Mendelian Randomization - 
      Stephen Burgess
      Simon G. Thompson

Mendelian Randomization

Methods for Using Genetic Variants in Causal Estimation

; Simon G. Thompson

«<p>"Mendelian randomization (MR) may be regarded as a modern integration of the concepts of instrumental variables from the field of economics with Mendel’s laws of inheritance in the field of genetics. … Stephen Burgess, a mathematician, and Simon Thompson, a biostatistician, present the fundamental ideas and methods behind MR in an easy-to-read book for researchers from different backgrounds such as statistics, genetics, or clinical research. The book is the first and currently only book published on MR, in which the authors not only summarize MR methodology but also share their experience with real data analysis in the field of MR for cardiovascular diseases. … The book has been very well written, and the authors have done an excellent job of bridging the gap between researchers from various backgrounds. … Overall, the book provides the most comprehensive MR resource to date, and it is a valuable resource for researchers even at the graduate level."<br />—Sandeep Grover, Universität zu Lübeck, »

Presents the Terminology and Methods of Mendelian Randomization for Epidemiological Studies

Mendelian randomization uses genetic instrumental variables to make inferences about causal effects based on observational data. Les mer
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Presents the Terminology and Methods of Mendelian Randomization for Epidemiological Studies

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

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.
Forlag: CRC Press Inc
Innbinding: Innbundet
Språk: Engelsk
Sider: 224
ISBN: 9781466573178
Format: 23 x 16 cm

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Gi vurdering
Les vurderinger
Using Genetic Variants as Instrumental Variables to Assess Causal Relationships
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

What is Mendelian randomization?
What is Mendelian randomization?
Why use Mendelian randomization?
A brief overview of genetics

Assumptions for causal inference
Observational and causal relationships
Finding a valid instrumental variable
Testing for a causal relationship
Estimating a causal effect

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

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

Generalizability of estimates from Mendelian randomization
Internal and external validity
Comparison of estimates

Statistical Issues in Instrumental Variable Analysis and Mendelian Randomization
Weak instruments and finite-sample bias
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
Key points from chapter

Multiple instruments and power
Allele scores
Power of IV estimates
Multiple variants and missing data
Key points from chapter

Multiple studies and evidence synthesis
Assessing the causal relationship
Study-level meta-analysis
Summary-level meta-analysis
Individual-level meta-analysis
Example: C-reactive protein and fibrinogen
Binary outcomes
Key points from chapter

Example: The CRP CHD Genetics Collaboration
Overview of the dataset
Single study: Cardiovascular Health Study
Meta-analysis of all studies
Key points from chapter

Prospects for Mendelian Randomization
Future directions
Methodological developments
Applied developments