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Bayesian bi-level variable selection for genome-wide survival study.

Eunjee Lee1, Joseph G Ibrahim2, Hongtu Zhu2

  • 1Department of Information and Statistics, Chungnam National University, Daejeon 34134, Korea.

Genomics & Informatics
|October 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian method to identify genetic markers associated with the rapid progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). The approach enhances early AD diagnosis and drug discovery by detecting subtle genetic influences.

Keywords:
Bayesian variable selectiongenome-wide association studiesgroup structurelinkage disequilibriumsurvival analysis

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Area of Science:

  • Genetics
  • Neuroscience
  • Biostatistics

Background:

  • Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD), and understanding its genetic basis is crucial for early diagnosis and treatment.
  • Genome-wide association studies (GWAS) have limitations in detecting genetic variants with small effect sizes and do not leverage SNP group structures.

Purpose of the Study:

  • To develop and validate a Bayesian bi-level variable selection method for identifying single nucleotide polymorphisms (SNPs) associated with the time to conversion from MCI to AD.
  • To improve early diagnosis and facilitate drug discovery for Alzheimer's disease by analyzing genetic factors influencing MCI progression.

Main Methods:

  • A Bayesian bi-level variable selection approach integrating group inclusion indicators into an accelerated failure time model.
  • Utilizing data augmentation for imputing censored time values via predictive posterior distributions.
  • Applying Dirichlet-Laplace shrinkage priors to incorporate SNP group structures for variable selection.

Main Results:

  • The proposed Bayesian method demonstrated superior performance in variable selection compared to competing methods in simulation studies.
  • Analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data identified several genes linked to AD, which were missed by traditional GWAS.
  • The method successfully detected SNPs associated with the time of conversion from MCI to AD, highlighting its potential for clinical application.

Conclusions:

  • The developed Bayesian method effectively identifies genetic markers associated with MCI to AD progression, outperforming standard GWAS.
  • This approach offers a promising tool for enhancing early Alzheimer's disease diagnosis and accelerating the discovery of novel therapeutic targets.
  • Leveraging SNP group structures and advanced statistical techniques provides deeper insights into the genetic architecture of neurodegenerative disease progression.