The candidate gene approach to pharmacogenetics is hypothesis driven, and anchored in biological plausibility. Whole genome scanning is hypothesis generating, and it may lead to new biology. While both approaches are important, the scientific community is rapidly reallocating resources toward the latter. We propose a step-wise approach to large-scale pharmacogenetic association studies that begins with candidate genes, then uses a pathway-based intermediate step, to inform subsequent analyses of data generated through whole genome scanning. Novel computational strategies are explored in the context of two clinically relevant examples, cholesterol synthesis and lipid signaling.