Mendelian Randomization

Book: Methods for Causal Inference Using Genetic Variants by Stephen Burgess and Simon G. Thompson

Bookcover 2ndeditionsmall Mendelian randomization uses genetic variants as instrumental variables to make inferences about causal effects based on observational data. It can be a reliable way of assessing the causal nature of risk factors, such as biomarkers, for a wide range of disease outcomes.

The book 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.

Features

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
Includes several real-world examples that show how Mendelian randomization can be used to address questions of disease aetiology, target validation, and drug development
Discusses possible future directions for research involving Mendelian randomization
Software code is provided in the relevant chapters and is also available on a 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 (2021): 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

The book can be bought from Routledge here.

Preface	Sample available!
PART I: Understanding and performing Mendelian Randomization
Chapter 1: Introduction and motivation 1.1 Shortcomings of classical epidemiology 1.2 The rise of genetic epidemiology 1.3 Motivating example: The inflammation hypothesis 1.4 Other examples of Mendelian randomization 1.5 Overview of book 1.6 Summary	Summary Download sample!
Chapter 2: What is Mendelian randomization? 2.1 What is Mendelian randomization? 2.2 Why use Mendelian randomization? 2.3 A brief overview of genetics 2.4 Classification of Mendelian randomization investigations 2.5 Summary	Summary
Chapter 3: Assumptions for causal inference 3.1 Observational and causal relationships 3.2 Finding a valid instrumental variable 3.3 Testing for a causal relationship 3.4 Example: Lp-PLA2 and coronary heart disease 3.5 Estimating a causal effect 3.6 Summary	Summary
Chapter 4: Methods for instrumental variable analysis 4.1 Ratio of coefficients method 4.2 Two-stage methods 4.3 Example: Body mass index and smoking intensity 4.4 Computer implementation 4.5 Summary	Summary
Chapter 5: Estimating a causal effect from summarized data 5.1 Motivating example: interleukin-1 and cardiovascular diseases 5.2 Inverse-variance weighted method 5.3 Heterogeneity and pleiotropy 5.4 Computer implementation 5.5 Example: body mass index and smoking intensity reprised 5.6 Summary	Summary
Chapter 6: Interpretation of estimates from Mendelian randomization 6.1 Internal and external validity 6.2 Comparison of estimates 6.3 Example: lipoprotein(a) and coronary heart disease 6.4 Discussion 6.5 Recap of examples considered so far 6.6 Summary	Summary
PART II: Advanced methods for Mendelian randomization
Chapter 7: Robust methods using variants from multiple gene regions 7.1 Motivating example: LDL- and HDL-cholesterol and coronary heart disease 7.2 Consensus methods 7.3 Outlier-robust methods 7.4 Modelling methods 7.5 Other methods and comparison 7.6 Example: LDL- and HDL-cholesterol and coronary heart disease reprised 7.7 Computer implementation 7.8 Summary	Summary Download sample!
Chapter 8: Other statistical issues for Mendelian randomization 8.1 Weak instrument bias 8.2 Allele scores 8.3 Sample overlap 8.4 Winner’s curse 8.5 Selection and collider bias 8.6 Covariate adjustment 8.7 Non-collapsibility 8.8 Time and time-varying effects 8.9 Power to detect a causal effect 8.10 Choosing variants from a single gene region 8.11 Binary exposure 8.12 Alternative estimation methods 8.13 Summary	Summary
Chapter 9: Extensions to Mendelian randomization 9.1 Multivariable Mendelian randomization 9.2 Network Mendelian randomization 9.3 Non-linear Mendelian randomization 9.4 Factorial Mendelian randomization 9.5 Bidirectional Mendelian randomization 9.6 Mendelian randomization and meta-analysis 9.7 Summary	Summary
Chapter 10: How to perform a Mendelian randomization investigation 10.1 Motivation and scope 10.2 Data sources 10.3 Selection of genetic variants 10.4 Variant harmonization 10.5 Primary analysis 10.6 Robust methods for sensitivity analysis 10.7 Other approaches for sensitivity analysis 10.8 Data presentation 10.9 Interpretation 10.10 Summary	Summary
PART III: Prospects for Mendelian randomization
Chapter 11: Future directions 11.1 GWAS: large numbers of genetic variants 11.2 -omics: large numbers of risk factors 11.3 Hypothesis-free: large numbers of outcomes 11.4 Biobanks: large numbers of participants 11.5 Clever designs: the role of epidemiologists 11.6 Conclusion	Summary