Abstract
Pharmacogenetics aims to elucidate the genetic factors underlying the individual's response to pharmacotherapy. Coupled with the recent (and ongoing) progress in high-throughput genotyping, sequencing and other genomic technologies, pharmacogenetics is rapidly transforming into pharmacogenomics, while pursuing the primary goals of identifying and studying the genetic contribution to drug therapy response and adverse effects, and existing drug characterization and new drug discovery. Accomplishment of both of these goals hinges on gaining a better understanding of the underlying biological systems; however, reverse-engineering biological system models from the massive datasets generated by the large-scale genetic epidemiology studies presents a formidable data analysis challenge. In this article, we review the recent progress made in developing such data analysis methodology within the paradigm of systems biology research that broadly aims to gain a 'holistic', or 'mechanistic' understanding of biological systems by attempting to capture the entirety of interactions between the components (genetic and otherwise) of the system.