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High-Dimensional Statistical Genomics in Plants

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.


"It 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."

Lutz Bunger, Genetic Research, 89, 2007

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

International Statistics Review, April 2008




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 ...

Basic Statistics

Basic Statistics

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 ...


Basic Genetics.

1.1 Introduction
1.2 Genes and Chromosomes
1.3 Meiosis
1.4 Mendelfs 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

Basic Statistics.

2.1 Introduction
2.2 Likelihood Estimation
2.3 Hypothesis Testing
2.4 Exercises

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

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

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

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

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

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

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

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

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

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

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

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

General Statistical Results and Algorithms.

R Programs.

References .

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