Evidence map›Paper›PMID 37207929›Full record

ReviewJournal of genetics and genomics = Yi chuan xue bao2023

eQTL studies: from bulk tissues to single cells.

Jingfei Zhang, Hongyu Zhao

Abstract readReview
In one paragraph

Review in Journal of genetics and genomics = Yi chuan xue bao, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 1 pooled it
–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

55 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Jingfei ZhangInformation Systems and Operations Management, Emory University, Atlanta, GA 30322, USA.
Hongyu ZhaoDepartment of Biostatistics, Yale School of Public Health, New Haven, CT 208034, USA. Electronic address: hongyu.zhao@yale.edu.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Sex-Specific Single Cell Expression Profiles, Genetic Risk and Drug Responsiveness in Alzheimer's DiseaseR56AG074015 · NIA · YALE UNIVERSITY · PI GHOSH, SOURAV, ROTHLIN, CARLA · 2021 to 2022
$2.5M
Novel statistical methods and tools to integrate multiple endophenotypes and functional annotation data to study the roles of rare variants in complex human diseases using sequencing dataR01GM134005 · NIGMS · YALE UNIVERSITY · PI WU, BAOLIN, ZHAO, HONGYU · 2020 to 2023
$1.6M
NCATS NIH HHS UL1 TR001863NIA NIH HHS R56 AG074015NIGMS NIH HHS R01 GM134005
6 · The paper itself

Abstract

An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.

Indexed as

Gene Expression RegulationQuantitative Trait LociGenome-Wide Association StudyPolymorphism, Single NucleotideBulk samplesCell-type-specificContext-dependenteQTLSingle cellTissues

Identifiers

PMID37207929
PMCPMC10656365

What OpenQuestion holds

Textmetadata
LicenceTDM
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.