Handbook of Statistical Genetics

Handbook of Statistical Genetics......Page 3
Contents......Page 6
List of Contributors......Page 30
Editor’s Preface to the Third Edition......Page 36
Glossary of Terms......Page 38
Abbreviations and Acronyms......Page 50
Part 1 GENOMES......Page 54
1.1 Introduction......Page 56
1.2.1 Mendel’s Two Laws......Page 58
1.2.2 Basic Principles in Genetic Mapping......Page 60
1.2.3 Meiosis, Chromatid Interference, Chiasma Interference, and Crossover Interference......Page 61
1.2.5 Genetic Mapping for Three Markers......Page 62
1.2.6 Genetic Mapping for Multiple Markers......Page 64
1.2.7 Tetrads......Page 67
1.2.10 Current State of Genetic Maps......Page 70
1.2.11 Programs for Genetic Mapping......Page 71
1.3 Physical Maps......Page 73
1.3.3 Restriction Maps......Page 74
1.3.5 Ordered Clone Maps......Page 75
1.3.6 Contig Mapping Using Restriction Fragments......Page 77
1.3.7 Sequence-tagged Site Maps......Page 79
1.4 Radiation Hybrid Mapping......Page 81
1.4.2 Diploid Data......Page 82
1.5 Other Physical Mapping Approaches......Page 84
References......Page 85
2.1 Introduction......Page 93
2.2 Statistical Significance and Biological Significance......Page 94
2.2.2 Examples of Similarity in Proteins......Page 95
2.2.3 Inferences from Protein Homology......Page 96
2.3.1 Measuring Sequence Similarity......Page 97
2.3.2 Statistical Significance of Local Similarity Scores......Page 100
2.3.3 Evaluating Statistical Estimates......Page 111
2.4 Summary: Exploiting Statistical Estimates......Page 115
References......Page 116
3.1 Introduction......Page 120
3.2.1 The Procedure......Page 121
3.2.3 Model Selection and Bayes Evidence......Page 122
3.2.4 Bayesian Computation......Page 123
3.3 Hidden Markov Model: A General Introduction......Page 124
3.4.1 Bayesian Pairwise Alignment......Page 126
3.5 Multiple Sequence Alignment......Page 129
3.5.1 The Rationale of Using HMM for Sequence Alignment......Page 130
3.5.2 Bayesian Estimation of HMM Parameters......Page 132
3.5.3 PROBE and Beyond: Motif-based MSA Methods......Page 135
3.5.4 Bayesian Progressive Alignment......Page 136
3.6.1 Block-motif Model with iid Background......Page 138
3.6.3 Block-motif Model with Inhomogeneous Background......Page 139
3.6.4 Extension to Multiple Motifs......Page 140
3.6.5 HMM for cis Regulatory Module Discovery......Page 141
3.7 Joint Analysis of Sequence Motifs and Expression Microarrays......Page 142
3.8 Summary......Page 143
Appendix A: Markov Chain Monte Carlo Methods......Page 144
References......Page 146
4.1 Structural Organization and Expression of Eukaryotic Genes......Page 150
4.2 Methods of Functional Signal Recognition......Page 153
4.2.1 Position-specific Measures......Page 155
4.2.4 Performance Measures......Page 157
4.3 Linear Discriminant Analysis......Page 158
4.4.1 Splice-sites Characteristics......Page 159
4.4.2 Donor Splice-site Characteristics......Page 163
4.4.3 Acceptor Splice-site Recognition......Page 165
4.5 Identification of Promoter Regions in Human DNA......Page 166
4.6 Recognition of PolyA Sites......Page 174
4.7 Characteristics for Recognition of 3\'-Processing Sites......Page 176
4.9 Discriminative and Probabilistic Approaches for Multiple Gene Prediction......Page 177
4.9.1 HMM-based Multiple Gene Prediction......Page 178
4.9.2 Pattern-based Multiple Gene-prediction Approach......Page 180
4.10 Internal Exon Recognition......Page 181
4.11.2 3\'-exon-coding Region Recognition......Page 182
4.12 Performance of Gene Identification Programs......Page 184
4.13 Using Protein Similarity Information to Improve Gene Prediction......Page 185
4.13.1 Components of Fgenesh++ Gene-prediction Pipeline......Page 186
4.14 Genome Annotation Assessment Project (EGASP)......Page 188
4.15 Annotation of Sequences from Genome Sequencing Projects......Page 189
4.15.2 Selecting Potential Pseudogenes......Page 190
4.15.3 Selecting a Reliable Part of Alignment......Page 193
4.16 Characteristics and Computational Identification of miRNA genes......Page 194
4.17 Prediction of microRNA Targets......Page 198
4.18 Internet Resources for Gene Finding and Functional Site Prediction......Page 200
References......Page 203
5.1 Introduction......Page 213
5.2 Homology......Page 216
5.3 Genomic Mutation......Page 217
5.4 Comparative Maps......Page 219
5.5.1 Gene Order......Page 223
5.5.2 Fragile Breakage versus Random Breakage Models......Page 230
5.5.3 Gene Content......Page 232
5.5.4 Comparison of Gene Order and Gene Content Methods......Page 236
5.6 Whole Genome Sequences......Page 237
5.6.1 Whole Genome Alignment......Page 238
5.6.2 Finding Conserved Blocks......Page 240
5.6.3 Dating Duplicated Genes and Blocks......Page 243
5.7 Conclusions and Future Research......Page 245
References......Page 246
Part 2 BEYOND THE GENOME......Page 254
6.1 Introduction......Page 256
6.2.1 Image Quantification......Page 258
6.2.3 Scatterplot......Page 259
6.2.4 Batch Effects......Page 262
6.3 Error Models, Calibration and Measures of Differential Expression......Page 263
6.3.1 Multiplicative Calibration and Noise......Page 264
6.3.2 Limitations......Page 265
6.3.3 Multiplicative and Additive Calibration and Noise......Page 267
6.4 Identification of Differentially Expressed Genes......Page 269
6.4.1 Regularized t-Statistics......Page 271
6.4.2 Multiple Testing......Page 272
6.5 Pattern Discovery......Page 279
6.5.1 Projection Methods......Page 280
6.5.2 Cluster Algorithms......Page 281
6.6 Conclusions......Page 283
References......Page 284
7.1 Introduction......Page 289
7.2 Initial Data Processing......Page 293
7.2.1 Normalization......Page 294
7.2.2 Filtering......Page 296
7.3.1 Two-group and k-group Comparisons......Page 298
7.3.3 Association with a Time-to-event Endpoint......Page 300
7.3.4 Computing p Values......Page 302
7.4.2 The False Discovery Rate......Page 303
7.4.3 Significance Criteria for Multiple Hypothesis Tests......Page 306
7.4.4 Significance Analysis of Microarrays......Page 307
7.4.5 Selecting an MTA Method for a Specific Application......Page 308
7.5 Annotation Analysis......Page 311
7.6 Validation Analysis......Page 312
7.7 Study Design and Sample Size......Page 314
7.8 Discussion......Page 317
References......Page 318
8.1 Introduction......Page 325
8.2 Extracting Signal From Observed Intensities......Page 327
8.2.1 Spotted cDNA Arrays......Page 330
8.2.2 Oligonucleotide Arrays......Page 331
8.3.1 Normalization......Page 333
8.3.2 Gene Variability......Page 334
8.3.3 Expression Levels......Page 335
8.3.4 Classifying Genes as Differentially Expressed......Page 338
8.4 Clustering Gene Expression Profiles......Page 341
8.4.1 Unordered Samples......Page 342
8.4.2 Ordered Samples......Page 345
8.5 Multivariate Gene Selection Models......Page 346
8.5.1 Variable Selection Approach......Page 347
8.5.2 Bayesian Shrinkage with Sparsity Priors......Page 348
References......Page 349
9 Inferring Causal Associations between Genes and Disease via the Mapping of Expression Quantitative Trait Loci......Page 354
9.1 Introduction......Page 355
9.2 An Overview of Transcription as a Complex Process......Page 357
9.3 Human Versus Experimental Models......Page 359
9.4 Heritability of Expression Traits......Page 360
9.5 Joint eQTL Mapping......Page 361
9.6 Multilocus Models AND FDR......Page 363
9.7 eQTL and Clinical Trait Linkage Mapping to Infer Causal Associations......Page 365
9.7.1 A Simple Model for Inferring Causal Relationships......Page 367
9.7.2 Distinguishing Proximal eQTL Effects from Distal......Page 370
9.8 Using eQTL Data to Reconstruct Coexpression Networks......Page 371
9.8.1 More Formally Assessing eQTL Overlaps in Reconstructing Coexpression Networks......Page 373
9.8.2 Identifying Modules of Highly Interconnected Genes in Coexpression Networks......Page 374
9.9 Using eQTL Data to Reconstruct Probabilistic Networks......Page 375
9.10 Conclusions......Page 379
9.11 Software......Page 380
References......Page 381
10.1 History......Page 385
10.2 Basic Structural Biology......Page 386
10.3 Protein Structure Prediction......Page 387
10.3.2 Threading......Page 388
10.3.5 New Fold (NF) Prediction (Ab initio and De novo Approaches)......Page 389
10.4.1 Decision Trees......Page 390
10.4.2 Genetic Algorithms......Page 392
10.4.3 k and Fuzzy k-nearest Neighbour......Page 393
10.4.4 Bayesian Approaches......Page 394
10.4.5 Artificial Neural Networks (ANNs)......Page 396
10.4.6 Support Vector Machines......Page 397
10.5 Conclusions......Page 400
11.1 Introduction......Page 405
11.1.1 Spectroscopic Techniques......Page 406
11.1.2 Data Pre-processing......Page 408
11.1.3 Example Data......Page 409
11.2.1 Principal Components Analysis......Page 410
11.2.2 Principal Components Regression......Page 412
11.3.1 Partial Least Squares......Page 413
11.3.2 PLS and Discrimination......Page 415
11.3.3 Orthogonal Projections to Latent Structure......Page 416
11.4 Clustering Procedures......Page 418
11.4.2 Hierarchical Clustering......Page 419
11.4.3 Model-based Hierarchical Clustering......Page 420
11.4.5 Displaying and Interpreting Clustering Results......Page 421
11.5.1 Mathematical Formulation......Page 422
11.5.2 Kernel Density Estimates, PNNs and CLOUDS......Page 429
11.6 Evolutionary Algorithms......Page 430
11.7 Conclusions......Page 432
References......Page 433
Part 3 EVOLUTIONARY GENETICS......Page 438
12.1 Introduction......Page 440
12.2 Markov Model of Codon Substitution......Page 442
12.3.1 Counting Methods......Page 444
12.3.2 Maximum Likelihood Estimation......Page 445
12.3.3 A Numerical Example and Comparison of Methods......Page 449
12.4 Likelihood Calculation on a Phylogeny......Page 451
12.5.1 Likelihood Calculation under Models of Variable ω Ratios among Lineages......Page 453
12.5.2 Adaptive Evolution in the Primate Lysozyme......Page 454
12.5.3 Comparison with Methods Based on Reconstructed Ancestral Sequences......Page 455
12.6.1 Likelihood Ratio Test under Models of Variable ω Ratios among Sites......Page 457
12.6.2 Methods That Test One Site at a Time......Page 459
12.6.3 Positive Selection in the HIV-1 vif Genes......Page 460
12.7.1 Branch-site Test of Positive Selection......Page 462
12.7.2 Similar Models......Page 463
12.8 Limitations of Current Methods......Page 464
References......Page 465
13.1 Introduction......Page 470
13.2.1 Genome Sequencing Projects......Page 472
13.2.2 The Origins and Functions of Introns......Page 474
13.3.1 The Relative Positions of Genes: Are They Adaptive?......Page 477
13.3.3 Gene Clusters......Page 478
13.3.4 Integration of Genetic Functions......Page 479
13.3.5 Gene Duplications as Individual Genes or Whole Genome Duplications?......Page 480
13.3.6 Apparent Genetic Redundancy......Page 484
13.4.1 The Impact of Chromosomal Position on Population Genetic Variability......Page 485
13.4.2 Codon Usage Bias......Page 486
13.4.3 Effective Population Size......Page 487
13.5.1 Repetitive Sequences......Page 488
13.5.4 Phylogenies of Transposable Elements......Page 489
13.6 Conclusions......Page 493
References......Page 494
14.1 Introduction......Page 502
14.2.1 The Dayhoff and Eck Model......Page 503
14.3 Amino Acid Composition......Page 505
14.4 Heterogeneity of Replacement Rates Among Sites......Page 506
14.5 Protein Structural Environments......Page 507
14.6 Variation of Preferred Residues Among Sites......Page 509
14.7 Models with a Physicochemical Basis......Page 510
14.8 Codon-Based Models......Page 511
14.9 Dependence Among Positions: Simulation......Page 512
14.10 Dependence Among Positions: Inference......Page 513
14.11 Conclusions......Page 516
References......Page 517
15.1 Introduction......Page 523
15.2.1 A Brief History of Maximum Likelihood in Phylogenetics......Page 525
15.3 Likelihood Function......Page 526
15.4 Developing an Intuition of Likelihood......Page 532
15.5 Method of Maximum Likelihood......Page 534
15.6 Bayesian Inference......Page 537
15.7 Markov Chain Monte Carlo......Page 539
15.8 Assessing Uncertainty of Phylogenies......Page 543
15.9 Hypothesis Testing and Model Choice......Page 544
15.10 Comparative Analysis......Page 545
References......Page 547
16.1 Introduction......Page 552
16.2 DATA......Page 553
16.2.2 Genetic Distances (Including Generalised Distances)......Page 554
16.2.3 Splits (Bipartitions)......Page 559
16.2.4 Sampling Error......Page 561
16.3.1 Terminology for Graphs and Trees......Page 562
16.3.2 Computational Complexity, Numbers of Trees......Page 564
16.3.3 Three Parts of an Evolutionary Model......Page 567
16.3.4 Stochastic Mechanisms of Evolution......Page 570
16.4 Methods for Inferring Evolutionary Trees......Page 572
16.4.1 Five Desirable Properties for a Method......Page 573
16.4.2 Optimality Criteria......Page 575
16.5.1 Reconstructing Reticulate Evolutionary Histories......Page 583
16.5.2 Displaying Conflicting Phylogenetic Signals......Page 584
16.6.1 Complete or Exact Searches......Page 587
16.6.2 Heuristic Searches I, Limited (Local) Searches......Page 589
16.6.3 Heuristic Searches II–Hill-climbing and Related Methods......Page 590
16.6.4 Quartets and Supertrees......Page 591
16.7 Overview and Conclusions......Page 592
References......Page 593
17.1 Introduction......Page 596
17.1.1 Resemblances, Variances, and Breeding Values......Page 597
17.1.2 Single Trait Parent–Offspring Regressions......Page 598
17.1.3 Multiple Trait Parent–Offspring Regressions......Page 599
17.2.1 The Infinitesimal Model......Page 600
17.2.2 Changes in Variances......Page 601
17.2.3 The Roles of Drift and Mutation under the Infinitesimal Model......Page 604
17.3 Fitness......Page 605
17.3.1 Individual Fitness......Page 606
17.3.2 Episodes of Selection......Page 607
17.3.4 The Opportunity for Selection......Page 608
17.3.5 Some Caveats in Using the Opportunity for Selection......Page 610
17.4.1 Individual and Mean Fitness Surfaces......Page 611
17.4.2 Measures of Selection on the Mean......Page 612
17.4.3 Measures of Selection on the Variance......Page 613
17.4.4 Gradients and the Geometry of Fitness Surfaces......Page 614
17.4.5 Estimating the Individual Fitness Surface......Page 615
17.4.6 Linear and Quadratic Approximations of W(z)......Page 616
17.5.1 Changes in the Mean Vector: The Directional Selection Differential......Page 618
17.5.2 The Directional Selection Gradient......Page 619
17.5.3 Directional Gradients, Fitness Surface Geometry, and Selection Response......Page 620
17.5.4 Changes in the Covariance Matrix: The Quadratic Selection Differential......Page 621
17.5.5 The Quadratic Selection Gradient......Page 622
17.5.7 Estimation, Hypothesis Testing, and Confidence Intervals......Page 624
17.5.8 Geometric Interpretation of the Quadratic Fitness Regression......Page 626
17.5.9 Unmeasured Characters and Other Biological Caveats......Page 628
17.6.2 The Effects of Genetic Correlations: Direct and Correlated Responses......Page 629
17.6.4 Inferring the Nature of Previous Selection......Page 630
17.6.5 Changes in G under the Infinitesimal Model......Page 631
17.7 Phenotypic Evolution Models......Page 633
17.7.1 Selection versus Drift in the Fossil Record......Page 634
17.7.2 Stabilizing Selection......Page 636
17.8 Theorems of Natural Selection: Fundamental and Otherwise......Page 638
17.8.1 The Classical Interpretation of Fishers’ Fundamental Theorem......Page 639
17.8.2 What did Fisher Really Mean?......Page 641
17.8.4 Robertson’s Secondary Theorem of Natural Selection......Page 643
References......Page 645
Part 4 ANIMAL AND PLANT BREEDING......Page 650
18.1.1 Mendelian Factors and Quantitative Traits......Page 652
18.1.2 The Genetics of Inbred Lines......Page 653
18.1.3 Phenotype, Genotype and Environment......Page 654
18.2.1 Visualisation of Quantitative Variation in a Histogram......Page 655
18.2.2 Plotting Mixture Distributions on Top of the Histogram......Page 657
18.2.3 Fitting Mixture Distributions......Page 658
18.2.4 Wanted: QTLs!......Page 659
18.3.1 Molecular Markers......Page 660
18.3.2 Mixture Models......Page 661
18.3.3 Alternative Regression Mapping......Page 665
18.3.5 ANOVA and Regression Tests......Page 666
18.3.6 Maximum Likelihood Tests......Page 667
18.3.7 Analysis-of-deviance Tests......Page 668
18.3.8 How Many Parameters Can We Fit Safely?......Page 669
18.4.1 Model Selection and Genome Scan......Page 670
18.4.2 Single-marker Analysis and Interval Mapping......Page 671
18.4.3 Composite Interval Mapping......Page 673
18.4.4 Multiple-QTL Mapping......Page 674
18.4.5 Uncritical use of Model Selection Procedures......Page 677
18.5.1 Statistical Approaches......Page 678
18.5.2 Learning More about Important Genetic Parameters......Page 679
18.5.3 QTL Analysis in Inbred Lines on a Large Scale......Page 680
References......Page 681
19.1 Introduction......Page 686
19.2.1 Least Squares......Page 688
19.2.2 Maximum Likelihood......Page 691
19.3.1 Identity-by-descent Probabilities of Alleles......Page 692
19.3.2 Mixed Linear Model with Random QTL Allelic Effects......Page 696
19.3.3 Mixed Linear Model with Random QTL Genotypic Effects......Page 697
19.3.4 Relationship with Other Likelihood Methods......Page 699
19.4.1 General......Page 701
19.4.2 Bayesian Mapping of a Monogenic Trait......Page 702
19.4.3 Bayesian QTL Mapping......Page 704
19.5 Deterministic Haplotyping In Complex Pedigrees......Page 713
19.6 Genotype Sampling In Complex Pedigrees......Page 716
19.7.2 Fine Mapping Using Historical Recombinations......Page 728
19.8 Concluding Remarks......Page 731
References......Page 732
20.1 Introduction......Page 741
20.2.1 Statistical Genetic Models......Page 743
20.2.2 Best Linear Unbiased Prediction (BLUP)......Page 744
20.2.3 Variance and Covariance Component Estimation......Page 747
20.2.4 BLUP and Unknown Dispersion Parameters......Page 750
20.2.5 Bayesian Procedures......Page 751
20.2.6 Nonlinear, Generalized Linear Models, and Longitudinal Responses......Page 753
20.2.7 Effects of Selection on Inferences......Page 758
20.2.8 Massive Molecular Data: Semiparametric Methods......Page 760
20.3 Future Developments......Page 768
20.3.2 Model Dimensionality......Page 769
20.3.3 Robustification of Inference......Page 770
20.3.5 Mixture Models......Page 771
Acknowledgments......Page 772
References......Page 773
21.1 Introduction......Page 781
21.2.1 Lande and Thompson’s Formula......Page 782
21.2.2 Efficiency of Marker-assisted Selection......Page 785
21.2.3 Refinements......Page 787
21.3 Marker-assisted selection: outbred populations......Page 793
21.3.1 MAS via BLUP......Page 794
21.3.2 Comments......Page 795
21.3.3 Within-family MAS......Page 797
21.4.1 Inbred Line Crosses......Page 799
21.4.2 Outbred Populations......Page 802
21.5 Marker-assisted Gene Pyramiding......Page 803
21.6 Discussion......Page 805
References......Page 808
Part 5 POPULATION GENETICS......Page 816
22.1 A Brief History of The Role of Selection......Page 818
22.2 Mutation, Random Genetic Drift, and Selection......Page 819
22.2.2 Random Genetic Drift......Page 820
22.2.4 The Wright–Fisher Model......Page 822
22.3 The Diffusion Approximation......Page 823
22.3.1 Fixation......Page 826
22.3.3 Random Genetic Drift Versus Mutation and Selection......Page 827
22.4 The Infinite Allele Model......Page 828
22.4.1 The Infinite Allele Model with Mutation......Page 829
22.4.3 The Infinite Allele Model with Selection and Mutation......Page 830
22.5.2 Frequency-dependent Selection......Page 831
22.6.1 The Neutral Coalescent......Page 832
22.6.2 The Ancestral Selection Graph......Page 834
22.6.3 Varying Population Size......Page 837
22.7 Detecting Selection......Page 838
References......Page 840
23.1 Genealogies as Graphs......Page 844
23.2.1 The Algebra of Pairwise Relationships......Page 845
23.2.2 Measures of Genetic Relationship......Page 848
23.2.3 Identity States for Two Individuals......Page 849
23.2.4 More Than Two Individuals......Page 850
23.2.5 Example: Two Siblings Given Parental States......Page 852
23.3.1 Theory of Junctions......Page 853
23.3.3 Other Methods......Page 854
23.4.1 Applying the Peeling Method......Page 855
23.4.2 Recursions......Page 856
23.4.3 More Complex Linear Systems......Page 858
23.5.1 Drawing Marriage Node Graphs......Page 859
23.5.2 Zero-loop Pedigrees......Page 861
23.6.1 Significance for Computation......Page 864
23.6.2 Derivation from Marriage Node Graphs......Page 865
23.6.3 Four Colourability and Triangulation......Page 867
References......Page 868
24.1 Introduction......Page 871
24.2.2 Conditional Independence......Page 872
24.2.4 Object-oriented Specification of Bayesian Networks......Page 873
24.3.1 Graphs for Pedigrees......Page 874
24.3.2 Pedigrees and Bayesian Networks......Page 875
24.4 Peeling and Related Algorithms......Page 879
24.4.1 Compilation......Page 880
24.4.2 Propagation......Page 884
24.4.3 Random and Other Propagation Schemes......Page 886
24.5.1 Single-point Linkage Analysis......Page 887
24.5.2 QTL Mapping......Page 888
24.5.3 Pedigree Uncertainty......Page 890
24.5.4 Forensic Applications......Page 892
24.6 Causal Inference......Page 895
24.6.2 Mendelian Randomisation......Page 896
24.7.1 Graph Learning for Genome-wide Associations......Page 899
References......Page 901
25.1 Introduction......Page 906
25.2.1 The Fundamental Insights......Page 907
25.2.2 The Coalescent Approximation......Page 910
25.3.1 Robustness and Scaling......Page 913
25.3.2 Variable Population Size......Page 914
25.3.3 Population Structure on Different Time Scales......Page 916
25.4 Geographical Structure......Page 917
25.4.1 The Structured Coalescent......Page 918
25.4.2 The Strong-migration Limit......Page 919
25.5.1 Hermaphrodites......Page 920
25.6 Recombination......Page 922
25.6.1 The Ancestral Recombination Graph......Page 923
25.6.2 Properties and Effects of Recombination......Page 926
25.7.1 Balancing Selection......Page 928
25.7.3 Background Selection......Page 931
25.8 Neutral Mutations......Page 932
25.9.2 The Coalescent and Phylogenetics......Page 933
References......Page 935
26.1 Introduction......Page 941
26.1.1 Likelihood-based Inference......Page 942
26.2 The Likelihood and the Coalescent......Page 946
26.3 Importance Sampling......Page 948
26.3.1 Likelihood Surfaces......Page 950
26.3.3 Application and Assessing Reliability......Page 951
26.4.1 Introduction......Page 952
26.4.3 Likelihood Surfaces......Page 954
26.4.4 Ancestral Inference......Page 956
26.4.5 Example Proposal Distributions......Page 957
26.4.6 Application and Assessing Reliability......Page 960
26.4.7 Extensions to More Complex Demographic and Genetic Models......Page 962
26.5.1 Rejection Sampling and Approximate Bayesian Computation......Page 963
26.5.2 Composite Likelihood Methods......Page 965
26.6 Software and Web Resources......Page 966
26.6.2 Inference Methods......Page 967
Acknowledgments......Page 968
References......Page 969
27.1 What Is Linkage Disequilibrium?......Page 972
27.2 Measuring Linkage Disequilibrium......Page 974
27.2.1 Single-number Summaries of LD......Page 976
27.2.2 The Spatial Distribution of LD......Page 977
27.2.3 Various Extensions of Two-locus LD Measures......Page 981
27.2.4 The Relationship between r2 and Power in Association Studies......Page 982
27.3.1 A Historical Perspective......Page 985
27.3.2 Coalescent Modelling......Page 987
27.3.3 Relating Genealogical History to LD......Page 993
27.4.1 Formulating the Hypotheses......Page 995
27.4.2 Parameter Estimation......Page 996
27.4.3 Hypothesis Testing......Page 1000
27.5 Prospects......Page 1001
Acknowledgments......Page 1002
References......Page 1003
28.1 Introduction......Page 1008
28.2.1 Assumptions and Parameters......Page 1009
28.3.1 F-statistics......Page 1011
28.3.2 Likelihood Computations......Page 1015
28.4.2 Island Model......Page 1018
28.4.3 Isolation by Distance......Page 1019
28.4.4 Likelihood Inferences......Page 1022
28.5 Separation of Timescales......Page 1023
28.6.1 Assignment and Clustering......Page 1025
28.6.2 Inferences from Clines......Page 1027
28.7 Integrating Statistical Techniques into the Analysis of Biological Processes......Page 1028
Related Chapters......Page 1029
References......Page 1030
Appendix A: Analysis of Variance and Probabilities of Identity......Page 1035
Appendix B: Likelihood Analysis of the Island Model......Page 1040
29.1.1 Effects of Population Subdivision......Page 1043
29.2 The Fixation Index F......Page 1045
29.3 Wright’s F Statistics in Hierarchic Subdivisions......Page 1046
29.3.1 Multiple Alleles......Page 1048
29.3.2 Sample Estimation of F Statistics......Page 1049
29.3.3 G Statistics......Page 1050
29.4 Analysis of Genetic Subdivision Under an Analysis of Variance Framework......Page 1051
29.4.1 The Model......Page 1052
29.4.2 Estimation Procedure......Page 1054
29.4.3 Dealing with Mutation and Migration using Identity Coefficients......Page 1059
29.5 Relationship Between Different Definitions of Fixation Indexes......Page 1060
29.6 F Statistics and Coalescence Times......Page 1062
29.7.1 Haplotypic Diversity......Page 1064
29.7.3 Multiallelic Molecular Data......Page 1067
29.7.4 Dominant Data......Page 1070
29.7.5 Relation of AMOVA with other Approaches......Page 1071
29.8.1 Resampling Techniques......Page 1072
29.9.1 Testing Departure from Hardy–Weinberg Equilibrium......Page 1074
29.9.3 What is the Underlying Genetic Structure of Populations?......Page 1075
References......Page 1076
30.1 Introduction......Page 1084
30.2 Estimating Effective Population Size......Page 1085
30.2.1 Estimating Ne Using Two Samples from the Same Population: The Temporal Method......Page 1086
30.2.2 Estimating Ne from Two Derived Populations......Page 1088
30.2.3 Estimating Ne Using One Sample......Page 1093
30.2.4 Inferring Past Changes in Population Size: Population Bottlenecks......Page 1096
30.2.5 Approximate Bayesian Computation......Page 1103
30.3 Admixture......Page 1104
30.4.1 Assignment Testing......Page 1109
30.4.2 Genetic Mixture Modelling and Clustering......Page 1111
30.4.3 Hybridisation and the Use of Partially Linked Markers......Page 1114
30.4.4 Inferring Current Migration Rates......Page 1115
30.4.5 Spatial Modelling......Page 1116
30.5 Relatedness and Pedigree Estimation......Page 1117
Related Chapters......Page 1120
References......Page 1121
31 Human Genetic Diversity and its History......Page 1130
31.1 Introduction......Page 1131
31.2.1 Some Data on Fossil Evidence......Page 1132
31.2.2 Models of Modern Human Origins......Page 1133
31.2.3 Methods for Inferring Past Demography......Page 1134
31.2.4 Reconstructing Past Human Migration and Demography......Page 1138
31.3.1 Catalogues of Humankind......Page 1144
31.3.2 Methods for Describing Population Structure......Page 1147
31.3.3 Identifying the Main Human Groups......Page 1150
31.3.4 Continuous versus Discontinuous Models of Human Variation......Page 1154
31.4 Final Remarks......Page 1155
References......Page 1159
Part 6 GENETIC EPIDEMIOLOGY......Page 1172
32.1 Introduction......Page 1174
32.2 Descriptive Epidemiology......Page 1175
32.2.1 Incidence and Prevalence......Page 1177
32.2.2 Modelling Correlated Responses......Page 1178
32.3.1 Is There Evidence of Phenotypic Aggregation within Families?......Page 1180
32.3.2 Is the Pattern of Correlation Consistent with a Possible Effect of Genes?......Page 1181
32.3.3 Segregation Analysis......Page 1189
32.3.4 Ascertainment......Page 1190
32.4 Studies Investigating Specific Aetiological Determinants......Page 1193
32.5 The Future......Page 1194
References......Page 1195
33.1 Introduction......Page 1204
33.2 The Early Years......Page 1205
33.3 The Development of Human Genetic Linkage Analysis......Page 1207
33.4 The Pedigree Years; Segregation and Linkage Analysis......Page 1209
33.5 Likelihood and Location Score Computation......Page 1212
33.6 Monte Carlo Multipoint Linkage Likelihoods......Page 1214
33.7 Linkage Analysis of Complex Traits......Page 1218
33.8 Map Estimation, Map Uncertainty, and The Meiosis Model......Page 1221
33.9 The Future......Page 1225
References......Page 1226
34.1 Introduction......Page 1231
34.2 Pros and Cons of Model-free Methods......Page 1232
34.3.1 Affected Sib-pair Methods......Page 1234
34.3.2 Parameter Estimation and Power Calculation Using Affected Sib Pairs......Page 1235
34.3.3 Typing Unaffected Relatives in Sib-pair Analyses......Page 1236
34.3.4 Application of Sib-pair Methods to Multiplex Sibships......Page 1237
34.3.5 Methods for Analysing Larger Pedigrees......Page 1238
34.3.6 Extensions to Multiple Marker Loci......Page 1239
34.3.8 Inclusion of Covariates......Page 1240
34.3.9 Multiple Disease Loci......Page 1242
34.3.11 Meta-analysis of Genome Scans......Page 1243
34.4 Model-free Methods for Analysing Quantitative Traits......Page 1244
Related Chapters......Page 1245
References......Page 1246
35 Population Admixture and Strati.cation in Genetic Epidemiology......Page 1253
35.1 Background......Page 1254
35.2.1 Basic Principles......Page 1255
35.2.2 Statistical Power and Sample Size......Page 1257
35.2.3 Distinguishing between Genetic and Environmental Explanations for Ethnic Variation in Disease Risk......Page 1259
35.3.1 Modelling Admixture......Page 1261
35.3.2 Modelling Stratification......Page 1262
35.3.3 Modelling Allele Frequencies......Page 1264
35.3.4 Fitting the Statistical Model......Page 1265
35.3.5 Model Comparison......Page 1266
35.3.6 Assembling and Evaluating Panels of Ancestry-informative Marker Loci......Page 1267
35.4 Testing For Linkage With Locus Ancestry......Page 1268
35.4.1 Modelling Population Stratification......Page 1270
35.5 Conclusions......Page 1275
References......Page 1276
36.1 Introduction......Page 1279
36.2 Measures of Association......Page 1280
36.3 Case-Control Studies......Page 1282
36.4 Tests For Association......Page 1284
36.5 Logistic Regression And Log-Linear Models......Page 1288
36.6 Stratification And Matching......Page 1290
36.7 Unmeasured Confounding......Page 1293
36.8 Multiple Alleles......Page 1295
36.9 Multiple Loci......Page 1297
References......Page 1299
37.1 Introduction......Page 1301
37.1.1 Linkage Disequilibrium and Tagging......Page 1302
37.1.2 Current WGA Studies......Page 1303
37.2 Genotype Quality Control......Page 1305
37.3 Single-Locus Analysis......Page 1308
37.3.1 Logistic Regression Modelling Framework......Page 1310
37.3.2 Interpretation of Results and Correction for Multiple Testing......Page 1312
37.4 Population Structure......Page 1313
37.5 Multi-Locus Analysis......Page 1314
37.5.1 Haplotype-based Analyses......Page 1315
37.5.2 Haplotype Clustering Techniques......Page 1316
37.6 Epistasis......Page 1317
37.7 Replication......Page 1319
37.8 Prospects for Whole-Genome Association Studies......Page 1320
References......Page 1321
38.1 Introduction......Page 1327
38.2 Transmission/Disequilibrium Test......Page 1329
38.3 Logistic Regression Models......Page 1331
38.4 Haplotype Analysis......Page 1334
38.5 General Pedigree Structures......Page 1336
38.6 Quantitative Traits......Page 1339
38.7 Association in the Presence of Linkage......Page 1341
38.8 Conclusions......Page 1344
References......Page 1345
39.1 Introduction......Page 1349
39.2.1 The Multistage Model......Page 1350
39.2.2 The Two-stage Model......Page 1352
References......Page 1361
40.1 A Brief Introduction......Page 1364
40.2 Technologies for CGI Methylation Interrogation......Page 1366
40.2.2 Methylation Microarrays......Page 1367
40.3.2 Methylation Patterns......Page 1368
40.3.3 Modeling Human Colon Crypts......Page 1369
40.4 Mixture Modeling......Page 1370
40.4.1 Cluster Analysis......Page 1371
40.4.2 Modeling Exposures for Latent Disease Subtypes......Page 1373
40.4.3 Differential Methylation with Single-slide Data......Page 1374
40.5.2 Heritable Clustering......Page 1376
40.5.3 Further Comments......Page 1378
Acknowledgments......Page 1379
References......Page 1380
Part 7 SOCIAL AND ETHICAL ASPECTS......Page 1386
41.1 Introduction: Scope of This Chapter......Page 1388
41.1.1 What is Ethics?......Page 1389
41.1.2 Models for Analysing the Ethics of Population Genetic Research......Page 1390
41.2 A Case Study in Ethical Regulation of Population Genetics Research: UK Biobank’s Ethics and Governance Framework......Page 1392
41.2.1 The Scientific and Clinical Value of the Research......Page 1393
41.2.2 Recruitment of Participants......Page 1395
41.2.3 Consent......Page 1397
41.3 Stewardship......Page 1402
41.3.1 Benefit Sharing......Page 1403
41.4.1 Geneticisation......Page 1404
41.4.2 Race, Ethnicity and Genetics......Page 1405
References......Page 1406
42.1.1 Long-term Insurance Pricing......Page 1409
42.1.2 Life Insurance Underwriting......Page 1411
42.1.4 Adverse Selection......Page 1412
42.1.5 Family Medical Histories......Page 1413
42.1.6 Legislation and Regulation......Page 1414
42.2.1 Actuarial Models for Life and Health Insurance......Page 1415
42.2.2 Parameterising Actuarial Models......Page 1417
42.2.3 Market Models and Missing Information......Page 1418
42.2.4 Modelling Strategies......Page 1420
42.2.5 Statistical Issues......Page 1421
42.3.1 Single-gene Disorders......Page 1422
42.3.2 Multifactorial Disorders......Page 1424
References......Page 1428
43.1 Introduction......Page 1431
43.2 Principles of Interpretation......Page 1432
43.3.1 Allelic Independence......Page 1434
43.3.2 Allele Frequencies......Page 1436
43.3.3 Joint Profile Probabilities......Page 1438
43.4 Parentage Issues......Page 1440
43.6 Mixtures......Page 1442
43.7.1 Allele Probabilities......Page 1446
43.7.2 Coancestry......Page 1447
43.8.2 Relevant Population......Page 1448
43.8.4 Uniqueness of Profiles......Page 1449
43.8.5 Assigning Individuals to Phenotypes, Populations or Families......Page 1451
43.8.6 Hierarchy of Propositions......Page 1452
References......Page 1453
Reference Author Index......Page 1456
Subject Index......Page 1518
Colour plate sections......Page 274