TY - JOUR
T1 - Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology
AU - Shadrin, A.A.
AU - Kaufmann, T.
AU - van der Meer, D.
AU - Palmer, C.E.
AU - Makowski, C.
AU - Loughnan, R.
AU - Jernigan, T.L.
AU - Seibert, T.M.
AU - Hagler, D.J.
AU - Smeland, O.B.
AU - Motazedi, E.
AU - Chu, Y.H.
AU - Lin, A.H.
AU - Cheng, W.Q.
AU - Hindley, G.
AU - Thompson, W.K.
AU - Fan, C.C.
AU - Holland, D.
AU - Westlye, L.T.
AU - Frei, O.
AU - Andreassen, O.A.
AU - Dale, A.M.
N1 - Funding Information:
Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development ℠ Study ( ABCD Study® ) ( https://abcdstudy.org ), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers:
Funding Information:
We were funded by the Research Council of Norway (276082, 213837, 223273, 204966/F20, 229129, 249795/F20, 225989, 248778, 249795), the South-Eastern Norway Regional Health Authority (2013-123, 2014-097, 2015-073, 2016-064, 2017-004), Stiftelsen Kristian Gerhard Jebsen (SKGJ-Med-008), The European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC Starting Grant, Grant Agreement No. 802998) and National Institutes of Health (R01MH100351, R01GM104400, NIDA/NCI: U24DA041123). This work was partly performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT). ([email protected]). Computations were also performed on resources provided by UNINETT Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) including COMET and OASYS resources at the UCSD through allocation TG-IBN200001. This research has been conducted using data from UK Biobank, a major biomedical database.
Funding Information:
We were funded by the Research Council of Norway (276082, 213837, 223273, 204966/F20, 229129, 249795/F20, 225989, 248778, 249795), the South-Eastern Norway Regional Health Authority (2013-123, 2014-097, 2015-073, 2016-064, 2017-004), Stiftelsen Kristian Gerhard Jebsen (SKGJ-Med-008), The European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC Starting Grant, Grant Agreement No. 802998) and National Institutes of Health (R01MH100351, R01GM104400, NIDA/NCI: U24DA041123). This work was partly performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT). ([email protected]). Computations were also performed on resources provided by UNINETT Sigma2?the National Infrastructure for High Performance Computing and Data Storage in Norway. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) including COMET and OASYS resources at the UCSD through allocation TG-IBN200001. This research has been conducted using data from UK Biobank, a major biomedical database. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development? Study (ABCD Study?) ( https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers:, U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147, A full list of supporters is available at https://abcdstudy.org/federal-partners/. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators.html. ABCD Study consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD Study consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this came from [NIMH Data Archive Digital Object Identifier (10.151.54/1519007)]. The data incorporated in the primary analysis were gathered from the public UK Biobank resource and will be made publicly available together with the code used to generate the data through the UK Biobank Returns Catalogue ( https://biobank.ndph.ox.ac.uk/showcase/docs.cgi?id=1). ABCD study data release 3.0 is available for approved researchers in NIMH Data Archive (NDA DOI:10.151.54/1519007). MOSTest code is freely available at https://github.com/precimed/mostest (GPLv3 license).
Publisher Copyright:
© 2021 The Authors
PY - 2021/12/1
Y1 - 2021/12/1
N2 - Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N = 35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study (R). This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.
AB - Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N = 35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study (R). This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.
KW - Multivariate vertex-wise analysis
KW - Cortical surface area
KW - Cortical thickness
KW - Genome-wide association study
KW - Distributed polygenic architecture
KW - SURFACE-AREA
KW - BRAIN
KW - THICKNESS
KW - SCHIZOPHRENIA
KW - EVOLUTION
KW - DISEASE
KW - SYSTEM
U2 - 10.1016/j.neuroimage.2021.118603
DO - 10.1016/j.neuroimage.2021.118603
M3 - Article
C2 - 34560273
SN - 1053-8119
VL - 244
JO - Neuroimage
JF - Neuroimage
M1 - 118603
ER -