Mendelian Randomization, 2nd Edition

Table of contents

1. Cover
2. Half Title
3. Series Page
4. Title Page
5. Copyright Page
6. Contents
7. Preface to the second edition
8. Abbreviations
9. Notation
10. I Understanding and performing Mendelian randomization Understanding and performing Mendelian randomization
    1. 1 Introduction and motivation
       1. 1.1 Shortcomings of classical epidemiology
       2. 1.2 The rise of genetic epidemiology
       3. 1.3 Motivating example: The inflammation hypothesis
       4. 1.4 Other examples of Mendelian randomization
       5. 1.5 Overview of book
       6. 1.6 Summary
    2. 2 What is Mendelian randomization?
       1. 2.1 What is Mendelian randomization?
       2. 2.2 Why use Mendelian randomization?
       3. 2.3 A brief overview of genetics
       4. 2.4 Classification of Mendelian randomization investigations
       5. 2.5 Summary
    3. 3 Assumptions for causal inference
       1. 3.1 Observational and causal relationships
       2. 3.2 Finding a valid instrumental variable
       3. 3.3 Testing for a causal relationship
       4. 3.4 Example: Lp-PLA2 and coronary heart disease
       5. 3.5 Estimating a causal effect
       6. 3.6 Summary
    4. 4 Estimating a causal effect from individual-level data
       1. 4.1 Ratio of coefficients method
       2. 4.2 Two-stage methods
       3. 4.3 Example: Body mass index and smoking intensity
       4. 4.4 Computer implementation
       5. 4.5 Summary
    5. 5 Estimating a causal effect from summarized data
       1. 5.1 Motivating example: interleukin-1 and cardiovascular diseases
       2. 5.2 Inverse-variance weighted method
       3. 5.3 Heterogeneity and pleiotropy
       4. 5.4 Computer implementation
       5. 5.5 Example: Body mass index and smoking intensity reprised
       6. 5.6 Summary
    6. 6 Interpretation of estimates from Mendelian randomization
       1. 6.1 Internal and external validity
       2. 6.2 Comparison of estimates
       3. 6.3 Example: Lipoprotein(a) and coronary heart disease
       4. 6.4 Discussion
       5. 6.5 Recap of examples considered so far
       6. 6.6 Summary
11. II Advanced methods for Mendelian randomization
    1. 7 Robust methods using variants from multiple gene regions
       1. 7.1 Motivating example: LDL- and HDL-cholesterol and coronary heart disease
       2. 7.2 Consensus methods
       3. 7.3 Outlier-robust methods
       4. 7.4 Modelling methods
       5. 7.5 Other methods and comparison
       6. 7.6 Example: LDL- and HDL-cholesterol and coronary heart disease reprised
       7. 7.7 Computer implementation
       8. 7.8 Summary
    2. 8 Other statistical issues for Mendelian randomization
       1. 8.1 Weak instrument bias
       2. 8.2 Allele scores
       3. 8.3 Sample overlap
       4. 8.4 Winner's curse
       5. 8.5 Selection and collider bias
       6. 8.6 Covariate adjustment
       7. 8.7 Non-collapsibility
       8. 8.8 Time and time-varying effects
       9. 8.9 Power to detect a causal effect
       10. 8.10 Choosing variants from a single gene region
       11. 8.11 Binary exposure
       12. 8.12 Alternative estimation methods
       13. 8.13 Summary
    3. 9 Extensions to Mendelian randomization
       1. 9.1 Multivariable Mendelian randomization
       2. 9.2 Network Mendelian randomization
       3. 9.3 Non-linear Mendelian randomization
       4. 9.4 Factorial Mendelian randomization
       5. 9.5 Bidirectional Mendelian randomization
       6. 9.6 Mendelian randomization and meta-analysis
       7. 9.7 Summary
    4. 10 How to perform a Mendelian randomization investigation
       1. 10.1 Motivation and scope
       2. 10.2 Data sources
       3. 10.3 Selection of genetic variants
       4. 10.4 Variant harmonization
       5. 10.5 Primary analysis
       6. 10.6 Robust methods for sensitivity analysis
       7. 10.7 Other approaches for sensitivity analysis
       8. 10.8 Data presentation
       9. 10.9 Interpretation
       10. 10.10 Summary
12. III Prospects for Mendelian randomization
    1. 11 Future directions
       1. 11.1 GWAS: large numbers of genetic variants
       2. 11.2 -omics: Large numbers of risk factors
       3. 11.3 Hypothesis-free: Large numbers of outcomes
       4. 11.4 Biobanks: Large numbers of participants
       5. 11.5 Clever designs: The role of epidemiologists
       6. 11.6 Conclusion
13. Bibliography
14. Index