Abstract
The cerebral ventricles are recognized as windows into brain development and disease, yet their genetic architectures, underlying neural mechanisms and utility in maintaining brain health remain elusive. Here we aggregated genetic and neuroimaging data from 61,974 participants (age range, 9 to 98 years) in five cohorts to elucidate the genetic basis of ventricular morphology and examined their overlap with neuropsychiatric traits. Genome-wide association analysis in a discovery sample of 31,880 individuals identified 62 unique loci and 785 candidate genes associated with ventricular morphology. We replicated over 80% of loci in a well-matched cohort of lateral ventricular volume. Gene set analysis revealed enrichment of ventricular-trait-associated genes in biological processes and disease pathogenesis during both early brain development and degeneration. We explored the age-dependent genetic associations in cohorts of different age groups to investigate the possible roles of ventricular-trait-associated loci in neurodevelopmental and neurodegenerative processes. We describe the genetic overlap between ventricular and neuropsychiatric traits through comprehensive integrative approaches under correlative and causal assumptions. We propose the volume of the inferior lateral ventricles as a heritable endophenotype to predict the risk of Alzheimer’s disease, which might be a consequence of prodromal Alzheimer’s disease. Our study provides an advance in understanding the genetics of the cerebral ventricles and demonstrates the potential utility of ventricular measurements in tracking brain disorders and maintaining brain health across the lifespan.
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Data availability
Our GWAS summary statistics for cerebral ventricles can be found at https://doi.org/10.6084/m9.figshare.21529491. The GWAS results are also available on the FUMA website (https://fuma.ctglab.nl/browse/; ID 619–625). The individual-level data used in the present study were obtained from UKB (https://www.ukbiobank.ac.uk/), ABCD (https://abcdstudy.org/), IMAGEN (http://imagen-project.org/), HCP (http://www.humanconnectome.org/) and ADNI (https://adni.loni.usc.edu/). The summary-level data for the CHARGE consortium were obtained from dbGaP (phs000930.v9.p1). The GWAS Catalog resource can be found at https://www.ebi.ac.uk/gwas/. The GWAS atlas can be found at https://atlas.ctglab.nl/PheWAS/. The DSigDB database can be found at http://dsigdb.tanlab.org/DSigDBv1.0/. Agora can be found at https://agora.adknowledgeportal.org/. The Human Protein Atlas can be found at https://www.proteinatlas.org/.
Code availability
This study used openly available software and code, specifically R (https://www.r-project.org/), PLINK (https://www.cog-genomics.org/plink/), GCTA (http://cnsgenomics.com/software/gcta/), IMPUTE (https://mathgen.stats.ox.ac.uk/impute/impute_v2.html), Michigan Imputation Server (https://imputationserver.sph.umich.edu/), MOSTest (https://github.com/precimed/mostest), METAL (http://csg.sph.umich.edu/abecasis/metal/), FUMA (https://fuma.ctglab.nl/), MAGMA (https://ctg.cncr.nl/software/magma/, also implemented in FUMA), SAIGE-GENE+ (https://saigegit.github.io/SAIGE-doc/), Enrichr (https://maayanlab.cloud/Enrichr/) and LDSC (https://github.com/bulik/ldsc/). Custom scripts for the analyses in this paper are available through GitHub (https://github.com/yjge/cerebral_ventricles).
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Acknowledgements
We thank all participants and cooperating institutions. The UKB analyses were conducted using the UKB Resource under application no. 19542. This study was supported by grants from the Science and Technology Innovation 2030 Major Projects (grant no. 2022ZD0211600 to J.-T.Y.), the National Natural Science Foundation of China (grant nos 92249305 and 82071201 to J.-T.Y., 82071997 to W.C. and 81971032 to L.T.), the Shanghai Municipal Science and Technology Major Project (grant no. 2018SHZDZX01 to J.-F.F.), the Research Start-up Fund of Huashan Hospital (grant no. 2022QD002 to J.-T.Y.), the Shanghai Rising-Star Program (grant no. 21QA1408700 to W.C.), the 111 Project (grant no. B18015 to J.-F.F.), and the ZHANGJIANG LAB, the Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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J.-T.Y. designed the study. Y.-J.G. and B.-S.W. organized the data, carried out the statistical analysis and participated in writing the first draft of the manuscript. Y.-J.G., B.-S.W. and Y.Z. designed and drew the figures. S.-D.C., J.-J.K., Y.-T.D., X.-Y.H., Y.-L.Z. and Q.M. organized and analysed the data. Y.Z., S.-D.C., Y.-R.Z., Y.-T.D., Y.-N.O., X.-Y.H., K.K., H.L., T.P., J.-F.F., Q.D., L.T., G.S., W.C. and J.-T.Y. critically revised the manuscript. IMAGEN provided data used for this study. All authors read and approved the final manuscript.
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T.B. served in an advisory or consultancy role for eye level, Infectopharm, Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Roche and Takeda. He received conference support or a speaker’s fee from Janssen, Medice and Takeda. He received royalties from Hogrefe, Kohlhammer, CIP Medien and Oxford University Press; the present work is unrelated to these relationships. G.J.B. has received honoraria from General Electric Healthcare for teaching on scanner programming courses. L.P. served in an advisory or consultancy role for Roche and Viforpharm and received a speaker’s fee from Shire. She received royalties from Hogrefe, Kohlhammer and Schattauer. The present work is unrelated to the above grants and relationships. The other authors report no competing interests.
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Extended data
Extended Data Fig. 1 Schematic diagram of the study design.
First, we aim to elucidate the genetic basis of six ventricular phenotypes, including volumes of the lateral, inferior lateral, third, and fourth ventricles, as well as asymmetries of the lateral and inferior lateral ventricles. Second, we aim to leverage genetic data to estimate the value of ventricular traits in monitoring common neuropsychiatric disorders and promoting brain health across the lifespan. The brain and DNA images were produced using Servier Medical Art (http://smart.servier.com/) licensed under CC BY 3.0.
Extended Data Fig. 2 Top hits in FUMA gene set enrichment analysis.
Bar plots show the top enriched GO terms (a), pathways (b), and phenotypes in the GWAS Catalog (c) through the summary statistics of multivariate ventricular GWAS using hypergeometric tests in FUMA. The length of the bars indicates the -log10 P-value of the enrichment analysis. The color of bars shows the number of genes overlapping between cerebral ventricles and other traits. This figure displays raw P-values without FDR correction and full results could be found in Supplementary Table 17.
Extended Data Fig. 3 Associations between age and volumetric metrics in cohorts with different age groups.
Individual-level data from ABCD, IMAGEN, HCP, and UKB were used to construct the density plots with no covariates included. This plot only includes data from cross-sectional genetic analyses (N = 38,441).
Extended Data Fig. 4 The volume of the inferior lateral ventricles is a consequence of prodromal AD and could predict AD risk.
a-b, Bidirectional MR shows high genetic liability to AD is potentially causal to large inferior lateral ventricles, but not vice versa. The dot refers to each variant’s mean value of association estimate with exposure and outcome, while the cross symbol represents SE. The slope of the line corresponds to the total estimated causal effect. The number of instruments is 31 for inferior lateral ventricular volume (a) and 31 for AD (b), respectively. c-f, Multivariate COX analyses indicate the volume of the inferior lateral ventricles could predict the risk of indicant dementia (c-d) and AD (e-f). Analyses were performed without restriction (c and e, sample size = 15,198) and restricted in participants with at least three years of follow-up (d and f, sample size = 4,930). Compared to volumes of the hippocampus, the volume of the inferior lateral ventricles has even stronger significance and larger effect size.
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Supplementary Data 1
Supplementary Tables 1–41.
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Ge, YJ., Wu, BS., Zhang, Y. et al. Genetic architectures of cerebral ventricles and their overlap with neuropsychiatric traits. Nat Hum Behav 8, 164–180 (2024). https://doi.org/10.1038/s41562-023-01722-6
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DOI: https://doi.org/10.1038/s41562-023-01722-6
