Mendelian Randomization: Methods For Causal Inference Using Genetic Variants provides thorough coverage of the methods and practical elements of Mendelian randomization analysis. It brings together diverse aspects of Mendelian randomization from the fields of epidemiology, statistics, genetics, and bioinformatics.
Through multiple examples, the first part of the book introduces the reader to the concept of Mendelian randomization, showing how to perform simple Mendelian randomization investigations and interpret the results. The second part of the book addresses specific methodological issues relevant to the practice of Mendelian randomization, including robust methods, weak instruments, multivariable methods, and power calculations. The authors present the theoretical aspects of these issues in an easy-to-understand way by using non-technical language. The last part of the book examines the potential for Mendelian randomization in the future, exploring both methodological and applied developments.
- Offers first-hand, in-depth guidance on Mendelian randomization from leaders in the field
- Makes the diverse aspects of Mendelian randomization understandable to newcomers
- Illustrates technical details using data from applied analyses
- Discusses possible future directions for research involving Mendelian randomization
- Software code is provided in the relevant chapters and is also available at the supplementary website
This book gives epidemiologists, statisticians, geneticists, and bioinformaticians the foundation to understand how to use genetic variants as instrumental variables in observational data.
New in Second Edition: The second edition of the book has been substantially re-written to reduce the amount of technical content, and emphasize practical consequences of theoretical issues. Extensive material on the use of two-sample Mendelian randomization and publicly-available summarized data has been added. The book now includes several real-world examples that show how Mendelian randomization can be used to address questions of disease aetiology, target validation, and drug development
Table of Contents
I Understanding and Performing Mendelian Randomization
1. Introduction and Motivation
2. What is Mendelian Randomization?
3. Assumptions for Causal Inference
4. Estimating a Causal Effect from Individual-level Data
5. Estimating a Causal Effect from Summarized Data
6. Interpretation of Estimates from Mendelian Randomization
II Advanced Methods for Mendelian Randomization
7. Robust Methods using Variants from Multiple Gene Regions
8. Other Statistical Issues for Mendelian Randomization
9. Extensions to Mendelian Randomization
10. How to Perform a Mendelian Randomization Investigation
III Prospects for Mendelian Randomization
11. Future Directions
Dr Stephen Burgess is an MRC Investigator at the MRC Biostatistics Unit in Cambridge, an internationally acclaimed research institute in medical statistics. He holds a Wellcome Trust Sir Henry Dale Fellowship, and leads a research group which aims to develop statistical methods that use genetic variation to answer clinically relevant questions about disease aetiology and prevention. He was previously located at the Cardiovascular Epidemiology Unit in the University of Cambridge, where he held a Sir Henry Wellcome Postdoctoral Fellowship. His main research interests are in causal inference and evidence synthesis.
Professor Simon Thompson was Director of Research in Biostatistics at the Cardiovascular Epidemiology Unit in the University of Cambridge until his retirement in 2018. He is a Fellow of the Academy of Medical Sciences. From 2000-2011, he was Director of the MRC Biostatistics Unit in Cambridge. He held previous academic appointments at the London School of Hygiene and Tropical Medicine, and as the first Professor of Medical Statistics and Epidemiology at Imperial College London. In retirement, he has cut down his research activities substantially, and is not getting involved in new research projects.
Praise for the First Edition
"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