In epidemiologic studies on direct genetic associations, hypothesis testing is primarily considered for evaluating the effects of each candidate genetic marker, eg, single nucleotide polymorphisms. To help investigators protect themselves from over-interpreting statistically significant findings that are not likely to signify a true effect-a problem connected to multiple comparisons-consideration of the false-positive report probability has been proposed. There have also been arguments advocating estimation of effect size rather than hypothesis testing (P value). Here, we propose an estimation-based approach that offers an attractive alternative to the test-based false-positive report probability, when the task is to select promising genetic markers for further analyses. We discuss the potential of this estimation-based approach for genome-wide association studies.