Recent Developments in Mendelian Randomization Studies

Zheng, Jie, Baird, Denis, Borges, Maria-Carolina, Bowden, Jack, Hemani, Gibran, Haycock, Philip, Evans, David M and Smith, George Davey (2017) Recent Developments in Mendelian Randomization Studies. Current epidemiology reports, 4 4: 330-345. doi:10.1007/s40471-017-0128-6

Author Zheng, Jie
Baird, Denis
Borges, Maria-Carolina
Bowden, Jack
Hemani, Gibran
Haycock, Philip
Evans, David M
Smith, George Davey
Title Recent Developments in Mendelian Randomization Studies
Journal name Current epidemiology reports   Check publisher's open access policy
ISSN 2196-2995
Publication date 2017-11-22
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1007/s40471-017-0128-6
Open Access Status Not yet assessed
Volume 4
Issue 4
Start page 330
End page 345
Total pages 16
Abstract 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.

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.

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.
Keyword Databases and automation tools for causal inference
Disease progression
Drug development
Hypothesis-free causality
Mendelian randomization
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collection: Pubmed Import
Version Filter Type
Citation counts: Google Scholar Search Google Scholar
Created: Wed, 13 Dec 2017, 12:04:00 EST