Genome-wide association study of cerebellar volume provides insights into heritable mechanisms underlying brain development and mental health.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
16 07 2022
16 07 2022
Historique:
received:
30
11
2021
accepted:
05
07
2022
entrez:
16
7
2022
pubmed:
17
7
2022
medline:
20
7
2022
Statut:
epublish
Résumé
Cerebellar volume is highly heritable and associated with neurodevelopmental and neurodegenerative disorders. Understanding the genetic architecture of cerebellar volume may improve our insight into these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail. A genome-wide associations study for cerebellar volume was performed in a discovery sample of 27,486 individuals from UK Biobank, resulting in 30 genome-wide significant loci and a SNP heritability of 39.82%. We pinpoint the likely causal variants and those that have effects on amino acid sequence or cerebellar gene-expression. Additionally, 85 genome-wide significant genes were detected and tested for convergence onto biological pathways, cerebellar cell types, human evolutionary genes or developmental stages. Local genetic correlations between cerebellar volume and neurodevelopmental and neurodegenerative disorders reveal shared loci with Parkinson's disease, Alzheimer's disease and schizophrenia. These results provide insights into the heritable mechanisms that contribute to developing a brain structure important for cognitive functioning and mental health.
Identifiants
pubmed: 35842455
doi: 10.1038/s42003-022-03672-7
pii: 10.1038/s42003-022-03672-7
pmc: PMC9288439
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
710Subventions
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom
Informations de copyright
© 2022. The Author(s).
Références
Villanueva, R. The cerebellum and neuropsychiatric disorders. Psychiatry Res. 198, 527–532 (2012).
pubmed: 22436353
doi: 10.1016/j.psychres.2012.02.023
Gottwald, B., Mihajlovic, Z., Wilde, B. & Mehdorn, H. M. Does the cerebellum contribute to specific aspects of attention? Neuropsychologia 41, 1452–1460 (2003).
pubmed: 12849763
doi: 10.1016/S0028-3932(03)00090-3
Ravizza, S. M. et al. Cerebellar damage produces selective deficits in verbal working memory. Brain 129, 306–320 (2006).
pubmed: 16317024
doi: 10.1093/brain/awh685
Gillig, P. M. & Sanders, R. D. Psychiatry, neurology, and the role of the cerebellum. Psychiatry 7, 38–43 (2010).
pubmed: 20941351
pmcid: 2952646
Posthuma, D. et al. Multivariate genetic analysis of brain structure in an extended twin design. Behav. Genet. 30, 311–319 (2000).
pubmed: 11206086
doi: 10.1023/A:1026501501434
Smith, S. M. et al. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat. Neurosci. 24, 737–745 (2021).
pubmed: 33875891
pmcid: 7610742
doi: 10.1038/s41593-021-00826-4
Zhao, B. et al. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat. Genet. 51, 1637–1644 (2019).
pubmed: 31676860
pmcid: 6858580
doi: 10.1038/s41588-019-0516-6
Chambers, T. et al. Genetic common variants associated with cerebellar volume and their overlap with mental disorders: a study on 33,265 individuals from the UK-Biobank. Mol. Psychiatry https://doi.org/10.1038/s41380-022-01443-8 (2022).
Uffelmann, E. & Posthuma, D. Emerging methods and resources for biological interrogation of neuropsychiatric polygenic-signal. Biol. Psychiatry https://doi.org/10.1016/j.biopsych.2020.05.022 (2020).
Owen, M. J. & Williams, N. M. Explaining the missing heritability of psychiatric disorders. World Psychiatry 20, 294–295 (2021).
pubmed: 34002520
pmcid: 8129850
doi: 10.1002/wps.20870
Heintzman, N. D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108–112 (2009).
pubmed: 19295514
pmcid: 2910248
doi: 10.1038/nature07829
Creyghton, M. P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl Acad. Sci. USA 107, 21931–21936 (2010).
pubmed: 21106759
pmcid: 3003124
doi: 10.1073/pnas.1016071107
Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934 (2013).
pubmed: 24119843
doi: 10.1016/j.cell.2013.09.053
Pott, S. & Lieb, J. D. What are super-enhancers? Nat. Genet. 47, 8–12 (2015).
pubmed: 25547603
doi: 10.1038/ng.3167
Igolkina, A. A. et al. H3K4me3, H3K9ac, H3K27ac, H3K27me3 and H3K9me3 histone tags suggest distinct regulatory evolution of open and condensed chromatin landmarks. Cells 8, 1–16 (2019).
doi: 10.3390/cells8091034
Frank, C. L. et al. Regulation of chromatin accessibility and Zic binding at enhancers in the developing cerebellum. Nat. Neurosci. 18, 647–656 (2015).
pubmed: 25849986
pmcid: 4414887
doi: 10.1038/nn.3995
Holland, D. et al. Beyond SNP heritability: polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model. PLoS Genet. 16, 1–30 (2020).
doi: 10.1371/journal.pgen.1008612
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
pubmed: 29184056
pmcid: 5705698
doi: 10.1038/s41467-017-01261-5
Roadmap Epigenomics Consortium. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–329 (2015).
pmcid: 4530010
doi: 10.1038/nature14248
Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. https://doi.org/10.1101/500090 (2019).
Boudjadi, S., Chatterjee, B., Sun, W., Vemu, P. & Barr, F. G. The expression and function of PAX3 in development and disease. Gene 666, 145–157 (2018).
pubmed: 29730428
pmcid: 6624083
doi: 10.1016/j.gene.2018.04.087
Jara, J. et al. Pax3 induces neural circuit repair through a developmental program of directed axon outgrowth. Preprint at https://www.biorxiv.org/content/10.1101/2021.02.25.432898v1 (2021).
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).
pubmed: 24487276
pmcid: 3992975
doi: 10.1038/ng.2892
Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).
pubmed: 22955989
pmcid: 3431494
doi: 10.1101/gr.137323.112
van de Bunt, M., Cortes, A., Brown, M. A., Morris, A. P. & McCarthy, M. I. Evaluating the performance of fine-mapping strategies at common variant GWAS loci. PLoS Genet. 11, 1–14 (2015).
Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).
pubmed: 26773131
pmcid: 4866522
doi: 10.1093/bioinformatics/btw018
Lee, J. & Cho, Y. Potential roles of stem cell marker genes in axon regeneration. Exp. Mol. Med. 53, 1–7 (2021).
pubmed: 33446881
pmcid: 8080715
doi: 10.1038/s12276-020-00553-z
Weiss, K. et al. Haploinsufficiency of ZNF462 is associated with craniofacial anomalies, corpus callosum dysgenesis, ptosis, and developmental delay. Eur. J. Hum. Genet. 25, 946–951 (2017).
pubmed: 28513610
pmcid: 5567153
doi: 10.1038/ejhg.2017.86
Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).
pubmed: 24695229
pmcid: 4105188
doi: 10.1038/nature13185
Miller, J. A. et al. BrainSpan atlas of the developing human brain dataset. http://brainspan.org (2014).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
pubmed: 21546393
pmcid: 3106198
doi: 10.1093/bioinformatics/btr260
Doan, R. N. et al. Mutations in human accelerated regions disrupt cognition and social behavior. Cell 167, 341–354.e12 (2016).
pubmed: 27667684
pmcid: 5063026
doi: 10.1016/j.cell.2016.08.071
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030.e16 (2018).
pubmed: 30096299
pmcid: 6447408
doi: 10.1016/j.cell.2018.07.028
Saunders, A. et al. DropViz Dataset. http://dropviz.org (2018).
Phillips, J. R., Hewedi, D. H., Eissa, A. M. & Moustafa, A. A. The cerebellum and psychiatric disorders. Front. public Heal. 3, 66 (2015).
Wu, T. & Hallett, M. The cerebellum in Parkinson’s disease. Brain 136, 696–709 (2013).
pubmed: 23404337
pmcid: 7273201
doi: 10.1093/brain/aws360
Jacobs, H. I. L. et al. The cerebellum in Alzheimer’s disease: evaluating its role in cognitive decline. Brain 141, 37–47 (2018).
pubmed: 29053771
doi: 10.1093/brain/awx194
Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).
pubmed: 30617256
pmcid: 6836675
doi: 10.1038/s41588-018-0311-9
Li, L. et al. Lysine acetyltransferase 8 is involved in cerebral development and syndromic intellectual disability. J. Clin. Invest. 130, 1431–1445 (2020).
pubmed: 31794431
pmcid: 7269600
doi: 10.1172/JCI131145
Nativio, R. et al. Dysregulation of the epigenetic landscape of normal aging in Alzheimer’s disease. Nat. Neurosci. 21, 1018 (2018).
pubmed: 29556027
doi: 10.1038/s41593-018-0124-2
Kumar, R. et al. Purkinje cell-specific males absent on the first (mMof) gene deletion results in an ataxia-telangiectasia-like neurological phenotype and backward walking in mice. Proc. Natl Acad. Sci. USA 108, 3636–3641 (2011).
pubmed: 21321203
pmcid: 3048124
doi: 10.1073/pnas.1016524108
Hackinger, S. et al. Evidence for genetic contribution to the increased risk of type 2 diabetes in schizophrenia. Transl. Psychiatry 8, 1–10 (2018).
Ma, C., Gu, C., Huo, Y., Li, X. & Luo, X. J. The integrated landscape of causal genes and pathways in schizophrenia. Transl. Psychiatry 8, 1–14 (2018).
Hoffmann, A. & Spengler, D. Chromatin remodeling complex NuRD in neurodevelopment and neurodevelopmental disorders. Front. Genet. 10, 682 (2019).
Choi, S. W. & O’Reilly, P. F. PRSice-2: polygenic risk score software for biobank-scale data. Gigascience 8, 1–6 (2019).
doi: 10.1093/gigascience/giz082
Privé, F., Arbel, J. & Vilhjálmsson, B. J. LDpred2: Better, faster, stronger. Bioinformatics 36, 5424–5431 (2020).
pmcid: 8016455
doi: 10.1093/bioinformatics/btaa1029
Ni, G. et al. A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts. Biol. Psychiatry 90, 611–620 (2021).
pubmed: 34304866
pmcid: 8500913
doi: 10.1016/j.biopsych.2021.04.018
Choi, S. W., Mak, T. S. H. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. https://doi.org/10.1038/s41596-020-0353-1 (2020).
Oxvig, C. The role of PAPP-A in the IGF system: location, location, location. J. Cell Commun. Signal. 9, 177–187 (2015).
pubmed: 25617049
pmcid: 4458251
doi: 10.1007/s12079-015-0259-9
Sveinsdóttir, K. et al. Impaired cerebellar maturation, growth restriction, and circulating insulin-like growth factor 1 in preterm rabbit pups. Dev. Neurosci. 39, 487–497 (2017).
pubmed: 28972955
doi: 10.1159/000480428
Wrigley, S., Arafa, D. & Tropea, D. Insulin-like growth factor 1: at the crossroads of brain development and aging. Front. Cell. Neurosci. 11, 1–15 (2017).
doi: 10.3389/fncel.2017.00014
Bondy, C., Werner, H., Roberts, C. T. & LeRoith, D. Cellular pattern of type-I insulin-like growth factor receptor gene expression during maturation of the rat brain: Comparison with insulin-like growth factors I and II. Neuroscience 46, 909–923 (1992).
pubmed: 1311816
doi: 10.1016/0306-4522(92)90193-6
Frontera, J. L. & Léna, C. When the cerebellum holds the starting gun. Neuron 109, 2207–2209 (2021).
pubmed: 34293289
doi: 10.1016/j.neuron.2021.06.027
Bach, M. A., Shen-Orr, Z., Lowe, W. L., Roberts, C. T. & Leroith, D. Insulin-like growth factor I mRNA levels are developmentally regulated in specific regions of the rat brain. Mol. Brain Res. 10, 43–48 (1991).
pubmed: 1647481
doi: 10.1016/0169-328X(91)90054-2
Hansen-Pupp, I. et al. Postnatal decrease in circulating insulin-like growth factor-I and low brain volumes in very preterm infants. J. Clin. Endocrinol. Metab. 96, 1129–1135 (2011).
pubmed: 21289247
doi: 10.1210/jc.2010-2440
Ye, P., Xing, Y., Dai, Z. & D’Ercole, A. J. In vivo actions of insulin-like growth factor-I (IGF-I) on cerebellum development in transgenic mice: Evidence that IGF-I increases proliferation of granule cell progenitors. Dev. Brain Res. 95, 44–54 (1996).
doi: 10.1016/0165-3806(96)00492-0
Yamada, T. et al. Promoter decommissioning by the NuRD chromatin remodeling complex triggers synaptic connectivity in the mammalian brain. Neuron 83, 122–134 (2014).
pubmed: 24991957
pmcid: 4266462
doi: 10.1016/j.neuron.2014.05.039
Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715.e16 (2018).
pmcid: 6432650
doi: 10.1016/j.cell.2018.05.046
Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
pubmed: 28686856
pmcid: 5501872
doi: 10.1016/j.ajhg.2017.06.005
Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).
pubmed: 28366442
pmcid: 5384097
doi: 10.1016/j.ajhg.2017.03.004
Marees, A. T. et al. Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits. Nat. Hum. Behav. https://doi.org/10.1038/s41562-021-01053-4 (2021).
Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).
pubmed: 27643430
pmcid: 5086094
doi: 10.1038/nn.4393
Jansen, P. R. et al. Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence. Nat. Commun. 11, 1–12 (2020).
Smith, S. M., Alfaro-almagro, F. & Miller, K. L. UK biobank brain imaging documentation. https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf (2020).
Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).
pubmed: 29079522
doi: 10.1016/j.neuroimage.2017.10.034
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
Abraham, G., Qiu, Y. & Inouye, M. FlashPCA2: principal component analysis of Biobank-scale genotype datasets. Bioinformatics 33, 2776–2778 (2017).
pubmed: 28475694
doi: 10.1093/bioinformatics/btx299
Alfaro-Almagro, F. et al. Confound modelling in UK Biobank brain imaging. Neuroimage 117002 https://doi.org/10.1016/j.neuroimage.2020.117002 (2020).
Bulik-Sullivan, B. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
pubmed: 25642630
pmcid: 4495769
doi: 10.1038/ng.3211
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
pubmed: 26414678
pmcid: 4626285
doi: 10.1038/ng.3404
Altshuler, D. L. et al. The 1000 Genomes Project Consortium: a map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).
pubmed: 20981092
doi: 10.1038/nature09534
Ardlie, K. G. et al. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
doi: 10.1126/science.1262110
Ramasamy, A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418–1428 (2014).
pubmed: 25174004
pmcid: 4208299
doi: 10.1038/nn.3801
Schaid, D. J., Chen, W. & Larson, N. B. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19, 491–504 (2018).
pubmed: 29844615
pmcid: 6050137
doi: 10.1038/s41576-018-0016-z
Benner, C. et al. Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies. Am. J. Hum. Genet. 101, 539–551 (2017).
pubmed: 28942963
pmcid: 5630179
doi: 10.1016/j.ajhg.2017.08.012
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, 1–19 (2015).
doi: 10.1371/journal.pcbi.1004219
Koopmans, F. et al. SynGO: an evidence-based, expert-curated knowledge base for the synapse. Neuron 103, 217–234.e4 (2019).
pubmed: 31171447
pmcid: 6764089
doi: 10.1016/j.neuron.2019.05.002
Koopmans, F. et al. SynGO Brain genes list dataset. https://www.syngoportal.org (2019).
Watanabe, K., Umićević Mirkov, M., de Leeuw, C. A., van den Heuvel, M. P. & Posthuma, D. Genetic mapping of cell type specificity for complex traits. Nat. Commun. 10, 3222 (2019).
pubmed: 31324783
pmcid: 6642112
doi: 10.1038/s41467-019-11181-1
Barton, R. A. & Venditti, C. Rapid evolution of the cerebellum in humans and other great apes. Curr. Biol. 24, 2440–2444 (2014).
pubmed: 25283776
doi: 10.1016/j.cub.2014.08.056
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
pubmed: 26414676
pmcid: 4797329
doi: 10.1038/ng.3406
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
Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).
pubmed: 30478444
doi: 10.1038/s41588-018-0269-7
Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).
pubmed: 35396580
doi: 10.1038/s41586-022-04434-5
Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).
pubmed: 31701892
pmcid: 8422160
doi: 10.1016/S1474-4422(19)30320-5
Zhang, Y. et al. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biol. 22, 1–30 (2021).
Yengo, L., Yang, J. & Visscher, P. M. Expectation of the intercept from bivariate LD score regression in the presence of population stratification. Preprint at bioRxiv https://doi.org/10.1101/310565 (2018).
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
pubmed: 24830394
pmcid: 4022491
doi: 10.1371/journal.pgen.1004383
Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
pubmed: 27225129
pmcid: 4883595
doi: 10.1038/nature17671
Palmer, C. & Pe’er, I. Statistical correction of the winner’s curse explains replication variability in quantitative trait genome-wide association studies. PLoS Genet. 13, 1–18 (2017).
doi: 10.1371/journal.pgen.1006916
Zhong, H. & Prentice, R. L. Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies. Biostatistics 9, 621–634 (2008).
pubmed: 18310059
pmcid: 2536726
doi: 10.1093/biostatistics/kxn001