Recent Developments in Mendelian Randomization Studies - PubMed (original) (raw)

Review

Recent Developments in Mendelian Randomization Studies

Jie Zheng et al. Curr Epidemiol Rep. 2017.

Abstract

Purpose of review: Mendelian randomization (MR) is a strategy for evaluating causality in observational epidemiological studies. MR exploits the fact that genotypes are not generally susceptible to reverse causation and confounding, due to their fixed nature and Mendel's First and Second Laws of Inheritance. MR has the potential to provide information on causality in many situations where randomized controlled trials are not possible, but the results of MR studies must be interpreted carefully to avoid drawing erroneous conclusions.

Recent findings: In this review, we outline the principles behind MR, as well as assumptions and limitations of the method. Extensions to the basic approach are discussed, including two-sample MR, bidirectional MR, two-step MR, multivariable MR, and factorial MR. We also consider some new applications and recent developments in the methodology, including its ability to inform drug development, automation of the method using tools such as MR-Base, and phenome-wide and hypothesis-free MR.

Summary: In conjunction with the growing availability of large-scale genomic databases, higher level of automation and increased robustness of the methods, MR promises to be a valuable strategy to examine causality in complex biological/omics networks, inform drug development and prioritize intervention targets for disease prevention in the future.

Keywords: Databases and automation tools for causal inference; Disease progression; Drug development; Hypothesis-free causality; Mendelian randomization.

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Conflict of interest statement

Conflict of Interest

Jie Zheng, Denis Baird, Maria-Carolina Borges, Jack Bowden, Gibran Hemani, Philip Haycock, David M. Evans and George Davey Smith each declare no potential conflicts of interest.

This article contains no studies with human or animal subjects performed by any of the authors.

Figures

Fig. 1

Fig. 1

Design strategies for Mendelian randomization. a Standard MR: The causal relationship between an exposure variable (X) and an outcome (Y) is estimated using genetic variants (Z) as an instrument, regardless of the presence of variables (C) that may confound the observational association between the exposure and outcome. One method of estimation involves calculation of the Wald Ratio, [see Burgess review paper for description of the various instrumental variable (IV) estimators available] [3], where the causal estimate (β^IV) is derived by dividing the estimated regression coefficient of the outcome on the single nucleotide polymorphism (SNP) (β^YZ) by the estimated regression coefficient of the exposure on the SNP (β^XZ). b Two-sample MR. c Bidirectional MR. d Mediation and two-step MR. e Multivariable MR. f Factorial MR

Fig. 1

Fig. 1

Design strategies for Mendelian randomization. a Standard MR: The causal relationship between an exposure variable (X) and an outcome (Y) is estimated using genetic variants (Z) as an instrument, regardless of the presence of variables (C) that may confound the observational association between the exposure and outcome. One method of estimation involves calculation of the Wald Ratio, [see Burgess review paper for description of the various instrumental variable (IV) estimators available] [3], where the causal estimate (β^IV) is derived by dividing the estimated regression coefficient of the outcome on the single nucleotide polymorphism (SNP) (β^YZ) by the estimated regression coefficient of the exposure on the SNP (β^XZ). b Two-sample MR. c Bidirectional MR. d Mediation and two-step MR. e Multivariable MR. f Factorial MR

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