Evidence map›Paper›PMID 36046661›Full record

ArticleJournal of inflammation research2022

Whole Exome Sequencing Revealed Variants That Predict Pulmonary Artery Involvement in Patients with Takayasu Arteritis.

Lingyu Liu, Jing Chen, Jing Li, Yunjiao Yang, Xiaofeng Zeng, Xinping Tian

Open access · goldAbstract read
In one paragraph

Article in Journal of inflammation research, 2022. 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
0.6field-weighted citation impact, top 31% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
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

6 authors at 2 institutions in 1 country.

Lingyu LiuDepartment of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, People's Republic of China.ORCID 0000-0003-1681-807X
Jing ChenDepartment of Rheumatology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, People's Republic of China.
Jing LiDepartment of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, People's Republic of China.
Yunjiao YangDepartment of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, People's Republic of China.
Xiaofeng ZengDepartment of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, People's Republic of China.
Xinping TianDepartment of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, People's Republic of China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNShandong Provincial Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To conduct the first whole exome sequencing (WES) on Takayasu arteritis (TAK) to identify common and rare variants responsible for disease susceptibility. Patients and Methods: A total of 200 patients and 1675 healthy controls from China were recruited for this study. Site-based association analysis for common variants and gene-based burden analysis for rare variants were conducted. A weighted genetic risk score (wGRS) was calculated for each patient with TAK based on the independent risk alleles identified in the association analyses. The ability of the patient wGRS to discriminate between different phenotypes was evaluated. Results: In the site-based analysis, the top association signal was CCHCR1 (rs1265067, p = 8.27 × 10 Conclusion: This WES study on TAK supports a previously reported association within the HLA region. Moreover, novel susceptibility loci were identified outside the HLA region. These risk alleles showed potential associations with pulmonary artery involvement in TAK. However, additional studies are warranted to verify our findings.

Indexed as

human leukocyte antigenpulmonary arteryTakayasu arteritiswhole exome sequencing

Identifiers

PMID36046661
PMCPMC9420927
OpenAlexW4293023373

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.