Mendelian Randomization: Methods for Using Genetic Variants in Causal Estimation (Chapman & Hall/CRC Interdisciplinary Statistics)

Presents the Terminology and Methods of Mendelian Randomization for Epidemiological Studies

Mendelian randomization uses genetic instrumental variables to make inferences about causal effects based on observational data. It, therefore, can be a reliable way of assessing the causal nature of risk factors, such as biomarkers, for a wide range of disease outcomes.

Mendelian Randomization: Methods for Using Genetic Variants in Causal Estimation provides thorough coverage of the methods and practical elements of Mendelian randomization analysis. It brings together diverse aspects of Mendelian randomization spanning epidemiology, statistics, genetics, and econometrics. Although the book mainly focuses on epidemiology, much of the material can be applied to other areas of research.

Through several examples, the first part of the book shows how to perform simple applied Mendelian randomization analyses and interpret their results. The second part addresses specific methodological issues, such as weak instruments, multiple instruments, power calculations, and meta-analysis, relevant to practical applications of Mendelian randomization. In this part, the authors draw on data from the C-reactive protein Coronary heart disease Genetics Collaboration (CCGC) to illustrate the analyses. They present the mathematics in an easy-to-understand way by using nontechnical language and reinforcing key points at the end of each chapter. The last part of the book examines the potential of Mendelian randomization in the future, exploring both methodological and applied developments.

This book gives statisticians, epidemiologists, and geneticists the foundation to understand issues concerning the use of genetic variants as instrumental variables. It will get them up to speed in undertaking and interpreting Mendelian randomization analyses. Chapter summaries, paper summaries, web-based applications, and software code for implementing the statistical techniques are available on a supplementary website.

"Mendelian randomization (MR) may be regarded as a modern integration of the concepts of instrumental variables from the field of economics with Mendel’s laws of inheritance in the field of genetics. ... Stephen Burgess, a mathematician, and Simon Thompson, a biostatistician, present the fundamental ideas and methods behind MR in an easy-to-read book for researchers from different backgrounds such as statistics, genetics, or clinical research. The book is the first and currently only book published on MR, in which the authors not only summarize MR methodology but also share their experience with real data analysis in the field of MR for cardiovascular diseases. ... The book has been very well written, and the authors have done an excellent job of bridging the gap between researchers from various backgrounds. ... Overall, the book provides the most comprehensive MR resource to date, and it is a valuable resource for researchers even at the graduate level."
―Sandeep Grover, Universität zu Lübeck, in Biometrical Journal, September 2017

"... The topic of the book is Mendelian Randomization (MR), a form of instrument variable (IV) analysis using genetic factors as instruments. ... I enjoyed reading the book very much. The authors give an excellent overview of the assumptions and statistics underlying MR and the book achieves a good balance between methods and theory, case studies and the discussion of practical issues. ... I particularly liked the fact that most if not all of the relevant models, methods and analytical derivations on IV analyses are in a single book. The authors are very precise in their definitions and discuss the issue of when ‘causal’ can be used in the context of inference and, importantly, when not. ... In summary, MR analyses have an important role to play in making sense of epidemiological observations and we can expect a plethora of applications in the near future. This book is a thorough practical guide to their assumptions, inference and pitfalls."
―Peter M. Visscher, University of Queensland, in Australian & New Zealand Journal of Statistics, June 2017

"The authors have aimed their book at epidemiologists and medical statisticians but anyone with a basic knowledge of regression will understand most of the contents, because the algebra is kept to a minimum and emphasis is on explaining the ideas that underlie MR...will serve as an excellent introduction to Mendelian randomization for anyone who wants to understand the underlying statistical issues..."
―John Thompson, University of Leicester, in Biometrics, March 2017

"Mendelian Randomization, by Stephen Burgess and Simon Thompson, represents a compact and accessible resource for Mendelian randomization, providing exactly what one needs to know in a logical, clear, very thorough, and yet pragmatic way. This book will appeal to applied researchers interested in learning more about Mendelian randomization as well as methodological researchers who work in the area. Those new to the field will find that this book covers everything they need to know, from designing the study (e.g., choosing the "instruments" and considering different options for pooling data across multiple studies) to investigating whether the assumptions may hold, analyzing the data, and interpreting the results... Researchers interested in methods for Mendelian randomization will find this book equally useful in bringing together methodological findings published across different disciplines (genetics, epidemiology, statistics, and econometrics), in an articulate and comprehensive way... Being targeted to epidemiologists and medical statisticians with diverse backgrounds, no prior knowledge of genetics is required, and the book explains in very simple terms the basic concepts needed to understand and apply Mendelian randomization... I really enjoyed reading this book and highly recommend it to anyone with an interest in Mendelian randomization."
―Cosetta Minelli, Imperial College London, in The American Statistician, August 2016