Statistical and Computational Pharmacogenomics
By Rongling Wu, Min Lin
Series Editor: Byron J.T. Morgan, Niels Keiding, Peter Van der Heijden, Terry Speed
Published August 8th 2008 by Chapman and Hall/CRC – 368 pages
Due to the tremendous accumulation of data for genetic markers, pharmacogenomics, the study of the functions and interactions of all genes in the overall variability of drug response, is one of the hottest areas of research in biomedical science. Statistical and Computational Pharmacogenomics presents recent developments in statistical methodology with a number of detailed worked examples that outline how these methods can be applied. This comprehensive volume provides key tools needed to understand and model the genetic variation for drug response and equips statisticians with a thorough understanding of this complex field and how computational skills can be employed.
… a statistically rigorous text that gives a systematic exposition of the subject of pharmacogenomics, the related analytical methods and the corresponding computational algorithms. … a good basis for further methodological, empirical and applied investigation into the field.
—Statistics in Medicine, 2011, 30
This text is one of the first books written by statisticians and for statisticians who need to know the basics of genetic markers based on genomic mapping and haplotyping. … this book is a welcome addition that will help me learn pharmacogenomics to the extent that I need it to apply appropriate statistical methodology in microarray analysis and classification problems. … I can recommend it for the statisticians … . I also hope that it will be successful at getting the chemists, biologists, and geneticists interested in the important statistical methods and mathematical modeling described in this book.
—Michael R. Chernick, Technometrics, February 2011
This book covers advanced topics in statistical genetics focusing on applications of interest in pharmacogenomics. The difficulties in estimating haplotype frequencies and their effects on quantitative trait loci (QTLs) are covered in detail for a variety of experimental designs. … of most interest for statisticians working in the pharmaceutical area that need to incorporate genetic variables into consideration in their studies.
—ISCB News, No. 50, December 2010
… [Pharmacogenomics] can address questions such as whether individuals with different versions of a gene are more or less likely to respond to a particular drug. However, Wu and Lin go well beyond this and discuss methods for relating genetic variation to dynamic pharmacokinetic and pharmacodynamic profiles of drugs. They refer to this as ‘functional mapping’. … One of the main clinical applications of these methods will be in predicting efficacy and toxicity of drugs, allowing treatment to be tailored to an individual’s genetic background, and this book makes a valuable contribution towards this.
—Significance, June 2010
…a volume that can be recommended to both statisticians and life scientists. Yes, there’s plenty of heavy-duty math for the theory lovers, but there are also many sections of explanations for the biologist. These explanations are not highly theoretical and give the scientist a better understanding of what the analysis is doing and why it is needed.
—John A. Wass, Ph.D., Scientific Computing, 2009
Designs and Strategies for Genomic Mapping and Haplotyping
Genetic Haplotyping in Natural Populations
Genetic Haplotyping in Experimental Crosses
A General Genetic Model for Genetic Haplotyping
Basic Principle of Functional Mapping
Functional Mapping of Pharmacokinetics and Pharmacodynamics
Haplotyping Drug Response with a Pharmacokinetics-Pharmacodynamics
Functional Mapping of Biological Clocks
Genetic Mapping of Allometric Scaling
Functional Mapping of Drug Response with Allometric Scaling
Joint Functional Mapping of Drug Efficacy and Toxicity
Modeling Epistatic Interactions in Drug Response
Mapping Genotype-Environment Interactions in Drug Response
Nonparametric Functional Mapping of Drug Response
Semiparametric Functional Mapping of Drug Response