Evidence map›Paper›PMID 33457407›Full record

ArticleBioMed research international2020

Integration of SNP Disease Association, eQTL, and Enrichment Analyses to Identify Risk SNPs and Susceptibility Genes in Chronic Obstructive Pulmonary Disease.

Yang Liu, Kun Huang, Yahui Wang, Erqiang Hu, Benliang Wei, Zhaona Song, Yuqing Zou, Luanfeng Ge, Lina Chen, Wan Li

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 21% 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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Analysis of theGenes · 2025
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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

10 authors at 1 institution in 1 country.

Yang LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID https://orcid.org/0000-0003-1680-0398
Kun HuangDepartment of Respiratory, The Second Affiliated Hospital, Harbin Medical University, Harbin, China.ORCID https://orcid.org/0000-0001-7427-9647
Yahui WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID https://orcid.org/0000-0001-6645-8485
Erqiang HuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID https://orcid.org/0000-0002-1798-7513
Benliang WeiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID https://orcid.org/0000-0003-0846-1277
Zhaona SongCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID https://orcid.org/0000-0003-2061-3333
Yuqing ZouCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID https://orcid.org/0000-0003-0611-9056
Luanfeng GeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID https://orcid.org/0000-0002-2775-4625
Lina ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID https://orcid.org/0000-0002-4576-6814
Wan LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID https://orcid.org/0000-0002-9797-0315
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic obstructive pulmonary disease (COPD) is a complex disease caused by the disturbance of genetic and environmental factors. Single-nucleotide polymorphisms (SNPs) play a vital role in the genetic dissection of complex diseases. In-depth analysis of SNP-related information could recognize disease-associated biomarkers and further uncover the genetic mechanism of complex diseases. Risk-related variants might act on the disease by affecting gene expression and gene function. Through integrating SNP disease association study and expression quantitative trait loci (eQTL) analysis, as well as functional enrichment of containing known causal genes, four risk SNPs and four corresponding susceptibility genes were identified utilizing next-generation sequencing (NGS) data of COPD. Of the four risk SNPs, one could be found in the SNPedia database that stored disease-related SNPs and has been linked to a disease in the literature. Four genes showed significant differences from the perspective of normal/disease or variant/nonvariant samples, as well as the high performance of sample classification. It is speculated that the four susceptibility genes could be used as biomarkers of COPD. Furthermore, three of our susceptibility genes have been confirmed in the literature to be associated with COPD. Among them, two genes had an impact on the significance of expression correlation of known causal genes they interact with, respectively. Overall, this research may present novel insights into the diagnosis and pathogenesis of COPD and susceptibility gene identification of other complex diseases.

Indexed as

Genetic Predisposition to DiseaseHigh-Throughput Nucleotide SequencingQuantitative Trait LociAlgorithmsBiomarkersCluster AnalysisComputational BiologyGene ExpressionGenome-Wide Association StudyGenotypeHumansPulmonary Disease, Chronic ObstructiveRiskRNA-SeqROC CurveSensitivity and SpecificityBiomarkers

Identifiers

PMID33457407
PMCPMC7785362
OpenAlexW3115428393

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.