Measuring weak effects in high dimensional mediation analysis for omics data

Music City Center 

Understanding the mediating role of omics data is crucial for uncovering the biological mechanisms through which an established risk factor influences an outcome of interest. Despite extensive research on high-dimensional mediation analysis, existing methods have often fallen short in accurately quantifying the global contribution of omics mediators, particularly those with weak effects. When investigating the proteins from environmental exposures to cardiovascular outcomes in the MESA dataset, we found that many likely have weak mediating effects that could collectively play a substantial mediating role. To address this issue, we propose new variance-based causal measures under the causal mediation analysis framework. Then, we develop a flexible and computationally efficient estimation procedure based on a mixed-effects working model. Through this innovative approach, we were able to accurately quantify the total mediation effect, and we discovered that a significant amount is attributed to weak mediators in simulation studies and real data analysis, which were largely mis-estimated by existing methods. This result offers valuable guidance for future study design and downstream analyses. The proposed approach is general and complements the existing methodologies by offering new perspectives on the global and weak effects in mediation analysis.