Statistical Genetics: Gene Mapping Through Linkage and Association

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Statistical Genetics is an advanced textbook focusing on conducting genome-wide linkage and association analysis in order to identify the genes responsible for complex behaviors and diseases. 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.

The text can be used in conjunction with an associated website ( that provides supplementary material and links to downloadable software.

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Editorial Reviews

Doody's Review Service
Reviewer: Christopher J Graver, PhD, ABPP-CN(Madigan Healthcare System)
Description: Studies on the genetic contributions to human behavior and disease have been invaluable in sorting out hereditary components, biological versus social constructs, and the interactions between genes and other factors. This book provides a guide to methodologies and technologies available to maximize research in this field.
Purpose: Arising from the 2005 Workshops on Methodology for Twin and Family Studies, this book concatenates the use of structural equation modeling (SEM) in the areas of behavioral genetics and genetic epidemiology.
Audience: The book is aimed at researchers and graduate students in the field of behavioral genetics and related fields, such as public health, psychology, sociology, biology, and medicine. It is written at a fairly advanced level that assumes familiarity with genetics, SEM, and associated computer programs. The editors are a young group of promising scholars backed by a notable level of senior faculty contributing to the chapters.
Features: For readers new to this field, the book starts with an introduction to genetics and other concepts of importance, such as statistical power. Each chapter builds upon the last, so that novice readers would be well advised to read the book from beginning to end. More advanced readers will probably be able to skip ahead to chapters of specific interest. A short introduction to each chapter is enough to gauge whether further reading is of interest. Both linkage analysis and association models are covered in depth. There is a chapter specific to learning PLINK with sample Unix commands and binary files. For users of Haploview, there is a discussion of this program, screen shots, and specific tips for running linkage disequilibrium analysis and tagging. The two chapters on Power and Type I Error in linkage and association studies are a nice addition. Plenty of figures and tables supplement the text. The references are well researched and current, but the index is somewhat abbreviated for a book of this size.
Assessment: This is a commendable account of statistical genetics, particularly in relation to linkage analysis and association models. While the subject matter is rather specific and presented at an intermediate to advanced level, students and researchers in this field will find it abundantly useful.
From the Publisher
"…well organized and is written and edited by enthusiastic experts. This will be a great starting place for anyone who wants to understand and, hopefully, get involved with research on the genetics of human complex traits." Twin Research and Human Genetics
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Product Details

  • ISBN-13: 9780415410403
  • Publisher: Taylor & Francis
  • Publication date: 1/29/2008
  • Edition description: New Edition
  • Pages: 608
  • Sales rank: 1,226,452
  • Product dimensions: 6.80 (w) x 9.60 (h) x 1.40 (d)

Table of Contents

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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