Although complete understanding of the mechanisms of rare genetic variants in disease continues to elude us, Next Generation Sequencing (NGS) has facilitated significant gene discoveries across the disease spectrum. However, the cost of NGS hinders its use for identifying rare variants in common diseases that require large samples. To circumvent the need for larger samples, designing efficient sampling studies is crucial in order to detect potential associations. Designs based on selective sampling have been shown to be powerful for the study of quantitative traits but result in selection bias. This research therefore presents a framework that accounts for selective sampling. In addition, sampling designs for rare variant - quantitative trait association studies are evaluated to assess the effect on power that freely available public cohort data can have in the design. Performing simulations and evaluating common and unconventional sampling schemes results in several noteworthy findings. Specifically, the extreme-trait design is the most powerful design for analyzing quantitative traits. This research also shows that sampling more individuals from the extreme of clinical interest does not increase power. The framework presented can serve as a catalyst to improve sampling design and to develop robust statistical methods for association testing.