Publication

Bayesian nonparametric method for genetic dissection of brain activation region

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Last modified
  • 06/25/2025
Type of Material
Authors
    Zhuxuan Jin, Emory UniversityJian Kang, University of Michigan, Ann ArborTianwei Yu, Chinese University of Hong Kong
Language
  • English
Date
  • 2023-10-18
Publisher
  • Frontiers
Publication Version
Copyright Statement
  • © 2023 Jin, Kang and Yu.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 17
Start Page
  • 1235321
Grant/Funding Information
  • his research was partially supported by NIH R01GM124061.
Supplemental Material (URL)
Abstract
  • Biological evidence indicewates that the brain atrophy can be involved at the onset of neuropathological pathways of Alzheimer's disease. However, there is lack of formal statistical methods to perform genetic dissection of brain activation phenotypes such as shape and intensity. To this end, we propose a Bayesian hierarchical model which consists of two levels of hierarchy. At level 1, we develop a Bayesian nonparametric level set (BNLS) model for studying the brain activation region shape. At level 2, we construct a regression model to select genetic variants that are strongly associated with the brain activation intensity, where a spike-and-slab prior and a Gaussian prior are chosen for feature selection. We develop efficient posterior computation algorithms based on the Markov chain Monte Carlo (MCMC) method. We demonstrate the advantages of the proposed method via extensive simulation studies and analyses of imaging genetics data in the Alzheimer's disease neuroimaging initiative (ADNI) study.
Author Notes
Keywords
Research Categories
  • Biology, Biostatistics
  • Biology, Bioinformatics
  • Biology, Neuroscience

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