Title |
Approximating Principal Genetic Components of Subcortical Shape
|
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Published in |
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, June 2017
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DOI | 10.1109/isbi.2017.7950738 |
Pubmed ID | |
Authors |
Boris A Gutman, Fabrizio Pizzagalli, Neda Jahanshad, Margaret J Wright, Katie L McMahon, Greig de Zubicaray, Paul M Thompson |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 33% |
Australia | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 17% |
Unknown | 5 | 83% |
Demographic breakdown
Readers by professional status | Count | As % |
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Professor | 2 | 33% |
Student > Ph. D. Student | 2 | 33% |
Student > Bachelor | 1 | 17% |
Unknown | 1 | 17% |
Readers by discipline | Count | As % |
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Engineering | 2 | 33% |
Mathematics | 1 | 17% |
Medicine and Dentistry | 1 | 17% |
Psychology | 1 | 17% |
Unknown | 1 | 17% |