  This paper addresses the challenge of efficiently capturing a high proportion
of true signals for subsequent data analyses when sample sizes are relatively
limited with respect to data dimension. We propose the signal missing rate as a
new measure for false negative control to account for the variability of false
negative proportion. Novel data-adaptive procedures are developed to control
signal missing rate without incurring many unnecessary false positives under
dependence. We justify the efficiency and adaptivity of the proposed methods
via theory and simulation. The proposed methods are applied to GWAS on human
height to effectively remove irrelevant SNPs while retaining a high proportion
of relevant SNPs for subsequent polygenic analysis.
