Handbook on Analyzing Human Genetic Data (eBook)

Preface and Introduction 5
Contents 10
Population Genetics 14
1 Introduction 14
2 Within-Population Analyses 15
2.1 Genotype and Allele Frequencies 15
2.2 Maximum Likelihood Estimation 16
2.3 Inbreeding Coefficient 17
2.4 Testing for Hardy–Weinberg Equilibrium 19
2.5 Linkage Disequilibrium 21
2.6 Composite Linkage Disequilibrium 22
2.7 Testing for Linkage Equilibrium 23
2.8 Application to Data 24
3 Between-Population and Analyses 29
3.1 F-statistics 29
3.2 Application to Data 33
4 Discussion 35
5 Web Resources 35
References 36
Haplotype Structure 37
1 Population Haplotype Structure 37
1.1 Haplotype Block Structure in Human Populations 37
1.2 Wright–Fisher Model 38
1.3 Coalescent Theory 40
2 Public Genotype/Haplotype Databases 42
2.1 International HapMap Project 43
Download Genotype Data from HapMap 44
2.2 The HapMap ENCODE Resequencingand Genotyping Project 45
2.2.1 Download ENCODE Genotype Data 46
2.3 Haplotype Simulation 46
3 Haploview 48
3.1 What is Haploview? 48
3.2 How to Download and Install Haploview 48
3.3 How to Run Haploview 49
3.3.1 How to Use HapMap Data in Haploview 49
3.3.2 How to Use Non-HapMap Genotype Data in Haploview 50
4 Haplotype Inference Methods 53
4.1 Clark's Algorithm 54
4.1.1 Software Usage 55
4.2 PHASE 58
4.2.1 PHASE Algorithm 59
4.2.2 Software Usage 60
4.3 HAPLOTYPER 65
4.3.1 Bayesian Model 65
4.3.2 Partition–Ligation (PL) 66
4.3.3 Software Usage 68
4.4 CHB 70
4.4.1 CHB Model 70
4.4.2 MCMC Sampling and Convergence 72
4.4.3 Software Usage 73
4.5 Comparison of Phasing Results 76
5 Estimation of Recombination Rate 76
5.1 LDhat 77
5.1.1 Composite Likelihood Estimation of 77
5.1.2 Likelihood Permutation Test 78
5.1.3 Software Usage 79
5.2 HOTSPOTTER 83
5.2.1 PAC Model 83
5.2.2 Computing the Conditional Distribution 85
5.2.3 Software Usage 86
Summary 88
Web Resources 89
References 89
Linkage Analysis of Qualitative Traits 92
1 Introduction 92
2 Model-Based Linkage Analysis 93
2.1 Phase-Known Pedigrees 93
2.2 Phase-Unknown Pedigrees 96
2.3 Linkage Analysis in General Case 98
2.4 Elston–Stewart Algorithm 99
3 Model-Free Linkage Analysis 101
3.1 Fundamental Principle of Model-Free Linkage Analysis 101
3.2 Measure of Genetic Similarity 102
3.3 Model-Free Linkage Analysis for Affected Sib Pairs 103
3.4 Multipoint Analysis for Affected Sib Pairs 106
3.5 Model-Free Linkage Analysis for General Pedigrees 108
3.5.1 Inheritance Vector 108
3.5.2 NPL Score When the Inheritance Vector Is Known 109
3.5.3 NPL Score When the Inheritance Vector Is Uncertain 110
3.6 Lander–Green Algorithm 111
4 Practical Examples 113
5 Identifying SNPs Responsible for a Linkage Signal 117
5.1 Assumptions and Definitions 117
5.2 Conditional Probability of Marker Data Given ASP 118
5.3 Relationship Between Disease Locus and Candidate SNP 119
5.4 Hypothesis Testing 120
5.5 Extension to Sibship Data and Nuclear Families 122
5.6 Summary 124
6 Comparison of Model-Based and Model-Free Linkage Analysis Methods 124
6.1 Software Packages for Linkage Analysis 126
Web Resources 126
References 127
Linkage Analysis of Quantitative Traits 130
1 Introduction and Description of Data 130
2 Methods 132
2.1 Classical Model-Based Linkage Analysis 134
2.2 Model-Free Haseman–Elston Regression Approach 138
2.3 Variance-Components Approaches 139
2.4 Model-Free Variance Regression 145
2.5 Multivariate Models 147
2.6 Joint Linkage and Association Analysis 149
3 Discussion 149
4 Web Resources 151
References 152
Markov Chain Monte Carlo Linkage Analysis Methods 157
1 Introduction 157
2 Test Data 159
2.1 Data from the Framingham study 159
2.2 Simulated data 160
3 MCMC Methods and Packages 161
4 Comparison of Methods 162
4.1 Analysis Strategies 162
4.1.1 Estimation of Segregation Models for TH 163
4.1.2 Linkage Analysis Based on Loki 164
4.1.3 Linkage Analysis Based on MORGAN 164
4.1.4 Linkage Analysis Based on SimWalk2 164
4.2 Comparison of the Three Linkage Analysis Software 165
4.2.1 Framingham Data 165
4.2.2 Simulated Data 169
5 Conclusions, Recommendations, and Other Considerations 173
6 Web Resources 177
References 177
Population-Based Association Studies 180
1 Introduction 180
2 The Data 181
2.1 Association of a Genetic Marker and a Disease 182
2.2 Testing for Association When No PopulationStratification Is Present 184
2.3 False Positive Can Be Aroused When PopulationStratification Is Present 186
3 Genome-Control Approach 186
4 Structured Association Approach 187
5 Methods Based on Principal Components (PC) 189
5.1 Mixture Model 190
5.2 Semi-Parametric Approach 192
5.3 Linear Model Approach 194
6 Discussion 195
Web Resources 196
References 197
Family-Based Association Studies 200
1 Introduction 201
2 Basic Notations 202
3 Qualitative Traits, Trios, Bi-Allelic Markers 203
3.1 Qualitative Traits, Trios, Multi-Allelic Markers 204
4 Family with Multiple Siblings 207
5 Families with Missing Parental Genotypes 210
6 Quantitative Phenotypes 217
7 Joint Analysis of Multiple Markers 221
8 Other Association Methods Using Family-Based Designs 228
8.1 General Pedigrees 228
8.2 Gene–Gene (GG) interaction and Gene–Environment (GE) Interaction 230
9 Software Packages and Power Consideration 231
10 Discussion 236
References 240
Haplotype Association Analysis 250
1 Introduction 250
1.1 The FUSION Study 252
1.2 General Notation 253
2 Haplotype Analysis of Unrelated Samples 253
2.1 Cross-Sectional Studies 253
2.1.1 Analyses Using Phased Haplotypes 253
2.1.2 Analyses Using Unphased Haplotypes 255
2.1.3 Stability Issues in Haplotype Analysis 256
2.1.4 Modeling Interaction Effects 257
2.1.5 Haplotype Clustering 257
2.1.6 Software Packages 259
2.1.7 Software Application to FUSION Data 261
2.2 Cohort Studies 263
2.2.1 Software Packages 264
2.3 Case–Control Studies 264
2.3.1 Related Study Designs 267
2.3.2 Haplotype Similarity Analyses 268
2.3.3 Software Packages 270
2.3.4 Software Application to FUSION Data 271
3 Haplotype Analysis of Family-based Samples 273
3.1 Haplotype Approach of Horvath et al. 274
3.2 Haplotype Approach of Allen and Satten 276
3.3 Software Packages 279
4 Summary 280
Electronic-Database Information 281
References 282
Multiple Comparisons/Testing Issues 286
1 Introduction 286
2 Bonferroni Correction 287
3 False Discovery Rate 288
4 Randomization Testing 289
5 Single Experiment-Wise Test Statistic 291
6 Example Dataset: Parkinson Disease 292
7 Discussion 294
Web Resources 295
References 295
Estimating the Absolute Risk of Disease Associated with Identified Mutations 297
1 Introduction 297
2 Population-Based Cohort Studies 300
3 Case–Control Designs 302
4 Case–Control Family Study Design 303
5 Kin–Cohort Design 308
6 Discussion 312
References 312
Processing Large-Scale, High-Dimension Genetic and Gene Expression Data 314
1 Introduction 314
2 Data Management, Access and Workflow 316
3 Analysis Issues with High-Dimensional Data 318
3.1 Power 318
3.2 Data Trends and Unaccounted for Heterogeneity 320
3.3 Outliers and Transformations 320
4 Implementing a Standard First-Pass Analysis Pipeline 321
4.1 The Model – Common vs. Individual 321
4.2 Estimating Heritability 322
4.3 Ethnicity and Substructure 323
4.4 Multiplicity 323
5 High-Performance Computing 324
6 Further Recommendations for Efficiency Gainsin GOGE Studies 326
7 Constructing Gene Networks to Enhance GWASand GOGE Results 327
7.1 Constructing Weighted and UnweightedCo-Expression Networks 328
7.2 Using Genetics in Constructing Co-Expression Networks 329
7.3 Identifying Modules of Highly Interconnected Genes in Co-Expression Networks 329
8 Looking Toward the Future: Probabilistic Causal Networks 331
9 Summary 332
Web Resources 333
References 334
Index 338