Behavior Genetics

, Volume 49, Issue 1, pp 112–121 | Cite as

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

  • Baptiste Couvy-DuchesneEmail author
  • Lachlan T. Strike
  • Katie L. McMahon
  • Greig I. de Zubicaray
  • Paul M. Thompson
  • Nicholas G. Martin
  • Sarah E. Medland
  • Margaret J. Wright
Original Research


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.


Twin models Statistical power Multivariate Univariate GWAS MRI imaging 



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.


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).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent

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.

Supplementary material

10519_2018_9936_MOESM1_ESM.docx (6.8 mb)
Supplementary material 1 (DOCX 6918 KB)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Baptiste Couvy-Duchesne
    • 1
    • 2
    • 3
    Email author
  • Lachlan T. Strike
    • 2
  • Katie L. McMahon
    • 4
    • 5
  • Greig I. de Zubicaray
    • 6
  • Paul M. Thompson
    • 7
  • Nicholas G. Martin
    • 3
  • Sarah E. Medland
    • 3
  • Margaret J. Wright
    • 2
    • 5
  1. 1.Institute of Molecular BioscienceThe University of QueenslandBrisbaneAustralia
  2. 2.Queensland Brain InstituteThe University of QueenslandBrisbaneAustralia
  3. 3.QIMR Berghofer Medical Research InstituteBrisbaneAustralia
  4. 4.Herston Imaging Research Facility (HIRF)Queensland Institute of TechnologyBrisbaneAustralia
  5. 5.Centre for Advanced ImagingThe University of QueenslandBrisbaneAustralia
  6. 6.Institute of Health and Biomedical InnovationsQueensland Institute of TechnologyBrisbaneAustralia
  7. 7.Imaging Genetics Center, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA

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