Evidence map›Paper›PMID 30804403›Full record

ArticleScientific reports2019

Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases.

Marco Cavalli, Nicholas Baltzer, Husen M Umer, Jan Grau, Ioana Lemnian, Gang Pan, Ola Wallerman, Rapolas Spalinskas, Pelin Sahlén, Ivo Grosse and 2 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

15 citing papers in PubMed, 33 citations in OpenAlex.

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  13. Epigenomics and the kidney.Current opinion in nephrology and hypertension · 2020
    Review
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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

12 authors at 4 institutions in 3 countries.

Marco CavalliScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Nicholas BaltzerDepartment of Cell and Molecular Biology, Computational Biology and Bioinformatics, Uppsala University, Uppsala, Sweden.ORCID 0000-0001-8505-403X
Husen M UmerDepartment of Cell and Molecular Biology, Computational Biology and Bioinformatics, Uppsala University, Uppsala, Sweden.
Jan GrauInstitute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.ORCID 0000-0003-2081-6405
Ioana LemnianInstitute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.
Gang PanScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Ola WallermanScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Rapolas SpalinskasScience for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID 0000-0002-1648-6426
Pelin SahlénScience for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
Ivo GrosseInstitute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.
Jan KomorowskiDepartment of Cell and Molecular Biology, Computational Biology and Bioinformatics, Uppsala University, Uppsala, Sweden.
Claes WadeliusScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden. claes.wadelius@igp.uu.se.
Uppsala University · SEMartin Luther University Halle-Wittenberg · DEScience for Life Laboratory · SEGerman Centre for Integrative Biodiversity Research · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several Genome Wide Association Studies (GWAS) have reported variants associated to immune diseases. However, the identified variants are rarely the drivers of the associations and the molecular mechanisms behind the genetic contributions remain poorly understood. ChIP-seq data for TFs and histone modifications provide snapshots of protein-DNA interactions allowing the identification of heterozygous SNPs showing significant allele specific signals (AS-SNPs). AS-SNPs can change a TF binding site resulting in altered gene regulation and are primary candidates to explain associations observed in GWAS and expression studies. We identified 17,293 unique AS-SNPs across 7 lymphoblastoid cell lines. In this set of cell lines we interrogated 85% of common genetic variants in the population for potential regulatory effect and we identified 237 AS-SNPs associated to immune GWAS traits and 714 to gene expression in B cells. To elucidate possible regulatory mechanisms we integrated long-range 3D interactions data to identify putative target genes and motif predictions to identify TFs whose binding may be affected by AS-SNPs yielding a collection of 173 AS-SNPs associated to gene expression and 60 to B cell related traits. We present a systems strategy to find functional gene regulatory variants, the TFs that bind differentially between alleles and novel strategies to detect the regulated genes.

Indexed as

AllelesBinding SitesChromatinGenetic Predisposition to DiseaseGenome-Wide Association StudyHLA AntigensHumansPolymorphism, Single NucleotideProtein BindingChromatinHLA Antigens

Identifiers

PMID30804403
PMCPMC6389883
OpenAlexW2917609425

What OpenQuestion 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 OpenQuestion graph.