Understanding the genetic determinants of the brain with MOSTest

Dennis van der Meer; Oleksandr Frei; Tobias Kaufmann; Alexey A. Shadrin; Anna Devor; Olav B. Smeland; Wesley K. Thompson; Chun Chieh Fan; Dominic Holland; Lars T. Westlye; Ole A. Andreassen; Anders M. Dale

doi:10.1038/s41467-020-17368-1

Understanding the genetic determinants of the brain with MOSTest

Dennis van der Meer^*, Oleksandr Frei, Tobias Kaufmann, Alexey A. Shadrin, Anna Devor, Olav B. Smeland, Wesley K. Thompson, Chun Chieh Fan, Dominic Holland, Lars T. Westlye, Ole A. Andreassen, Anders M. Dale^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of alpha =5x10(-8), MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation. Regional brain morphology has a complex genetic architecture. Here the authors present MOSTest, a multivariate statistical framework, apply it to UK Biobank data, and discover hundreds of loci associated with regional brain morphology.

Original language	English
Number of pages	9
Journal	Nature Communications
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41467-020-17368-1
Publication status	Published - 14 Jul 2020

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1 Erratum / corrigendum / retractions

Author Correction: Understanding the genetic determinants of the brain with MOSTest: Understanding the genetic determinants of the brain with MOSTest
van der Meer, D., Frei, O., Kaufmann, T., Shadrin, A. A., Devor, A., Smeland, O. B., Thompson, W. K., Fan, C. C., Holland, D., Westlye, L. T., Andreassen, O. A. & Dale, A. M., 14 Sept 2020, In: Nature Communications. 11, 1, 1 p., 4700.
Research output: Contribution to journal › Erratum / corrigendum / retractions › Academic

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@article{b766af4d088541dfb5c43c1b68089286,

title = "Understanding the genetic determinants of the brain with MOSTest",

abstract = "Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of alpha =5x10(-8), MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation. Regional brain morphology has a complex genetic architecture. Here the authors present MOSTest, a multivariate statistical framework, apply it to UK Biobank data, and discover hundreds of loci associated with regional brain morphology.",

author = "{van der Meer}, Dennis and Oleksandr Frei and Tobias Kaufmann and Shadrin, {Alexey A.} and Anna Devor and Smeland, {Olav B.} and Thompson, {Wesley K.} and Fan, {Chun Chieh} and Dominic Holland and Westlye, {Lars T.} and Andreassen, {Ole A.} and Dale, {Anders M.}",

note = "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{\textquoteright}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. Publisher Copyright: {\textcopyright} 2020, The Author(s).",

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doi = "10.1038/s41467-020-17368-1",

language = "English",

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AU - van der Meer, Dennis

AU - Frei, Oleksandr

AU - Kaufmann, Tobias

AU - Shadrin, Alexey A.

AU - Devor, Anna

AU - Smeland, Olav B.

AU - Thompson, Wesley K.

AU - Fan, Chun Chieh

AU - Holland, Dominic

AU - Westlye, Lars T.

AU - Andreassen, Ole A.

AU - Dale, Anders M.

N1 - 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. Publisher Copyright: © 2020, The Author(s).

PY - 2020/7/14

Y1 - 2020/7/14

N2 - Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of alpha =5x10(-8), MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation. Regional brain morphology has a complex genetic architecture. Here the authors present MOSTest, a multivariate statistical framework, apply it to UK Biobank data, and discover hundreds of loci associated with regional brain morphology.

AB - Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of alpha =5x10(-8), MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation. Regional brain morphology has a complex genetic architecture. Here the authors present MOSTest, a multivariate statistical framework, apply it to UK Biobank data, and discover hundreds of loci associated with regional brain morphology.

U2 - 10.1038/s41467-020-17368-1

DO - 10.1038/s41467-020-17368-1

M3 - Article

C2 - 32665545

SN - 2041-1723

VL - 11

JO - Nature Communications

JF - Nature Communications

IS - 1

ER -

Understanding the genetic determinants of the brain with MOSTest

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Author Correction: Understanding the genetic determinants of the brain with MOSTest: Understanding the genetic determinants of the brain with MOSTest

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