Evidence map›Paper›PMID 31911605›Full record

ArticleNature communications2020

Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization.

Verena Zuber, Johanna Maria Colijn, Caroline Klaver, Stephen Burgess

Abstract readEvaluation Study
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 164 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
164citing papers in PubMed, 7 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

164 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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104 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

4 authors.

Verena ZuberMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0001-9827-1877
Johanna Maria ColijnDepartment of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.ORCID http://orcid.org/0000-0002-8324-7898
Caroline KlaverDepartment of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.ORCID http://orcid.org/0000-0002-2355-5258
Stephen BurgessMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK. sb452@medschl.cam.ac.uk.ORCID http://orcid.org/0000-0001-5365-8760

Funding

British Heart Foundation RG/13/13/30194British Heart Foundation RG/18/13/33946Medical Research Council MC_UU_00002/7Medical Research Council MR/S019669/1Wellcome Trust
6 · The paper itself

Abstract

Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors. Here, we propose a two-sample multivariable MR approach based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration.

Indexed as

Models, GeneticBayes TheoremGenome-Wide Association StudyHumansMendelian Randomization AnalysisRisk Factors

Identifiers

PMID31911605
PMCPMC6946691

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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.