Evidence map›Paper›PMID 24875393›Full record

ArticlePLoS genetics2014

A genome-wide assessment of the role of untagged copy number variants in type 1 diabetes.

Manuela Zanda, Suna Onengut-Gumuscu, Neil Walker, Corina Shtir, Daniel Gallo, Chris Wallace, Deborah Smyth, John A Todd, Matthew E Hurles, Vincent Plagnol and 1 more

Abstract read
In one paragraph

Article in PLoS genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Type 1 Diabetes Genetics Consortium.The Journal of clinical endocrinology and metabolism · 2025
    Review
  3. The missing heritability in type 1 diabetes.Diabetes, obesity & metabolism · 2022
    Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. 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

11 authors.

Manuela ZandaUniversity College London (UCL) Genetics Institute (UGI), London, United Kingdom; Wellcome Trust Sanger Institute, Hinxton, United Kingdom.
Suna Onengut-GumuscuUniversity of Virginia, Charlottesville, Virginia, United States of America.
Neil WalkerJDRF/Wellcome Trust Diabetes and Inflammation laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.
Corina ShtirJDRF/Wellcome Trust Diabetes and Inflammation laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.
Daniel GalloUniversity of Virginia, Charlottesville, Virginia, United States of America.
Chris WallaceJDRF/Wellcome Trust Diabetes and Inflammation laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.
Deborah SmythJDRF/Wellcome Trust Diabetes and Inflammation laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.
John A ToddJDRF/Wellcome Trust Diabetes and Inflammation laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.
Matthew E HurlesWellcome Trust Sanger Institute, Hinxton, United Kingdom.
Vincent PlagnolUniversity College London (UCL) Genetics Institute (UGI), London, United Kingdom.
Stephen S RichUniversity of Virginia, Charlottesville, Virginia, United States of America.

Funding

The role of copy number variants (CNV) in type 1 diabetesDP3DK085695 · NIDDK · UNIVERSITY OF VIRGINIA · PI RICH, STEPHEN S. · 2009 to 2009
$6.4M
Medical Research Council G1001158NIDDK NIH HHS DP3 DK085695Wellcome Trust 089989Wellcome Trust 091157
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) for type 1 diabetes (T1D) have successfully identified more than 40 independent T1D associated tagging single nucleotide polymorphisms (SNPs). However, owing to technical limitations of copy number variants (CNVs) genotyping assays, the assessment of the role of CNVs has been limited to the subset of these in high linkage disequilibrium with tag SNPs. The contribution of untagged CNVs, often multi-allelic and difficult to genotype using existing assays, to the heritability of T1D remains an open question. To investigate this issue, we designed a custom comparative genetic hybridization array (aCGH) specifically designed to assay untagged CNV loci identified from a variety of sources. To overcome the technical limitations of the case control design for this class of CNVs, we genotyped the Type 1 Diabetes Genetics Consortium (T1DGC) family resource (representing 3,903 transmissions from parents to affected offspring) and used an association testing strategy that does not necessitate obtaining discrete genotypes. Our design targeted 4,309 CNVs, of which 3,410 passed stringent quality control filters. As a positive control, the scan confirmed the known T1D association at the INS locus by direct typing of the 5' variable number of tandem repeat (VNTR) locus. Our results clarify the fact that the disease association is indistinguishable from the two main polymorphic allele classes of the INS VNTR, class I-and class III. We also identified novel technical artifacts resulting into spurious associations at the somatically rearranging loci, T cell receptor, TCRA/TCRD and TCRB, and Immunoglobulin heavy chain, IGH, loci on chromosomes 14q11.2, 7q34 and 14q32.33, respectively. However, our data did not identify novel T1D loci. Our results do not support a major role of untagged CNVs in T1D heritability.

Indexed as

Comparative Genomic HybridizationDNA Copy Number VariationsGenome-Wide Association StudyAllelesDiabetes Mellitus, Type 1Genetic Predisposition to DiseaseGenotypeHumansLinkage DisequilibriumPolymorphism, Single Nucleotide

Identifiers

PMID24875393
PMCPMC4038470

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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.