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

Tian Ge1,2, Thomas E Nichols3, Debashis Ghosh4

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital / Harvard Medical School, Charlestown, MA 02129, USA.

Neuroimage
|January 21, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to analyze how gene and cardiovascular risk factors interact to affect brain structure in Alzheimer's disease (AD). It found that CR1 and EPHA1 genes interact with cardiovascular risks, impacting hippocampal volume.

Keywords:
Alzheimer's diseaseCardiovascular diseaseImaging geneticsInteractionKernel machines

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Area of Science:

  • Neuroimaging
  • Genetics
  • Computational Biology

Background:

  • Neuroimaging measures are valuable biomarkers for disease and development, often heritable.
  • Current imaging genetics studies primarily examine isolated genetic effects, neglecting interactions with non-genetic factors.
  • Understanding gene-environment interactions is crucial for elucidating disease mechanisms.

Purpose of the Study:

  • To develop a general kernel machine-based method for detecting interactions between multidimensional variable sets.
  • To model joint and epistatic effects of single nucleotide polymorphisms (SNPs) and other moderating factors.
  • To test for nonlinear interactions between variable sets in a flexible framework.

Main Methods:

  • Applied a novel kernel machine method to Alzheimer's Disease Neuroimaging Initiative (ADNI) data.
  • Investigated interactions between Alzheimer's disease (AD) risk genes and cardiovascular disease (CVD) risk factors.
  • Analyzed effects on hippocampal volume using structural brain magnetic resonance imaging (MRI) scans.

Main Results:

  • Identified significant interactions between CR1 and EPHA1 genes and CVD risk factors on hippocampal volume.
  • Demonstrated the utility of the kernel machine method in analyzing complex genetic and environmental interactions.
  • Provided evidence for the role of CR1 and EPHA1 in AD-related neurodegeneration influenced by CVD risks.

Conclusions:

  • The developed method effectively detects gene-environment interactions in neuroimaging studies.
  • CR1 and EPHA1 genes show significant interaction effects with CVD risk factors on brain structure relevant to AD.
  • These findings highlight the importance of considering combined genetic and non-genetic influences in AD pathogenesis.