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A Fast Method for Estimating Statistical Power of Multivariate GWAS in Real Case Scenarios: Examples from the Field of Imaging Genetics

Behavior Genetics volume 49, pages 112–121 (2019)Cite this article

Abstract

In GWAS of imaging phenotypes (e.g., by the ENIGMA and CHARGE consortia), the growing number of phenotypes considered presents a statistical challenge that other fields are not experiencing (e.g. psychiatry and the Psychiatric Genetics Consortium). However, the multivariate nature of MRI measurements may also be an advantage as many of the MRI phenotypes are correlated and multivariate methods could be considered. Here, we compared the statistical power of a multivariate GWAS versus the current univariate approach, which consists of multiple univariate analyses. To do so, we used results from twin models to estimate pertinent vectors of SNP effect sizes on brain imaging phenotypes, as well as the residual correlation matrices, necessary to estimate analytically the statistical power. We showed that for subcortical structure volumes and hippocampal subfields, a multivariate GWAS yields similar statistical power to the current univariate approach. Our analytical approach is as accurate but ~ 1000 times faster than simulations and we have released the code to facilitate the investigation of other scenarios, may they be outside the field of imaging genetics.

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Acknowledgements

We are very grateful to the twins for their generosity of time and willingness to participate in our studies. We thank research nurses Marlene Grace and Ann Eldridge for twin recruitment, Richard Parker for data collection, research assistants Lachlan Strike, Kori Johnson, Aaron Quiggle, and Natalie Garden, and radiographers Matthew Meredith, Peter Hobden, Kate Borg, Aiman Al Najjar, and Anita Burns for data acquisition, David Butler, Daniel Park, David Smyth and Anthony Conciatore for IT support.

Funding

The MRI imaging of QTIM was supported by grants from National Institute of Health (NIH) (R01 HD050735, U54 EB020403) and the National Health and Medical Research Council (NHMRC) (496682, 1009064). PT and MJW are supported in part by NIH Grant to the ENIGMA Center for Worldwide Medicine, Imaging & Genomics. SEM is supported by an NHMRC fellowship (APP1103623). The funding was supported by Australian Research Council (Grant Nos. A7960034, A79906588, A79801419, DP0212016).

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Author notes

  1. Sarah E. Medland and Margaret J. Wright have contributed equally to this work.

Authors and Affiliations

  1. Institute of Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, 4072, Australia

    Baptiste Couvy-Duchesne

  2. Queensland Brain Institute, The University of Queensland, Brisbane, 4072, Australia

    Baptiste Couvy-Duchesne, Lachlan T. Strike & Margaret J. Wright

  3. QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia

    Baptiste Couvy-Duchesne, Nicholas G. Martin & Sarah E. Medland

  4. Herston Imaging Research Facility (HIRF), Queensland Institute of Technology, Brisbane, 4006, Australia

    Katie L. McMahon

  5. Centre for Advanced Imaging, The University of Queensland, Brisbane, 4072, Australia

    Katie L. McMahon & Margaret J. Wright

  6. Institute of Health and Biomedical Innovations, Queensland Institute of Technology, Brisbane, 4059, Australia

    Greig I. de Zubicaray

  7. Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, Los Angeles, CA, 90292, USA

    Paul M. Thompson

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  1. Baptiste Couvy-Duchesne
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Correspondence to Baptiste Couvy-Duchesne.

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Written informed consent was obtained from all participants including a parent or guardian for those aged less than 18 years.

Research involving human and animal rights

The QTIM study was approved by the ethics review boards of the Queensland Institute of Medical Research, the University of Queensland, and Uniting Health Care, Wesley Hospital, Brisbane. QTIM participants received an honorarium in appreciation of their time. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Couvy-Duchesne, B., Strike, L.T., McMahon, K.L. et al. A Fast Method for Estimating Statistical Power of Multivariate GWAS in Real Case Scenarios: Examples from the Field of Imaging Genetics. Behav Genet 49, 112–121 (2019). https://doi.org/10.1007/s10519-018-9936-9

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