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Statistical Genetics: Gene Mapping Through Linkage and Association

Edited By: Benjamin Neale, Manuel Ferreira, Sarah Medland and Danielle Posthuma

574 pages, no illustrations

Taylor & Francis

Paperback | Nov 2007 | #168097 | ISBN-13: 9780415410403
Availability: Usually dispatched within 6 days Details
NHBS Price: £52.99 $69/€60 approx

About this book

The mapping of the human genome means it is now feasible to conduct large genome-wide linkage and association studies for complex behaviours and diseases. This means that the focus of research is now to identify the genes responsible for such conditions. Identifying these genes will help readers to understand why some people are prone to mental and physical disease, improve early diagnosis, and eventually could lead to treatments.

Statistical Genetics is an advanced textbook focusing on conducting genetic linkage and association analysis in the post-genomic era. Starting with an introductory section on statistics and quantitative genetics, it covers both established and new methodologies, providing the genetic and statistical theory on which they are based. Each chapter is written by leading researchers, who give the reader the benefit of their experience with worked examples, study design, and sources of error.


1. Introduction 2. DNA 3. Introduction to Biometrical Genetics 4. Introduction to Statistics 5. Statistical Power 6. Population Genetics and its Relevance to Gene Mapping 7. Principles of Linkage Analysis 8. IBD Estimation 9. Regression Methods for Linkage Analysis 10. Variance Components Linkage Analysis for Quantitative Traits 11. Extensions to Univariate Linkage Analysis 12. QTL Detection in Multivariate Data from Sibling Pairs 13. Factors Affecting Type I Error and Power of Linkage Analysis 14. Introduction to Association 15. Single-locus Association Models 16. Genome-wide Association 17. Haplotype Estimation 18. Multi-locus Association Models 19. Linkage Disequilibrium and Tagging 20. Haploview 21. Factors Affecting Type I Error and Power in Association 22. Resampling Approaches to Statistical Interference

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