Recent Developments in Mendelian Randomization Studies

By Jie Zheng, Denis Baird, M. Borges, J. Bowden, Gibran Hemani, Philip C. Haycock, David M. Evans, George Davey Smith. Current Epidemiology Reports, 2017

945 citations recorded by the source corpus.

Purpose of ReviewMendelian 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 FindingsIn 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.SummaryIn 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.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2a270c8df89f1ffb1f564b0b037db9339b17c9d5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. E. Sanderson
2. George Davey Smith
3. J. Bowden
4. Gibran Hemani
5. Stephen Burgess
6. N. Davies
7. Philip C. Haycock
8. S. Thompson

In Statistics and Probability

1. E. Sanderson
2. J. Bowden
3. George Davey Smith
4. Gibran Hemani
5. Stephen Burgess
6. N. Davies
7. L. Howe
8. Nicholas J. Timpson

Citations in the corpus

• Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, background): “have appeared recently that relate to the scope of MR (10,11), emerging methods (11,12), applications to drug discovery (13– 15), and comparisons to other causal methods (16–18).”
• Mendelian randomisation for mediation analysis: current methods and challenges for implementation (2019, background): “For estimates of the direct effect and indirect effect from two-step MR to be unbiased, the homogeneity assumption must be satisfied between the exposure-mediator association and mediator-outcome association and there should be no interaction between the exposure and mediator [23, 53].”
• The MR-Base platform supports systematic causal inference across the human phenome (2018, background): “…2014 ; Haycock et al., 2016 ; Swerdlow et al., 2016 ; Holmes et al., 2017 ; VanderWeele et al., 2014 ; Lawlor et al., 2008 ; Lawlor et al., 2016 ; Zheng et al., 2017a ), and we summarise how these relate to MR-Base in Supplementary file 1E , with particular focus on the most important strengths…”