search for books and compare prices
Tables of Contents for Statistical Genomics
Chapter/Section Title
Page #
Page Count
FOREWORD
v
2
DR. RONALD R. SEDEROFF
PREFACE
vii
6
CHAPTER LIST
xiii
2
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 Modern Biology
3
1
1.1.3 Genomics and Its Practical Applications
4
2
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
2
Terminology
10
1
Mendelian Laws
11
1
Gene Linkage
12
1
2.2.2 Mechanisms of Mendelian Heredity -- Cytogenetics
12
5
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
4
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
2
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
4
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
4
2.5.1 DNA
37
1
DNA Structure
37
1
DNA Sequence
37
1
2.5.2 DNA-RNA-Protein
37
3
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
2
Genetic Map Construction
49
1
Comparative Mapping
50
1
Mapping Genes of Interest
51
1
3.2.2 Physical Mapping
51
4
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
3
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
20
3.4.1 Polymorphism and Informativity
63
1
3.4.2 Morphological and Cytogenetic Markers
63
2
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
2
"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
1
EXERCISES
82
3
CHAPTER 4 STATISTICS IN GENOMICS
85
54
4.1 INTRODUCTION
85
1
4.2 DISTRIBUTIONS
86
7
4.2.1 Distributions
86
4
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
3
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
5
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
3
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
4
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
3
Single Parameter
110
1
Multiple Parameters
110
1
Example: Newton-Raphson Iteration
111
2
4.5.5 Expectation-Maximization (EM) Algorithm
113
3
Example: EM Algorithm
114
2
4.5.6 Moment Estimation
116
2
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
2
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
3
4.7.1 Sample Size Needed for Specific Statistical Power
131
1
4.7.2 Sample Size Needed for a Specific Confidence Interval
132
2
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
2
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
15
5.3.1 Number of Alleles and Their Frequencies
146
9
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
2
Di-Allelic System
155
2
Multiple-Allelic System
157
1
5.3.3 Heterozygosity
157
4
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
4
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
2
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
2
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
3
Example: EM Algorithm
177
1
Example: Heterogeneity Test
178
1
6.4 STATISTICAL PROPERTIES
179
9
6.4.1 Variance and Bias
181
2
Parametric Variance
181
1
Empirical Variance and Bias
182
1
6.4.2 Distribution and Confidence Intervals
183
5
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
3
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
4
Low Linkage Information Content
193
1
Bias Estimator for Recombination Fraction
194
3
6.6.2 Use of Trans Dominant Linked Markers (TDLM)
197
2
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
8
6.7.1 Segregation Ratio Distortion
199
5
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
3
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
13
7.2.1 Model
222
2
7.2.2 Allelic Frequency in Pollen Pool and Outcrossing Rate
224
2
Allelic Frequency in Pollen Pool
224
1
Outcrossing Rate
224
2
7.2.3 Estimation of Recombination Fraction
226
3
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
5
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
2
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
5
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
3
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
5
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
11
9.4.1 Likelihood Approach
290
1
9.4.2 Bootstrap Approach
291
4
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
5
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
3
Configurations
311
2
Double Crossover Issue
313
1
10.2.3 Likelihood Function
314
4
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
9
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
4
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
7
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
10
10.5.1 Definitions
345
1
10.5.2 Factors Influencing Marker Coverage and Map Density
346
3
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
6
Prediction of Map Density and Marker Coverage
349
4
Simulation Approach
353
2
EXERCISES
355
4
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
3
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
5
12.3.1 Data Structure
380
1
12.3.2 The Barley Data
381
4
Marker Data
381
1
Phenotype Data
381
4
EXERCISES
385
2
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
3
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
6
Example: Analysis of the Barley Data Using a Likelihood Approach
399
3
13.3 SINGLE-MARKER ANALYSIS USING F2 PROGENY
402
11
13.3.1 Joint Segregation of QTL and Marker Genotypes
402
2
13.3.2 Analysis of Variance Using F2 Progeny
404
2
Codominant Marker Model
404
1
Dominant Marker Model
405
1
13.3.3 Linear Regression Using F2 Progeny
406
3
Codominant Marker Model
406
2
Dominant Marker Model
408
1
13.3.4 Likelihood Approach
409
2
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
4
Example: A Likelihood Approach
421
2
14.2.3 A Nonlinear Regression Approach
423
9
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
3
Codominant Markers
437
2
Dominant Markers
439
1
14.3.3 Regression Approach
440
4
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
11
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
4
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
10
15.3.1 Model for QTL Locating On Marker
469
5
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
3
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
5
17.1.1 Multiple-QTL Model
493
2
Practical Implementations
493
2
Problems
495
1
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
2
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
5
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
3
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
4
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
3
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
2
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 Multinormal Distribution
565
1
19.8 SIMULATION OF DATA WITH MULTIPLE GENERATIONS
566
4
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