Evidence map›Paper›PMID 32683804›Full record

ArticleClinical and experimental pediatrics2021

Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis.

Seoung Wan Nam, Kwang Seob Lee, Jae Won Yang, Younhee Ko, Michael Eisenhut, Keum Hwa Lee, Jae Il Shin, Andreas Kronbichler

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical and experimental pediatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Seoung Wan NamDepartment of Rheumatology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea.
Kwang Seob LeeSeverance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Jae Won YangDepartment of Nephrology, Yonsei University Wonju College of Medicine, Wonju, Korea.
Younhee KoDivision of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea.
Michael EisenhutDepartment of Pediatrics, Luton & Dunstable University Hospital NHS Foundation Trust, Luton, UK.
Keum Hwa LeeDepartment of Pediatrics, Yonsei University College of Medicine, Seoul, Korea.
Jae Il ShinDepartment of Pediatrics, Yonsei University College of Medicine, Seoul, Korea.
Andreas KronbichlerDepartment of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, Innsbruck, Austria.

Funding

Hankuk University of Foreign StudiesMinistry of EducationNational Research Foundation of Korea 2017R1D1A1B03032457
6 · The paper itself

Abstract

The publication of genetic epidemiology meta-analyses has increased rapidly, but it has been suggested that many of the statistically significant results are false positive. In addition, most such meta-analyses have been redundant, duplicate, and erroneous, leading to research waste. In addition, since most claimed candidate gene associations were false-positives, correctly interpreting the published results is important. In this review, we emphasize the importance of interpreting the results of genetic epidemiology meta-analyses using Bayesian statistics and gene network analysis, which could be applied in other diseases.

Indexed as

Bayesian false-discovery probabilityFalse-positive report probabilityProtein-protein interactionSTRING databaseSystemic lupus erythematosus

Identifiers

PMID32683804
PMCPMC8103040

What Socratic holds

Textmetadata
LicenceCC BY-NC
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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 Socratic graph.