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

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) Haldanes 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) Morgans Map Function 320(1) Haldanes Map Function 320(2) Kosambis 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

Liu, Ben Hui