Approximating principal genetic components of subcortical shape

Boris A. Gutman, Fabrizio Pizzagalli, Neda Jahanshad, Margaret J. Wright, Katie L. McMahon, Greig De Zubicaray, Paul M. Thompson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Optimal representations of the genetic structure underlying complex neuroimaging phenotypes lie at the heart of our quest to discover the genetic code of the brain. Here, we suggest a strategy for achieving such a representation by decomposing the genetic covariance matrix of complex phenotypes into maximally heritable and genetically independent components. We show that such a representation can be approximated well with eigenvectors of the genetic covariance based on a large family study. Using 520 twin pairs from the QTIM dataset, we estimate 500 principal genetic components of 54,000 vertex-wise shape features representing fourteen subcortical regions. We show that our features maintain their desired properties in practice. Further, the genetic components are found to be significantly associated with the CLU and PICALM genes in an unrelated Alzheimer's Disease (AD) dataset. The same genes are not significantly associated with other volume and shape measures in this dataset.

Original languageEnglish
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781509011711
StatePublished - 15 Jun 2017
Externally publishedYes
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: 18 Apr 201721 Apr 2017

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference14th IEEE International Symposium on Biomedical Imaging, ISBI 2017


  • Alzheimer's disease
  • Brain imaging
  • Genome-wide association study
  • Imaging genetics
  • Subcortical shape


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