Evidence map›Paper›PMID 33619490›Full record

ArticlebioRxiv : the preprint server for biology2021

Association of

Yulin Dai, Junke Wang, Hyun-Hwan Jeong, Wenhao Chen, Peilin Jia, Zhongming Zhao

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2021. 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, 9 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Yulin DaiCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Junke WangMD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.
Hyun-Hwan JeongCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Wenhao ChenImmunobiology and Transplant Science Center, Department of Surgery, Houston Methodist Research Institute and Institute for Academic Medicine, Houston Methodist Hospital, Houston, TX 77030, USA.
Peilin JiaCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Zhongming ZhaoCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
The University of Texas Health Science Center at Houston · USThe University of Texas MD Anderson Cancer Center · USHouston Methodist · US

Funding

Transforming dbGaP genetic and genomic data to FAIR-ready by artificial intelligence and machine learning algorithmsR01LM012806 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Zhongming Zhao · 2017 to 2026
$3.7M
NLM NIH HHS R01 LM012806
6 · The paper itself

Abstract

backgroundThe coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ∼80% asymptomatic or mild cases and ∼5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms.

methodsWe implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients.

resultsWe discovered and replicated the genetically regulated expression of

conclusions

Identifiers

PMID33619490
PMCPMC7899454
OpenAlexW3131044806

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.