Pharmacogenomics offers one of the best use cases for widespread clinical implementation of genomic medicine, as variants tend to have moderate allele frequencies, many of the affected medications are relatively common, and the magnitude of effect tends to be clinically meaningful. Using pharmacogenomic-guided warfarin dosing as an example, this dissertation addresses potential barriers and solutions to the clinical implementation of pharmacogenomics. Warfarin is a blood thinner that has a narrow therapeutic index and wide dosing variation, with many known pharmacogenomic markers associated with stable warfarin dose. A number of different methods to reduce disparities in pharmacogenomic-guided warfarin dosing among African Americans were tested. Additionally data from Vanderbiltâs clinical implementation of pharmacogenomic-guided warfarin dosing were analyzed to assess process outcomes (e.g. how did the actual warfarin dose ordered compare to the recommended dose) and patient outcomes (e.g., what kinds of clinical events did the patient experience immediately following warfarin initiation). A summary of policy and technological challenges for clinical implementation of precision medicine, along with potential solutions, are presented.

Age-related macular degeneration (AMD) is a neurodegenerative disease that leads to a loss of central vision and is the leading cause of blindness in the elderly in developed countries. AMD can significantly reduce quality of life and there is no way to cure or prevent the disease. The prevalence of AMD is expected to increase worldwide as lifespans increase. Of the two subtypes of AMD â geographic atrophy and neovascular AMD â only the neovascular form has treatment options, yet treatment does not reverse vision loss and is required for the life of a patient. AMD is known to be highly heritable (45-70%) but previous genetic studies only explain a portion of the heritability. In this work we perform genetic pathway analyses of AMD to localize additional heritability and develop novel methods to test for cumulative epistatic (interaction) effects potentially influencing risk for AMD. We show that genetic variation in complement activation-related genes, excluding previously associated risk variants, contributes to a statistically significant proportion of risk for AMD (9.7%). Additionally, we develop methods to calculate orthogonal genetic relationship matrices to estimate additive, dominant, and epistatic genetic effects. We applied this method to test for interactions between the ARMS2 gene and three AMD-related pathways and were able to conclusively rule out epistatic effects influencing risk for AMD from the specific interactions that were tested.

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