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dc.contributor.authorde Vlaming, Ronald
dc.contributor.authorSlob, Eric A W
dc.contributor.authorJansen, Philip R
dc.contributor.authorDagher, Alain
dc.contributor.authorKoellinger, Philipp D
dc.contributor.authorGroenen, Patrick J F
dc.contributor.authorRietveld, Cornelius A
dc.date.accessioned2021-11-22T14:36:54Z
dc.date.available2021-11-22T14:36:54Z
dc.date.issued2021-10-12
dc.identifier.issn2399-3642
dc.identifier.otherPMC8511103
dc.identifier.other34642422
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330806
dc.description.abstractHuman variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets.
dc.languageeng
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2399-3642
dc.sourcenlmid: 101719179
dc.titleMultivariate analysis reveals shared genetic architecture of brain morphology and human behavior.
dc.typeArticle
dc.date.updated2021-11-22T14:36:54Z
prism.issueIdentifier1
prism.publicationNameCommunications biology
prism.volume4
dc.identifier.doi10.17863/CAM.78249
rioxxterms.versionofrecord10.1038/s42003-021-02712-y
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidde Vlaming, Ronald [0000-0001-6416-6067]
dc.contributor.orcidJansen, Philip R [0000-0003-1550-2444]
dc.contributor.orcidDagher, Alain [0000-0002-0945-5779]
dc.contributor.orcidGroenen, Patrick J F [0000-0001-6683-8971]
dc.contributor.orcidRietveld, Cornelius A [0000-0003-4053-1861]
pubs.funder-project-idUW | Office of the Vice Chancellor for Research and Graduate Education, University of Wisconsin-Madison (VCRGE, UW) (N/A)
pubs.funder-project-idDutch Research Council (NWO) (EINF-403)
pubs.funder-project-idEuropean Research Council (647648, 647648 EdGe, 946647, 946647 GEPSI)


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International