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

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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
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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
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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