Foreword

The field of genetic epidemiology is barely 50 years old and, concomitant with the spectacular technological advances in molecular biology that have taken place over the past 25 years, there have been many advances in statistical methodology developed for the analysis of data specific to this field. Although there is no dearth of textbooks on either statistical methods or epidemiology in general, very little is available for the person who already has a basic statistical background but is new to the special needs of genetic epidemiology. It is therefore with great pleasure that I write a foreword to this new text, written by two expert statisticians who have gathered together the important concepts and methods into a comprehensive introductory textbook for upper-class undergraduate or graduate students. The next decade will see an enormous need for persons to analyze the massive amounts of genetic information that will be produced as a result of the Human Genome Project and the HapMap Project and I see this new text, complete with examples, homework questions and copious references, as filling a need in the training of such persons.

ROBERT C. ELSTON

Cleveland, OH, U.S.A., December 2005

Preface

This book presents a statistical approach to genetic epidemiology. But what do we mean by genetic epidemiology? Following the definition of Khoury, Beaty and Cohen [239], it is the discipline investigating genetic and environmental factors that influence the development and distribution of diseases. It differs from epidemiology in that explicitly genetic factors and similarities within families are taken into account. On the other hand, it can be distinguished from medical genetics by considering populations rather than single patients or families.

As the name implies, genetic epidemiology is an interdisciplinary subject, and it is a working field for scientists from different backgrounds. In contrast to many outstanding textbooks on molecular genetic techniques, such as Human Molecular Genetics 3 by Strachan and Read [414], this book focuses on introducing statistical concepts to current approaches used in genetic epidemiology. It is written at a level that it should make it useful to undergraduate and graduate students as well as researchers. The necessary background in statistics is an introductory course to statistical testing and estimation. Excellent books allowing one to revive this knowledge include, e.g., Ref. [323]; for a little knowledge about likelihood ratio, score, and Wald tests, see, e.g., Hills [208], Section "Hypothesis Testing" or Kleinbaum [242], pp. 128-136.

Just like Gaul at Cesar's time [67], this volume is divided into three different parts. Each part comprises four chapters. At the end of each chapter, the reader will find a number of problems covering both theoretical derivations and practical calculations. They can be solved by paper and pencil and require only the help of a pocket calculator. However, we refer to the software that could be utilized for computation in the text and list the relevant URLs at the end of each chapter. The solutions to all problems are detailed at the end of the book. Throughout the book, new, relevant terminology is indicated in italics where first introduced.

The material in this text assumes no background in biology, molecular biology or genetics. The required fundamentals of these topics are described in the first part of this book, covering Chapters 1 through 4. Chapter 1 provides an introduction to the basic molecular genetical mechanisms that are required as a background for understanding the statistical methods in the later chapters. However, for comprehensive overviews on human molecular genetics the reader is referred to standard textbooks [179, 414]. Mendel's laws and their consequences for familial inheritance patterns as well as the important population genetic Hardy-Weinberg law are discussed in Chapter 2 on formal genetics. Genetic variability is the key to studying the genetic architecture of disease, and it can be measured by genetic markers, which are the topic of Chapter 3. Many different types of molecular genetic markers exist and are still being developed, and we therefore sketch only currently used genetic marker systems and technologies. Before we introduce the techniques for statistically analyzing genetic disorders, Chapter 4 considers data quality control in genetic epidemiological studies.

The second part of this book deals with linkage analysis and starts with a discussion of measures for genetic distance in Chapter 5. Model-based linkage analysis is the topic of Chapter 6, while model-free linkage approaches to dichotomous and quantitative traits are considered in Chapters 7 and 8, respectively. An in-depth understanding of the linkage methods relies on some standard algorithms enabling one to deduce genetic marker information. Because these are quite technical, two of them are described but have been placed in the Appendix.

While linkage analysis relies on segregation information in families, association studies focus on differences at the population level. They are the subject of the final part of this book, starting with Chapter 9, where the fundamental concepts of association analyses are discussed. The analysis of association itself using single markers is discussed in Chapters 10 and 11. While Chapter 10 deals with studies utilizing data from unrelated individuals, Chapter 11 is concerned with family-based association studies. The concluding chapter is devoted to the rapidly evolving topic of studying haplotypes. In this last chapter, we consider single nucleotide polymorphisms (SNPs) as markers and data from unrelated individuals due to their widespread use in applications.

ANDREAS ZIEGLER AND INKE R. KÖNIG

Lübeck, Germany