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Statistical and Computational Pharmacogenomics

Building a bridge between pharmacogenomics and statistics, Statistical and Computational Pharmacogenomics allows 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 Therapies

In 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 plans" based on patients' individual genetic makeup. This will ideally lead to optimalprescriptions, optimal administration times, and optimal dosage scheduling.




Although pharmacogenetics or pharmacogenomics, the study of inherited variation in patients's responses to drugs, is still in its infancy, tremendous accumulation of data for genetic markers and pharmacodynamic tests have made it one of the hottest and most promising areas in biomedical research. The central goal of pharmacogenetics is to understand the association of interpatient variability in drug response with specific genomic sites through ...


Designs and Strategies for Genomic Mapping and Haplotyping.

1.1 Fundamental Genetics
1.2 Pharmacogenetics and Pharmacogenomics
1.3 Genetic Designs
1.4 Strategies for Genomic Mapping
1.5 From QTL to QTN
1.6 Functional Mapping of Drug Response

Genetic Haplotyping in Natural Populationsz.

2.1 Notation and Definitions
2.2 Likelihoods
2.3 The EM Algorithm
2.4 Sampling Variances of Parameter Estimates
2.5 Model Selection
2.6 Hypothesis Tests
2.7 Haplotyping with Multiple SNPs
2.8 R-SNP Model

Genetic Haplotyping in Experimental Crosses.

3.1 LD Analysis in the F1fs Gamete Population
3.2 LD Analysis in the Backcross
3.3 LD Analysis in the F2
3.4 LD Analysis in a Full-Sib Family
3.5 Prospects

A General QuantitativeModel for Genetic Haplotyping.

4.1 Quantitative Genetic Models
4.2 Likelihood
4.3 Three-SNP Haplotyping
4.4 Haplotyping in a Non-Equilibrium Population
4.5 Prospects

Basic Principle of Functional Mapping.

5.1 Dynamic Genetic Control
5.2 Structure of Functional Mapping
5.3 Estimation of Functional Mapping
5.4 Hypothesis Tests of Functional Mapping
5.5 Transform-Both-Sides Model of Functional Mapping
5.6 Structured AntedependenceModel of Functional Mapping
5.7 An Optimal Strategy of Structuring the Covariance
5.8 Functional Mapping Meets Ontology

FunctionalMapping of Pharmacokinetics and Pharmacodynamics.

6.1 Mathematical Modeling of Pharmacokinetics and Pharmacodynamics8
6.2 Functional Mapping of Pharmacokinetics
6.3 Functional Mapping of Pharmacodynamics
6.4 Sequencing Pharmacodynamics

Haplotyping Drug Response by Linking Pharmacokinetics and Pharmacodynamics.

7.1 A Unifying Model for Functional Mapping
7.2 Algorithms and Determination of Risk Haplotypes
7.3 Hypothesis Tests
7.4 Computer Simulation
7.5 Genetic and Statistical Considerations

FunctionalMapping of Biological Clocks.

8.1 Mathematical Modeling of Circadian Rhythms
8.2 Haplotyping Circadian Rhythms
8.3 Hypothesis Testing
8.4 Simulation
8.5 Fourier Series Approximationof Circadian Rhythms
8.6 Further Considerations

Genetic Mapping of Allometric Scaling.

9.1 Allometric Models
9.2 Allometric Mapping
9.3 Hypothesis Testing
9.4 Allometric Mapping with a Pleiotropic Model
9.5 Allometric Mapping with General Power Equations

Functional Mapping of Drug Response with Allometric Scaling.

10.1 Allometric Scaling of Pharmacokinetic and Pharmacodynamic Responses
10.2 ModelDerivations
10.3 APleiotropicModel forAllometricMapping
10.4 GeneticHaplotypingwithDevelopmentalAllometry

Joint Functional Mapping of Drug Efficacy and Toxicity.

11.1 A Joint Model
11.2 Hypothesis Testing
11.3 Closed Forms for the SAD Structure
11.4 Allometric Mapping of Drug Efficacy and Drug Toxicity

Modeling Epistatic Interactions in Drug Response.

12.1 Quantitative Genetic Models for Epistasis
12.2 Haplotyping Epistasis
12.3 Haplotyping Epistasis of Drug Response
12.4 Prospects

Mapping Genotype-Environment Interactions in Drug Response.

13.1 HaplotypingGenotype-EnvironmentInteractions
13.2 Haplotyping Genotype-Environment Interactions for PharmacologicalProcesses
13.3 GeneticConsiderations

Nonparametric Functional Mapping of Drug Response.

14.1 Nonparametric Modeling with Legendre Polynomial
14.2 Nonparametric Modeling of Event Processes with Legendre Polynomial
14.3 Nonparametric Functional Mapping with B-Spline
14.4 Nonparametric Functional Mapping of Pharmacokinetics and Pharmacodynamics
14.5 Nonparametric Modeling of the Covariance Structure

Semiparametric Functional Mapping of Drug Response.

15.1 Problems
15.2 SemiparametricModeling of Functional Mapping: HIV Dynamics
15.3 SemiparametricModelingofFunctionalMapping: PCD

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