Evidence map›Paper›PMID 29226067›Full record

ReviewCurrent epidemiology reports2017

Recent Developments in Mendelian Randomization Studies.

Jie Zheng, Denis Baird, Maria-Carolina Borges, Jack Bowden, Gibran Hemani, Philip Haycock, David M Evans, George Davey Smith

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current epidemiology reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 642 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
642citing papers in PubMed, 10 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

642 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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582 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jie Zheng *MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.
Denis Baird *MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.
Maria-Carolina BorgesMRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.
Jack BowdenMRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.
Gibran HemaniMRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.
Philip HaycockMRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.
David M EvansMRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.
George Davey SmithMRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK.

Funding

Cancer Research UK 20138Medical Research Council MC_UU_12013/1Medical Research Council MC_UU_12013/4Medical Research Council MR/N501906/1
6 · The paper itself

Abstract

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. 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.

Indexed as

Databases and automation tools for causal inferenceDisease progressionDrug developmentHypothesis-free causalityMendelian randomization

Identifiers

PMID29226067
PMCPMC5711966

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.