*B*uilding 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.

*E*xciting Implications for the Future of Drug Therapies

*I*n 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.

*I*ndicative 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.

"**I**t is a big help and guidance in the field of statistical developments for genetic mapping, synthesised all in one volume helping to build a bridge between genetics and statistics."

"...**T**his is an ideal book for a young researcher looking for an exciting and developing field to get into."

Most traits in nature and of importance to agriculture are quantitatively inherited.These traits are difficult to study due to the complex nature of their inheritance.However, recent developments of genomic technologies provide a revolutionary means for unraveling the secrets of genetic variation in ...

Now that we have seen the basics of genetics, we turn to an introduction to the statistical methodologies that we will use throughout this book. Most of the statistical inferences that we will make will be based on likelihood analysis, and we will be concerned not only with constructing the appropriate ...

- 01
- Basic Genetics.

1.1 Introduction

1.2 Genes and Chromosomes

1.3 Meiosis

1.4 Mendelfs Laws

1.5 Linkage and Mapping

1.6 Interference

1.7 Quantitative Genetics

1.8 Molecular Genetics

1.9 SNP

1.10 Exercises

1.11 Note

- 02
- Basic Statistics.

2.1 Introduction

2.2 Likelihood Estimation

2.3 Hypothesis Testing

2.4 Exercises

- 03
- Linkage Analysis and Map Construction.

3.1 Introduction

3.2 Experimental Design

3.3 Mendelian Segregation

3.3.1 Testing Marker Segregation Patterns

3.4 Segregation Patterns in a Full-Sib Family

3.5 Two-Point Analysis

3.6 Three-Point Analysis

3.7 Multilocus Likelihood and Locus Ordering

3.8 Estimation with Many Loci

3.9 Mixture Likelihoods and Order Probabilities

3.10 Map Functions

3.11 Exercises

3.12 Notes: Algorithms and Software for Map Construction

- 04
- A General Model for Linkage Analysis in Controlled Crosses.

4.1 Introduction

4.2 Fully Informative Markers: A Diplotype Model

4.3 Fully Informative Markers: A Genotype Model

4.4 Joint modeling of the Linkage, Parental Diplotype, and Gene Order

4.5 Partially Informative Markers

4.6 Exercises

4.7 Notes

- 05
- Linkage Analysis with Recombinant Inbred Lines.

5.1 Introduction

5.2 RILs by Selfing

5.3 RILs by Sibling Mating

5.4 Bias Reduction

5.5 Multiway RILs

5.6 Exercises

5.7 Note

- 06
- Linkage Analysis for Distorted and Misclassified Markers

6.1 Introduction

6.2 Gametic Differential Viability

6.3 Zygotic Differential Viability

6.4 Misclassification

6.5 Simulation

6.6 Exercises

- 07
- Special Considerations in Linkage Analysis

7.1 Introduction

7.2 Linkage Analysis with a Complicated Pedigree

7.3 Information Analysis of Dominant Markers

7.4 Exercises

- 08
- Marker Analysis of Phenotypes.

8.1 Introduction

8.2 QTL Regression Model

8.3 Analysis at the Marker

8.4 Moving Away from the Marker

8.5 Power Calculation

8.6 Marker Interaction Analysis

8.7 Whole-Genome Marker Analysis

8.8 Exercises

- 09
- The Structure of QTL Mapping.

9.1 Introduction

9.2 The Mixture Model

9.3 Population Genetic Structure of the Mixture Model

9.4 Quantitative Genetic Structure of the Mixture Model

9.5 Experimental Setting of the Mixture Model

9.6 Estimation in the Mixture Model

9.7 Computational Algorithms for the Mixture Model

9.8 Exercises

- 10
- Interval Mapping with Regression Analysis.

10.1 Introduction

10.2 Linear Regression Model

10.3 Interval Mapping in the Backcross

10.4 Interval Mapping in an F2

10.5 Remarks

10.6 Exercises

- 11
- Interval Mapping by Maximum Likelihood Approach.

11.1 Introduction

11.2 QTL Interval Mapping in a Backcross

11.3 Hypothesis Testing

11.4 QTL Interval Mapping in an F2

11.5 Factors That Affect QTL Detection

11.6 Procedures for QTL Mapping

11.7 Exercises

- 12
- Threshold and Precision Analysis.

12.1 Introduction

12.2 Threshold Determination

12.3 Precision of Parameter Estimation

12.4 Confidence Intervals for the QTL Location

12.5 Exercises

- 13
- Composite QTL Mapping.

13.1 Introduction

13.2 Composite Interval Mapping for a Backcross

13.3 Composite Interval Mapping for an F2

13.4 A Statistical Justification of Composite Interval Mapping

13.5 Comparisons Between Composite Interval Mapping and Interval Mapping 13.6 Multiple Interval Mapping

13.7 Exercises

- 14
- QTL Mapping in Outbred Pedigrees .

14.1 Introduction

14.2 A Fixed-Effect Model for a Full-Sib Family

14.3 Random-Effect Mapping Model for a Complicated Pedigree

14.4 Exercises

- A
- General Statistical Results and Algorithms.

- B
- R Programs.

- C
- References .