Evidence map›Paper›PMID 37755483›Full record

ReviewHuman genetics2023

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective.

Zijun Zhu, Xinyu Chen, Sainan Zhang, Rui Yu, Changlu Qi, Liang Cheng, Xue Zhang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Human genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 19% 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

4 citing papers in PubMed, 3 citations in OpenAlex.

  1. Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026
    Review
  2. Article
  3. Review
  4. Review
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

7 authors at 1 institution in 1 country.

Zijun Zhu *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
Xinyu Chen *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
Sainan Zhang *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
Rui YuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
Changlu QiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
Liang ChengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China. liangcheng@hrbmu.edu.cn.ORCID http://orcid.org/0000-0002-6665-6710
Xue ZhangNHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China.
Harbin Medical University · CN

Funding

Heilongjiang Postdoctoral Fund LBH-Q20030National Natural Science Foundation of China 62172130National Natural Science Foundation of China 62222104Tou-Yan Innovation Team Program of the Heilongjiang Province 2019-15
6 · The paper itself

Abstract

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.

Indexed as

Genome-Wide Association StudyQuantitative Trait LociHumansMultifactorial InheritancePhenotypeTranscriptome

Identifiers

PMID37755483
OpenAlexW4387077045

What Socratic holds

Textmetadata
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.