Home > Research > Book > Statistical Genetics of Quantitative Traits by Rongling Wu Chang-Xing Ma, and George Casella

Statistical Genetics of Quantitative Traits

The book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of DNA-based marker and phenotypic data that arise in agriculture, forrestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping and assumes a background in regression analysis and maximum likelihood approaches. The strengths of this book lie in the construction of general models and algorithms for linkage analysis and QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops and plant and animal model systems or outbred lines in forest trees and wildlife species.

The book includes a detailed description of each approach and the step-by-step demonstration of live-example analyses designed to explain the utilization and usefulness of statistical methods. The book also includes exercise sets and computer codes for all the analyses used.

This book can serve as a textbook for graduates and senior undergraduates in genetics, agronomy, forest biology, plant breeding and animal sciences. It will also be useful to researchers and other professionals in the areas of statistics, biology and agriculture.

Rongling Wu is Associate Professor of Statistics at the University of Florida, Gainesville. He currently serves as Associate Editor for six genetics and bioinformatics journals. Chang-Xing Ma is Assistant Professor of Biostatistics at the State University of New York at Buffalo. George Casella is Distinguished Professor of Statistics and Distinguished Member of the Genetics Institute at the Univesity of Florida, Gainesville. He is a fellow of the American Statistical Association and the Institute of Mathematical Sciences, and the author of four other statistics books.

This book can serve as a textbook for graduates and senior undergraduates in genetics, agronomy, forest biology, plant breeding and animal sciences. It will also be useful to researchers and other professionals in the areas of statistics, biology and agriculture.

Contents

"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

Contents

Perface

Preface

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 ...


Contents

01
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

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
3.4 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 .

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