Mendelian Randomization Methods for Causal Inference Using Genetic Variants

240 Pages·2021·12.803 MB·English

by  Stephen Burgess, Simon G. Thompson

Mendelian
Randomization
Methods for Causal Inference
Using Genetic Variants
CHAPMAN & HALL/CRC Interdisciplinary Statistics Series
Series editors: B.J.T. Morgan, C.K. Wikle, P.G.M. van der Heijden
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Mendelian Randomization: Methods for Causal Inference Using Genetic Variants,
Second Edition
S. Burgess, S. G. Thompson
For more information about this series, please visit: https://www.crcpress.com/
Chapman--HallCRC-Interdisciplinary-Statistics/book-series/CHINTSTASER
Mendelian
Randomization
Methods for Causal Inference
Using Genetic Variants
Second Edition
Stephen Burgess
Simon G. Thompson
First edition published 2015
by CRC Press
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and by CRC Press
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Contents
Preface to the second edition ix
Abbreviations xi
Notation xiii
I Understanding and performing Mendelian
randomization 1
1 Introduction and motivation 3
1.1 Shortcomings of classical epidemiology . . . . . . . . . . . . 3
1.2 The rise of genetic epidemiology . . . . . . . . . . . . . . . 5
1.3 Motivating example: The inflammation hypothesis . . . . . 6
1.4 Other examples of Mendelian randomization . . . . . . . . 9
1.5 Overview of book . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 What is Mendelian randomization? 13
2.1 What is Mendelian randomization? . . . . . . . . . . . . . . 13
2.2 Why use Mendelian randomization? . . . . . . . . . . . . . 19
2.3 A brief overview of genetics . . . . . . . . . . . . . . . . . . 20
2.4 Classification of Mendelian randomization investigations . . 24
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3 Assumptions for causal inference 27
3.1 Observational and causal relationships . . . . . . . . . . . . 27
3.2 Finding a valid instrumental variable . . . . . . . . . . . . . 30
3.3 Testing for a causal relationship . . . . . . . . . . . . . . . . 42
3.4 Example: Lp-PLA and coronary heart disease . . . . . . . 43
2
3.5 Estimating a causal effect . . . . . . . . . . . . . . . . . . . 45
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4 Estimating a causal effect from individual-level data 51
4.1 Ratio of coefficients method . . . . . . . . . . . . . . . . . . 51
4.2 Two-stage methods . . . . . . . . . . . . . . . . . . . . . . . 61
4.3 Example: Body mass index and smoking intensity . . . . . 63
4.4 Computer implementation . . . . . . . . . . . . . . . . . . . 64
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
v
vi Contents
5 Estimating a causal effect from summarized data 67
5.1 Motivating example: interleukin-1 and cardiovascular diseases 67
5.2 Inverse-variance weighted method . . . . . . . . . . . . . . . 69
5.3 Heterogeneity and pleiotropy . . . . . . . . . . . . . . . . . 74
5.4 Computer implementation . . . . . . . . . . . . . . . . . . . 78
5.5 Example: Body mass index and smoking intensity reprised . 80
5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6 Interpretation of estimates from Mendelian randomization 83
6.1 Internal and external validity . . . . . . . . . . . . . . . . . 83
6.2 Comparison of estimates . . . . . . . . . . . . . . . . . . . . 86
6.3 Example: Lipoprotein(a) and coronary heart disease . . . . 89
6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.5 Recap of examples considered so far . . . . . . . . . . . . . 95
6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
II Advanced methods for Mendelian randomization 99
7 Robust methods using variants from multiple gene regions 101
7.1 Motivatingexample:LDL-andHDL-cholesterolandcoronary
heart disease . . . . . . . . . . . . . . . . . . . . . . . . . . 102
7.2 Consensus methods . . . . . . . . . . . . . . . . . . . . . . . 104
7.3 Outlier-robust methods . . . . . . . . . . . . . . . . . . . . 108
7.4 Modelling methods . . . . . . . . . . . . . . . . . . . . . . . 110
7.5 Other methods and comparison . . . . . . . . . . . . . . . . 116
7.6 Example: LDL- and HDL-cholesterol and coronary heart
disease reprised . . . . . . . . . . . . . . . . . . . . . . . . . 117
7.7 Computer implementation . . . . . . . . . . . . . . . . . . . 119
7.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
8 Other statistical issues for Mendelian randomization 121
8.1 Weak instrument bias . . . . . . . . . . . . . . . . . . . . . 121
8.2 Allele scores . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
8.3 Sample overlap . . . . . . . . . . . . . . . . . . . . . . . . . 132
8.4 Winner’s curse . . . . . . . . . . . . . . . . . . . . . . . . . 133
8.5 Selection and collider bias . . . . . . . . . . . . . . . . . . . 134
8.6 Covariate adjustment . . . . . . . . . . . . . . . . . . . . . 137
8.7 Non-collapsibility . . . . . . . . . . . . . . . . . . . . . . . . 138
8.8 Time and time-varying effects . . . . . . . . . . . . . . . . . 139
8.9 Power to detect a causal effect . . . . . . . . . . . . . . . . 141
8.10 Choosing variants from a single gene region . . . . . . . . . 142
8.11 Binary exposure . . . . . . . . . . . . . . . . . . . . . . . . 143
8.12 Alternative estimation methods . . . . . . . . . . . . . . . . 145
8.13 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
Contents vii
9 Extensions to Mendelian randomization 149
9.1 Multivariable Mendelian randomization . . . . . . . . . . . 149
9.2 Network Mendelian randomization . . . . . . . . . . . . . . 154
9.3 Non-linear Mendelian randomization . . . . . . . . . . . . . 155
9.4 Factorial Mendelian randomization . . . . . . . . . . . . . . 160
9.5 Bidirectional Mendelian randomization . . . . . . . . . . . . 164
9.6 Mendelian randomization and meta-analysis . . . . . . . . . 165
9.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
10 How to perform a Mendelian randomization investigation 167
10.1 Motivation and scope . . . . . . . . . . . . . . . . . . . . . 170
10.2 Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . 171
10.3 Selection of genetic variants . . . . . . . . . . . . . . . . . . 172
10.4 Variant harmonization . . . . . . . . . . . . . . . . . . . . . 175
10.5 Primary analysis . . . . . . . . . . . . . . . . . . . . . . . . 176
10.6 Robust methods for sensitivity analysis . . . . . . . . . . . 177
10.7 Other approaches for sensitivity analysis . . . . . . . . . . . 178
10.8 Data presentation . . . . . . . . . . . . . . . . . . . . . . . 180
10.9 Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . 182
10.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
III Prospects for Mendelian randomization 185
11 Future directions 187
11.1 GWAS: large numbers of genetic variants . . . . . . . . . . 187
11.2 -omics: Large numbers of risk factors . . . . . . . . . . . . . 189
11.3 Hypothesis-free: Large numbers of outcomes . . . . . . . . . 190
11.4 Biobanks: Large numbers of participants . . . . . . . . . . . 190
11.5 Clever designs: The role of epidemiologists . . . . . . . . . . 191
11.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
Bibliography 193
Index 223

Preface to the second edition
The volume of research into the genetics of common diseases has exploded
overthelast25years.Whilemanygeneticvariantsrelatedtovariousdiseases
have been identified, their usefulness may lie more in what they offer to our
understanding of the biological mechanisms leading to disease rather than
to, for example, predicting disease risk. To understand mechanisms, we need
to separate the relationships of risk factors with diseases into those that are
causal and those that are not. This is where Mendelian randomization can
play an important role.
The technique of Mendelian randomization itself has undergone rapid
development, mostly in the last 15 years, and applications now abound in
medicalandepidemiologicaljournals.Itsbasisisthatofinstrumentalvariable
analysis, which has a much longer history in statistics and particularly in
econometrics. Relevant papers on Mendelian randomization are therefore
dispersed across the multiple fields of genetics, epidemiology, statistics and
econometrics – and increasingly bioinformatics. The intention of this book is
tobringtogetherthisliteratureonthemethodsandpracticalitiesofMendelian
randomization, especially to help those who are relatively new to this area.
In writing this book, we envisage the target audience comprising three
main groups, Epidemiologists, Bioinformaticians, and Medical Statisticians,
whowanttoperformappliedMendelianrandomizationanalysesorunderstand
how to interpret their results. We therefore assume a familiarity with basic
epidemiological terminology, such as prospective and case-control studies,
and basic statistical methods, such as linear and logistic regression. While
Mendelian randomization methods can be applied in a diverse range of areas,
we concentrate on applications in epidemiology, and hope that researchers in
other areas will still find the content relevant.
While we have tried to ensure that this book will be accessible to a wide
audience, a geneticist may baulk at the simplistic explanations of Mendelian
inheritance,astatisticianmayyearnforadeeperleveloftechnicalexposition,
and an epidemiologist may wonder why we don’t just cut to the chase of how
to perform the analyses. Our aim is that enough detail is given for those who
need it, references are available for those who want more, and a section can
simply be glossed over by those for whom it is redundant. We have also tried
as far as possible to allow each chapter to be read in isolation. The price of
this is that we may introduce a topic in one chapter, only to return to it in
more detail in a later chapter.
ix