TY - JOUR
T1 - Methodological and statistical issues in pharmacogenomics
AU - Peters, Bas J. M.
AU - Rodin, Andrei S.
AU - de Boer, Anthonius
AU - Maitland-van der Zee, Anke-Hilse
PY - 2010
Y1 - 2010
N2 - 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
AB - 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
U2 - https://doi.org/10.1211/jpp/62.02.0002
DO - https://doi.org/10.1211/jpp/62.02.0002
M3 - Review article
C2 - 20487194
SN - 0022-3573
VL - 62
SP - 161
EP - 166
JO - Journal of Pharmacy and Pharmacology
JF - Journal of Pharmacy and Pharmacology
IS - 2
ER -