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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Semiparametric Bayes conditional graphical models for imaging genetics applications.

Suprateek Kundu1, Jian Kang2

  • 1Department of Biostatistics, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA.

Stat (International Statistical Institute)
|June 16, 2017
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Summary
This summary is machine-generated.

This study introduces a new Bayesian model to find brain imaging and genetic markers for neurological disorders. It improves understanding of brain networks and identifies predictive biomarkers by accounting for demographic and genetic variations.

Keywords:
brain functional networkconditional graphical modelimaging geneticsmodularitysemiparametric Bayesvariable selection

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Area of Science:

  • Neuroscience
  • Genetics
  • Biostatistics

Background:

  • Large-scale imaging genetic studies are crucial for understanding neurological disorders.
  • Current methods for analyzing brain connectivity and genetic biomarkers often overlook interdependencies and population heterogeneity.
  • Identifying predictive neuroimaging and genetic biomarkers is a key objective.

Purpose of the Study:

  • To propose a novel semiparametric Bayesian conditional graphical model for joint variable selection and graph estimation.
  • To simultaneously estimate brain functional connectivity while accounting for demographic and genetic heterogeneity.
  • To infer significant genetic biomarkers predictive of neurological disorders.

Main Methods:

  • Developed a semiparametric Bayesian conditional graphical model.
  • Incorporated priors on regression coefficients for dimension reduction by clustering brain regions.
  • Utilized a novel graphical prior to promote modular brain organization (dense within-cluster, sparse across-cluster connections).
  • Employed Markov chain Monte Carlo for posterior computation.

Main Results:

  • The model successfully estimates brain networks while accounting for heterogeneity.
  • Identified significant genetic biomarkers associated with neurological disorders.
  • Demonstrated numerical advantages through application to Alzheimer's Disease Neuroimaging Initiative data and simulation studies.

Conclusions:

  • The proposed model offers a robust framework for joint analysis of neuroimaging and genetic data.
  • It enhances the identification of biomarkers for neurological disorders by integrating network structure and genetic information.
  • The approach provides a more comprehensive understanding of the interplay between brain networks, genetics, and disease.