Evidence map›Paper›PMID 26857735›Full record

ReviewInternational immunology2016

Genetics of autoimmune diseases: perspectives from genome-wide association studies.

Yuta Kochi

Abstract readReview
In one paragraph

Review in International immunology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 4 pooled it
–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

40 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

1 author.

Yuta KochiLaboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo 113-8655, Japan yuta.kochi@riken.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-wide association studies (GWASs) for autoimmune diseases (ADs) have identified many risk loci and have provided insights into the etiology of each disease. Some of these loci, such asPTPN22,IL23RandSTAT4, are shared among different ADs, and the combination of risk loci may determine an individual's susceptibility for a disease. The majority of GWAS loci are expression quantitative trait loci (eQTLs), where disease-causing variants regulate expression of neighboring (or sometimes distant) genes. Because the eQTL effects are often cell type-specific, the incorporation of epigenetic data from disease-related cell types and tissues is expected to refine the identification of causal variants. The cumulative eQTL effects in multiple genes may influence the activity or fate of immune cells, which in turn may affect the function of the immune system in individuals. In this paper, I review the etiology of ADs by focusing on important immune cells (Th1 cells, Th17 cells and regulatory T cells), important pathways (antigen-receptor signaling and type I interferon signaling) and relevant genes identified in GWASs.

Indexed as

Genetic Predisposition to DiseaseGenome-Wide Association StudyAnimalsAntigen PresentationAutoimmune DiseasesGene Expression RegulationHistocompatibility AntigensHumansInterferon Type IQuantitative Trait LociReceptors, Antigen, T-CellSignal TransductionT-Lymphocyte SubsetsHistocompatibility AntigensInterferon Type IReceptors, Antigen, T-Cellautoimmune diseaseexpression quantitative trait locusgenome-wide association study

Identifiers

PMID26857735
PMCPMC4889885

What OpenQuestion holds

Textmetadata
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.