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A kernel machine method for detecting effects of interaction between multidimensional variable sets : an imaging genetics application

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Ge, Tian, Nichols, Thomas E. , Ghosh, Debashis, Mormino, Elizabeth C., Smoller, Jordan W. and Sabuncu, Mert R. (2015) A kernel machine method for detecting effects of interaction between multidimensional variable sets : an imaging genetics application. NeuroImage, Volume 109 . pp. 505-514. doi:10.1016/j.neuroimage.2015.01.029 ISSN 1053-8119.

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Official URL: http://dx.doi.org/10.1016/j.neuroimage.2015.01.029

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Abstract

Measurements derived from neuroimaging data can serve as markers of disease and/or healthy development, are largely heritable, and have been increasingly utilized as (intermediate) phenotypes in genetic association studies. To date, imaging genetic studies have mostly focused on discovering isolated genetic effects, typically ignoring potential interactions with non-genetic variables such as disease risk factors, environmental exposures, and epigenetic markers. However, identifying significant interaction effects is critical for revealing the true relationship between genetic and phenotypic variables, and shedding light on disease mechanisms. In this paper, we present a general kernel machine based method for detecting effects of the interaction between multidimensional variable sets. This method can model the joint and epistatic effect of a collection of single nucleotide polymorphisms (SNPs), accommodate multiple factors that potentially moderate genetic influences, and test for nonlinear interactions between sets of variables in a flexible framework. As a demonstration of application, we applied the method to the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to detect the effects of the interactions between candidate Alzheimer's disease (AD) risk genes and a collection of cardiovascular disease (CVD) risk factors, on hippocampal volume measurements derived from structural brain magnetic resonance imaging (MRI) scans. Our method identified that two genes, CR1 and EPHA1, demonstrate significant interactions with CVD risk factors on hippocampal volume, suggesting that CR1 and EPHA1 may play a role in influencing AD-related neurodegeneration in the presence of CVD risks.

Item Type: Journal Article
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Journal or Publication Title: NeuroImage
Publisher: Elsevier
ISSN: 1053-8119
Official Date: 1 April 2015
Dates:
DateEvent
1 April 2015Published
16 January 2015Available
9 January 2015Accepted
Volume: Volume 109
Page Range: pp. 505-514
DOI: 10.1016/j.neuroimage.2015.01.029
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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