High-dimensional association testing in biomedical research often uses covariates to boost accuracy. A new method for controlling errors in these tests has been developed, improving power significantly. By mapping values and adjusting estimators, the method doubles potential power gains compared to existing techniques. This approach was successfully applied to transcriptome-wide studies, showing its effectiveness in enhancing analysis power.