Statistical Optimization of Pharmacogenomics Association Studies: Key Considerations from Study Design to Analysis

Benjamin      J. Grady; Marylyn      D. Ritchie

doi:10.2174/187569211794728805

Abstract

Research in human genetics and genetic epidemiology has grown significantly over the previous decade, particularly in the field of pharmacogenomics. Pharmacogenomics presents an opportunity for rapid translation of associated genetic polymorphisms into diagnostic measures or tests to guide therapy as part of a move towards personalized medicine. Expansion in throughput of genotyping technology and reduction of its cost have cleared the way for widespread use of whole-genome genotyping in the effort to identify novel biology and new genetic markers associated with pharmacokinetic and pharmacodynamic endpoints. With new technology and methodology regularly becoming available for use in genetic studies, a discussion on the application of such tools becomes necessary. In particular, quality control criteria have evolved with the use of genome wide association studies (GWAS) as we have come to understand potential systematic errors which can be introduced into the data during high-throughput genotyping. There have been several replicated pharmacogenomic associations, some of which have moved to the clinic to enact change in treatment decisions. These examples of translation illustrate the pertinence of systematic evidence from welldesigned studies to successfully and effectively translate a genetic discovery. In this paper, the design of pharmacogenomic association studies is examined with the goal of optimizing the downstream impact and utility of this research in clinical and public health practice. Issues of ascertainment, genotyping, quality control, analysis and interpretation are also considered.

Keywords: Epistasis, genotyping, personalized medicine, pharmacogenomics, quality control, statistics, study design, genome wide association studies (GWAS), adverse drug reaction (ADR, single nucleotide polymorphisms (SNPs), linkage disequilibrium (LD)