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Statistical Genomics: Linkage, Mapping, and QTL Analysis [Pehme köide]

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(Bio-Informatics Group, North Carolina, USA)
  • Formaat: Paperback / softback, 644 pages, kõrgus x laius: 246x174 mm, kaal: 1190 g
  • Ilmumisaeg: 01-Nov-2019
  • Kirjastus: CRC Press
  • ISBN-10: 036740074X
  • ISBN-13: 9780367400743
Teised raamatud teemal:
Statistical Genomics: Linkage, Mapping, and QTL AnalysisSuurem pilt
  • Formaat: Paperback / softback, 644 pages, kõrgus x laius: 246x174 mm, kaal: 1190 g
  • Ilmumisaeg: 01-Nov-2019
  • Kirjastus: CRC Press
  • ISBN-10: 036740074X
  • ISBN-13: 9780367400743
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367400743.html
Märksõnad:
Genomics, the mapping of the entire genetic complement of an organism, is the new frontier in biology. This handbook on the statistical issues of genomics covers current methods and the tried-and-true classical approaches.
Foreword v
Dr. Ronald R. Sederoff
Preface vii
Chapter List xiii
Contents xv
Chapter 1 Introduction
1(8)
1.1 Introducing Genomics
1(5)
1.1.1 Genomics and This Book
1(2)
1.1.2 Genomics and Modem Biology
3(1)
1.1.3 Genomics and Its Practical Applications
4(1)
The Potential of Genome Research
4(1)
Population and Quantitative Genetics
4(1)
DNA Diagnosis of Human Genetic Disorders
5(1)
Applications in Agriculture and Forestry
5(1)
1.2 Statistical Genomics
6(1)
1.3 Related Books
7(2)
General Genetics
7(1)
Molecular Biology
7(1)
Population Genetics
7(1)
Quantitative Genetics
7(1)
Genetic Linkage Analysis
8(1)
Statistical Methods
8(1)
Statistical Theory
8(1)
Mathematics and Algorithms
8(1)
Computational Biology
8(1)
History of Genome Research
8(1)
Chapter 2 Biology In Genomics
9(36)
2.1 Introduction
9(1)
2.2 Mendelian Genetics And Cytogenetics
10(13)
2.2.1 Mendelian Genetics
10(1)
Terminology
10(1)
Mendelian Laws
11(1)
Gene Linkage
12(1)
2.2.2 Mechanisms of Mendelian Heredity - Cytogenetics
12(1)
Cell Division and Chromosomes
12(1)
Meiosis
13(1)
Linkage and Recombination
13(2)
The Mechanism of Recombination
15(1)
Linkage Phase
16(1)
Factors Affecting Recombination
16(1)
Importance of Manipulation of Genetic Recombination
17(1)
2.2.3 Measurement of Genetic Recombination
17(1)
Recombination Fraction
17(1)
Interference
18(1)
Haldane's Mapping Function
19(1)
Chromosome Rearrangements
20(1)
2.2.4 Approaches Used for Genetic Recombination Studies
21(1)
Cytology
21(1)
Genetics
21(1)
2.2.5 Applications for Manipulating Recombination
21(1)
Application to Fine Genetic Mapping
21(1)
Application to Map-Based Cloning
22(1)
Application to QTL Mapping
22(1)
Application to Plant and Animal Breeding
22(1)
Theory of Genetic Mapping
23(1)
2.3 Population Genetics
23(7)
2.3.1 Allelic Frequency
23(1)
2.3.2 Hardy-Weinberg Equilibrium
24(2)
2.3.3 Changes In Gene Frequency
26(4)
2.4 Quantitative Genetics
30(6)
2.4.1 Single-Gene Model
30(1)
Notation
30(1)
Average Effect of Gene Substitution
30(1)
Breeding Value
31(1)
Dominance Deviation
32(1)
Variance
32(2)
2.4.2 Trait Models
34(1)
2.4.3 Heritability
35(1)
2.4.4 Genetic Correlation
35(1)
2.5 Molecular Genetics
36(9)
2.5.1 DNA
37(1)
DNA Structure
37(1)
DNA Sequence
37(1)
2.5.2 DNA-RNA-Protein
37(1)
Gene Expression
37(2)
RNA Processing
39(1)
Reading Frame
40(1)
Exercises
40(5)
Chapter 3 Introduction To Genomics
45(40)
3.1 Genome
45(3)
3.1.1 Genome Description
46(1)
3.1.2 Genome Structure
46(1)
3.1.3 Genome Variation and Colinearity
47(1)
3.1.4 Sources of Genome Variation
47(1)
Chromosomal Rearrangement
47(1)
Point Mutation
48(1)
3.2 Biological Techniques In Genomics
48(11)
3.2.1 Genetic Mapping
49(1)
Genetic Map Construction
49(1)
Comparative Mapping
50(1)
Mapping Genes of Interest
51(1)
3.2.2 Physical Mapping
51(1)
DNA Fragmentation
51(1)
DNA Vector
52(1)
Physical Map Assembly
53(2)
3.2.3 DNA Sequencing
55(1)
3.2.4 Genomic Informatics
56(1)
3.2.5 Relating Genetic Maps, Physical Maps and DNA Sequence
56(2)
Traits, Maps and Sequence
58(1)
3.3 Mapping Populations
59(3)
3.3.1 Populations from Controlled Crosses
59(1)
3.3.2 Natural Populations
60(2)
3.3.3 Mating Schemes and Genetic Marker Systems
62(1)
3.4 Genetic Markers
62(23)
3.4.1 Polymorphism and Informativity
63(1)
3.4.2 Morphological and Cytogenetic Markers
63(1)
Morphological Markers
63(1)
Cytogenetic Markers
64(1)
In Situ Hybridization (ISH)
64(1)
3.4.3 Protein Markers
65(1)
3.4.4 DNA Markers (Rationale)
65(1)
3.4.5 RFLP and Southern Blotting
66(4)
3.4.6 PCR
70(1)
3.4.7 Mini- and Micro-satellite Markers
70(3)
3.4.8 STS and EST
73(1)
3.4.9 Single-Strand Conformational Polymorphism (SSCP)
74(1)
3.4.10 Random Amplified Polymorphic DNA (RAPD) Markers
74(3)
3.4.11 Amplified Fragment Length Polymorphism (AFLP)
77(2)
3.4.12 Comparison among Different Marker Systems
79(1)
"Evolution" of Genetic Markers
79(1)
Characteristics of Commonly Used Marker Systems
80(1)
Marker Conversion
80(1)
3.4.13 Automation
81(1)
Robotic-Assisted Assay
81(1)
Automated Scoring Systems
81(4)
Chapter 4 Statistics In Genomics
85(54)
4.1 Introduction
85(1)
4.2 Distributions
86(7)
4.2.1 Distributions
86(1)
Example: Data from Mendel
86(1)
Distributions
86(1)
Cumulative Distribution
87(1)
Expectation and Variance
88(1)
Joint, Marginal and Conditional Distributions
88(2)
4.2.2 Standard Distributions Used in Genomic Analysis
90(1)
Moments and Moment Generating Functions
90(1)
The Binomial and Multinomial Distributions
91(1)
The Poisson Distribution
92(1)
The Normal Distribution
93(1)
The Chi-Square Distribution
93(1)
4.3 Likelihood
93(2)
4.3.1 Definitions
93(2)
4.3.2 Score
95(1)
4.3.3 Information Content
95(1)
4.4 Hypothesis Tests
95(9)
4.4.1 Method of Hypothesis Testing
96(1)
Critical Region
96(1)
Significance Level
97(1)
Chi-Square Tests
97(2)
Likelihood Ratio Test
99(1)
The Lod Score Approach
100(1)
Nonparametric Hypothesis Test
101(1)
4.4.2 The Power of the Test
101(1)
Probability of False Positive and False Negative Errors
101(2)
The Power of the Test
103(1)
4.5 Estimation
104(15)
4.5.1 Maximum Likelihood Point Estimation
104(1)
4.5.2 Analytical Approach Obtaining ML Estimator
105(1)
4.5.3 Grid Search to Obtain ML Estimator
106(1)
Example: Mapping a Gene for Resistance to Fusiform Rust Disease
107(1)
Example: Grid Search
108(2)
4.5.4 Newton-Raphson Iteration for Obtaining ML Estimator
110(1)
Single Parameter
110(1)
Multiple Parameters
110(1)
Example: Newton-Raphson Iteration
111(2)
4.5.5 Expectation-Maximization (EM) Algorithm
113(1)
Example: EM Algorithm
114(2)
4.5.6 Moment Estimation
116(1)
Example: Moment Estimate
117(1)
4.5.7 Least Squares Estimation
118(1)
4.6 Statistical Properties Of An Estimator
119(12)
4.6.1 Variance of an Estimator
119(1)
Example: Variance
120(1)
4.6.2 Variance of a Linear Function
120(1)
4.6.3 Variance of a General Function
120(1)
4.6.4 Mean Square Error (MSE) and Bias
121(1)
4.6.5 Confidence Interval
122(1)
4.6.6 Normal Approximation for Obtaining a Confidence Interval
123(1)
Example: Confidence Interval
123(1)
4.6.7 A Nonparametric Approach to Obtain a Confidence Interval
124(1)
Example: Confidence Intervals (Bootstrap Approach)
124(1)
4.6.8 A Likelihood Approach for Obtaining a Confidence Interval
125(2)
Example: Likelihood Approach
127(1)
4.6.9 Lod Score Support for a Confidence Interval
127(1)
Example: Lod Score Support
128(1)
4.6.10 What Is a Good Estimator of a Confidence Interval?
129(1)
4.6.11 What Is a Good Estimator?
129(2)
4.7 Sample Size Determination
131(8)
4.7.1 Sample Size Needed for Specific Statistical Power
131(1)
4.7.2 Sample Size Needed for a Specific Confidence Interval
132(1)
Example: Sample Size Determination
132(2)
Summary
134(1)
Exercises
134(5)
Chapter 5 Single-Locus Models
139(24)
5.1 Expected Segregation Ratios
139(3)
5.1.1 Single Population
139(1)
5.1.2 Multiple Populations
140(1)
Example
141(1)
5.2 Marker Screening
142(4)
5.2.1 Screening for Polymorphism
142(2)
5.2.2 Screening 1:1 over 3:1
144(1)
5.2.3 Distinguishing between Two-Class Segregations
145(1)
5.3 Natural Populations
146(17)
5.3.1 Number of Alleles and Their Frequencies
146(1)
Notation
146(1)
Estimating within Population Allelic Frequency
147(2)
Single Allele Detection
149(1)
Multiple Allele Detection
150(5)
5.3.2 Hardy-Weinberg Equilibrium for a Single Locus
155(1)
Di-Allelic System
155(2)
Multiple-Allelic System
157(1)
5.3.3 Heterozygosity
157(1)
Definition
157(2)
Screening Polymorphic Markers
159(2)
Exercises
161(2)
Chapter 6 Two-Locus Models: The Controlled Crosses
163(52)
6.1 Introduction
163(1)
6.2 Linkage Detection
163(7)
6.2.1 Partition of Test Statistic
164(1)
Partition of Goodness of Fit Statistic
164(1)
Example: Partition of Goodness of Fit Statistic
165(1)
Partitioning of Log Likelihood Ratio Test Statistic
166(1)
Example: Partition of Log Likelihood Ratio Test Statistic
167(1)
6.2.2 A Generalized Likelihood Approach
168(1)
Log Likelihood Approach
168(1)
Example: Log Likelihood Approach
169(1)
The Lod Score
170(1)
Example: Lod Score
170(1)
6.3 Recombination Fraction Estimation
170(9)
6.3.1 Backcross Model
171(1)
6.3.2 F2 Model
171(2)
Example: Data
173(1)
6.3.3 Likelihood Profile Method
173(1)
Example: Graphic Approach
174(1)
6.3.4 Newton-Raphson Iteration for a Single Parameter
175(1)
Example: Newton-Raphson Iteration
176(1)
6.3.5 EM Algorithm
176(1)
Example: EM Algorithm
177(1)
Example: Heterogeneity Test
178(1)
6.4 Statistical Properties
179(9)
6.4.1 Variance and Bias
181(1)
Parametric Variance
181(1)
Empirical Variance and Bias
182(1)
6.4.2 Distribution and Confidence Intervals
183(1)
Distribution
183(1)
Confidence Intervals
183(2)
Example: Confidence Interval (Normal Approximation)
185(1)
Example: Confidence Interval (Bootstrap)
186(1)
Example: Confidence Interval (Likelihood Approach)
186(1)
Example: Confidence Interval (Lod Score Support)
186(1)
Quality of a Confidence Interval
187(1)
6.5 Sample Size
188(5)
6.5.1 Expected Likelihood Ratio Test Statistic and Power
189(1)
Expected Log Likelihood Ratio Test Statistic
189(1)
Power and Sample Size
190(2)
6.5.2 Minimum Confidence Interval
192(1)
6.6 Dominant Markers In F2 Progeny
193(6)
6.6.1 Disadvantage of Dominant Markers in F2 Progeny
193(1)
Low Linkage Information Content
193(1)
Bias Estimator for Recombination Fraction
194(3)
6.6.2 Use of Trans Dominant Linked Markers (TDLM)
197(1)
TDLM
197(1)
Linkage Information Content for TDLM
198(1)
Estimate of Recombination Fraction between a TDLM and a Marker
198(1)
6.7 Violation Of Assumptions
199(16)
6.7.1 Segregation Ratio Distortion
199(1)
Additive Distortion
199(3)
Penetrance Distortions
202(1)
Impact of Segregation Ratio Distortion in Practical Linkage Analysis
203(1)
6.7.2 Linkage Analysis Involving Lethal Genes
204(1)
Single Gene Defect
204(1)
Two-Locus Recessive Lethal
205(2)
Exercises
207(8)
Chapter 7 Two-Locus Models: Natural Populations
215(26)
7.1 The Linkage Phase Problem
215(7)
7.1.1 Linkage Phase Configurations for Two-Locus Models
215(1)
7.1.2 Linkage Phase Determination
216(3)
7.1.3 Phase-Unknown Linkage Analysis
219(2)
7.1.4 Linkage Analysis with a Mixture of Linkage Phases
221(1)
7.2 Mixtures Of Selfs And Random Mating
222(19)
7.2.1 Model
222(2)
7.2.2 Allelic Frequency in Pollen Pool and Outcrossing Rate
224(1)
Allelic Frequency in Pollen Pool
224(1)
Outcrossing Rate
224(2)
7.2.3 Estimation of Recombination Fraction
226(1)
Method I
227(1)
Method II Using EM Algorithm
227(1)
Method II Using Newton-Raphson Iteration
228(1)
7.2.4 Efficiency and Variances
229(1)
Information Content for Codominant Markers
229(3)
Information Content for Dominant Markers
232(1)
Empirical Variance and Bias
232(2)
7.2.5 Mapping Using Cross Between Two Heterozygotes
234(1)
Exercises
235(6)
Chapter 8 Two-Locus Models: Using Linkage Disequilibrium
241(32)
8.1 Linkage Disequilibrium
241(10)
8.1.1 Two-Locus Disequilibrium Model
241(1)
8.1.2 Detection and Estimation
242(1)
Detection
242(1)
Detection Power
243(1)
8.1.3 Disequilibrium and Linkage
244(4)
8.1.4 Disequilibrium-Based Analysis
248(3)
8.2 The Transmission Disequilibrium Test (TDT)
251(10)
8.2.1 Genetic Model
251(1)
8.2.2 Transmission/Disequilibrium Test
252(1)
8.2.3 Genetic Interpretation of TDT
253(1)
Example: Insulin-Dependent Diabetes Mellitus (IDDM)
254(1)
8.2.4 Statistical Power of TDT
254(1)
8.2.5 Why TDT?
255(6)
8.3 Other Disequilibrium Based Analyses
261(3)
8.3.1 Relative Risk
261(1)
8.3.2 Genotype and Haplotype Relative Risk (GRR and HRR)
262(2)
8.3.3 Linkage Analysis Using Population Admixture
264(1)
8.4 Estimation Of Recombination Fraction
264(9)
8.4.1 Fixed Large Population Size
265(2)
8.4.2 Model
267(1)
8.4.3 The Luria-Delbruck Algorithm
268(1)
8.4.4 Maximum Likelihood Approach
268(1)
Exercises
269(4)
Chapter 9 Linkage Grouping And Locus Ordering
273(32)
9.1 Linkage Grouping
273(1)
9.1.1 Linkage Grouping Criteria
273(1)
9.1.2 Procedures
274(1)
9.2 Three-Locus Order
274(7)
9.2.1 Introduction to Locus Ordering
274(2)
9.2.2 Three-Locus Likelihood and The Concept of Interference
276(2)
9.2.3 Double Crossover Approach
278(1)
9.2.4 Two-Locus Recombination Fraction Approach
279(1)
9.2.5 Log Likelihood Approach
279(2)
9.3 Multiple-Locus Ordering
281(8)
9.3.1 Multiple-Locus Ordering Statistic
281(1)
Notation
281(1)
Three-Locus Approach
282(1)
Maximum Likelihood Approach
282(1)
Minimum Sum or Product of Adjacent Recombination Fractions (SARF and PARF)
282(1)
Maximum Sum of Adjacent Lod Score (SALOD)
283(1)
Least Square Method
284(1)
9.3.2 The Traveling Salesman Problem
284(1)
Problem
284(1)
Algorithms
284(1)
Seriation
285(1)
Simulated Annealing Algorithm
286(1)
Branch-and-Bound (BB)
287(2)
A Combination of SA and BB
289(1)
9.4 Probability Of Estimated Locus Orders
289(16)
9.4.1 Likelihood Approach
290(1)
9.4.2 Bootstrap Approach
291(1)
Percentage of Correct Gene Order
291(1)
An Example
292(2)
Sample Size and PCO
294(1)
9.4.3 Interval Support for Locus Order
295(1)
Framework Map
295(1)
Confidence Interval for Gene Order
296(1)
An Example
297(1)
Combination of Jackknife and Bootstrap
298(2)
Exercises
300(5)
Chapter 10 Multi-Locus Models
305(54)
10.1 Interpretation Of Map Distance
305(5)
10.1.1 Map and Physical Distances
305(2)
10.1.2 Possible Genetic Control of Crossover
307(1)
10.1.3 Genome Structure Variation Among Parents
308(2)
10.2 Three-Locus Models
310(8)
10.2.1 Three-locus model
310(1)
10.2.2 Crossover in a Three-Locus Model
311(1)
Configurations
311(2)
Double Crossover Issue
313(1)
10.2.3 Likelihood Function
314(1)
Triple-Backcross
315(1)
F2 Progeny
316(2)
10.3 Mapping Functions
318(12)
10.3.1 Definitions
318(1)
10.3.2 Commonly Used Map Functions
319(1)
Morgan's Map Function
320(1)
Haldane's Map Function
320(2)
Kosambi's Map Function
322(2)
Other Map Functions
324(4)
10.3.3 Comparison of Commonly Used Mapping Functions
328(2)
10.4 Estimation Of Multi-Locus Map Distance
330(15)
10.4.1 Least Squares
331(1)
Notation
331(1)
Likelihood
332(1)
Example
333(1)
Variance of Estimated Map Distance
333(1)
Least Square Approach Using Lod Score
334(1)
10.4.2 EM Algorithm
335(2)
10.4.3 Joint Estimation of Recombination and Interference
337(1)
10.4.4 Simulation Approach
338(1)
Crossover Distribution
338(2)
Example
340(1)
Simulation
341(2)
Multilocus Feasible Map Function
343(1)
Practical Implementation
344(1)
10.5 Marker Coverage And Map Density"
345(14)
10.5.1 Definitions
345(1)
10.5.2 Factors Influencing Marker Coverage and Map Density
346(1)
Number of Markers
346(1)
Marker and Crossover Distribution
346(2)
Mapping Population
348(1)
Data Analysis
348(1)
10.5.3 Prediction of Marker Coverage and Map Density
349(1)
Prediction of Map Density and Marker Coverage
349(4)
Simulation Approach
353(6)
Chapter 11 Linkage Map Merging
359(16)
11.1 Introduction
359(3)
11.1.1 Linkage Mapping
359(2)
11.1.2 Hypothesis Tests Are Needed
361(1)
11.1.3 Why Linkage Map Pooling and Bridging?
361(1)
Cross Validation of Mapping Strategies
361(1)
Applications of Genome Information to Applied Plant and Animal Breeding
361(1)
Comparative Mapping
362(1)
Structures of Genome Database
362(1)
11.2 Factors Related To Linkage Map Merging
362(2)
11.2.1 Biology and Linkage Map Merging
362(1)
Mating and Genetic Marker Systems
362(1)
Cytogenetics
363(1)
11.2.2 Statistics and Linkage Map Merging
363(1)
Sampling Variation
363(1)
Different Screening Strategies
364(1)
Missing Data and Missing Linkage Information
364(1)
Sample Size and Data Quality
364(1)
11.3 Hypotheses About Gene Orders
364(4)
11.3.1 Heterogeneity Test between Two-Point Recombination Fractions
365(1)
11.3.2 Likelihood Ratio Tests Among Locus Orders and Multipoint Map Distances
366(1)
11.3.3 Nonparametric Heterogeneity Tests for Locus Orders
367(1)
11.4 Linkage Map Pooling
368(7)
11.4.1 Anchor Map Approach
368(1)
11.4.2 Estimation of Missing Recombination Fractions Using EM Algorithm
369(1)
11.4.3 Linkage Map Bridging
370(1)
Exercises
371(4)
Chapter 12 QTL Mapping: Introduction
375(12)
12.1 History
375(2)
12.2 Quantitative Genetics Models
377(3)
12.2.1 Single-QTL Model
377(2)
12.2.2 Multiple-Locus Model
379(1)
12.3 Data For QTL Mapping
380(7)
12.3.1 Data Structure
380(1)
12.3.2 The Barley Data
381(1)
Marker Data
381(1)
Phenotype Data
381(6)
Chapter 13 QTL Mapping: Single-Marker Analysis
387(30)
13.1 Rationale
387(2)
13.2 Single-Marker Analysis In Backcross Progeny
389(13)
13.2.1 Joint Segregation of QTL and Marker Genotypes
390(1)
13.2.2 Simple t-Test Using Backcross Progeny
391(2)
Example: Analysis of the Barley Malt Extract Data Using t-Test
393(1)
13.2.3 Analysis of Variance Using Backcross Progeny
394(1)
13.2.4 Linear Regression Using Backcross Progeny
394(2)
Example: Analysis of the Barley Data Using Linear Regression
396(1)
13.2.5 A Likelihood Approach Using Backcross Progeny
396(3)
Example: Analysis of the Barley Data Using a Likelihood Approach
399(3)
13.3 Single-Marker Analysis Using F2 Progeny
402(15)
13.3.1 Joint Segregation of QTL and Marker Genotypes
402(2)
13.3.2 Analysis of Variance Using F2 Progeny
404(1)
Codominant Marker Model
404(1)
Dominant Marker Model
405(1)
13.3.3 Linear Regression Using F2 Progeny
406(1)
Codominant Marker Model
406(2)
Dominant Marker Model
408(1)
13.3.4 Likelihood Approach
409(1)
Codominant Marker Model
409(2)
Dominant Marker Model
411(1)
13.3.5 Use of Trans Dominant Linked Markers in F2 Progeny
411(2)
Summary
413(1)
Exercises
414(3)
Chapter 14 QTL Mapping: Interval Mapping
417(42)
14.1 Introduction
417(1)
14.2 Interval Mapping Of QTL Using Backcross Progeny
418(17)
14.2.1 Joint Segregation of QTL and Markers
418(1)
14.2.2 A Likelihood Approach for QTL Mapping with Backcross Progeny
419(2)
Example: A Likelihood Approach
421(2)
14.2.3 A Nonlinear Regression Approach
423(1)
Nonlinear Regression
423(2)
Hypothesis Test
425(1)
Confidence Interval for the Parameters
425(1)
Example: Estimation, Hypothesis Tests and Confidence Interval
426(1)
Multiple Environments Model
427(2)
Implementation of the Nonlinear Regression
429(1)
Example: The Multiple Environments Problem
430(2)
14.2.4 The Linear Regression Approach
432(3)
14.3 Interval Mapping Using F2 Progeny
435(9)
14.3.1 Joint Segregation of QTLs and Markers
435(2)
14.3.2 A Likelihood Approach for QTL Analysis Using F2 Progeny
437(1)
Codominant Markers
437(2)
Dominant Markers
439(1)
14.3.3 Regression Approach
440(1)
Nonlinear Regression Approach (Codominant Markers)
440(2)
Linear Regression (Codominant Markers)
442(1)
Linear Regression (Dominant Markers)
442(2)
14.3.4 Problems with the Simple Interval Mapping Approaches
444(1)
14.4 Composite Interval Mapping
444(15)
14.4.1 Model
444(2)
14.4.2 Solutions
446(1)
14.4.3 Hypothesis Test
447(1)
14.4.4 CIM Using Regression
448(1)
Example: CIM Using Regression
449(3)
14.4.5 Implementing CIM
452(1)
14.4.6 CIM Using F2 Progeny
453(2)
14.4.7 Advantages of the CIM
455(1)
Summary
455(1)
Exercises
456(3)
Chapter 15 QTL Mapping: Natural Populations
459(22)
15.1 Introduction
459(1)
15.2 Open-Pollinated Populations
460(9)
15.2.1 Joint Segregation of QTL and Markers
460(1)
15.2.2 Model
461(1)
15.2.3 Complete Outcrossing
462(2)
15.2.4 Half Outcrossing
464(2)
15.2.5 Expectation of the Additive Contrast
466(3)
15.3 SIB-Pair Methods
469(12)
15.3.1 Model for QTL Locating On Marker
469(1)
Model
469(1)
Identity by Descent
469(1)
Sib-Pair Difference
470(2)
Expected Square of the Sib-Pair Difference
472(1)
Solutions for the Linear Model
472(2)
15.3.2 Marker Model
474(4)
15.3.3 Implementation of the Sib-Pair Method
478(1)
Exercises
479(2)
Chapter 16 QTL Mapping: Statistical Power
481(12)
16.1 Introduction
481(1)
16.2 Single QTL Detection Power
482(5)
16.2.1 Single Marker Analysis
482(3)
16.2.2 Interval Mapping
485(2)
16.3 Multiple QTLS
487(6)
16.3.1 Rationale
487(1)
16.3.2 A Simulation Approach
488(1)
16.3.3 The Percent of Genetic Variation Explained by QTL
488(2)
Exercises
490(3)
Chapter 17 QTL Mapping: Future Considerations
493(26)
17.1 Problems With QTL Mapping
493(1)
17.1.1 Multiple-QTL Model
493(1)
Practical Implementations
493(1)
Problems
493(2)
17.1.2 Multiple-Test Problem
495(1)
17.1.3 Multiple Related Traits Problem
496(1)
17.1.4 Are the QTLs Real?
497(1)
17.2 QTL Resolution
498(4)
17.2.1 QTL Location
498(2)
17.2.2 High Resolution QTL Mapping
500(1)
Quantitative Analysis of the Trait
500(2)
Conditional Marker Analysis
502(1)
Mapping Population Extension
502(1)
17.3 Mapping Strategies
502(5)
17.3.1 Bulk Segregant Analysis
502(4)
17.3.2 Selective Genotyping
506(1)
17.3.3 Increase Marker Coverage
506(1)
Comparative Mapping
506(1)
Increase Useful Progeny Size
507(1)
17.4 What Are QTLS?
507(2)
17.4.1 What Are QTLs?
507(1)
Limitations of QTL Mapping
508(1)
17.4.2 Quantitative Genetics, Genomic Mapping and Molecular Biology
508(1)
17.5 Future QTL Mapping
509(10)
17.5.1 Genetic and Physical Maps
510(1)
17.5.2 Trait - Maps - Sequence
511(1)
17.5.3 Metabolic Genetic Model (MGM)
512(2)
Exercises
514(5)
Chapter 18 Computer Tools
519(26)
18.1 Computer Tools For Genomic Data Analysis
520(6)
18.1.1 Linkage Analysis and Map Construction
520(3)
18.1.2 Specific Packages for QTL Mapping
523(1)
18.1.3 QTL Analysis Using SAS
523(1)
Interval Mapping Using Nonlinear Regression
524(1)
Composite Interval Mapping Using Regression
525(1)
18.2 Future Considerations
526(3)
18.2.1 Commercial Quality Software Is Needed
526(2)
18.2.2 Structure of Bioinformation Analysis and Management System (BIAMS)
528(1)
18.2.3 Data Quality Problem
528(1)
18.3 Plant Genome Research Initiative (PGRI)
529(16)
18.3.1 Data Type
530(1)
18.3.2 Data Format
531(2)
18.3.3 Linkage Analysis and Map Construction
533(5)
18.3.4 Linkage Map Merging
538(1)
18.3.5 QTL Analysis and Breeding Plan
538(3)
18.3.6 Output Samples
541(1)
Linkage Map Merging
541(2)
QTL Analysis
543(2)
Chapter 19 Resampling And Simulation In Genomics
545(26)
19.1 Introduction
545(1)
19.2 Resampling
546(6)
19.2.1 Bootstrap
546(1)
Bootstrap Sample
546(1)
Bootstrap Replication
546(1)
Bootstrap Mean, Variance and Bias
547(1)
Bootstrap Confidence Interval
547(1)
Example: Bootstrap Approach
547(2)
19.2.2 Jackknife
549(1)
Jackknife Sample
549(1)
Jackknife Mean, Variance and Bias
549(1)
19.2.3 Combination of Jackknife and Bootstrap
549(1)
19.2.4 Shuffling or Permutation Test
550(1)
Shuffling a Sample and Permutation
550(1)
Empirical Distribution of the Test Statistic
551(1)
Example: Permutation Test
551(1)
19.3 Computer Simulations
552(3)
19.3.1 Overview
552(1)
19.3.2 Random Sampling from Continuous Distributions
553(1)
19.3.3 Random Sampling from Discrete Distributions
554(1)
19.4 Simulation Of Discrete Markers
555(2)
19.4.1 A Joint Distribution Approach for Two Loci
555(1)
19.4.2 A Conditional Frequency Approach for Multiple Loci
556(1)
19.4.3 Conversion of Map Distance to Recombination Fraction
557(1)
19.5 Quantitative Traits: A Two-Gene Model
557(3)
19.5.1 Two-Gene Model
557(1)
19.5.2 Model Specification
558(1)
19.5.3 Simulation of the Trait Values
559(1)
19.6 Quantitative Trait: Multiple-Gene Model
560(2)
19.6.1 Multiple-Gene Model
560(1)
19.6.2 Distribution of Genetic Effects
560(2)
19.6.3 Simulation of Trait Values
562(1)
19.7 Multiple Quantitative Traits
562(4)
19.7.1 The Model
562(1)
19.7.2 Model Specification
563(1)
19.7.3 Linkage and Genetic Correlation
564(1)
19.7.4 Multinomial Distribution
565(1)
19.8 Simulation Of Data With Multiple Generations
566(5)
19.8.1 Introduction
566(1)
19.8.2 Single Gene
567(1)
19.8.3 Two Loci
568(2)
19.8.4 Multiple Loci
570(1)
Exercises
570(1)
Glossary 571(8)
Bibliography 579(18)
Author Index 597(4)
Subject Index 601
Liu, Ben Hui