A kernel machine method for detecting effects of interaction between multidimensional variable sets: an imaging genetics application.
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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. |
Year of Publication | 2015
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Journal | Neuroimage
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Volume | 109
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Pages | 505-14
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Date Published | 2015 Apr 01
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ISSN | 1095-9572
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URL | |
DOI | 10.1016/j.neuroimage.2015.01.029
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PubMed ID | 25600633
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PubMed Central ID | PMC4339421
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Grant list | R01 NS070963 / NS / NINDS NIH HHS / United States
K25 EB013649 / EB / NIBIB NIH HHS / United States
NIH NIBIB 1K25EB013649-01 / EB / NIBIB NIH HHS / United States
K24 MH094614 / MH / NIMH NIH HHS / United States
R01 AG016495 / AG / NIA NIH HHS / United States
100309/Z/12/Z / Wellcome Trust / United Kingdom
R01 EB015611 / EB / NIBIB NIH HHS / United States
U01 AG024904 / AG / NIA NIH HHS / United States
R01 EB015611-01 / EB / NIBIB NIH HHS / United States
K24MH094614 / MH / NIMH NIH HHS / United States
U54 MH091657-03 / MH / NIMH NIH HHS / United States
R01 AG008122 / AG / NIA NIH HHS / United States
Canadian Institutes of Health Research / Canada
100309 / Wellcome Trust / United Kingdom
P41EB015896 / EB / NIBIB NIH HHS / United States
U54 MH091657 / MH / NIMH NIH HHS / United States
098369/Z/12/Z / Wellcome Trust / United Kingdom
P41 EB015896 / EB / NIBIB NIH HHS / United States
R01 NS083534 / NS / NINDS NIH HHS / United States
R01 MH101486 / MH / NIMH NIH HHS / United States
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