Book Review: A Statistical Approach to Genetic Epidemiology. By A. Ziegler and I. R. König
✍ Scribed by Ronja Foraita; Iris Pigeot
- Publisher
- John Wiley and Sons
- Year
- 2008
- Tongue
- English
- Weight
- 38 KB
- Volume
- 50
- Category
- Article
- ISSN
- 0323-3847
No coin nor oath required. For personal study only.
✦ Synopsis
Genetic epidemiology is an emerging field of research where different disciplines come together as there are, among others, genetics, epidemiology and last, but not least, statistics. It investigates the influence of genetic, environmental factors, and their interaction on the development of diseases in families and populations. It is not just epidemiology because genetic factors and family structures are taken into account, which also asks for special statistical methods being able to cope with such complex data structures. Lectures on genetic epidemiology have become a standard course for students of epidemiology and it is nowadays also possible to get a Master of Science in Genetic Epidemiology. Therefore, good textbooks on this topic are required that can be used by students and researchers in this field. This book provides a comprehensive coverage of the fundamental statistical approaches applied in genetic epidemiology. It is primarily useful as introductory textbook for graduate students and for statisticians, mathematicians, and geneticists, where those with expertise in statistics but minor knowledge of genetics or epidemiology will benefit most.
The book is subdivided into three parts each consisting of four chapters. The first part is devoted to the fundamentals of genetics. Chapter 1 is highly recommended to be read by beginners in the field of genetics. This chapter and Chapter 2 provide an excellent introduction into molecular genetics and formal genetics. Chapter 3 describes properties and types of genetic markers that are of special importance "for studying the genetic architecture of a disease". Chapter 4 discusses possible error sources and easy-to-apply algorithms to ensure and check for data quality. The second part deals with linkage analysis, which is based on the principle of recombination in course of inheritance and therefore relies on segregation information in families. For this purpose, measures of genetic distance have to be considered that are presented in Chapter 5 where the focus is on genetic mapping, radiation hybrid distances, and linkage disequilibrium units. Chapters 6 to 8 describe modelbased and model-free linkage analysis in detail. Besides further extensions of classical statistical approaches like the affected sib-pair analysis for dichotomous data or the Haseman-Elston method for quantitative traits, topics like choosing adequate significance levels in LOD-score (LOD ¼ logarithm of the odds) analyses, power and sample size calculations are addressed. The third part of this book treats association studies that focus on populations, i.e. on unrelated subjects with and without the disease of interest, and that do not necessarily require family data. Chapter 9 gives an overview of the principal concepts of genetic association and linkage disequilibrium. Chapter 10 discusses the statistical analysis of association studies with case-control and cohort designs with focus on case-control designs including sample size calculations. Special emphasis is on the problem of population stratification and on approaches for dealing with this type of bias. Chapter 11 treats the analysis of family-based association studies introducing among others the haplotype relative risk (HRR), the classical transmission disequilibrium test (TDT), risk estimates, sample size calculations, alternative test statistics and their extensions to short tandem repeat markers, different family structures, and quantitative traits. Chapter 12 focuses on haplotypes in association analyses: how haplotypes can be inferred, how tests for association should be built using haplotypes and how markers can be selected on the basis of haplotypes are examples for questions that are answered in this chapter. The appendix gives a detailed explanation of the Lander-Green algorithm and an outline of the Cardon-Fulker algorithm. We would like to note that scientists who expect to find more about statistical approaches for genome wide association or a more detailed discussion of populationbased association studies might be disappointed.
Although this book treats rather complex statistical approaches and therefore gives numerous formulae it is easy to read and the material can be well understood by working through the examples and problems that are provided at the end of each chapter and that can be easily solved with a pocket calculator, where the solutions can also be found at the end of the book. In addition, important formulae are derived step by step which supports a better understanding of the concepts. Of course, some basic knowledge in statistical testing and estimation is needed. Numerous illustrative figures, diagrams, and tables are provided to support further understanding of the presented approaches. Each chapter gives plentiful references and ends with useful URL references. A five-page errata list is available on the authors' web site.
In our opinion, it is a pleasure to read this valuable, concisely written and nicely illustrated textbook because of the numerous examples and illustrations, the close connection to the real world e.g., by real study examples,