Zeggini / Morris Analysis of Complex Disease Association Studies

A Practical Guide
1. Auflage 2010
ISBN: 978-0-12-375143-0
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 340 Seiten

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

Autoren/Hrsg.

Zeggini, Eleftheria

Morris, Andrew

Inhaltsverzeichnis

1;Front Cover;1
2;Analysis of Complex Disease Association Studies;4
3;Copyright;5
4;Table of Contents;6
5;List of Contributors;8
6;Chapter 1 Genetic Architecture ofComplex Diseases;10
6.1;Introduction;10
6.2;Genetic Modeling: Twin, Adoption andFamily Studies;11
6.3;Disease Gene Mapping: Linkage Studies;14
6.4;Disease Gene Mapping: Association Studies;16
6.5;Conclusion;19
6.6;References;20
7;Chapter 2 Population Genetics and Linkage Disequilibrium;24
7.1;The Origin and Structure of Variation inThe Human Genome;24
7.2;Pair-Wise Measurement of LD;26
7.3;Predicted and Observed Patterns ofRecombination;28
7.4;The International HapMap Project;30
7.5;Conclusion;31
7.6;References;32
8;Chapter 3 Genetic Association Study Design;34
8.1;Concepts and Scope of Association Studies;35
8.2;Population-Based Study Designs;39
8.3;Conclusions;52
8.4;References;53
9;Chapter 4 Tag SNP Selection;58
9.1;Introduction;58
9.2;Approaches;60
9.3;Tools;69
9.4;Genotyping Platforms;72
9.5;References;74
10;Chapter 5 Genotype Calling;78
10.1;Bias and Error in Genotype Calling;78
10.2;Genotyping Platforms;79
10.3;Normalization Algorithms;80
10.4;Genotype Calling From a Single Array;83
10.5;Genotype Calling of Multiple Arrays Simultaneously;86
10.6;Other Genotype Calling Algorithms;91
10.7;References;94
11;Chapter 6 Data Handling;96
11.1;References;103
12;Chapter 7 Data Quality Control;104
12.1;Introduction;105
12.2;Sample-based QC;107
12.3;Marker-based QC;113
12.4;Family-based Studies;115
12.5;Post-analysis QC;115
12.6;Summary;116
12.7;References;116
13;Chapter 8 Single-locus Tests of Association for Population-based Studies;118
13.1;Introduction;119
13.2;Genetic Models;121
13.3;Covariates;122
13.4;Genome - wide Associations Studies and General Interpretation;126
13.5;Quantitative Traits;128
13.6;Conclusion;130
13.7;References;130
14;Chapter 9 Effects of Population Structure in Genome-wide Association Studies;132
14.1;Introduction;133
14.2;Genetic Structure of Populations;134
14.3;Effects of Population Structure on Standard Tests for Association;143
14.4;Analysis of Structured Populations;154
14.5;LINKS;163
14.6;References;164
15;Chapter 10 Genotype Imputation;166
15.1;Uses of Imputation;168
15.2;Genotype Imputation Methods;170
15.3;SNP Tagging-Based Approaches;171
15.4;Hidden Markov Model-Based Approaches;171
15.5;Perspectives and Future Directions;180
15.6;References;181
16;Chapter 11 Haplotype Methods for Population-based Association Studies;186
16.1;Haplotype Reconstruction in Population-Based Association Studies;188
16.2;Population-Based Haplotype Association Analysis;197
16.3;Summary;201
16.4;References;202
17;Chapter 12 Gene-Gene Interaction and Epistasis;206
17.1;Introduction;207
17.2;What is ``Epistasis''?;207
17.3;``Biological'' Epistasis;208
17.4;Statistical Epistasis;208
17.5;Two-Locus Quantitative Trait Models Incorporating Epistatic Interactions;210
17.6;Test for Association Incorporating Interactions;212
17.7;Two-Locus Binary Models Incorporating Interaction;215
17.8;Why Model Epistasis?;215
17.9;Strategies for Detecting Epistasis in Genome-WideAssociation Studies;216
17.10;Two-Stage Strategies to Detect Epistasis;218
17.11;Other Simple Tests for Gene-Gene Interaction;218
17.12;Higher-Order Interactions;219
17.13;More Sophisticated Approaches to Modeling and Detecting Interactions;220
17.14;Conclusions;220
17.15;Reference;220
18;Chapter 13 Copy Number Variant Association Studies;224
18.1;Introduction;224
18.2;The Value of CNV Association Studies;226
18.3;Differences Between SNP and CNV Association Studies;227
18.4;Normalization of CNV Intensity Data;229
18.5;Normalization of SNP Genotyping Data forCNV Studies;230
18.6;Effect of SNPS on CNV Probes;231
18.7;Association Tests and Software for CNV Data;232
18.8;Differential Bias and The Advantage ofUsing Trio Data;233
18.9;Summarizing Signal Across Multiple Probes;234
18.10;References;238
19;Chapter 14 Family-based Association Methods;240
19.1;FBATs;241
19.2;Genetic Data;246
19.3;Phenotypes;249
19.4;Multivariate Phenotypes and Genotypes;252
19.5;Testing Strategies for Large-Scale Association Studies;252
19.6;Gene-Environment Interaction;255
19.7;Software;255
19.8;Discussion;255
19.9;References;257
20;Chapter 15 Bioinformatics Approaches;260
20.1;Prioritizing Association Signals forFollow-Up;260
20.2;Using Linkage Disequilibrium (LD) to Define Regions SurroundingSignals;261
20.3;Sources of Bioinformation;262
20.4;Tools for Annotating Bioinformation;262
20.5;References;268
21;Chapter 16 Interpreting Association Signals;270
21.1;Introduction;271
21.2;The Importance and Definition ofReplication;271
21.3;Power of Replication Studies;274
21.4;Guidelines for Reporting Association Results;279
21.5;Follow-Up of Associations;280
21.6;Conclusions;283
21.7;References;284
22;Chapter 17 Delineating Signals from Association Studies;286
22.1;Introduction;287
22.2;Locus Fine-Mapping: Design and a Case Study;291
22.3;Delineating Association Signals in the Future;295
22.4;Concluding Remarks;299
22.5;World Wide Web Url Links;299
22.6;References;299
23;Chapter 18 A Genome-wide Association Case Study on Obesity;304
23.1;References;312
24;Chapter 19 Case Study on Rheumatoid Arthritis;316
24.1;Candidate Gene on Studies;317
24.2;Genome-Wide Association Studies;319
24.3;Future Studies;327
24.4;Overall Conclusion;328
24.5;References;328
25;Index;334
26;Color Plates;344