Pharmacogenomics is the study of the relationship between individual genetic variation and drug response. One of the major goals of the field is the use of an individual's genomic information in conjunction with other demographic and environmental covariates to personalize a previously uniform treatment regimen. Realizing this ambition requires nothing less than the ability to derive a genotype-to-phenotype map for a trait of interest. In the specific case of pharmacogenomics this trait is often a drug dosage, efficacy, toxicity, or a variable indicating response/non-response or adverse-event/no-adverse-event, and the genotype is frequently a vector of SNP measurements. As such, progress in this area is intimately tied to progress in the more general search for the genetic determinants of complex traits.

As with any complex trait, the molecular, epidemiological, and analytical techniques used in pharmacogenomics are under constant evolution and development. Parallel to human statistical genetics, the most common methodology/design has evolved from linkage analysis to candidate gene association studies, and now the mainstream study design is genome-wide association studies (GWAS). That said, over the course of the next 18-36 months, this trend is likely to shift to next-generation sequence data, structural variation, rare variants, and gene-gene-drug interactions. To maximize our ability to dissect complex patterns from these complex datasets, it is important to continue developing novel analytic approaches. In response to RFA-GM-10-001 ‘Pharmacogenomics Research Network (PGRN), we have created a network resource, the PGRN STatistical Analysis Resource (P-STAR) for coordination of statistical analysis and methods development in the PGRN.


A network resource for coordination of statistical analysis and methods development in the PGRN.