I Understanding and Performing Mendelian
Randomization1. 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
Randomization7. 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 Randomization11. 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" . . . the book is well written, in clear
language and has many real data analysis examples. This book is an
excellent introduction for those who want to learn more about
mendelian randomization and a great guide for those who want to
perform analysis, including methodological researchers who work in
the area. I highly recommend the book to anyone interested in
learning more about mendelian randomization."
_Patrícia Soares, ISCB Book Reviews, September 2022"With the rapid
advancement in human genetics and the availability of extensive
genetic data, this book presents essential concepts for utilizing
genetic data to achieve randomization using observational study
data. This new edition was needed to address the use of summary
statistics data and two-sample Mendelian randomization, which are
becoming increasingly popular."— Azeez A Alade, BDS, MS, University
of Iowa College of Public Health
"In this regard, Mendelian Randomization by Stephen Burgess and
Simon G. Thompson (2021, second edition) is a must-read for anyone
interested in MR. This book provides a broad introduction to the
field, covering the basics as well as advanced topics in MR
methodology, making it an ideal textbook for students and an
essential reference for researchers new to the field. In addition
to its introductory value, advanced readers can also benefit from
this book, as it reviews the state-of-the-art MR methods and lays
out the potential future developments of MR."
~Biometrics, Chia-Yen Chen, Biogen
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