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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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A Bayesian high-dimensional mediation analysis for multilevel genome-wide epigenetic data.

Xi Qiao¹, Duy Ngo¹, Bilinda Straight²

¹Statistics, Western Michigan University, Kalamazoo, MI, USA.

Journal of Applied Statistics

|February 10, 2025

Summary

This study introduces a Bayesian causal mediation analysis for high-dimensional mediators in multilevel studies. The method effectively identifies causal pathways in complex intergenerational epigenetic mechanisms.

Keywords:

62F15 92D10 Bayesian variable selection High-dimensional mediation analysis epigenetic study multilevel data modeling

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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Area of Science:

Epidemiology
Biostatistics
Genetics

Background:

Causal mediation analysis is vital in clinical trials and epidemiology for understanding exposure-response pathways.
Existing methods are limited to low-dimensional mediators and single-level data.
Multilevel intergenerational epigenetic mechanisms present unique analytical challenges.

Purpose of the Study:

To propose a novel Bayesian causal mediation analysis method.
To address high-dimensional mediators within multilevel data structures.
To identify active mediators in complex biological pathways.

Main Methods:

Developed a Bayesian hierarchical model for complex, multilevel data.
Utilized Bayesian spike-and-slab priors to pinpoint significant exposure-mediator-outcome pathways.
Derived natural indirect and direct effects using Markov chain Monte Carlo (MCMC) inference.

Main Results:

The proposed Bayesian method demonstrates superior performance across various simulation scenarios.
Successfully identified key mediators in intergenerational epigenetic mechanisms.
Provided robust statistical inference for causal pathways.

Conclusions:

The novel Bayesian approach effectively handles high-dimensional mediators in multilevel settings.
This method advances causal inference in intergenerational epigenetic studies.
Applicable to understanding environmental exposures, like climate extremes, on offspring health via DNA methylation.