Multivariate models to detect genomic signatures for a class of drugs: application to thiopurines pharmacogenomics.
Often, analysis for pharmacogenomic studies involving multiple drugs from the same class is completed by analyzing each drug individually for association with genomic variation. However, by completing the analysis of each drug individually, we may be losing valuable information. When studying multiple drugs from the same drug class, one may wish to determine genomic variation that explains the difference in response between individuals for the drug class, as opposed to each individual drug. Therefore, we have developed a multivariate model to assess whether genomic variation impacts a class of drugs. In addition to determine genomic effects that are similar for the drugs, we will also be able to determine genomic effects that differ between the drugs (that is, interaction). We will illustrate the utility of this multivariate model for cytotoxicity and genomic data collected on the Coriell Human Variation Panel for the class of anti-purine metabolites (6-mercaptopurine and 6-thioguanine).
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. fridley.brooke@mayo.edu
B L
BL
Fridley
G D
GD
Jenkins
A
A
Batzler
L
L
Wang
Y
Y
Ji
F
F
Li
R M
RM
Weinshilboum
D002461:Cell Line, Transformed; D049109:Cell Proliferation / Q000187:drug effects; D006801:Humans; D015122:Mercaptopurine / Q000494:pharmacology*; D015999:Multivariate Analysis; D010597:Pharmacogenetics; D013866:Thioguanine / Q000494:pharmacology*
D015122:Mercaptopurine; D013866:Thioguanine