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Bioinformatics Advance Access published online on March 28, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm110
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Modelling sequence-sequence interactions for drug response

Min Lin 1,4, Hongying Li 1, Wei Hou 2, Julie A. Johnson 3 and Rongling Wu 1,*

1Department of Statistics, 2Department of Epidemiology and Health Policy Research, 3Department of Pharmacy Practice, University of Florida, Gainesville, FL 32611, USA and 4Department Biostatistics and Bioinformatics, Duke University, Durham, NC 27715, USA

*To whom correspondence should be addressed. Rongling Wu, E-mail: rwu{at}mail.ifas.ufl.edu


  Abstract

Motivation: Genetic interactions or epistasis may play an important role in the genetic etiology of drug response. With the availability of large-scale, high-density single-nucleotide polymorphism markers, a great challenge is how to associate haplotype structures and complex drug response through its underlying pharmacodynamic mechanisms.

Results: We have derived a general statistical model for detecting a interactive network of DNA sequence variants that encode pharmacodynamic processes based on the haplotype map constructed by single nucleotide polymorphisms. The model was validated by a pharmacogenetic study for two predominant beta-adrenergic receptor (ßAR) subtypes expressed in the heart, ß1AR and ß2AR. Haplotypes from these two receptors trigger significant interaction effects on the response of heart rate to different dose levels of dobutamine. This model will have implications for pharmacogenetic and pharmacogenomic research and drug discovery.

Availability: A computer program written in Matlab can be downloaded from the webpage of statistical genetics group at the University of Florida.

Associate Editor: Prof. Martin Bishop

Received on April 20, 2006; revised on March 1, 2007; accepted on March 14, 2007

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