Visual exploration of genetic association with voxel-based imaging phenotypes in an MCI/AD study.

Sungeun Kim, Li Shen, Andrew J Saykin, John D West
Author Information
  1. Sungeun Kim: Center for Neuroimaging, Department of Radiology, Indianapolis, IN 46202, USA. sk31@iupui.edu

Abstract

Neuroimaging genomics is a new transdisciplinary research field, which aims to examine genetic effects on brain via integrated analyses of high throughput neuroimaging and genomic data. We report our recent work on (1) developing an imaging genomic browsing system that allows for whole genome and entire brain analyses based on visual exploration and (2) applying the system to the imaging genomic analysis of an existing MCI/AD cohort. Voxel-based morphometry is used to define imaging phenotypes. ANCOVA is employed to evaluate the effect of the interaction of genotypes and diagnosis in relation to imaging phenotypes while controlling for relevant covariates. Encouraging experimental results suggest that the proposed system has substantial potential for enabling discovery of imaging genomic associations through visual evaluation and for localizing candidate imaging regions and genomic regions for refined statistical modeling.

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Grants

  1. R03 EB008674/NIBIB NIH HHS
  2. UL1 TR001108/NCATS NIH HHS
  3. UL1 RR025761-01/NCRR NIH HHS
  4. R01 AG019771-01/NIA NIH HHS
  5. R01 AG19771/NIA NIH HHS
  6. R01 CA101318/NCI NIH HHS
  7. R01 CA101318-02/NCI NIH HHS
  8. R01 AG019771/NIA NIH HHS
  9. UL1 RR025761/NCRR NIH HHS
  10. R03 EB008674-01/NIBIB NIH HHS
  11. P30 AG10133/NIA NIH HHS
  12. P30 AG010133-17/NIA NIH HHS
  13. P30 AG010133/NIA NIH HHS

MeSH Term

Aged
Aging
Alzheimer Disease
Artificial Intelligence
Brain Mapping
Cognition Disorders
Cohort Studies
Computer Graphics
Diagnostic Imaging
Genome-Wide Association Study
Genomics
Humans
Neurons
Pattern Recognition, Automated
Phenotype

Word Cloud

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