Rare coding variation provides insight into the genetic architecture and phenotypic context of autism.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
received:
07
12
2021
accepted:
24
05
2022
pubmed:
19
8
2022
medline:
16
9
2022
entrez:
18
8
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 35982160
doi: 10.1038/s41588-022-01104-0
pii: 10.1038/s41588-022-01104-0
pmc: PMC9653013
mid: NIHMS1844621
doi:
Types de publication
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1320-1331Subventions
Organisme : NIMH NIH HHS
ID : R01 MH123184
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH123155
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH099134
Pays : United States
Organisme : NHGRI NIH HHS
ID : UM1 HG008895
Pays : United States
Organisme : NIEHS NIH HHS
ID : U24 ES028533
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH111660
Pays : United States
Organisme : NIMH NIH HHS
ID : R56 MH097849
Pays : United States
Organisme : NHGRI NIH HHS
ID : T32 HG002295
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH116999
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD081256
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH111662
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH122412
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH123619
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH115957
Pays : United States
Organisme : NIMH NIH HHS
ID : R37 MH057881
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH097849
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH129751
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH100027
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH057881
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH129725
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30 ES023513
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH111658
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH129724
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH111661
Pays : United States
Organisme : NIMH NIH HHS
ID : R56 MH115957
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH069359
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH094400
Pays : United States
Organisme : NICHD NIH HHS
ID : P50 HD103537
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH109900
Pays : United States
Organisme : NIEHS NIH HHS
ID : R24 ES028533
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD096326
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH100233
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH129722
Pays : United States
Investigateurs
Branko Aleksic
(B)
Mykyta Artomov
(M)
Elisa Benetti
(E)
Monica Biscaldi-Schafer
(M)
Anders D Børglum
(AD)
Angel Carracedo
(A)
Andreas G Chiocchetti
(AG)
Hilary Coon
(H)
Ryan N Doan
(RN)
Montserrat Fernández-Prieto
(M)
Christine M Freitag
(CM)
Sherif Gerges
(S)
Stephen Guter
(S)
David M Hougaard
(DM)
Christina M Hultman
(CM)
Suma Jacob
(S)
Miia Kaartinen
(M)
Alexander Kolevzon
(A)
Itaru Kushima
(I)
Terho Lehtimäki
(T)
Caterina Lo Rizzo
(CL)
Nell Maltman
(N)
Marianna Manara
(M)
Gal Meiri
(G)
Idan Menashe
(I)
Judith Miller
(J)
Nancy Minshew
(N)
Matthew Mosconi
(M)
Norio Ozaki
(N)
Aarno Palotie
(A)
Mara Parellada
(M)
Kaija Puura
(K)
Abraham Reichenberg
(A)
Sven Sandin
(S)
Stephen W Scherer
(SW)
Sabine Schlitt
(S)
Lauren Schmitt
(L)
Katja Schneider-Momm
(K)
Paige M Siper
(PM)
Pål Suren
(P)
John A Sweeney
(JA)
Karoline Teufel
(K)
Maria Del Pilar Trelles
(M)
Lauren A Weiss
(LA)
Ryan Yuen
(R)
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
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).
pubmed: 34855727
pmcid: 8639027
doi: 10.15585/mmwr.ss7011a1
Sandin, S. et al. The heritability of autism spectrum disorder. JAMA 318, 1182–1184 (2017).
pubmed: 28973605
pmcid: 5818813
doi: 10.1001/jama.2017.12141
Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).
pubmed: 30804558
pmcid: 6454898
doi: 10.1038/s41588-019-0344-8
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).
pubmed: 31981491
pmcid: 7250485
doi: 10.1016/j.cell.2019.12.036
Kaplanis, J. et al. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature 586, 757–762 (2020).
pubmed: 33057194
pmcid: 7116826
doi: 10.1038/s41586-020-2832-5
Coe, B. P. et al. Neurodevelopmental disease genes implicated by de novo mutation and copy number variation morbidity. Nat. Genet. 51, 106–116 (2019).
pubmed: 30559488
doi: 10.1038/s41588-018-0288-4
Singh, T. et al. Rare coding variants in ten genes confer substantial risk for schizophrenia. Nature 604, 509–516 (2022).
pubmed: 35396579
doi: 10.1038/s41586-022-04556-w
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).
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).
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).
Lord, J. et al. Prenatal exome sequencing analysis in fetal structural anomalies detected by ultrasonography (PAGE): a cohort study. Lancet 393, 747–757 (2019).
pubmed: 30712880
pmcid: 6386638
doi: 10.1016/S0140-6736(18)31940-8
Turner, T. N. & Eichler, E. E. The role of de novo noncoding regulatory mutations in neurodevelopmental disorders. Trends Neurosci. 42, 115–127 (2019).
pubmed: 30563709
doi: 10.1016/j.tins.2018.11.002
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).
pubmed: 32846283
doi: 10.1016/j.gde.2020.07.001
Sebat, J. et al. Strong association of de novo copy number mutations with autism. Science 316, 445–449 (2007).
pubmed: 17363630
pmcid: 2993504
doi: 10.1126/science.1138659
Talkowski, M. E. et al. Sequencing chromosomal abnormalities reveals neurodevelopmental loci that confer risk across diagnostic boundaries. Cell 149, 525–537 (2012).
pubmed: 22521361
pmcid: 3340505
doi: 10.1016/j.cell.2012.03.028
Cooper, G. M. et al. A copy number variation morbidity map of developmental delay. Nat. Genet. 43, 838–846 (2011).
pubmed: 21841781
pmcid: 3171215
doi: 10.1038/ng.909
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).
pubmed: 21658581
pmcid: 3939065
doi: 10.1016/j.neuron.2011.05.002
Marshall, C. R. et al. Structural variation of chromosomes in autism spectrum disorder. Am. J. Hum. Genet. 82, 477–488 (2008).
pubmed: 18252227
pmcid: 2426913
doi: 10.1016/j.ajhg.2007.12.009
Pinto, D. et al. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am. J. Hum. Genet. 94, 677–694 (2014).
pubmed: 24768552
pmcid: 4067558
doi: 10.1016/j.ajhg.2014.03.018
Iafrate, A. J. et al. Detection of large-scale variation in the human genome. Nat. Genet. 36, 949–951 (2004).
pubmed: 15286789
doi: 10.1038/ng1416
Lupski, J. R. Genomic disorders ten years on. Genome Med. 1, 42 (2009).
pubmed: 19439022
pmcid: 2684663
doi: 10.1186/gm42
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).
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).
Mills, R. E. et al. Mapping copy number variation by population-scale genome sequencing. Nature 470, 59–65 (2011).
pubmed: 21293372
pmcid: 3077050
doi: 10.1038/nature09708
Collins, R. L. et al. A structural variation reference for medical and population genetics. Nature 581, 444–451 (2020).
pubmed: 32461652
pmcid: 7334194
doi: 10.1038/s41586-020-2287-8
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).
pubmed: 29700473
pmcid: 5961723
doi: 10.1038/s41588-018-0107-y
Brandler, W. M. et al. Paternally inherited cis-regulatory structural variants are associated with autism. Science 360, 327–331 (2018).
pubmed: 29674594
pmcid: 6449150
doi: 10.1126/science.aan2261
Trost, B. et al. Genome-wide detection of tandem DNA repeats that are expanded in autism. Nature 586, 80–86 (2020).
pubmed: 32717741
pmcid: 9348607
doi: 10.1038/s41586-020-2579-z
Turner, T. N. et al. Genomic patterns of de novo mutation in simplex autism. Cell 171, 710–722.e12 (2017).
pubmed: 28965761
pmcid: 5679715
doi: 10.1016/j.cell.2017.08.047
Ruzzo, E. K. et al. Inherited and de novo genetic risk for autism impacts shared networks. Cell 178, 850–866.e26 (2019).
pubmed: 31398340
pmcid: 7102900
doi: 10.1016/j.cell.2019.07.015
Chaisson, M. J. P. et al. Multi-platform discovery of haplotype-resolved structural variation in human genomes. Nat. Commun. 10, 1784 (2019).
pubmed: 30992455
pmcid: 6467913
doi: 10.1038/s41467-018-08148-z
Ebert, P. et al. Haplotype-resolved diverse human genomes and integrated analysis of structural variation. Science 372, eabf7117 (2021).
pubmed: 33632895
pmcid: 8026704
doi: 10.1126/science.abf7117
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).
pubmed: 33789087
pmcid: 8206509
doi: 10.1016/j.ajhg.2021.03.014
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
pubmed: 32461654
pmcid: 7334197
doi: 10.1038/s41586-020-2308-7
Samocha, K. E. et al. Regional missense constraint improves variant deleteriousness prediction. Preprint at bioRxiv https://doi.org/10.1101/148353 (2017).
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).
pubmed: 23966865
pmcid: 3744441
doi: 10.1371/journal.pgen.1003671
Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).
pubmed: 26402605
pmcid: 4624267
doi: 10.1016/j.neuron.2015.09.016
O’Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012).
pubmed: 22495309
pmcid: 3350576
doi: 10.1038/nature10989
Glessner, J. T. et al. Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459, 569–573 (2009).
pubmed: 19404257
pmcid: 2925224
doi: 10.1038/nature07953
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).
pubmed: 21552272
pmcid: 3270583
doi: 10.1038/nbt.1852
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).
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).
pubmed: 33675682
pmcid: 8059337
doi: 10.1016/j.ajhg.2021.02.012
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).
pubmed: 23431162
pmcid: 3612665
doi: 10.1073/pnas.1211070110
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).
pubmed: 34183854
doi: 10.1038/s41588-021-00885-0
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).
pubmed: 25284784
pmcid: 4194132
doi: 10.1016/j.celrep.2014.08.068
Jónsson, H. et al. Parental influence on human germline de novo mutations in 1,548 trios from Iceland. Nature 549, 519–522 (2017).
pubmed: 28959963
doi: 10.1038/nature24018
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).
pubmed: 26749307
doi: 10.1016/j.ajhg.2015.11.017
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).
doi: 10.1016/j.neuron.2012.02.014
Doan, R. N. et al. Recessive gene disruptions in autism spectrum disorder. Nat. Genet. 51, 1092–1098 (2019).
pubmed: 31209396
pmcid: 6629034
doi: 10.1038/s41588-019-0433-8
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).
pubmed: 20938640
doi: 10.1007/s00127-010-0294-z
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).
pubmed: 24323995
pmcid: 3876178
doi: 10.1542/peds.2013-0819
Sanders, S. J. et al. A framework for the investigation of rare genetic disorders in neuropsychiatry. Nat. Med. 25, 1477–1487 (2019).
pubmed: 31548702
pmcid: 8656349
doi: 10.1038/s41591-019-0581-5
Nowakowski, T. J. et al. Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. Science 358, 1318–1323 (2017).
pubmed: 29217575
pmcid: 5991609
doi: 10.1126/science.aap8809
Carroll, L. S. & Owen, M. J. Genetic overlap between autism, schizophrenia and bipolar disorder. Genome Med. 1, 102 (2009).
pubmed: 19886976
pmcid: 2784305
doi: 10.1186/gm102
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).
pubmed: 33658382
pmcid: 7958425
doi: 10.1073/pnas.2024383118
Peng, M. et al. Cell type hierarchy reconstruction via reconciliation of multi-resolution cluster tree. Nucleic Acids Res. 49, e91 (2021).
pubmed: 34125905
pmcid: 8450107
doi: 10.1093/nar/gkab481
Polioudakis, D. et al. A single-cell transcriptomic atlas of human neocortical development during mid-gestation. Neuron 103, 785–801.e8 (2019).
pubmed: 31303374
pmcid: 6831089
doi: 10.1016/j.neuron.2019.06.011
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).
pubmed: 30349098
doi: 10.1038/s41436-018-0330-z
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).
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).
Buxbaum, J. D. et al. The autism sequencing consortium: large-scale, high-throughput sequencing in autism spectrum disorders. Neuron 76, 1052–1056 (2012).
pubmed: 23259942
doi: 10.1016/j.neuron.2012.12.008
De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).
pubmed: 25363760
pmcid: 4402723
doi: 10.1038/nature13772
SPARK Consortium. SPARK: a US cohort of 50,000 families to accelerate autism research. Neuron 97, 488–493 (2018).
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).
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).
pubmed: 31768057
pmcid: 6884695
doi: 10.1038/s41593-019-0527-8
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).
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).
pubmed: 28545751
doi: 10.1016/j.jaac.2017.03.013
Jiang, H. & Doerge, R. W. Estimating the proportion of true null hypotheses for multiple comparisons. Cancer Inform. 6, 25–32 (2008).
pubmed: 19259400
pmcid: 2623313
doi: 10.1177/117693510800600001
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
pubmed: 25605792
pmcid: 4402510
doi: 10.1093/nar/gkv007
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).
doi: 10.18637/jss.v032.i06