Evidence map›Paper›PMID 35773720›Full record

ArticleEpigenetics & chromatin2022

3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk.

Ning Liu, Timothy Sadlon, Ying Y Wong, Stephen Pederson, James Breen, Simon C Barry

Open access · goldAbstract read
In one paragraph

Article in Epigenetics & chromatin, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 25% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Aberrant Enhancer Regulation, Phase Separation, and Autoimmune Diseases.Clinical reviews in allergy & immunology · 2025
    Review
  3. Article
  4. Review
  5. 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

6 authors at 4 institutions in 1 country.

Ning Liu *South Australian Health and Medical Research Institute, Adelaide, Australia.
Timothy Sadlon *Robinson Research Institute, University of Adelaide, Adelaide, Australia.
Ying Y WongRobinson Research Institute, University of Adelaide, Adelaide, Australia.
Stephen PedersonBioinformatics Hub, School of Biological Sciences, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia.
James Breen *South Australian Health and Medical Research Institute, Adelaide, Australia. jimmy.breen@telethonkids.org.au.ORCID 0000-0001-6184-0925
Simon C Barry *Robinson Research Institute, University of Adelaide, Adelaide, Australia.
Women's and Children's Hospital · AUAustralian National University · AUSouth Australian Health and Medical Research Institute · AUThe University of Adelaide · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenome-wide association studies (GWAS) have enabled the discovery of single nucleotide polymorphisms (SNPs) that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the identified variants lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. To address this problem, we developed a variant filtering workflow called 3DFAACTS-SNP to link genetic variants to target genes in a cell-specific manner. Here, we use 3DFAACTS-SNP to identify candidate SNPs and target genes associated with the loss of immune tolerance in regulatory T cells (Treg) in T1D.

resultsUsing 3DFAACTS-SNP, we identified from a list of 1228 previously fine-mapped variants, 36 SNPs with plausible Treg-specific mechanisms of action. The integration of cell type-specific chromosome conformation capture data in 3DFAACTS-SNP identified 266 regulatory regions and 47 candidate target genes that interact with these variant-containing regions in Treg cells. We further demonstrated the utility of the workflow by applying it to three other SNP autoimmune datasets, identifying 16 Treg-centric candidate variants and 60 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of all known common (> 10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 9376 candidate variants and 4968 candidate target genes, generating a list of potential sites for future T1D or other autoimmune disease research.

conclusionsWe demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function, and illustrate the power of using cell type-specific multi-omics datasets to determine disease mechanisms. Our workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

Indexed as

Diabetes Mellitus, Type 1T-Lymphocytes, RegulatoryEpigenesis, GeneticGenome-Wide Association StudyHumansPolymorphism, Single NucleotideAutoimmune diseaseFunctional annotationHi-CRegulatory T cellsTranscription factor bindingType 1 diabetes

Identifiers

PMID35773720
PMCPMC9244893
OpenAlexW4283732064

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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.