TY - JOUR
T1 - Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior
AU - de Vlaming, Ronald
AU - Slob, Eric A. W.
AU - Jansen, Philip R.
AU - Dagher, Alain
AU - Koellinger, Philipp D.
AU - Groenen, Patrick J. F.
AU - Rietveld, Cornelius A.
N1 - Funding Information:
UK Biobank has obtained ethical approval from the National Research Ethics Committee (11/NW/0382). This research has been conducted using the UK Biobank Resource under application number 11425. We would like to thank the participants and researchers from UK Biobank Imaging Study who contributed or collected data. We also thank the Pan-UKB team for providing the UK Biobank specific LD scores (https://pan.ukbb.broadinstitute.org). This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative (NWO Call for Compute Time EINF-403 to E.A.W.S.). P.D.K. and R.d.V. were supported by a European Research Council Consolidator Grant (647648 EdGe to P.D.K.). P.D.K. was also supported by the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin–Madison with funding from the Wisconsin Alumni Research Foundation. C.A.R. was supported by a European Research Council Starting Grant (946647 GEPSI). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12/1
Y1 - 2021/12/1
N2 - Human 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.
AB - Human 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.
UR - http://www.scopus.com/inward/record.url?scp=85117362948&partnerID=8YFLogxK
U2 - 10.1038/s42003-021-02712-y
DO - 10.1038/s42003-021-02712-y
M3 - Article
C2 - 34642422
VL - 4
JO - Communications Biology
JF - Communications Biology
SN - 2399-3642
IS - 1
M1 - 1180
ER -