Abstract: One problem that plagues omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. While there is a vast literature on multiple testing methodologies, methods that simultaneously take into account confounders and multiple testing are lacking. To fill this methodological gap, we develop 2dFDR, a linear model-based two-dimensional false discovery rate control procedure (2dFDR), for powerful confounder adjustment under multiple testing. Through extensive simulation studies and a large-scale evaluation on real data, we demonstrate that 2dFDR is substantially more powerful than the traditional procedure while controlling for false positives. In the presence of strong confounding and weak signals, power improvement could be more than 100%.
Zoom Link: https://illinois.zoom.us/j/96251642520?pwd=UUl2dit2VEExcVVXL0dHMDYvWFVrZz09
Meeting ID: 962 5164 2520
Password: 475311
