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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
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Genome-wide interaction analysis reveals replicated epistatic effects on brain structure.

Derrek P Hibar1, Jason L Stein1, Neda Jahanshad1

  • 1Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.

Neurobiology of Aging
|September 30, 2014
PubMed
Summary
This summary is machine-generated.

Researchers identified a significant gene interaction (SNP-SNP) affecting temporal lobe volume, a key brain region. This finding offers new insights into brain structure and potential protective effects against neurodegenerative diseases.

Keywords:
EpistasisGWASGWIAGenome-wideInteractionSure independence screeningTensor-based morphometry

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Area of Science:

  • Neurogenetics
  • Brain Imaging
  • Computational Biology

Background:

  • Alzheimer's disease risk is influenced by genetic factors, prompting research into common genetic variants (SNPs).
  • Genome-wide analysis of SNP-SNP interactions is computationally intensive due to the vast number of potential tests.
  • Machine learning, specifically iterative sure independence screening, enables analysis of datasets with more predictors than observations.

Purpose of the Study:

  • To perform a genome-wide interaction analysis of all possible SNP-SNP interactions.
  • To identify SNP-SNP interactions affecting regional brain volumes measured by magnetic resonance imaging (MRI) and tensor-based morphometry.
  • To investigate the association of identified interactions with brain volume and potential protective effects.

Main Methods:

  • Utilized an implementation of the sure independence screening algorithm (EPISIS) for genome-wide interaction analysis.
  • Tested all possible SNP-SNP interactions influencing regional brain volumes.
  • Validated findings in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and an independent twin imaging dataset (Queensland Twin Imaging).

Main Results:

  • Identified a significant SNP-SNP interaction between rs1345203 and rs1213205, explaining 1.9% of the variance in temporal lobe volume.
  • Mapped voxelwise effects of the interaction across the whole brain in both ADNI and Queensland Twin Imaging datasets.
  • Each unit increase in interaction loading correlated with approximately 5% greater regional brain volume, indicating a protective effect.

Conclusions:

  • A specific SNP-SNP interaction significantly influences temporal lobe volume.
  • This interaction demonstrates a protective effect, associated with increased regional brain volume.
  • The findings highlight the potential of advanced machine learning methods for uncovering complex genetic interactions in neuroimaging studies.