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.

Approximating principal genetic components of subcortical shape

Pizzagalli F.;
2017-01-01

Abstract

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.
2017
14th IEEE International Symposium on Biomedical Imaging
Melbourne
18 April 2017 through 21 April 2017
IEEE Computer Society
IEEE
2017
1226
1230
978-150901171-1
Alzheimer's disease; Brain imaging; Genome-wide association study; Imaging genetics; Subcortical shape
Gutman B.A.; Pizzagalli F.; Jahanshad N.; Wright M.J.; McMahon K.L.; De Zubicaray G.; Thompson P.M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1766850
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