Evidence map›Paper›PMID 35507585›Full record

ArticlePLoS genetics2022

A practical problem with Egger regression in Mendelian randomization.

Zhaotong Lin, Isaac Pan, Wei Pan

Open access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 4 pooled it
4.5field-weighted citation impact, top 5% 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

25 citing papers in PubMed, 4 syntheses or guidelines pooled it, 27 citations in OpenAlex.

  1. Pooled it
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  3. Efficacy of traditional Chinese medicineFrontiers in medicine · 2024
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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

3 authors at 2 institutions in 1 country.

Zhaotong LinDivision of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.
Isaac PanPomona College, Claremont, California, United States of America.ORCID 0000-0001-5918-7058
Wei PanDivision of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.ORCID 0000-0002-1159-0582
University of Minnesota · USPomona College · US

Funding

Causal and integrative deep learning for Alzheimer's disease geneticsU01AG073079 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2021 to 2025
$3.5M
Deep Learning with Neuroimaging Genetic Data for Alzheimer's DiseaseR01AG069895 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2020 to 2024
$3.4M
Association analysis of rare variants with sequencing dataR01HL116720 · NHLBI · UNIVERSITY OF MINNESOTA · PI PAN, WEI, WEI, PENG · 2013 to 2020
$3.3M
Discovering causal genes, brain regions and other risk factors for Alzheimer's DiseaseR01AG065636 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2020 to 2024
$3.1M
Integrating Alzheimer's disease GWAS with proteomic and metabolomic QTL dataRF1AG067924 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2020 to 2020
$1.9M
Estimation and Inference of Gene Regulatory NetworksR01GM126002 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2017 to 2020
$1.4M
Statistical Methods for Genomic DataR01GM113250 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2014 to 2017
$702k
NIA NIH HHS R01 AG065636NIA NIH HHS R01 AG069895NIA NIH HHS RF1 AG067924NIA NIH HHS U01 AG073079NIGMS NIH HHS R01 GM113250NIGMS NIH HHS R01 GM126002
6 · The paper itself

Abstract

Mendelian randomization (MR) is an instrumental variable (IV) method using genetic variants such as single nucleotide polymorphisms (SNPs) as IVs to disentangle the causal relationship between an exposure and an outcome. Since any causal conclusion critically depends on the three valid IV assumptions, which will likely be violated in practice, MR methods robust to the IV assumptions are greatly needed. As such a method, Egger regression stands out as one of the most widely used due to its easy use and perceived robustness. Although Egger regression is claimed to be robust to directional pleiotropy under the instrument strength independent of direct effect (InSIDE) assumption, it is known to be dependent on the orientations/coding schemes of SNPs (i.e. which allele of an SNP is selected as the reference group). The current practice, as recommended as the default setting in some popular MR software packages, is to orientate the SNPs to be all positively associated with the exposure, which however, to our knowledge, has not been fully studied to assess its robustness and potential impact. We use both numerical examples (with both real data and simulated data) and analytical results to demonstrate the practical problem of Egger regression with respect to its heavy dependence on the SNP orientations. Under the assumption that InSIDE holds for some specific (and unknown) coding scheme of the SNPs, we analytically show that other coding schemes would in general lead to the violation of InSIDE. Other related MR and IV regression methods may suffer from the same problem. Cautions should be taken when applying Egger regression (and related MR and IV regression methods) in practice.

Indexed as

Genetic PleiotropyMendelian Randomization AnalysisCausalityGenome-Wide Association StudyPolymorphism, Single NucleotideRegression Analysis

Identifiers

PMID35507585
PMCPMC9109933
OpenAlexW4229007227

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.