,7,8,9,10 in ASD. Recessive mutations have also been implicated11,12,13,14 but their contribution remains less well defined. Here we demonstrate an excess of biallelic loss-of-function and damaging missense mutations in a large ASD cohort, corresponding to approximately 5% of total cases, including 10% of females, consistent with a female protective effect. We document biallelic disruption of known or emerging recessive neurodevelopmental genes (CA2, DDHD1, NSUN2, PAH, RARB, ROGDI, SLC1A1, USH2A) as well as other genes not previously implicated in ASD including FEV (FEV transcription factor, ETS family member), which encodes a key regulator of the serotonergic circuitry. Our data refine estimates of the contribution of recessive mutation to ASD and suggest new paths for illuminating previously unknown biological pathways responsible for this condition.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: An excess of rare, damaging, biallelic mutation in ASD.
Fig. 2: Biallelic mutations in ASD: the effects of sex.
Fig. 3: Biallelic mutations in ASD: ExAC filtration.

Data availability

Data included in this manuscript have been deposited at the database of Genotypes and Phenotypes, merged with published data under accession number phs000298.v4.p3. Correspondence and requests for materials should be addressed to Timothy.yu@childrens.harvard.edu.

References

  1. 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. 63, 1–22 (2014).

    Google Scholar 

  2. 2.

    Robinson, E. B. et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat. Genet. 48, 552–555 (2016).

    CAS  Article  Google Scholar 

  3. 3.

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

    CAS  Article  Google Scholar 

  4. 4.

    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).

    CAS  Article  Google Scholar 

  5. 5.

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

    CAS  Article  Google Scholar 

  6. 6.

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

    CAS  Article  Google Scholar 

  7. 7.

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

    CAS  Article  Google Scholar 

  8. 8.

    Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012).

    CAS  Article  Google Scholar 

  9. 9.

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

    CAS  Article  Google Scholar 

  10. 10.

    Ronemus, M., Iossifov, I., Levy, D. & Wigler, M. The role of de novo mutations in the genetics of autism spectrum disorders. Nat. Rev. Genet. 15, 133–141 (2014).

    CAS  Article  Google Scholar 

  11. 11.

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

    CAS  Article  Google Scholar 

  12. 12.

    Chahrour, M. H. et al. Whole-exome sequencing and homozygosity analysis implicate depolarization-regulated neuronal genes in autism. PLoS Genet. 8, e1002635 (2012).

    CAS  Article  Google Scholar 

  13. 13.

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

    CAS  Article  Google Scholar 

  14. 14.

    Lim, E. T. et al. Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron 77, 235–242 (2013).

    CAS  Article  Google Scholar 

  15. 15.

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

    CAS  Article  Google Scholar 

  16. 16.

    Huang, N., Lee, I., Marcotte, E. M. & Hurles, M. E. Characterising and predicting haploinsufficiency in the human genome. PLoS Genet. 6, e1001154 (2010).

    Article  Google Scholar 

  17. 17.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    Betancur, C. Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res. 1380, 42–77 (2011).

    CAS  Article  Google Scholar 

  19. 19.

    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).

    CAS  Article  Google Scholar 

  20. 20.

    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).

    CAS  Article  Google Scholar 

  21. 21.

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

    CAS  Article  Google Scholar 

  22. 22.

    Blaker-Lee, A., Gupta, S., McCammon, J. M., De Rienzo, G. & Sive, H. Zebrafish homologs of genes within 16p11.2, a genomic region associated with brain disorders, are active during brain development, and include two deletion dosage sensor genes. Dis. Model. Mech. 5, 834–851 (2012).

    CAS  Article  Google Scholar 

  23. 23.

    Park, S. M., Littleton, J. T., Park, H. R. & Lee, J. H. Drosophila homolog of human KIF22 at the autism-linked 16p11.2 loci influences synaptic connectivity at larval neuromuscular junctions. Exp. Neurobiol. 25, 33–39 (2016).

    Article  Google Scholar 

  24. 24.

    Bailey, C. G. et al. Loss-of-function mutations in the glutamate transporter SLC1A1 cause human dicarboxylic aminoaciduria. J. Clin. Invest. 121, 446–453 (2011).

    CAS  Article  Google Scholar 

  25. 25.

    Teijema, H. L., van Gelderen, H. H., Giesberts, M. A. & Laurent de Angulo, M. S. Dicarboxylic aminoaciduria: an inborn error of glutamate and aspartate transport with metabolic implications, in combination with a hyperprolinemia. Metab. Clin. Exp. 23, 115–123 (1974).

    CAS  Article  Google Scholar 

  26. 26.

    Swarna, M., Rao, D. N. & Reddy, P. P. Dicarboxylic aminoaciduria associated with mental retardation. Hum. Genet. 82, 299–300 (1989).

    CAS  Article  Google Scholar 

  27. 27.

    Rothstein, J. D. et al. Localization of neuronal and glial glutamate transporters. Neuron 13, 713–725 (1994).

    CAS  Article  Google Scholar 

  28. 28.

    Ross, J. R., Porter, B. E., Buckley, P. T., Eberwine, J. H. & Robinson, M. B. mRNA for the EAAC1 subtype of glutamate transporter is present in neuronal dendrites in vitro and dramatically increases in vivo after a seizure. Neurochem. Int. 58, 366–375 (2011).

    CAS  Article  Google Scholar 

  29. 29.

    Nieoullon, A. et al. The neuronal excitatory amino acid transporter EAAC1/EAAT3: does it represent a major actor at the brain excitatory synapse? J. Neurochem. 98, 1007–1018 (2006).

    CAS  Article  Google Scholar 

  30. 30.

    Diamond, J. S. Neuronal glutamate transporters limit activation of NMDA receptors by neurotransmitter spillover on CA1 pyramidal cells. J. Neurosci. 21, 8328–8338 (2001).

    CAS  Article  Google Scholar 

  31. 31.

    Bianchi, M. G., Bardelli, D., Chiu, M. & Bussolati, O. Changes in the expression of the glutamate transporter EAAT3/EAAC1 in health and disease. Cell. Mol. Life Sci. 71, 2001–2015 (2014).

    CAS  Article  Google Scholar 

  32. 32.

    Stafford, M. M., Brown, M. N., Mishra, P., Stanwood, G. D. & Mathews, G. C. Glutamate spillover augments GABA synthesis and release from axodendritic synapses in rat hippocampus. Hippocampus 20, 134–144 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Mathews, G. C. & Diamond, J. S. Neuronal glutamate uptake contributes to GABA synthesis and inhibitory synaptic strength. J. Neurosci. 23, 2040–2048 (2003).

    CAS  Article  Google Scholar 

  34. 34.

    Scimemi, A., Tian, H. & Diamond, J. S. Neuronal transporters regulate glutamate clearance, NMDA receptor activation, and synaptic plasticity in the hippocampus. J. Neurosci. 29, 14581–14595 (2009).

    CAS  Article  Google Scholar 

  35. 35.

    Peghini, P., Janzen, J. & Stoffel, W. Glutamate transporter EAAC-1-deficient mice develop dicarboxylic aminoaciduria and behavioral abnormalities but no neurodegeneration. EMBO J. 16, 3822–3832 (1997).

    CAS  Article  Google Scholar 

  36. 36.

    Lee, S., Park, S. H. & Zuo, Z. Effects of isoflurane on learning and memory functions of wild-type and glutamate transporter type 3 knockout mice. J. Pharm. Pharmacol. 64, 302–307 (2012).

    CAS  Article  Google Scholar 

  37. 37.

    Lucki, I. The spectrum of behaviors influenced by serotonin. Biol. Psychiatry 44, 151–162 (1998).

    CAS  Article  Google Scholar 

  38. 38.

    Muller, C. L., Anacker, A. M. J. & Veenstra-VanderWeele, J. The serotonin system in autism spectrum disorder: from biomarker to animal models. Neuroscience 321, 24–41 (2016).

    CAS  Article  Google Scholar 

  39. 39.

    Hendricks, T., Francis, N., Fyodorov, D. & Deneris, E. S. The ETS domain factor Pet-1 is an early and precise marker of central serotonin neurons and interacts with a conserved element in serotonergic genes. J. Neurosci. 19, 10348–10356 (1999).

    CAS  Article  Google Scholar 

  40. 40.

    Hendricks, T. J. et al. Pet-1 ETS gene plays a critical role in 5-HT neuron development and is required for normal anxiety-like and aggressive behavior. Neuron 37, 233–247 (2003).

    CAS  Article  Google Scholar 

  41. 41.

    Liu, C. et al. Pet-1 is required across different stages of life to regulate serotonergic function. Nat. Neurosci. 13, 1190–1198 (2010).

    CAS  Article  Google Scholar 

  42. 42.

    Iyo, A. H., Porter, B., Deneris, E. S. & Austin, M. C. Regional distribution and cellular localization of the ETS-domain transcription factor, FEV, mRNA in the human postmortem brain. Synapse 57, 223–228 (2005).

    CAS  Article  Google Scholar 

  43. 43.

    Maurer, P. et al. The Ets transcription factor Fev is specifically expressed in the human central serotonergic neurons. Neurosci. Lett. 357, 215–218 (2004).

    CAS  Article  Google Scholar 

  44. 44.

    Cao, H. et al. FCHSD1 and FCHSD2 are expressed in hair cell stereocilia and cuticular plate and regulate actin polymerization in vitro. PLoS ONE 8, e56516 (2013).

    CAS  Article  Google Scholar 

  45. 45.

    Shao, W., Halachmi, S. & Brown, M. ERAP140, a conserved tissue-specific nuclear receptor coactivator. Mol. Cell. Biol. 22, 3358–3372 (2002).

    CAS  Article  Google Scholar 

  46. 46.

    Poon, C. L. C., Mitchell, K. A., Kondo, S., Cheng, L. Y. & Harvey, K. F. The Hippo pathway regulates neuroblasts and brain size in Drosophila melanogaster. Curr. Biol. 26, 1034–1042 (2016).

    CAS  Article  Google Scholar 

  47. 47.

    Cheng, Y. Z. et al. Investigating embryonic expression patterns and evolution of AHI1 and CEP290 genes, implicated in Joubert syndrome. PLoS ONE 7, e44975 (2012).

    CAS  Article  Google Scholar 

  48. 48.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  Google Scholar 

  49. 49.

    Stenson, P. D. et al. The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum. Genet. 136, 665–677 (2017).

    CAS  Article  Google Scholar 

  50. 50.

    Ng, P. C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).

    CAS  Article  Google Scholar 

  51. 51.

    Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    CAS  Article  Google Scholar 

  52. 52.

    Schwarz, J. M., Rödelsperger, C., Schuelke, M. & Seelow, D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7, 575–576 (2010).

    CAS  Article  Google Scholar 

  53. 53.

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

    CAS  Article  Google Scholar 

  54. 54.

    Chun, S. & Fay, J. C. Identification of deleterious mutations within three human genomes. Genome Res. 19, 1553–1561 (2009).

    CAS  Article  Google Scholar 

  55. 55.

    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).

    CAS  Article  Google Scholar 

  56. 56.

    Gerrelli, D., Lisgo, S., Copp, A. J. & Lindsay, S. Enabling research with human embryonic and fetal tissue resources. Development 142, 3073–3076 (2015).

    CAS  Article  Google Scholar 

  57. 57.

    Kerwin, J. et al. The HUDSEN Atlas: a three-dimensional (3D) spatial framework for studying gene expression in the developing human brain. J. Anat. 217, 289–299 (2010).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank A. Hossain and N. Hatem for their help with sample preparation, and J. Kerwin for her help with analysis of in situ hybridization results. R.N.D. was supported by an NIH T32 fellowship from the Fundamental Neurobiology Training Grant (no. 5 T32 NS007484-14) and the Nancy Lurie Marks Clinical and Research Fellowship Program in Autism. The ASC is supported by the National Institute of Mental Health (NIMH; grant nos. MH100233, MH100229, MH100209, MH100239, MH111661, MH111660, MH111662 and MH111658). Collection of the PAGES cohort is supported by the NIMH (grant no. MH097849). This work was supported in part through the computational resources provided by the Department of Scientific Computing at the Icahn School of Medicine at Mount Sinai, and the Research Information Technology Group at Harvard Medical School, which is partially supported by National Institutes of Health grant no. NCRR 1S10RR028832-01. Human embryonic and fetal material was provided jointly by the MRC/Wellcome Trust (grant no. MR/R006237/1) Human Developmental Biology Resource (www.hdbr.org). C.A.W. is an Investigator of the Howard Hughes Medical Institute. C.A.W. and T.W.Y. were supported by NIMH grant nos. RC2MH089952 and RO1MH083565. T.W.Y. was supported by grant nos. NIH/NIMH R01MH113761, NICHD/NHGRI/NIH U19HD077671 and NIH/NICHD U24HD0938487, and by a SFARI Pilot Research Award. S.D.R. and J.D.B. are supported by the Beatrice and Samuel A. Seaver Foundation.

Author information

Affiliations

Authors

Consortia

Contributions

T.W.Y., R.N.D., E.T.L. and M.J.D. designed the study, with important additional contributions from C.B., D.J.C, C.A.W. and J.D.B. R.N.D., E.T.L. and A.S. performed the data analyses. S.D.R. and S.G. performed the Sanger validation. A.G.C. and C.M.F. characterized the FEV family. L.O., S.G. and T.W.Y. designed and performed the in situ expression analyses. R.N.D. and T.W.Y. wrote the manuscript. All authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Timothy W. Yu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–11 and Supplementary Note

Reporting Summary

Supplementary Tables

Supplementary Tables 1–13

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Doan, R.N., Lim, E.T., De Rubeis, S. et al. Recessive gene disruptions in autism spectrum disorder. Nat Genet 51, 1092–1098 (2019). https://doi.org/10.1038/s41588-019-0433-8

Download citation

Further reading