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We consider Bayesian high-dimensional mediation analysis to identify among alarge set of correlated potential mediators the active ones that mediate theeffect from an exposure variable to an outcome of interest. Correlations amongmediators are commonly observed in modern data analysis; examples include theactivated voxels within connected regions in brain image data, regulatorysignals driven by gene networks in genome data and correlated exposure datafrom the same source. When correlations are present among active mediators,mediation analysis that fails to account for such correlation can besub-optimal and may lead to a loss of power in identifying active mediators.Building upon a recent high-dimensional mediation analysis framework, wepropose two Bayesian hierarchical models, one with a Gaussian mixture priorthat enables correlated mediator selection and the other with a Potts mixtureprior that accounts for the correlation among active mediators in mediationanalysis. We develop efficient sampling algorithms for both methods. Varioussimulations demonstrate that our methods enable effective identification ofcorrelated active mediators, which could be missed by using existing methodsthat assume prior independence among active mediators. The proposed methods areapplied to the LIFECODES birth cohort and the Multi-Ethnic Study ofAtherosclerosis (MESA) and identified new active mediators with importantbiological implications.

Bayesian Hierarchical Models for High-Dimensional Mediation Analysis with Coordinated Selection of Correlated Mediators