Evidence map›Paper›PMID 32797243›Full record

ReviewDiabetologia2020

Next steps in the identification of gene targets for type 1 diabetes.

Struan F A Grant, Andrew D Wells, Stephen S Rich

Open access · hybridAbstract readReview
In one paragraph

Review in Diabetologia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

16 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Type 1 Diabetes Genetics Consortium.The Journal of clinical endocrinology and metabolism · 2025
    Review
  5. Advancing Animal Models of Human Type 1 Diabetes.Cold Spring Harbor perspectives in medicine · 2024
    Review
  6. Review
  7. Review
  8. Article
  9. Clinical and experimental treatment of type 1 diabetes.Clinical and experimental immunology · 2022
    Review
  10. Review
  11. Review
  12. Review
  13. De-coding genetic risk variants in type 1 diabetes.Immunology and cell biology · 2021
    Review
  14. Type 1 diabetes mellitus: much progress, many opportunities.The Journal of clinical investigation · 2021
    Review
  15. Review
  16. 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

3 authors at 2 institutions in 1 country.

Struan F A GrantCenter for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.ORCID 0000-0003-2025-5302
Andrew D WellsCenter for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.ORCID 0000-0002-3630-2145
Stephen S RichCenter for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA. ssr4n@virginia.edu.ORCID 0000-0003-3872-7793
Children's Hospital of Philadelphia · USUniversity of Virginia · US

Funding

Type 1 Diabetes Genetics ConsortiumU01DK062418 · NIDDK · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI RICH, STEPHEN S. · 2002 to 2007
$59.8M
Growth Factor Signaling, Cell Division and T cell AnergyR01AI054643 · NIAID · CHILDREN'S HOSP OF PHILADELPHIA · PI WELLS, ANDREW D · 2002 to 2020
$5.7M
Functional Mechanisms of T1D Risk Variants and their Target Genes using 3D Epigenomics and Single Cell ApproachesR01DK122586 · NIDDK · UNIVERSITY OF VIRGINIA · PI GRANT, STRUAN F A, RICH, STEPHEN S. · 2019 to 2022
$4.8M
Systematic identification of functional T1D-associated non-coding SNPs using genetic, transcriptomic and epigenetic methodsDP3DK111906 · NIDDK · UNIVERSITY OF VIRGINIA · PI RICH, STEPHEN S. · 2016 to 2016
$4.0M
Genome Wide Association Study of Latent Autoimmune Diabetes in AdultsR01DK085212 · NIDDK · CHILDREN'S HOSP OF PHILADELPHIA · PI GRANT, STRUAN F A · 2011 to 2016
$3.4M
Network-based algorithms for target identification and drug repositioning from genetic associationsR01HG010067 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI GREENE, CASEY S · 2018 to 2022
$3.2M
Epigenetic imprinting of follicular helper T cell fate and function in lupusR01AI123539 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI LAUFER, TERRI M. · 2017 to 2020
$3.1M
Treating chronic viral infection by epigenetic reprogramming of exhausted CD8 T cellsR01AI130115 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI ABT, MICHAEL C. · 2017 to 2021
$2.7M
NHGRI NIH HHS R01 HG010067NIAID NIH HHS R01 AI054643NIAID NIH HHS R01 AI123539NIAID NIH HHS R01 AI130115NIDDK NIH HHS DP3 DK111906NIDDK NIH HHS R01 DK085212NIDDK NIH HHS R01 DK122586NIDDK NIH HHS U01 DK062418
6 · The paper itself

Abstract

The purpose of this review is to provide a view of the future of genomics and other omics approaches in defining the genetic contribution to all stages of risk of type 1 diabetes and the functional impact and clinical implementations of the associated variants. From the recognition nearly 50 years ago that genetics (in the form of HLA) distinguishes risk of type 1 diabetes from type 2 diabetes, advances in technology and sample acquisition through collaboration have identified over 60 loci harbouring SNPs associated with type 1 diabetes risk. Coupled with HLA region genes, these variants account for the majority of the genetic risk (~50% of the total risk); however, relatively few variants are located in coding regions of genes exerting a predicted protein change. The vast majority of genetic risk in type 1 diabetes appears to be attributed to regions of the genome involved in gene regulation, but the target effectors of those genetic variants are not readily identifiable. Although past genetic studies clearly implicated immune-relevant cell types involved in risk, the target organ (the beta cell) was left untouched. Through emergent technologies, using combinations of genetics, gene expression, epigenetics, chromosome conformation and gene editing, novel landscapes of how SNPs regulate genes have emerged. Furthermore, both the immune system and the beta cell and their biological pathways have been implicated in a context-specific manner. The use of variants from immune and beta cell studies distinguish type 1 diabetes from type 2 diabetes and, when they are combined in a genetic risk score, open new avenues for prediction and treatment. Graphical abstract.

Indexed as

Diabetes Mellitus, Type 1Genetic Predisposition to DiseaseGenome-Wide Association StudyHumansPolymorphism, Single NucleotideQuantitative Trait LociChromatinEnhancerseQTLsGeneticsPredictionReviewTarget genesType 1 Diabetes

Identifiers

PMID32797243
PMCPMC7527360
OpenAlexW3049231614

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.