Power estimates for voxel-based genetic association studies using diffusion imaging

Neda Jahanshad, Peter Kochunov, David C. Glahn, John Blangero, Thomas E. Nichols, Katie L. McMahon, Greig I. De Zubicaray, Nicholas G. Martin, Margaret J. Wright, Clifford R. Jack, Matt A. Bernstein, Michael W. Weiner, Arthur W. Toga, Paul M. Thompson

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


The quest to discover genetic variants that affect the human brain will be accelerated by screening brain images from large populations. Even so, the wealth of information in medical images is often reduced to a single numeric summary, such as a regional volume or an average signal, which is then analyzed in a genome wide association study (GWAS). The high cost and penalty formultiple comparisons often constrains us from searching over the entire image space. Here, we developed a method to compute and boost power to detect genetic associations in brain images. We computed voxel-wise heritability estimates for fractional anisotropy in over 1,100 DTI scans, and used the results to threshold FA images from new studies. We describe voxel selection criteria to optimally boost power, as a function of the sample size and allele frequency cut-off. We illustrate our methods by analyzing publicly-available data from the ADNI2 project.

Original languageEnglish (US)
Pages (from-to)229-238
Number of pages10
JournalMathematics and Visualization
StatePublished - 2014
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 26 2013


  • DTI
  • GWAS
  • Heritability
  • Multiple comparisons correction
  • Neuroimaging genetics

ASJC Scopus subject areas

  • Modeling and Simulation
  • Geometry and Topology
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics


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