Recent advances in genotyping technology have resulted in a dramatic change in the way hypothesis-based genetic association studies are conducted. Previous investigators who were limited by cost and power to investigate only a handful of common variants within the most interesting genes are now able to conduct whole genome studies involving millions of both common and rare genetic variants. Thus, the bottleneck in understanding many important complex diseases has become how to sift through millions of exchangeable variants. While the most common approach to this problem has been to apply a marginal test to all genetic markers, the analytical strategies that are a focus of my research aim to improve upon these methods by modeling the outcome variable as a function of a multivariate genetic profile using Bayesian model uncertainty techniques. Throughout this talk I will demonstrate that these techniques can be extremely powerful and advantageous in high-dimensional variable selection problems due to their flexibility and ability to provide formal intuitive multi-level inference. In particular, I will describe several methods that have been developed to gain insight into the genetic contribution (of both common and rare variants) of various complex diseases within the North Carolina Ovarian Cancer Study (NCOCS), Womenâ€™s Environmental Cancer and Radiation Epidemiology (WECARE) Study, and the Pharmacogenetics of Nicotine Addiction and Treatment Consortium (PNAT). While this work is motivated by a very specific application, I will also highlight several areas of general methodological development throughout in efficient model search algorithms and multiplicity corrected model space priors. Finally, I will describe a recent extension of this work that allows the incorporation of external variant specific biological information to guide the variable selection procedure.