Evidence map›Paper›PMID 41630952›Full record

ReviewGenes & diseases2026

eQTL analysis: A bridge from genome to mechanism.

Zhe Jia, Jing Xu, Yingnan Ma, Siyu Wei, Chen Sun, Xingyu Chen, Jingxuan Kang, Haiyan Chen, Chen Zhang, Yu Dong and 6 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Genes & diseases, 2026. 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. 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

16 authors.

Zhe JiaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Jing XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Yingnan MaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Siyu WeiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Chen SunCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Xingyu ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Jingxuan KangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Haiyan ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Chen ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Yu DongCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Junxian TaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Xuying GuoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Hongchao LvCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Guoping TangDepartment of Medical Engineering, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang 322000, China.
Yongshuai JiangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Mingming ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Expression quantitative trait locus (eQTL) refers to a genetic variation associated with the expression of specific genes. It has been widely applied to explain the regulatory mechanisms linking genetic variations to complex traits or diseases. Several eQTLs have been identified from tissues and single cells in individuals. Furthermore, the integration of eQTL and other omics data can be used to detect novel susceptibility genes and consequently understand the dynamic regulation of trait-associated genetic variations at the system level. Here, we review the identification methods, analysis tools, research progress, and common data resources of eQTLs, as well as their role in four typical diseases. Finally, we discussed the application fields, challenges, and future development perspectives of eQTL.

Indexed as

Expression quantitative trait locusGene expressionGenetic variationGenome-wide association studySingle nucleotide polymorphism

Identifiers

PMID41630952
PMCPMC12860985

What Socratic holds

Textmetadata
LicenceCC BY
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.