Evidence map›Paper›PMID 34936225›Full record

ArticleCurrent protocols2021

Two-Sample Multivariable Mendelian Randomization Analysis Using R.

Danielle Rasooly, Gina M Peloso

Open access · greenAbstract read
In one paragraph

Article in Current protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed, 1 pooled it
9.9field-weighted citation impact, top 1% of its field
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

56 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.

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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

2 authors at 2 institutions in 1 country.

Danielle RasoolyDivision of Aging, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Gina M PelosoDepartment of Biostatistics, Boston University School of Public Health, Boston, Massachusetts.
Boston University · USBrigham and Women's Hospital · US

Funding

Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Using genetic variation to study biology of blood lipids & coronary heart diseaseR01HL127564 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Pradeep Natarajan, Gina Marie Peloso · 2015 to 2026
$7.3M
Whole genome sequences in individuals to comprehensively characterize the genetic mechanisms of dyslipidemiasR01HL142711 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Satoshi Koyama, Gina Marie Peloso · 2019 to 2026
$4.7M
NHGRI NIH HHS T32 HG002295NHLBI NIH HHS R01 HL127564NHLBI NIH HHS R01HL127564NHLBI NIH HHS R01 HL142711NHLBI NIH HHS R01HL142711
6 · The paper itself

Abstract

Mendelian randomization is a framework that uses measured variation in genes for assessing and estimating the causal effect of an exposure on an outcome. Multivariable Mendelian randomization is an extension that can assess the causal effect of multiple exposures on an outcome, and can be advantageous when considering a set (>1) of potentially correlated candidate risk factors in evaluating the causal effect of each on a health outcome, accounting for measured pleiotropy. This can be seen, for example, in determining the causal effects of lipids and cholesterol on type 2 diabetes risk, where the correlated risk factors share genetic predictors. Similar to univariate Mendelian randomization, multivariable Mendelian randomization can be conducted using two-sample summary-level data where the gene-exposure and gene-outcome associations are derived from separate samples from the same underlying population. Here, we present a protocol for conducting a two-sample multivariable Mendelian randomization study using the 'MVMR' package in R and summary-level genetic data. We also provide a protocol for searching and obtaining instruments using available data sources in the 'MRInstruments' R package. Finally, we provide general guidelines and discuss the utility of performing a multivariable Mendelian randomization analysis for simultaneously assessing causality of multiple exposures. © 2021 Wiley Periodicals LLC. Basic Protocol: Performing a two-sample multivariable Mendelian randomization analysis using the 'MVMR' package in R and summarized genetic data Support Protocol 1: Installing the 'MVMR' R package Support Protocol 2: Obtaining instruments from the 'MRInstruments' R package.

Indexed as

Diabetes Mellitus, Type 2Mendelian Randomization AnalysisCausalityGenetic VariationHumansRisk Factorscausal inferencegenetic epidemiologyinstrumental variable analysismendelian randomizationmultivariableMVMR

Identifiers

PMID34936225
PMCPMC8767787
OpenAlexW4200582942

What OpenQuestion holds

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Registered trials

None linked

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.