Evidence map›Paper›PMID 34386270›Full record

ArticleQuantitative biology (Beijing, China)2021

Mendelian randomization and pleiotropy analysis.

Xiaofeng Zhu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Quantitative biology (Beijing, China), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 2 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

52 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

1 author.

Xiaofeng ZhuDepartment of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.

Funding

Statistical methods for analyzing high-throughput genotype dataR01HG003054 · NHGRI · LOYOLA UNIVERSITY CHICAGO · PI ZHU, XIAOFENG · 2005 to 2017
$3.6M
Statistical Analysis of Large Genomic Data SetsR01HG011052 · NHGRI · CASE WESTERN RESERVE UNIVERSITY · PI XIAOFENG ZHU · 2020 to 2026
$3.3M
NHGRI NIH HHS R01 HG003054NHGRI NIH HHS R01 HG011052
6 · The paper itself

Abstract

backgroundMendelian randomization (MR) analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies. Many statistical approaches have been developed and each of these methods require specific assumptions.

resultsIn this article, we review the pros and cons of these methods. We use an example of high-density lipoprotein cholesterol on coronary artery disease to illuminate the challenges in Mendelian randomization investigation.

conclusionThe current available MR approaches allow us to study causality among risk factors and outcomes. However, novel approaches are desirable for overcoming multiple source confounding of risk factors and an outcome in MR analysis.

Indexed as

causalityconfoundinginstrumental variableMendelian randomizationsummary statistics

Identifiers

PMID34386270
PMCPMC8356909

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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.