Genome-wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability.
Journal
Molecular psychiatry
ISSN: 1476-5578
Titre abrégé: Mol Psychiatry
Pays: England
ID NLM: 9607835
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
received:
01
07
2020
accepted:
30
11
2020
revised:
30
10
2020
pubmed:
9
1
2021
medline:
12
10
2021
entrez:
8
1
2021
Statut:
ppublish
Résumé
Genomic copy number variants (CNVs) are routinely identified and reported back to patients with neuropsychiatric disorders, but their quantitative effects on essential traits such as cognitive ability are poorly documented. We have recently shown that the effect size of deletions on cognitive ability can be statistically predicted using measures of intolerance to haploinsufficiency. However, the effect sizes of duplications remain unknown. It is also unknown if the effect of multigenic CNVs are driven by a few genes intolerant to haploinsufficiency or distributed across tolerant genes as well. Here, we identified all CNVs > 50 kilobases in 24,092 individuals from unselected and autism cohorts with assessments of general intelligence. Statistical models used measures of intolerance to haploinsufficiency of genes included in CNVs to predict their effect size on intelligence. Intolerant genes decrease general intelligence by 0.8 and 2.6 points of intelligence quotient when duplicated or deleted, respectively. Effect sizes showed no heterogeneity across cohorts. Validation analyses demonstrated that models could predict CNV effect sizes with 78% accuracy. Data on the inheritance of 27,766 CNVs showed that deletions and duplications with the same effect size on intelligence occur de novo at the same frequency. We estimated that around 10,000 intolerant and tolerant genes negatively affect intelligence when deleted, and less than 2% have large effect sizes. Genes encompassed in CNVs were not enriched in any GOterms but gene regulation and brain expression were GOterms overrepresented in the intolerant subgroup. Such pervasive effects on cognition may be related to emergent properties of the genome not restricted to a limited number of biological pathways.
Identifiants
pubmed: 33414497
doi: 10.1038/s41380-020-00985-z
pii: 10.1038/s41380-020-00985-z
pmc: PMC8953148
mid: NIHMS1668378
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2663-2676Subventions
Organisme : Medical Research Council
ID : MR/T030852/1
Pays : United Kingdom
Organisme : NIMH NIH HHS
ID : U01 MH119690
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH119739
Pays : United States
Organisme : Wellcome Trust
Pays : United Kingdom
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature Limited.
Références
Coe BP, Witherspoon K, Rosenfeld JA, van Bon BWM, Vulto-van Silfhout AT, Bosco P, et al. Refining analyses of copy number variation identifies specific genes associated with developmental delay. Nat Genet. 2014;46:1063–71.
doi: 10.1038/ng.3092
Coe BP, Stessman HAF, Sulovari A, Geisheker MR, Bakken TE, Lake AM, et al. Neurodevelopmental disease genes implicated by de novo mutation and copy number variation morbidity. Nat Genet. 2019;51:106–16.
doi: 10.1038/s41588-018-0288-4
Wilfert AB, Sulovari A, Turner TN, Coe BP, Eichler EE. Recurrent de novo mutations in neurodevelopmental disorders: properties and clinical implications. Genome Med. 2017;9:101.
doi: 10.1186/s13073-017-0498-x
Huguet G, Ey E, Bourgeron T. The genetic landscapes of autism spectrum disorders. Annu Rev Genomics Hum Genet. 2013;14:191–213.
doi: 10.1146/annurev-genom-091212-153431
Pinto D, Delaby E, Merico D, Barbosa M, Merikangas A, Klei L, et al. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am J Hum Genet. 2014;94:677–94.
doi: 10.1016/j.ajhg.2014.03.018
Maillard AM, Ruef A, Pizzagalli F, Migliavacca E, Hippolyte L, Adaszewski S, et al. The 16p11.2 locus modulates brain structures common to autism, schizophrenia and obesity. Mol Psychiatry. 2015;20:140–7.
doi: 10.1038/mp.2014.145
Sakai M, Watanabe Y, Someya T, Araki K, Shibuya M, Niizato K, et al. Assessment of copy number variations in the brain genome of schizophrenia patients. Mol Cytogenet. 2015;8:46.
doi: 10.1186/s13039-015-0144-5
Szatkiewicz JP, O’Dushlaine C, Chen G, Chambert K, Moran JL, Neale BM, et al. Copy number variation in schizophrenia in Sweden. Mol Psychiatry. 2014;19:762–73.
doi: 10.1038/mp.2014.40
Riggs ER, Andersen EF, Cherry AM, Kantarci S, Kearney H, Patel A, et al. Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen). Genet Med. 2020;22:245–57.
doi: 10.1038/s41436-019-0686-8
Miller DT, Adam MP, Aradhya S, Biesecker LG, Brothman AR, Carter NP, et al. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am J Hum Genet. 2010;86:749–64.
doi: 10.1016/j.ajhg.2010.04.006
Kendall KM, Bracher-Smith M, Fitzpatrick H, Lynham A, Rees E, Escott-Price V, et al. Cognitive performance and functional outcomes of carriers of pathogenic copy number variants: analysis of the UK Biobank. Br J Psychiatry. 2019;214:297–304.
doi: 10.1192/bjp.2018.301
Stefansson H, Meyer-Lindenberg A, Steinberg S, Magnusdottir B, Morgen K, Arnarsdottir S, et al. CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature. 2014;505:361–6.
doi: 10.1038/nature12818
Posthuma D, de Geus EJC, Boomsma DI. Perceptual speed and IQ are associated through common genetic factors. Behav Genet. 2001;31:593–602.
doi: 10.1023/A:1013349512683
Hill WD, Arslan RC, Xia C, Luciano M, Amador C, Navarro P, et al. Genomic analysis of family data reveals additional genetic effects on intelligence and personality. Mol Psychiatry. 2018;23:2347–62.
doi: 10.1038/s41380-017-0005-1
Savage JE, Jansen PR, Stringer S, Watanabe K, Bryois J, de Leeuw CA, et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet. 2018;50:912–9.
doi: 10.1038/s41588-018-0152-6
Hill WD, Marioni RE, Maghzian O, Ritchie SJ, Hagenaars SP, McIntosh AM, et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol Psychiatry. 2019;24:169–81.
doi: 10.1038/s41380-017-0001-5
Huguet G, Schramm C, Douard E, Jiang L, Labbe A, Tihy F, et al. Measuring and estimating the effect sizes of copy number variants on general intelligence in community-based samples. JAMA Psychiatry. 2018;75:447–57.
doi: 10.1001/jamapsychiatry.2018.0039
Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285–91.
doi: 10.1038/nature19057
Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. BioRxiv. 2019. https://doi.org/10.1101/531210 .
Deary IJ. Intelligence. Annu Rev Psychol. 2011;63:453–82.
doi: 10.1146/annurev-psych-120710-100353
Yuen RKC, Merico D, Bookman M, Howe JL, Thiruvahindrapuram B, Patel RV, et al. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat Neurosci. 2017;20:602–11.
doi: 10.1038/nn.4524
Trost B, Walker S, Wang Z, Thiruvahindrapuram B, MacDonald JR, Sung WWL, et al. A comprehensive workflow for read depth-based identification of copy-number variation from whole-genome sequence data. Am J Hum Genet. 2018;102:142–55.
doi: 10.1016/j.ajhg.2017.12.007
Ramasamy A, Trabzuni D, Guelfi S, Varghese V, Smith C, Walker R, et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci. 2014;17:1418–28.
doi: 10.1038/nn.3801
McRae JF, Clayton S, Fitzgerald TW, Kaplanis J, Prigmore E, Rajan D, et al. Prevalence and architecture of de novo mutations in developmental disorders. Nature. 2017;542:433–8.
doi: 10.1038/nature21062
Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979;86:420–8.
doi: 10.1037/0033-2909.86.2.420
Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57.
doi: 10.1038/nprot.2008.211
Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One. 2011;6:e21800.
doi: 10.1371/journal.pone.0021800
D’Angelo D, Lebon S, Chen Q, Martin-Brevet S, Snyder LG, Hippolyte L, et al. Defining the effect of the 16p11.2 duplication on cognition, behavior, and medical comorbidities. JAMA Psychiatry. 2016;73:20–30.
doi: 10.1001/jamapsychiatry.2015.2123
Moreno-De-Luca A, Evans DW, Boomer KB, Hanson E, Bernier R, Goin-Kochel RP, et al. The role of parental cognitive, behavioral, and motor profiles in clinical variability in individuals with chromosome 16p11.2 deletions. JAMA Psychiatry. 2015;72:119–26.
doi: 10.1001/jamapsychiatry.2014.2147
Bernier R, Steinman KJ, Reilly B, Wallace AS, Sherr EH, Pojman N, et al. Clinical phenotype of the recurrent 1q21.1 copy-number variant. Genet Med. 2016;18:341–9.
doi: 10.1038/gim.2015.78
Douard E, Zeribi A, Schramm C, Tamer P, Loum MA, Nowak S, et al. Effect sizes of deletions and duplications on autism risk across the genome. Am J Psychiatry. 2020. https://doi.org/10.1101/2020.03.09.979815 .
Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An J-Y, et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell. 2020;180:568–84.e23.
doi: 10.1016/j.cell.2019.12.036
Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: from polygenic to omnigenic. Cell. 2017;169:1177–86.
doi: 10.1016/j.cell.2017.05.038
An open resource of structural variation for medical and population genetics. bioRxiv. https://www.biorxiv.org/content/10.1101/578674v1.full . Accessed 31 Dec 2019.
Wray NR, Wijmenga C, Sullivan PF, Yang J, Visscher PM. Common disease is more complex than implied by the core gene omnigenic model. Cell. 2018;173:1573–80.
doi: 10.1016/j.cell.2018.05.051
Schumann G, Loth E, Banaschewski T, Barbot A, Barker G, Büchel C, et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol Psychiatry. 2010;15:1128–39.
doi: 10.1038/mp.2010.4
Pausova Z, Paus T, Abrahamowicz M, Bernard M, Gaudet D, Leonard G, et al. Cohort profile: The Saguenay Youth Study (SYS). Int J Epidemiol. 2017;46:e19.
pubmed: 27018016
Deary IJ, Gow AJ, Pattie A, Starr JM. Cohort profile: The Lothian Birth Cohorts of 1921 and 1936. Int J Epidemiol. 2012;41:1576–84.
doi: 10.1093/ije/dyr197
Awadalla P, Boileau C, Payette Y, Idaghdour Y, Goulet J-P, Knoppers B, et al. Cohort profile of the CARTaGENE study: Quebec’s population-based biobank for public health and personalized genomics. Int J Epidemiol. 2013;42:1285–99.
doi: 10.1093/ije/dys160
Smith BH, Campbell A, Linksted P, Fitzpatrick B, Jackson C, Kerr SM, et al. Cohort profile: Generation Scotland: Scottish Family Health Study (GS:SFHS). The study, its participants and their potential for genetic research on health and illness. Int J Epidemiol. 2013;42:689–700.
doi: 10.1093/ije/dys084
Fischbach GD, Lord C. The Simons simplex collection: a resource for identification of autism genetic risk factors. Neuron. 2010;68:192–5.
doi: 10.1016/j.neuron.2010.10.006