Case-control designs are used widely in epidemiology and other fields to identify
the factors that may contribute to a disease of interest. In these studies, analyzing associations with secondary outcomes, which are biomarkers of the underlying disease, is another important way to understand the disease progress and mechanism. Most of the existing methods have been developed for inference on the mean of the secondary outcomes. In this paper, we propose a quantile-based approach. We construct a new family of estimating equations to make consistent and efficient estimation of conditional quantiles using the entire case-control sample, and also develop related statistical tools for inference. Simulations are conducted to evaluate the practical performance of the proposed approach, and a case-control study on genetic association with asthma is used to demonstrate the method.