Group-guided high dimensional mediation analysis of omics data

Music City Center 

Mediation analysis is increasingly used to identify omics molecules that mediate the relationship between an exposure and an outcome. Since high-dimensional omics data often exhibit clustered structures and biologically defined sets, group selection may improve detection power and interpretability. We propose a group-guided approach that leverages biologically informed omics groups, followed by sparse group lasso for mediator selection, allowing selection within and between groups. We adapted and implemented de-biased beta coefficients and variance estimates for inference. In simulations, de-biased sparse group lasso provided more accurate mediation effect estimates than sparse group lasso, lasso, and one-by-one regression. Our method also performed well under reasonable group misspecification, particularly when mediation effects were small. We will apply this approach to Framingham Heart Study data to demonstrate its utility.

omics

high-dimensional data

mediation analysis

debiasing

sparse group lasso 

Section on Statistics in Epidemiology