A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex.
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:
25
02
2021
accepted:
28
03
2022
pubmed:
3
6
2022
medline:
16
9
2022
entrez:
2
6
2022
Statut:
ppublish
Résumé
The genetic etiology of autism spectrum disorder (ASD) is multifactorial, but how combinations of genetic factors determine risk is unclear. In a large family sample, we show that genetic loads of rare and polygenic risk are inversely correlated in cases and greater in females than in males, consistent with a liability threshold that differs by sex. De novo mutations (DNMs), rare inherited variants and polygenic scores were associated with various dimensions of symptom severity in children and parents. Parental age effects on risk for ASD in offspring were attributable to a combination of genetic mechanisms, including DNMs that accumulate in the paternal germline and inherited risk that influences behavior in parents. Genes implicated by rare variants were enriched in excitatory and inhibitory neurons compared with genes implicated by common variants. Our results suggest that a phenotypic spectrum of ASD is attributable to a spectrum of genetic factors that impact different neurodevelopmental processes.
Identifiants
pubmed: 35654974
doi: 10.1038/s41588-022-01064-5
pii: 10.1038/s41588-022-01064-5
pmc: PMC9474668
mid: NIHMS1793519
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1284-1292Subventions
Organisme : NIMH NIH HHS
ID : R01 MH113715
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH124847
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH119738
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH119746
Pays : United States
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
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
Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).
pubmed: 25363768
pmcid: 4313871
doi: 10.1038/nature13908
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
Sebat, J., Levy, D. L. & McCarthy, S. E. Rare structural variants in schizophrenia: one disorder, multiple mutations; one mutation, multiple disorders. Trends Genet. 25, 528–535 (2009).
pubmed: 19883952
pmcid: 3351381
doi: 10.1016/j.tig.2009.10.004
Bergen, S. E. et al. Joint contributions of rare copy number variants and common SNPs to risk for schizophrenia. Am. J. Psychiatry 176, 29–35 (2019).
pubmed: 30392412
doi: 10.1176/appi.ajp.2018.17040467
Davies, R. W. et al. Using common genetic variation to examine phenotypic expression and risk prediction in 22q11.2 deletion syndrome. Nat. Med. 26, 1912–1918 (2020).
pubmed: 33169016
pmcid: 7975627
doi: 10.1038/s41591-020-1103-1
Lim, E. T. et al. Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron 77, 235–242 (2013).
pubmed: 23352160
pmcid: 3613849
doi: 10.1016/j.neuron.2012.12.029
Zhao, X. et al. A unified genetic theory for sporadic and inherited autism. Proc. Natl Acad. Sci. USA 104, 12831–12836 (2007).
pubmed: 17652511
pmcid: 1933261
doi: 10.1073/pnas.0705803104
Werling, D. M. & Geschwind, D. H. Recurrence rates provide evidence for sex-differential, familial genetic liability for autism spectrum disorders in multiplex families and twins. Mol. Autism 6, 27 (2015).
pubmed: 25973164
pmcid: 4429923
doi: 10.1186/s13229-015-0004-5
Robinson, E. B., Lichtenstein, P., Anckarsater, H., Happe, 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
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
Desachy, G. et al. Increased female autosomal burden of rare copy number variants in human populations and in autism families. Mol. Psychiatry 20, 170–175 (2015).
pubmed: 25582617
doi: 10.1038/mp.2014.179
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).
pubmed: 24581740
pmcid: 3951938
doi: 10.1016/j.ajhg.2014.02.001
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
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
Krumm, N. et al. Excess of rare, inherited truncating mutations in autism. Nat. Genet. 47, 582–588 (2015).
pubmed: 25961944
pmcid: 4449286
doi: 10.1038/ng.3303
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).
pubmed: 28504703
pmcid: 5552240
doi: 10.1038/ng.3863
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
Spark Consortium. SPARK: a US cohort of 50,000 families to accelerate autism research. Neuron 97, 488–493 (2018).
doi: 10.1016/j.neuron.2018.01.015
Lloyd-Jones, L. R. et al. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat. Commun. 10, 5086 (2019).
pubmed: 31704910
pmcid: 6841727
doi: 10.1038/s41467-019-12653-0
Werling, D. M. The role of sex-differential biology in risk for autism spectrum disorder. Biol. Sex. Differ. 7, 58 (2016).
pubmed: 27891212
pmcid: 5112643
doi: 10.1186/s13293-016-0112-8
Falconer, D. S. Inheritance of liability to certain diseases estimated from incidence among relatives. Ann. Hum. Genet. 29, 51–76 (1965).
doi: 10.1111/j.1469-1809.1965.tb00500.x
Reich, T., Morris, C. A. & James, J. W. Use of multiple thresholds in determining mode of transmission of semi-continuous traits. Ann. Hum. Genet. 36, 163 (1972).
pubmed: 4676360
doi: 10.1111/j.1469-1809.1972.tb00767.x
Antaki, D. et al. A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex. Preprint at medRxiv https://doi.org/2021.03.30.21254657 (2021).
Robinson, E. B. et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat. Genet. 48, 552–555 (2016).
pubmed: 26998691
pmcid: 4986048
doi: 10.1038/ng.3529
Buja, A. et al. Damaging de novo mutations diminish motor skills in children on the autism spectrum. Proc. Natl Acad. Sci. USA 115, E1859–E1866 (2018).
pubmed: 29434036
pmcid: 5828599
doi: 10.1073/pnas.1715427115
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
Michaelson, J. J. et al. Whole-genome sequencing in autism identifies hot spots for de novo germline mutation. Cell 151, 1431–1442 (2012).
pubmed: 23260136
pmcid: 3712641
doi: 10.1016/j.cell.2012.11.019
Kong, A. et al. Rate of de novo mutations and the importance of father’s age to disease risk. Nature 488, 471–475 (2012).
pubmed: 22914163
pmcid: 3548427
doi: 10.1038/nature11396
Goriely, A. & Wilkie, A. O. Missing heritability: paternal age effect mutations and selfish spermatogonia. Nat. Rev. Genet. 11, 589 (2010).
pubmed: 20634812
doi: 10.1038/nrg2809-c1
Gratten, J. et al. Risk of psychiatric illness from advanced paternal age is not predominantly from de novo mutations. Nat. Genet. 48, 718–724 (2016).
pubmed: 27213288
doi: 10.1038/ng.3577
Mullins, N. et al. Reproductive fitness and genetic risk of psychiatric disorders in the general population. Nat. Commun. 8, 15833 (2017).
pubmed: 28607503
pmcid: 5474730
doi: 10.1038/ncomms15833
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
Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).
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
Wainschtein, P. et al. Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nat. Genet. 54, 263–273 (2022).
pubmed: 35256806
pmcid: 9119698
doi: 10.1038/s41588-021-00997-7
Turner, T. N. et al. Sex-based analysis of de novo variants in neurodevelopmental disorders. Am. J. Hum. Genet. 105, 1274–1285 (2019).
pubmed: 31785789
pmcid: 6904808
doi: 10.1016/j.ajhg.2019.11.003
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
Werling, D. M. & Geschwind, D. H. Sex differences in autism spectrum disorders. Curr. Opin. Neurol. 26, 146–153 (2013).
pubmed: 23406909
pmcid: 4164392
doi: 10.1097/WCO.0b013e32835ee548
D’Angelo, D. et al. Defining the effect of the 16p11.2 duplication on cognition, behavior, and medical comorbidities. JAMA Psychiatry 73, 20–30 (2016).
pubmed: 26629640
pmcid: 5894477
doi: 10.1001/jamapsychiatry.2015.2123
Malaspina, D. et al. Paternal age and intelligence: implications for age-related genomic changes in male germ cells. Psychiatr. Genet. 15, 117–125 (2005).
pubmed: 15900226
doi: 10.1097/00041444-200506000-00008
Lyall, K. et al. The association between parental age and autism-related outcomes in children at high familial risk for autism. Autism Res. 13, 998–1010 (2020).
pubmed: 32314879
pmcid: 7396152
doi: 10.1002/aur.2303
Frans, E. M. et al. Autism risk across generations: a population-based study of advancing grandpaternal and paternal age. JAMA Psychiatry 70, 516–521 (2013).
pubmed: 23553111
pmcid: 3701020
doi: 10.1001/jamapsychiatry.2013.1180
Lampi, K. M. et al. Parental age and risk of autism spectrum disorders in a Finnish national birth cohort. J. Autism Dev. Disord. 43, 2526–2535 (2013).
pubmed: 23479075
doi: 10.1007/s10803-013-1801-3
Lundstrom, S. et al. Trajectories leading to autism spectrum disorders are affected by paternal age: findings from two nationally representative twin studies. J. Child Psychol. Psychiatry 51, 850–856 (2010).
pubmed: 20214699
doi: 10.1111/j.1469-7610.2010.02223.x
Fulco, C. J., Henry, K. L., Rickard, K. M. & Yuma, P. J. Time-varying outcomes associated with maternal age at first birth. J. Child Fam. Stud. 29, 1537–1547 (2020).
doi: 10.1007/s10826-019-01616-0
Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).
pubmed: 28622505
pmcid: 5536862
doi: 10.1016/j.cell.2017.05.038
Liu, X., Li, Y. I. & Pritchard, J. K. Trans effects on gene expression can drive omnigenic inheritance. Cell 177, 1022–1034.e6 (2019).
pubmed: 31051098
pmcid: 6553491
doi: 10.1016/j.cell.2019.04.014
Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).
pubmed: 22955828
pmcid: 3771521
doi: 10.1126/science.1222794
Jacquemont, S. et al. Genes to mental health (G2MH): a framework to map the mombined effects of rare and common variants on dimensions of cognition and psychopathology. Am. J. Psychiatry 179, 189–203 (2022).
pubmed: 35236119
doi: 10.1176/appi.ajp.2021.21040432
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
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
pubmed: 17701901
pmcid: 1950838
doi: 10.1086/519795
Lam, M. et al. RICOPILI: Rapid Imputation for COnsortias PIpeLIne. Bioinformatics 36, 930–933 (2020).
pubmed: 31393554
doi: 10.1093/bioinformatics/btz633
Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).
pubmed: 30038396
pmcid: 6393768
doi: 10.1038/s41588-018-0147-3
Choi, S. W. & O’Reilly, P. F. PRSice-2: polygenic risk score software for biobank-scale data. Gigascience 8, giz082 (2019).
pubmed: 31307061
pmcid: 6629542
doi: 10.1093/gigascience/giz082
Lian, A., Guevara, J., Xia, K. & Sebat, J. Customized de novo mutation detection for any variant calling pipeline: SynthDNM. Bioinformatics 37, 3640–3641 (2021).
pmcid: 8545295
doi: 10.1093/bioinformatics/btab225
Feliciano, P. et al. Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes. npj Genom. Med. 4, 19 (2019).
pubmed: 31452935
pmcid: 6707204
doi: 10.1038/s41525-019-0093-8
Antaki, D., Brandler, W. M. & Sebat, J. SV2: accurate structural variation genotyping and de novo mutation detection from whole genomes. Bioinformatics 34, 1774–1777 (2018).
pubmed: 29300834
doi: 10.1093/bioinformatics/btx813
Samocha, K. E. et al. Regional missense constraint improves variant deleteriousness prediction. Preprint at bioRxiv (2017).
Kosmicki, J. A. et al. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples. Nat. Genet. 49, 504–510 (2017).
pubmed: 28191890
pmcid: 5496244
doi: 10.1038/ng.3789
Brainstorm Consortium et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
doi: 10.1126/science.aap8757
Hagenaars, S. P. et al. Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N = 112 151) and 24 GWAS consortia. Mol. Psychiatry 21, 1624–1632 (2016).
pubmed: 26809841
pmcid: 5078856
doi: 10.1038/mp.2015.225
Clarke, T. K. et al. Common polygenic risk for autism spectrum disorder (ASD) is associated with cognitive ability in the general population. Mol. Psychiatry 21, 419–425 (2016).
pubmed: 25754080
doi: 10.1038/mp.2015.12
Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014).
pubmed: 25086666
pmcid: 4222185
doi: 10.1038/ng.3050
Sey, N. Y. A. et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat. Neurosci. 23, 583–593 (2020).
pubmed: 32152537
pmcid: 7131892
doi: 10.1038/s41593-020-0603-0
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
pubmed: 26000488
pmcid: 4481139
doi: 10.1016/j.cell.2015.05.002
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
pubmed: 29608179
pmcid: 6700744
doi: 10.1038/nbt.4096