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Related Experiment Videos

A Bayesian group sparse multi-task regression model for imaging genetics.

Keelin Greenlaw1, Elena Szefer2, Jinko Graham2

  • 1Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.

Bioinformatics (Oxford, England)
|April 19, 2017
PubMed
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This study introduces a Bayesian method for imaging genomics, enabling statistical inference for genetic influences on brain structure. The approach provides reliable interval estimates, outperforming existing methods and offering valuable insights into neuroimaging and genetic data analysis.

Area of Science:

  • Neuroscience
  • Genetics
  • Statistical Modeling

Background:

  • Advances in brain imaging and genotyping necessitate methods for analyzing imaging genomic studies.
  • Existing penalized regression methods offer point estimates but lack statistical inference capabilities.
  • A novel Bayesian approach is proposed to address these limitations.

Purpose of the Study:

  • To develop a Bayesian hierarchical model for imaging genomics that allows for full posterior inference.
  • To enable the construction of interval estimates for regression parameters in imaging genomic analyses.
  • To overcome the limitations of point-estimate-only methods.

Main Methods:

  • A Bayesian hierarchical modeling formulation is developed.
  • The model is expressed as a three-level Gaussian scale mixture.

Related Experiment Videos

  • A Gibbs sampling algorithm is utilized for posterior simulation.

    • Main Results:

    • The posterior mode of the Bayesian model corresponds to the penalized multi-task regression estimator.
    • Interval estimates achieve adequate coverage probabilities, outperforming nonparametric bootstrap methods.
    • The methodology is successfully applied to Alzheimer's Disease Neuroimaging Initiative (ADNI) data.

    Conclusions:

    • The proposed Bayesian method provides a robust framework for statistical inference in imaging genomics.
    • Incorporating interval estimation enhances the understanding of relationships between SNPs and brain imaging endophenotypes.
    • The R package 'bgsmtr' facilitates the application of this methodology.