Evidence map›Paper›PMID 32681667›Full record

ArticleGenetic epidemiology2020

SNP-HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC-centric analyses in genomics.

Nicolas Vince, Venceslas Douillard, Estelle Geffard, Diogo Meyer, Erick C Castelli, Steven J Mack, Sophie Limou, Pierre-Antoine Gourraud

Abstract read
In one paragraph

Article in Genetic epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. The genetic variantFrontiers in oncology · 2023
    Article
  7. Article
  8. Review
  9. An immunogenetic view of COVID-19.Genetics and molecular biology · 2021
    Article
  10. 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

8 authors.

Nicolas VinceCentre de Recherche en Transplantation et Immunologie, ITUN, UMR 1064, Université de Nantes, CHU Nantes, Inserm, Nantes, France.ORCID 0000-0002-3767-6210
Venceslas DouillardCentre de Recherche en Transplantation et Immunologie, ITUN, UMR 1064, Université de Nantes, CHU Nantes, Inserm, Nantes, France.ORCID 0000-0002-6762-4083
Estelle GeffardCentre de Recherche en Transplantation et Immunologie, ITUN, UMR 1064, Université de Nantes, CHU Nantes, Inserm, Nantes, France.
Diogo MeyerUniversity of São Paulo, São Paulo, Brazil.
Erick C CastelliUNESP-Universidade Estadual Paulista, Botucatu, São Paulo, Brazil.
Steven J MackDepartment of Pediatrics, University of California, San Francisco, UCSF Benioff Children's Hospital Oakland, Oakland, California.
Sophie LimouCentre de Recherche en Transplantation et Immunologie, ITUN, UMR 1064, Université de Nantes, CHU Nantes, Inserm, Nantes, France.
Pierre-Antoine GourraudCentre de Recherche en Transplantation et Immunologie, ITUN, UMR 1064, Université de Nantes, CHU Nantes, Inserm, Nantes, France.

Funding

Integrated Exchange and Storage of Current- and Future-Generation Immunogenomic DataR01AI128775 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI JILL Allison HOLLENBACH, STEVEN JOHN MACK · 2017 to 2026
$4.5M
6 · The paper itself

Abstract

Genome-wide associations studies have repeatedly identified the major histocompatibility complex genomic region (6p21.3) as key in immune pathologies. Researchers have also aimed to extend the biological interpretation of associations by focusing directly on human leukocyte antigen (HLA) polymorphisms and their combination as haplotypes. To circumvent the effort and high costs of HLA typing, statistical solutions have been developed to infer HLA alleles from single-nucleotide polymorphism (SNP) genotyping data. Though HLA imputation methods have been developed, no unified effort has yet been undertaken to share large and diverse imputation models, or to improve methods. By training the HIBAG software on SNP + HLA data generated by the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) to create reference panels, we highlighted the importance of (a) the number of individuals in reference panels, with a twofold increase in accuracy (from 10 to 100 individuals) and (b) the number of SNPs, with a 1.5-fold increase in accuracy (from 500 to 24,504 SNPs). Results showed improved accuracy with CAAPA compared to the African American models available in HIBAG, highlighting the need for precise population-matching. The SNP-HLA Reference Consortium is an international endeavor to gather data, enhance HLA imputation and broaden access to highly accurate imputation models for the immunogenomics community.

Indexed as

AllelesAsthmaBlack or African AmericanGene FrequencyGenome, HumanGenomicsGenotypeHaplotypesHLA AntigensHumansInformation DisseminationModels, GeneticPolymorphism, Single NucleotideWhite PeopleHLA AntigensconsortiumHLAimputationSNP

Identifiers

PMID32681667
PMCPMC7540691

What OpenQuestion holds

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LicenceCC BY-NC
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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.