Tuesday, Aug 6: 10:30 AM - 12:20 PM
3166
Contributed Posters
Oregon Convention Center
Pathway mediation analysis is widely used to identify biological mechanisms linking environmental exposures with disease outcomes. Previous methods such as HIMA1 and HIMA2 use penalized regression to identify individual omics biomarker as potential mediators among high-dimensional omics data. However, these methods overlook correlated omics biomarkers within pathway and shared omics biomarkers across multiple pathways, failing to identify key pathways.
We proposed a novel method using overlapped group lasso and principal component analysis to assess association of pathways with exposures and outcomes. Joint significance test was applied to identify mediators among overlapped and correlated pathways. Simulations were done based on correlation structure from a real study. Our method demonstrated power of 0.86, and 5-minutes computation time which showed higher power and less computation time than using HIMA1 or HIMA2 for 1000 simulations in detecting three mediation pathways under high-dimensional setting.
Our method offered higher power, and more efficient computing time in detecting mediation pathways than the existing method for both high and low dimensional data.
Mediation Analysis
high dimensional omics data
pathway analysis
Main Sponsor
Section on Statistics in Epidemiology