Evidence map›Paper›PMID 42328235›Full record

ArticleFrontiers in systems biology2026

Prioritizing long COVID related single nucleotide polymorphisms by mining genome-wide association studies of COVID-19 susceptibility and hospitalization.

Zhong-Shan Cheng

Abstract read
In one paragraph

Article in Frontiers in systems biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Zhong-Shan ChengCenter for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long coronavirus disease (COVID) presents a significant public health challenge, characterized by over 200 reported symptoms across multiple organ systems. Genetic studies of long COVID have been hindered by the disorder's symptom heterogeneity and the limited sample size of available datasets. To overcome these challenges, a proxy-based, hypothesis-generating strategy was conducted to prioritize candidate risk loci on studying long COVID by analyzing GWAS summary statistics of coronavirus disease 2019 (COVID-19) susceptibility, hospitalization, and long COVID from the COVID-19 Host Genetics Initiative (Release 7), resulting in 62 candidate loci represented by independent variants. These variants are grouped into three categories: (1) severe COVID-19-specific variants, exhibiting reduced signals in non-hospitalized cases; (2) variants associated with both severe and mild COVID-19, and (3) non-hospitalization-specific variants associated with mild cases. Evaluation using recently published long COVID datasets from the same consortium demonstrated that most candidate variants displayed weaker associations around nominal significance, with only a single genome-wide significant signal at rs12660421 of

Indexed as

COVID-19GWAShospitalizationlong COVIDSARS-CoV-2susceptibilityTWAS

Identifiers

PMID42328235
PMCPMC13279042

What OpenQuestion holds

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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.