Evidence map›Paper›PMID 25569255›Full record

ArticlePLoS genetics2015

The genetic and mechanistic basis for variation in gene regulation.

Athma A Pai, Jonathan K Pritchard, Yoav Gilad

Open access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
97citing papers in PubMed, 1 pooled it
10.2field-weighted citation impact, top 1% 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

97 citing papers in PubMed, 1 synthesis or guideline pooled it, 178 citations in OpenAlex.

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  20. Role of non-coding variants in cardiovascular disease.Journal of cellular and molecular medicine · 2023
    Review

37 more citing papers are in PubMed but not listed here.

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

3 authors at 3 institutions in 1 country.

Athma A PaiDepartment of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Jonathan K PritchardDepartments of Genetics and Biology, and Howard Hughes Medical Institute; Stanford University, Stanford, California, United States of America.
Yoav GiladDepartment of Human Genetics, University of Chicago, Chicago, Illinois, United States of America.
Howard Hughes Medical Institute · USMassachusetts Institute of Technology · USUniversity of Chicago · US

Funding

Re-Engineering Translational Research at the University of ChicagoUL1TR000430 · NCATS · UNIVERSITY OF CHICAGO · PI SOLWAY, JULIAN · 2012 to 2016
$20.2M
PREDOCTORAL TRAINING IN GENETICS AND REGULATIONT32GM007197 · NIGMS · UNIVERSITY OF CHICAGO · PI ROTHMAN-DENES, LUCIA B. B · 1985 to 2020
$17.0M
Analysis and interpretation of noncoding regulatory variationR01MH084703 · NIMH · UNIVERSITY OF CHICAGO · PI GILAD, YOAV, PRITCHARD, JONATHAN K · 2008 to 2015
$3.4M
Mapping QTLs Associated with Variation in RNA Decay RatesR01HG006123 · NHGRI · UNIVERSITY OF CHICAGO · PI GILAD, YOAV · 2011 to 2013
$1.3M
NCATS NIH HHS UL1 TR000430NHGRI NIH HHS HG006123NHGRI NIH HHS R01 HG006123NIGMS NIH HHS GM007197NIGMS NIH HHS T32 GM007197NIMH NIH HHS MH084703NIMH NIH HHS R01 MH084703
6 · The paper itself

Abstract

It is now well established that noncoding regulatory variants play a central role in the genetics of common diseases and in evolution. However, until recently, we have known little about the mechanisms by which most regulatory variants act. For instance, what types of functional elements in DNA, RNA, or proteins are most often affected by regulatory variants? Which stages of gene regulation are typically altered? How can we predict which variants are most likely to impact regulation in a given cell type? Recent studies, in many cases using quantitative trait loci (QTL)-mapping approaches in cell lines or tissue samples, have provided us with considerable insight into the properties of genetic loci that have regulatory roles. Such studies have uncovered novel biochemical regulatory interactions and led to the identification of previously unrecognized regulatory mechanisms. We have learned that genetic variation is often directly associated with variation in regulatory activities (namely, we can map regulatory QTLs, not just expression QTLs [eQTLs]), and we have taken the first steps towards understanding the causal order of regulatory events (for example, the role of pioneer transcription factors). Yet, in most cases, we still do not know how to interpret overlapping combinations of regulatory interactions, and we are still far from being able to predict how variation in regulatory mechanisms is propagated through a chain of interactions to eventually result in changes in gene expression profiles.

Indexed as

Gene Expression RegulationGenome, HumanChromatinChromosome MappingDNA MethylationGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideQuantitative Trait LociRNA, MessengerTranscription FactorsChromatinRNA, MessengerTranscription Factors

Identifiers

PMID25569255
PMCPMC4287341
OpenAlexW2023155906

What OpenQuestion holds

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LicenceCC BY
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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.