A new standard for medicine is emerging that aims to improve individual drug responses through
studying associations with genetic variations. This field, pharmacogenomics, is undergoing a rapid expansion
due to a variety of technological advancements that are enabling higher throughput with reductions in cost.
Here we review the advantages, limitations, and opportunities for using lymphoblastoid cell lines (LCL) as a
model system for human pharmacogenomic studies.
There are a wide range of publicly available resources with genome-wide data available for LCLs from both
related and unrelated populations, removing the cost of genotyping the data for drug response studies.
Furthermore, in contrast to human clinical trials or in vivo model systems, with high-throughput in vitro
screening technologies, pharmacogenomics studies can easily be scaled to accommodate large sample sizes.
An important component to leveraging genome-wide data in LCL models is association mapping. Several
methods are discussed herein, and include multivariate concentration response modeling, issues with multiple
testing, and successful examples of the ‘triangle model’ to identify candidate variants. Once candidate gene
variants have been determined, their biological roles can be elucidated using pathway analyses and
functionally confirmed using siRNA knockdown experiments.
The wealth of genomics data being produced using related and unrelated populations is creating many exciting
opportunities leading to new insights into the genetic contribution and heritability of drug response.