Evidence map›Paper›PMID 29101457›Full record

ArticleHuman genetics2017

A functional strategy to characterize expression Quantitative Trait Loci.

Elena Grassi, Elisa Mariella, Mattia Forneris, Federico Marotta, Marika Catapano, Ivan Molineris, Paolo Provero

Abstract read
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In one paragraph

Article in Human genetics, 2017. 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. Exploring functionally annotated transcriptional consensus regulatory elements with CONREL.Database : the journal of biological databases and curation · 2020
    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

7 authors.

Elena GrassiDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Turin, Turin, Italy.
Elisa MariellaDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Turin, Turin, Italy.
Mattia FornerisDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Turin, Turin, Italy.
Federico MarottaDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Turin, Turin, Italy.
Marika CatapanoDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Turin, Turin, Italy.
Ivan MolinerisCenter for Translational Genomics and Bioinformatics, San Raffaele Scientific Institute IRCCS, Milan, Italy.
Paolo ProveroDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Turin, Turin, Italy. paolo.provero@unito.it.ORCID 0000-0001-8664-8630

Funding

Compagnia di San Paolo Torino_call2014_L2_252
6 · The paper itself

Abstract

The study of genetic variation has been revolutionized by the advent of high-throughput technologies able to determine the complete genomic sequence of thousands of individuals. Understanding the functional relevance of variants is, however, still a difficult task, especially when focusing on non-coding variants. Most of the variants associated with disease by Genome-Wide Association Studies (GWAS) are indeed non-coding, and presumably exert their effects by altering gene regulation. Expression Quantitative Trait Loci (eQTL) studies represent an important step in understanding the functional relevance of regulatory variants. We propose a new strategy to detect and characterize eQTLs, based on the effect of variants on the Total Binding Affinity (TBA) profiles of regulatory regions. Using a large dataset of coupled genome and expression data, we show that TBA-based inference allows the identification of eQTLs not revealed by traditional methods and helps in their interpretation in terms of altered transcription factor binding.

Indexed as

Gene Expression RegulationGenome-Wide Association StudyPolymorphism, Single NucleotideQuantitative Trait LociRegulatory Sequences, Nucleic AcidTranscription, GeneticGenomicsHumans

Identifiers

PMID29101457

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.