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Understanding the genetic determinants of the brain with MOSTest

Author

Listed:
  • Dennis Meer

    (University of Oslo
    Maastricht University)

  • Oleksandr Frei

    (University of Oslo
    University of Oslo)

  • Tobias Kaufmann

    (University of Oslo)

  • Alexey A. Shadrin

    (University of Oslo)

  • Anna Devor

    (University of Oslo
    University of California at San Diego
    MGH/HMS)

  • Olav B. Smeland

    (University of Oslo)

  • Wesley K. Thompson

    (University of Oslo
    University of California at San Diego)

  • Chun Chieh Fan

    (University of California at San Diego)

  • Dominic Holland

    (University of California at San Diego
    University of California at San Diego)

  • Lars T. Westlye

    (University of Oslo
    University of Oslo)

  • Ole A. Andreassen

    (University of Oslo)

  • Anders M. Dale

    (University of Oslo
    University of California at San Diego)

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 α = 5 × 10−8, 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.

Suggested Citation

  • Dennis 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, 2020. "Understanding the genetic determinants of the brain with MOSTest," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17368-1
    DOI: 10.1038/s41467-020-17368-1
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    References listed on IDEAS

    as
    1. Matthew Stephens, 2013. "A Unified Framework for Association Analysis with Multiple Related Phenotypes," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-19, July.
    2. Christiaan A de Leeuw & Joris M Mooij & Tom Heskes & Danielle Posthuma, 2015. "MAGMA: Generalized Gene-Set Analysis of GWAS Data," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-19, April.
    3. Kyoko Watanabe & Erdogan Taskesen & Arjen Bochoven & Danielle Posthuma, 2017. "Functional mapping and annotation of genetic associations with FUMA," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
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    1. Shahram Bahrami & Kaja Nordengen & Jaroslav Rokicki & Alexey A. Shadrin & Zillur Rahman & Olav B. Smeland & Piotr P. Jaholkowski & Nadine Parker & Pravesh Parekh & Kevin S. O’Connell & Torbjørn Elvsås, 2024. "The genetic landscape of basal ganglia and implications for common brain disorders," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Runye Shi & Shitong Xiang & Tianye Jia & Trevor W. Robbins & Jujiao Kang & Tobias Banaschewski & Gareth J. Barker & Arun L. W. Bokde & Sylvane Desrivières & Herta Flor & Antoine Grigis & Hugh Garavan , 2024. "Investigating grey matter volumetric trajectories through the lifespan at the individual level," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Chun Chieh Fan & Robert Loughnan & Carolina Makowski & Diliana Pecheva & Chi-Hua Chen & Donald J. Hagler & Wesley K. Thompson & Nadine Parker & Dennis van der Meer & Oleksandr Frei & Ole A. Andreassen, 2022. "Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Eva-Maria Stauffer & Richard A. I. Bethlehem & Lena Dorfschmidt & Hyejung Won & Varun Warrier & Edward T. Bullmore, 2023. "The genetic relationships between brain structure and schizophrenia," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    5. Guanghao Qi & Surya B. Chhetri & Debashree Ray & Diptavo Dutta & Alexis Battle & Samsiddhi Bhattacharjee & Nilanjan Chatterjee, 2024. "Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    6. Michael Wainberg & Natalie J. Forde & Salim Mansour & Isabel Kerrebijn & Sarah E. Medland & Colin Hawco & Shreejoy J. Tripathy, 2024. "Genetic architecture of the structural connectome," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    7. Shahram Bahrami & Kaja Nordengen & Alexey A. Shadrin & Oleksandr Frei & Dennis Meer & Anders M. Dale & Lars T. Westlye & Ole A. Andreassen & Tobias Kaufmann, 2022. "Distributed genetic architecture across the hippocampal formation implies common neuropathology across brain disorders," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    8. E. P. Tissink & A. A. Shadrin & D. Meer & N. Parker & G. Hindley & D. Roelfs & O. Frei & C. C. Fan & M. Nagel & T. Nærland & M. Budisteanu & S. Djurovic & L. T. Westlye & M. P. Heuvel & D. Posthuma & , 2024. "Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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