Pharmacogenomics strives to explain the interindividual variability in response 
to drugs due to genetic variation. Although technological advances have provided 
us with relatively easy and cheap methods for genotyping, promises about 
personalised medicine have not yet met our high expectations. Successful results 
that have been achieved within the field of pharmacogenomics so far are, to name 
a few, HLA-B*5701 screening to avoid hypersensitivity to the antiretroviral 
abacavir, thiopurine S-methyltransferase (TPMT) genotyping to avoid thiopurine 
toxicity, and CYP2C9 and VKORC1 genotyping for better dosing of the 
anticoagulant warfarin. However, few pharmacogenetic examples have made it into 
clinical practice in the treatment of complex diseases. Unfortunately, lack of 
reproducibility of results from observational studies involving many genes and 
diseases seems to be a common pattern in pharmacogenomic studies. In this 
article we address some of the methodological and statistical issues within 
study design, gene and single nucleotide polymorphism (SNP) selection and data 
analysis that should be considered in future pharmacogenomic research. First, we 
discuss some of the issues related to the design of epidemiological studies, 
specific to pharmacogenomic research. Second, we describe some of the pros and 
cons of a candidate gene approach (including gene and SNP selection) and a 
genome-wide scan approach. Finally, conventional as well as several innovative 
approaches to the analysis of large pharmacogenomic datasets are proposed that 
deal with the issues of multiple testing and systems biology in different ways.