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Bayesian Variable Selection for High-Dimensional Mediation Analysis: Application to Metabolomics Data in

Youngho Bae, Chanmin Kim, Fenglei Wang

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    This study introduces a new Bayesian method to identify how diet affects heart health through blood biomarkers. The approach effectively finds active pathways in complex, high-dimensional data.

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    Area of Science:

    • Epidemiology
    • Biostatistics
    • Genomics

    Background:

    • Causal mediation analysis is vital for understanding exposure-outcome relationships in health.
    • High-dimensional omics data, like plasma metabolomes, present challenges for mediation analysis due to complex mediator dependencies.

    Purpose of the Study:

    • To develop a novel Bayesian framework for identifying active pathways and estimating indirect effects in high-dimensional multivariate mediation.
    • To address challenges in mediation analysis with omics data, focusing on complex dependencies and variable selection.

    Main Methods:

    • Proposed a multivariate stochastic search variable selection method within a Bayesian framework.
    • Introduced novel priors: a Markov random field prior to leverage mediator correlations and sequential subsetting Bernoulli priors for simultaneous selection.

    Main Results:

    • Demonstrated superior power in detecting active mediating pathways through comprehensive simulation studies.
    • Successfully applied the method to metabolome data from two cohort studies, showing practical utility.

    Conclusions:

    • The novel Bayesian framework effectively identifies causal pathways and estimates indirect effects in high-dimensional mediation analysis.
    • The method offers a powerful and coherent approach for analyzing complex omics data in epidemiological research.