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Bivariate Genome-Wide Association Study of Genetically Correlated Neuroimaging Phenotypes from DTI and MRI through a Seemingly Unrelated Regression Model

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Multimodal Brain Image Analysis (MBIA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8159))

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Abstract

Large multisite efforts (e.g., the ENIGMA Consortium), have shown that neuroimaging traits including tract integrity (from DTI fractional anisotropy, FA) and subcortical volumes (from T1-weighted scans) are highly heritable and promising phenotypes for discovering genetic variants associated with brain structure. However, genetic correlations (r g) among measures from these different modalities for mapping the human genome to the brain remain unknown. Discovering these correlations can help map genetic and neuroanatomical pathways implicated in development and inherited risk for disease. We use structural equation models and a twin design to find r g between pairs of phenotypes extracted from DTI and MRI scans. When controlling for intracranial volume, the caudate as well as related measures from the limbic system - hippocampal volume - showed high r g with the cingulum FA. Using an unrelated sample and a Seemingly Unrelated Regression model for bivariate analysis of this connection, we show that a multivariate GWAS approach may be more promising for genetic discovery than a univariate approach applied to each trait separately.

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Author information

Authors and Affiliations

  1. Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA

    Neda Jahanshad, Priya Bhatt, Derrek P. Hibar, Julio E. Villalon, Talia M. Nir, Arthur W. Toga & Paul M. Thompson

  2. Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA

    Clifford R. Jack Jr. & Matt A. Bernstein

  3. Department of Radiology, Medicine, and Psychiatry, UC San Francisco, CA, USA

    Michael W. Weiner

  4. Department of Veterans Affairs Medical Center, San Francisco, CA, USA

    Michael W. Weiner

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

    Katie L. McMahon

  6. School of Psychology, University of Queensland, Brisbane, Australia

    Greig I. de Zubicaray

  7. Queensland Institute of Medical Research, Brisbane, Australia

    Nicholas G. Martin & Margaret J. Wright

Authors
  1. Neda Jahanshad
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  2. Priya Bhatt
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  3. Derrek P. Hibar
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  4. Julio E. Villalon
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  5. Talia M. Nir
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  6. Arthur W. Toga
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  7. Clifford R. Jack Jr.
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  8. Matt A. Bernstein
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  9. Michael W. Weiner
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  10. Katie L. McMahon
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  11. Greig I. de Zubicaray
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  12. Nicholas G. Martin
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  13. Margaret J. Wright
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  14. Paul M. Thompson
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Editor information

Editors and Affiliations

  1. Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA

    Li Shen

  2. Cortical Architecture Imaging and Discovery Lab, Department of Computer Science, University of Georgia, Athens, GA, USA

    Tianming Liu

  3. Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, U.S.A.

    Pew-Thian Yap

  4. Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA

    Heng Huang

  5. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA

    Dinggang Shen

  6. Surgical Planning Laboratory, BWH, Harvard Medical School, Boston, MA, USA

    Carl-Fredrik Westin

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© 2013 Springer International Publishing Switzerland

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Jahanshad, N. et al. (2013). Bivariate Genome-Wide Association Study of Genetically Correlated Neuroimaging Phenotypes from DTI and MRI through a Seemingly Unrelated Regression Model. In: Shen, L., Liu, T., Yap, PT., Huang, H., Shen, D., Westin, CF. (eds) Multimodal Brain Image Analysis. MBIA 2013. Lecture Notes in Computer Science, vol 8159. Springer, Cham. https://doi.org/10.1007/978-3-319-02126-3_19

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  • DOI: https://doi.org/10.1007/978-3-319-02126-3_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02125-6

  • Online ISBN: 978-3-319-02126-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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