This burgeoning science merges the principles of statistics and genetics studies to make sense of the vast amounts of information available with the mapping of genomes. In order to make the most of the information available, statistical tools must be tailored and translated for the analytical issues which are original to large-scale association studies. This book will provide researchers with advanced biological knowledge who are entering the field of genome-wide association studies with the groundwork to apply statistical analysis tools appropriately and effectively. With the use of consistent examples throughout the work, chapters will provide readers with best practice for getting started (design), analyzing, and interpreting data according to their research interests. Frequently used tests will be highlighted and a critical analysis of the advantages and disadvantage complimented by case studies for each will provide readers with the information they need to make the right choice for their research. Additional tools including links to analysis tools, tutorials, and references will be available electronically to ensure the latest information is available.
* Easy access to key information including advantages and disadvantage of tests for particular applications, identification of databases, languages and their capabilities, data management risks, frequently used tests
* Extensive list of references including links to tutorial websites
* Case studies and Tips and Tricks
According to the National Institute of Health, a genome-wide association study is defined as any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition. Whole genome information, when combined with clinical and other phenotype data, offers the potential for increased understanding of basic biological processes affecting human health, improvement in the prediction of disease and patient care, and ultimately the realization of the promise of personalized medicine. In addition, rapid advances in understanding the patterns of human genetic variation and maturing high-throughput, cost-effective methods for genotyping are providing powerful research tools for identifying genetic variants that contribute to health and disease. This burgeoning science merges the principles of statistics and genetics studies to make sense of the vast amounts of information available with the mapping of genomes. In order to make the most of the information available, statistical tools must be tailored and translated for the analytical issues which are original to large-scale association studies. Analysis of Complex Disease Association Studies will provide researchers with advanced biological knowledge who are entering the field of genome-wide association studies with the groundwork to apply statistical analysis tools appropriately and effectively. With the use of consistent examples throughout the work, chapters will provide readers with best practice for getting started (design), analyzing, and interpreting data according to their research interests. Frequently used tests will be highlighted and a critical analysis of the advantages and disadvantage complimented by case studies for each will provide readers with the information they need to make the right choice for their research. Additional tools including links to analysis tools, tutorials, and references will be available electronically to ensure the latest information is available. Easy access to key information including advantages and disadvantage of tests for particular applications, identification of databases, languages and their capabilities, data management risks, frequently used tests Extensive list of references including links to tutorial websites Case studies and Tips and Tricks
Front Cover 1
Analysis of Complex Disease Association Studies 4
Copyright 5
Table of Contents 6
List of Contributors 8
Chapter 1 Genetic Architecture ofComplex Diseases 10
Introduction 10
Genetic Modeling: Twin, Adoption andFamily Studies 11
Disease Gene Mapping: Linkage Studies 14
Disease Gene Mapping: Association Studies 16
Conclusion 19
References 20
Chapter 2 Population Genetics and Linkage Disequilibrium 24
The Origin and Structure of Variation inThe Human Genome 24
Pair-Wise Measurement of LD 26
Predicted and Observed Patterns ofRecombination 28
The International HapMap Project 30
Conclusion 31
References 32
Chapter 3 Genetic Association Study Design 34
Concepts and Scope of Association Studies 35
Population-Based Study Designs 39
Conclusions 52
References 53
Chapter 4 Tag SNP Selection 58
Introduction 58
Approaches 60
Tools 69
Genotyping Platforms 72
References 74
Chapter 5 Genotype Calling 78
Bias and Error in Genotype Calling 78
Genotyping Platforms 79
Normalization Algorithms 80
Genotype Calling From a Single Array 83
Genotype Calling of Multiple Arrays Simultaneously 86
Other Genotype Calling Algorithms 91
References 94
Chapter 6 Data Handling 96
References 103
Chapter 7 Data Quality Control 104
Introduction 105
Sample-based QC 107
Marker-based QC 113
Family-based Studies 115
Post-analysis QC 115
Summary 116
References 116
Chapter 8 Single-locus Tests of Association for Population-based Studies 118
Introduction 119
Genetic Models 121
Covariates 122
Genome - wide Associations Studies and General Interpretation 126
Quantitative Traits 128
Conclusion 130
References 130
Chapter 9 Effects of Population Structure in Genome-wide Association Studies 132
Introduction 133
Genetic Structure of Populations 134
Effects of Population Structure on Standard Tests for Association 143
Analysis of Structured Populations 154
LINKS 163
References 164
Chapter 10 Genotype Imputation 166
Uses of Imputation 168
Genotype Imputation Methods 170
SNP Tagging-Based Approaches 171
Hidden Markov Model-Based Approaches 171
Perspectives and Future Directions 180
References 181
Chapter 11 Haplotype Methods for Population-based Association Studies 186
Haplotype Reconstruction in Population-Based Association Studies 188
Population-Based Haplotype Association Analysis 197
Summary 201
References 202
Chapter 12 Gene-Gene Interaction and Epistasis 206
Introduction 207
What is ``Epistasis''? 207
``Biological'' Epistasis 208
Statistical Epistasis 208
Two-Locus Quantitative Trait Models Incorporating Epistatic Interactions 210
Test for Association Incorporating Interactions 212
Two-Locus Binary Models Incorporating Interaction 215
Why Model Epistasis? 215
Strategies for Detecting Epistasis in Genome-WideAssociation Studies 216
Two-Stage Strategies to Detect Epistasis 218
Other Simple Tests for Gene-Gene Interaction 218
Higher-Order Interactions 219
More Sophisticated Approaches to Modeling and Detecting Interactions 220
Conclusions 220
Reference 220
Chapter 13 Copy Number Variant Association Studies 224
Introduction 224
The Value of CNV Association Studies 226
Differences Between SNP and CNV Association Studies 227
Normalization of CNV Intensity Data 229
Normalization of SNP Genotyping Data forCNV Studies 230
Effect of SNPS on CNV Probes 231
Association Tests and Software for CNV Data 232
Differential Bias and The Advantage ofUsing Trio Data 233
Summarizing Signal Across Multiple Probes 234
References 238
Chapter 14 Family-based Association Methods 240
FBATs 241
Genetic Data 246
Phenotypes 249
Multivariate Phenotypes and Genotypes 252
Testing Strategies for Large-Scale Association Studies 252
Gene-Environment Interaction 255
Software 255
Discussion 255
References 257
Chapter 15 Bioinformatics Approaches 260
Prioritizing Association Signals forFollow-Up 260
Using Linkage Disequilibrium (LD) to Define Regions SurroundingSignals 261
Sources of Bioinformation 262
Tools for Annotating Bioinformation 262
References 268
Chapter 16 Interpreting Association Signals 270
Introduction 271
The Importance and Definition ofReplication 271
Power of Replication Studies 274
Guidelines for Reporting Association Results 279
Follow-Up of Associations 280
Conclusions 283
References 284
Chapter 17 Delineating Signals from Association Studies 286
Introduction 287
Locus Fine-Mapping: Design and a Case Study 291
Delineating Association Signals in the Future 295
Concluding Remarks 299
World Wide Web Url Links 299
References 299
Chapter 18 A Genome-wide Association Case Study on Obesity 304
References 312
Chapter 19 Case Study on Rheumatoid Arthritis 316
Candidate Gene on Studies 317
Genome-Wide Association Studies 319
Future Studies 327
Overall Conclusion 328
References 328
Index 334
Color Plates 344
Erscheint lt. Verlag | 17.11.2010 |
---|---|
Sprache | englisch |
Themenwelt | Sachbuch/Ratgeber ► Gesundheit / Leben / Psychologie |
Informatik ► Weitere Themen ► Bioinformatik | |
Studium ► 2. Studienabschnitt (Klinik) ► Humangenetik | |
Studium ► Querschnittsbereiche ► Prävention / Gesundheitsförderung | |
Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie | |
Technik | |
ISBN-10 | 0-12-375143-8 / 0123751438 |
ISBN-13 | 978-0-12-375143-0 / 9780123751430 |
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