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. This research therefore evaluates sampling designs for rare variant - quantitative trait association studies and assesses 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.

Variant filtering has served as a "proof-of-concept" approach for the discovery of disease-causing genes in Mendelian traits and formal statistical methods have been lacking in this area. However, combining variant filtering schemes with existing rare variant association tests is a practical alternative. Thus, this thesis also compares the robustness of six burden-based rare variant association tests for Mendelian traits after a variant filtering step in the presence of genetic heterogeneity and genotyping errors. This research shows that with low locus heterogeneity, these tests are powerful for testing association. With the exception of the weighted sum statistic (WSS), the remaining tests were very conservative in preserving the type I error when the number of affected and unaffected individuals was unequal. The WSS, on the other hand, had inflated type I error as the number of unaffected individuals increased. The framework presented can serve as a catalyst to improve sampling design and to develop robust statistical methods for association testing.