By: Rongling Wu, Min Lin, Byron J.T. Morgan (Editor), Terry Speed (Editor), Peter Van Der Heijden (Editor)

Hardcover

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Synthesizes the Vast Literature on One of the Hottest Areas in Biomedical ResearchBuilding a bridge between pharmacogenomics and statistics, Statistical and Computational Pharmacogenomicsallows researchers to readily familiarize themselves with this promising and revolutionary area of science. It outlines the powerful statistical techniques used in the fast-growing field of pharmacogenomics, which seeks to understand the relationships between interpatient variability in drug response and specific genomic sites. Providing geneticists with the tools needed to understand and model the genetic variations for drug responses, this seminal work also equips statisticians with the motivation and ideas needed to explore genomic data.Exciting Implications for the Future of Drug TherapiesIn addition to providing a synthesis of statistical methodology for the pharmocogenomic study of drug response, this cutting-edge, authoritative text developseach method step-by-step, while keeping theoretical details to a minimum. It also presents detailed, worked examples that outline how to apply the discussed methods and outlines the necessary statistical and computational theories for genetic mapping of dynamic traits.Indicative of the depth of this groundbreaking, multidisciplinary research and its exciting implications for the future of drug therapies, it is now possible to document, map, and understand the structure and patterns of the human genome linked to drug response. The pioneering process of functional mapping has the potential to revolutionize the use of many medications with tailored treatment plansbased on patients' individual genetic makeup. This will ideally lead to optimal prescriptions, optimal administration times, and optimal dosage scheduling.

... 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	p. 1
Fundamental Genetics	p. 1
Chromosomes and Map	p. 1
Genotype and Phenotype	p. 2
Molecular Genetic Markers	p. 3
The HapMap	p. 4
Pharmacogenetics and Pharmacogenomics	p. 5
Genetic Designs	p. 8
Experimental Crosses	p. 8
Nuclear Families	p. 9
Natural Populations with Unrelated Individuals	p. 9
Natural Populations with Unrelated Families	p. 10
Strategies for Genomic Mapping	p. 11
Linkage Mapping	p. 11
Linkage Disequilibrium Mapping	p. 13
Joint Linkage and Linkage Disequilibrium Mapping	p. 15
From QTL to QTN	p. 16
Genotype and Diplotype	p. 17
Identification of QTNs	p. 18
Functional Mapping of Drug Response	p. 19
Genetic Haplotyping in Natural Populations	p. 21
Notation and Definitions	p. 21
Likelihoods	p. 23
The EM Algorithm	p. 24
Sampling Variances of Parameter Estimates	p. 25
Model Selection	p. 27
Hypothesis Tests	p. 28
Haplotyping with Multiple SNPs	p. 29
Haplotype Structure	p. 29
Likelihoods and Algorithms	p. 31
R-SNP Model	p. 35
Genetic Haplotyping in Experimental Crosses	p. 39
LD Analysis in the F[subscript 1]'s Gamete Population	p. 40
A General Model	p. 40
A Special Case: Two-Point LD	p. 41
A Special Case: Three-Point LD	p. 41
LD Analysis in the Backcross	p. 43
Design	p. 43
Analysis of Variance	p. 44
t-Test	p. 44
LD Analysis in the F[subscript 2]	p. 47
Mixture Model	p. 47
Likelihoods, Estimation, and Hypothesis Tests	p. 48
Model Selection: Two- vs. Three-Point LD Analysis	p. 48
LD Analysis in a Full-Sib Family	p. 51
Introduction	p. 51
A General Model	p. 52
Estimation	p. 53
Multiple Segregating Types of Markers	p. 56
Three-Point Haplotyping	p. 57
Prospects	p. 58
A General Quantitative Model for Genetic Haplotyping	p. 61
Quantitative Genetic Models	p. 62
Population Structure	p. 62
Biallelic Model	p. 62
Triallelic Model	p. 63
Quadriallelic Model	p. 64
Likelihood	p. 65
The EM Algorithm	p. 67
Model Selection	p. 67
Hypothesis Tests	p. 67
Three-SNP Haplotyping	p. 68
Haplotyping in a Non-Equilibrium Population	p. 70
Prospects	p. 73
Basic Principle of Functional Mapping	p. 75
Dynamic Genetic Control	p. 76
Structure of Functional Mapping	p. 77
Mixture Model	p. 78
Modeling the Mean-Covariance Structure	p. 79
Estimation of Functional Mapping	p. 82
Likelihood	p. 82
Algorithm	p. 83
Hypothesis Tests of Functional Mapping	p. 84
Transform-Both-Sides Model of Functional Mapping	p. 88
Structured Antedependence Model of Functional Mapping	p. 90
Antedependence Model	p. 90
Structured Antedependence Model	p. 90
Model Selection	p. 91
An Optimal Strategy of Structuring the Covariance	p. 91
Standard Deviation Function	p. 92
Correlation Function	p. 92
Model Selection	p. 93
Functional Mapping Meets Ontology	p. 94
Functional Mapping of Pharmacokinetics and Pharmacodynamics	p. 97
Mathematical Modeling of Pharmacokinetics and Pharmacodynamics	p. 98
Modeling Pharmacokinetics	p. 98
Modeling Pharmacodynamics	p. 98
Linking Pharmacokinetics and Pharmacodynamics	p. 100
Functional Mapping of Pharmacokinetics	p. 101
Kinetic Derivation of a Bi-Exponential Model	p. 102
QTL Mapping with a Bi-Exponential Model	p. 104
Functional Mapping Based on Ho et al.'s Kinetic Model	p. 112
Functional Mapping of Pharmacodynamics	p. 114
Patterns of Genetic Control in Pharmacodynamics	p. 114
Sequencing Pharmacodynamics	p. 115
Basic Model	p. 115
A Pharmacogenetic Study of Heart Rate Responses	p. 117
Haplotyping Drug Response by Linking Pharmacokinetics and Pharmacodynamics	p. 123
A Unifying Model for Functional Mapping	p. 123
Clinical Design	p. 123
Likelihood	p. 124
Modeling the Mean Vector	p. 128
Modeling the Covariance Structure	p. 128
Algorithms and Determination of Risk Haplotypes	p. 133
Hypothesis Tests	p. 133
Computer Simulation	p. 135
Genetic and Statistical Considerations	p. 138
Functional Mapping of Biological Clocks	p. 141
Mathematical Modeling of Circadian Rhythms	p. 142
Haplotyping Circadian Rhythms	p. 143
Study Design	p. 143
Antedependence Model	p. 144
Likelihood	p. 146
Algorithm and Determination of Risk Haplotypes	p. 147
Hypothesis Testing	p. 148
Existence of Risk Haplotypes	p. 148
Pleiotropic Effect on mRNA or Protein Rhythms	p. 149
Risk Haplotypes for the Behavior and Shape of Circadian Rhythms	p. 149
Simulation	p. 150
Fourier Series Approximation of Circadian Rhythms	p. 150
Introduction	p. 150
Fourier Model	p. 151
Genetic Haplotyping	p. 153
Further Considerations	p. 156
Genetic Mapping of Allometric Scaling	p. 159
Allometric Models	p. 159
Allometric Mapping	p. 161
Genetic Design	p. 161
Likelihood and Estimation	p. 162
Hypothesis Testing	p. 164
Allometric Mapping with a Pleiotropic Model	p. 165
Design	p. 165
Genetic Model	p. 165
Statistical Estimation	p. 167
Hypothesis Tests	p. 167
Allometric Mapping with General Power Equations	p. 168
Estimating Power Coefficients Using Model II Non-Linear Regression	p. 168
Predicting Dependent Variables	p. 170
Functional Mapping of Drug Response with Allometric Scaling	p. 175
Allometric Scaling of Pharmacokinetic and Pharmacodynamic Responses	p. 176
Model Derivations	p. 177
Experimental Design	p. 177
Model Structure and Estimation	p. 178
Hypothesis Testing	p. 179
A Pleiotropic Model for Allometric Mapping	p. 184
Genetic Haplotyping with Developmental Allometry	p. 184
Likelihood	p. 185
Algorithm	p. 187
Hypothesis Testing	p. 189
Joint Functional Mapping of Drug Efficacy and Toxicity	p. 195
A Joint Model	p. 196
Genetic Design	p. 196
Clinical Design	p. 197
Statistical Design	p. 198
The Haplotyping Framework	p. 198
Covariance Structure	p. 201
Algorithm and Determination of Risk Haplotypes	p. 203
Hypothesis Testing	p. 203
Existence of Risk Haplotypes	p. 204
Different Risk Haplotypes for PK and PD	p. 204
Different Risk Haplotypes for Drug Efficacy and Drug Toxicity	p. 205
Risk Haplotypes Responsible for Individual Curve Parameters	p. 206
Closed Forms for the SAD Structure	p. 210
Allometric Mapping of Drug Efficacy and Drug Toxicity	p. 211
Modeling Epistatic Interactions in Drug Response	p. 213
Quantitative Genetic Models for Epistasis	p. 214
Definition and Type	p. 214
Quantifying Epistasis	p. 215
From Static to Dynamic	p. 216
Haplotyping Epistasis	p. 217
Population Genetic Structure	p. 217
Genetic Design	p. 218
Population Genetic Model	p. 219
Likelihood for Estimating Across-Block Haplotype Frequencies	p. 219
Likelihood for Haplotype-Haplotype Interaction Effects	p. 226
Hypothesis Tests	p. 228
Haplotyping Epistasis of Drug Response	p. 233
Introduction	p. 233
Model and Estimation	p. 233
Hypothesis Tests	p. 235
Prospects	p. 243
Mapping Genotype-Environment Interactions in Drug Response	p. 245
Haplotyping Genotype-Environment Interactions	p. 246
Environmental Sensitivity and Genotype-Environment Interactions	p. 246
Genetic Design	p. 248
Likelihoods	p. 249
The EM Algorithm	p. 252
Model Selection	p. 252
Hypothesis Tests	p. 252
Haplotyping with Multiple SNPs	p. 255
Haplotyping Genotype-Environment Interactions for Pharmacological Processes	p. 259
Introduction	p. 259
Dynamic Model	p. 261
Hypothesis Testing	p. 262
Genetic Considerations	p. 268
Nonparametric Functional Mapping of Drug Response	p. 271
Nonparametric Modeling with Legendre Polynomial	p. 272
Legendre Orthogonal Polynomials	p. 272
Genetic Design	p. 273
Likelihoods	p. 275
Model Selection	p. 276
Hypothesis Tests	p. 277
Nonparametric Modeling of Event Processes with Legendre Polynomial	p. 278
Introduction	p. 278
Model and Estimation	p. 278
Hypothesis Testing	p. 280
Nonparametric Functional Mapping with B-Spline	p. 282
Basics of B-Splines	p. 282
Haplotyping Model for DNA Sequence Variants	p. 285
Nonparametric Functional Mapping of Pharmacokinetics and Pharmacodynamics	p. 285
Nonparametric Modeling of the Covariance Structure	p. 286
Semiparametric Functional Mapping of Drug Response	p. 287
Problems	p. 288
Long-Term HIV Dynamics	p. 288
Different Phases of Programmed Cell Death	p. 290
Semiparametric Modeling of Functional Mapping: HIV Dynamics	p. 291
Genetic Design	p. 291
Model Structure	p. 292
Model Estimation	p. 296
Hypothesis Testing	p. 297
Semiparametric Modeling of Functional Mapping: PCD	p. 299
Phase Dissection of Growth	p. 299
Haplotyping Model	p. 302
Computation Algorithms	p. 303
Hypothesis Testing	p. 304
References	p. 309
Author Index	p. 333
Subject Index	p. 341
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9781584888284
ISBN-10: 1584888288
Series: Chapman & Hall/CRC Interdisciplinary Statistics Series
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 368
Published: 1st July 2008
Dimensions (cm): 23.5 x 15.6  x 2.2
Weight (kg): 0.658