| Literature DB >> 29201284 |
Boris A Gutman1, Fabrizio Pizzagalli1, Neda Jahanshad1, Margaret J Wright2,3, Katie L McMahon2, Greig de Zubicaray4, Paul M Thompson1.
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.Entities:
Keywords: Alzheimer’s disease; brain imaging; genome-wide association study; imaging genetics; subcortical shape
Year: 2017 PMID： 29201284 PMCID： PMC5705101 DOI： 10.1109/ISBI.2017.7950738
Source DB: PubMed Journal: Proc IEEE Int Symp Biomed Imaging ISSN： 1945-7928