Evidence map›Paper›PMID 27342221›Full record

SynthesisInternational journal of epidemiology2016

Selecting instruments for Mendelian randomization in the wake of genome-wide association studies.

Daniel I Swerdlow, Karoline B Kuchenbaecker, Sonia Shah, Reecha Sofat, Michael V Holmes, Jon White, Jennifer S Mindell, Mika Kivimaki, Eric J Brunner, John C Whittaker and 2 more

Abstract readSystematic Review
In one paragraph

Synthesis in International journal of epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 221 papers, 16 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
221citing papers in PubMed, 16 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

221 citing papers in PubMed, 16 syntheses or guidelines pooled it.

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Daniel I SwerdlowInstitute of Cardiovascular Science, University College London, London, UK d.swerdlow@ucl.ac.uk.
Karoline B KuchenbaeckerCentre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.
Sonia ShahInstitute of Cardiovascular Science, University College London, London, UK.
Reecha SofatInstitute of Cardiovascular Science, University College London, London, UK.
Michael V HolmesInstitute of Cardiovascular Science, University College London, London, UK.
Jon WhiteInstitute of Cardiovascular Science, University College London, London, UK.
Jennifer S MindellResearch Department of Epidemiology & Public Health, University College London, London, UK.
Mika KivimakiResearch Department of Epidemiology & Public Health, University College London, London, UK.
Eric J BrunnerResearch Department of Epidemiology & Public Health, University College London, London, UK.
John C WhittakerDepartment of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Juan P CasasDepartment of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Aroon D HingoraniInstitute of Cardiovascular Science, University College London, London, UK.

Funding

Socioeconomic gradient in CHD in early old ageR01HL036310 · NHLBI · UNIVERSITY OF LONDON · PI KIVIMAKI, MIKA J, MARMOT, MICHAEL G · 1986 to 2012
$3.1M
Health behaviors over the adult lifecourse and cognitive agingR01AG034454 · NIA · UNIVERSITY COLLEGE LONDON · PI KIVIMAKI, MIKA J, SINGH-MANOUX, ARCHANA · 2009 to 2013
$1.5M
British Heart Foundation FS/07/011British Heart Foundation PG/11/63/29011British Heart Foundation RG/10/12/28456British Heart Foundation RG/13/2/30098Department of HealthMedical Research Council G0802432Medical Research Council MR/K006584/1Medical Research Council MR/K013351/1NHLBI NIH HHS R01 HL036310NIA NIH HHS R01 AG034454
6 · The paper itself

Abstract

Mendelian randomization (MR) studies typically assess the pathogenic relevance of environmental exposures or disease biomarkers, using genetic variants that instrument these exposures. The approach is gaining popularity-our systematic review reveals a greater than 10-fold increase in MR studies published between 2004 and 2015. When the MR paradigm was first proposed, few biomarker- or exposure-related genetic variants were known, most having been identified by candidate gene studies. However, genome-wide association studies (GWAS) are now providing a rich source of potential instruments for MR analysis. Many early reviews covering the concept, applications and analytical aspects of the MR technique preceded the surge in GWAS, and thus the question of how best to select instruments for MR studies from the now extensive pool of available variants has received insufficient attention. Here we focus on the most common category of MR studies-those concerning disease biomarkers. We consider how the selection of instruments for MR analysis from GWAS requires consideration of: the assumptions underlying the MR approach; the biology of the biomarker; the genome-wide distribution, frequency and effect size of biomarker-associated variants (the genetic architecture); and the specificity of the genetic associations. Based on this, we develop guidance that may help investigators to plan and readers interpret MR studies.

Indexed as

BiomarkersGenome-Wide Association StudyPolymorphism, Single NucleotideCausalityGenetic VariationHumansMendelian Randomization AnalysisBiomarkersbiomarkerscausal inferencegenome-wide association studyMendelian randomization

Identifiers

PMID27342221
PMCPMC5100611

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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.