Mendelian randomization: methods for using genetic variants in causal estimation
Burgess, Stephen
- Boca Raton CRC Press 2015
- xiv, 210 p.
- Chapman and Hall/CRC interdisciplinary statistics series .
Table of Contents:
I USING GENETIC VARIANTS AS INSTRUMENTAL VARIABLES TO ASSESS CAUSAL RELATIONSHIPS
1. Introduction and motivation Shortcomings of classical epidemiology The rise of genetic epidemiology Motivating example: The inflammation hypothesis Other examples of Mendelian randomization Overview of book Summary
2. What is Mendelian randomization? What is Mendelian randomization? Why use Mendelian randomization? A brief overview of genetics Summary
3. Assumptions for causal inference Observational and causal relationships Finding a valid instrumental variable Testing for a causal relationship Estimating a causal effect Summary
4. Methods for instrumental variable analysis Ratio of coefficients method Two-stage methods Likelihood-based methods Semi-parametric methods Efficiency and validity of instruments Computer implementation Summary
5. Examples of Mendelian randomization analysis Fibrinogen and coronary heart disease Adiposity and blood pressure Lipoprotein(a) and myocardial infarction High-density lipoprotein cholesterol and myocardial infarction Discussion
6. Generalizability of estimates from Mendelian randomization Internal and external validity Comparison of estimates Discussion Summary
II STATISTICAL ISSUES IN INSTRUMENTAL VARIABLE ANALYSIS AND MENDELIAN RANDOMIZATION
7. Weak instruments and finite-sample bias Introduction Demonstrating the bias of IV estimates Explaining the bias of IV estimates Properties of IV estimates with weak instruments Bias of IV estimates with different choices of IV Minimizing the bias of IV estimates Discussion Key points from chapter
8. Multiple instruments and power Introduction Allele scores Power of IV estimates Multiple variants and missing data Discussion Key points from chapter
9. Multiple studies and evidence synthesis Introduction Assessing the causal relationship Study-level meta-analysis Summary-level meta-analysis Individual-level meta-analysis Example: C-reactive protein and fibrinogen Binary outcomes Discussion Key points from chapter
10. Example: The CRP CHD Genetics Collaboration Overview of the dataset Single study: Cardiovascular Health Study Meta-analysis of all studies Discussion Key points from chapter
Mendelian Randomization: Methods for Using Genetic Variants in Causal Estimation provides thorough coverage of the methods and practical elements of Mendelian randomization analysis. It brings together diverse aspects of Mendelian randomization spanning epidemiology, statistics, genetics, and econometrics. Although the book mainly focuses on epidemiology, much of the material can be applied to other areas of research.
Through several examples, the first part of the book shows how to perform simple applied Mendelian randomization analyses and interpret their results. The second part addresses specific methodological issues, such as weak instruments, multiple instruments, power calculations, and meta-analysis, relevant to practical applications of Mendelian randomization. In this part, the authors draw on data from the C-reactive protein Coronary heart disease Genetics Collaboration (CCGC) to illustrate the analyses. They present the mathematics in an easy-to-understand way by using nontechnical language and reinforcing key points at the end of each chapter. The last part of the book examines the potential of Mendelian randomization in the future, exploring both methodological and applied developments.
This book gives statisticians, epidemiologists, and geneticists the foundation to understand issues concerning the use of genetic variants as instrumental variables. It will get them up to speed in undertaking and interpreting Mendelian randomization analyses. Chapter summaries, paper summaries, web-based applications, and software code for implementing the statistical techniques are available on a supplementary website.