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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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A Bayesian spatial model for imaging genetics.

Yin Song1, Shufei Ge2, Jiguo Cao3

  • 1Department of Mathematics and Statistics, University of Victoria, British Columbia, Canada.

Biometrics
|March 25, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian spatial model to analyze genetic influences on brain structure, outperforming standard methods in Alzheimer's Disease Neuroimaging Initiative data. The model helps identify genetic variations associated with brain imaging phenotypes.

Keywords:
Bayesian modelGbbs samplingimaging geneticsspatial modelvariational Bayes

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Area of Science:

  • Neuroscience
  • Genetics
  • Biostatistics

Background:

  • Imaging genetics studies investigate the relationship between genetic variations and brain structure.
  • Structural brain imaging data, like MRI scans, exhibit complex spatial correlation patterns.
  • Previous models may not fully capture these intricate correlations.

Purpose of the Study:

  • To develop a novel Bayesian bivariate spatial model for multivariate regression analysis.
  • To examine the association between brain imaging phenotypes (volumetric and cortical thickness) and single nucleotide polymorphisms (SNPs).
  • To accommodate spatial correlation structures in brain imaging data, including inter-hemispheric correlations.

Main Methods:

  • Developed a Bayesian bivariate spatial process model.
  • Incorporated spatial correlation on a graph structure within hemispheres and between hemispheres.
  • Utilized mean-field variational Bayes and Gibbs sampling algorithms for model fitting.
  • Applied Bayesian false discovery rate (FDR) procedures for SNP selection.
  • Implemented the methodology in the R package bgsmtr.

Main Results:

  • The proposed spatial model demonstrated superior performance compared to a standard model in the ADNI dataset.
  • Successfully identified associations between genetic variations and brain structure measures.
  • The model effectively handles spatial correlations in both same-hemisphere and inter-hemisphere brain imaging data.

Conclusions:

  • The new Bayesian bivariate spatial model provides a powerful tool for imaging genetics research.
  • It enhances the ability to detect genetic influences on brain structure.
  • The methodology offers improved analytical capabilities for complex neuroimaging and genetic datasets.