B0832
Title: High dimensional mediation analysis via difference in coefficients with applications in genetics
Authors: Qi Zhang - University of New Hampshire (United States) [presenting]
Abstract: High-dimensional mediation analysis has been enjoying increasing popularity, largely motivated by the scientific problems in genomics and biomedical imaging. Previous literature has been primarily focused on mediator selection. There has also been work on estimating the overall indirect effect for low dimensional exposure. We aim at estimation and inference of the overall indirect effect for high dimensional exposures and high dimensional mediators. We propose MedDiC, a novel debiased estimator of the high dimensional overall indirect effect based on the difference-in-coefficients approach. We evaluate the proposed method using intensive simulations, and find the MedDiC provides valid inference and offers higher power and shorter computing time than the competitors for both low dimensional and high dimensional exposures. We also apply MedDiC to a mouse f2 dataset for diabetes study, and a dataset composed of diverse maize inbred lines for flowering time, and show that MedDiC yields more biologically meaningful gene lists. The results are reproducible across different measures of identical biological signal.
