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

Authors
  • Vince, Nicolas1
  • Douillard, Venceslas1
  • Geffard, Estelle1
  • Meyer, Diogo2
  • Castelli, Erick C3
  • Mack, Steven J4
  • Limou, Sophie1, 5
  • Gourraud, Pierre-Antoine1
  • 1 Centre de Recherche en Transplantation et Immunologie, ITUN, UMR 1064, Université de Nantes, CHU Nantes, Inserm, Nantes, France. , (France)
  • 2 University of São Paulo, São Paulo, Brazil. , (Brazil)
  • 3 UNESP-Universidade Estadual Paulista, Botucatu, São Paulo, Brazil. , (Brazil)
  • 4 Department of Pediatrics, University of California, San Francisco, UCSF Benioff Children's Hospital Oakland, Oakland, California.
  • 5 Ecole Centrale de Nantes, Nantes, France. , (France)
Type
Published Article
Journal
Genetic Epidemiology
Publisher
Wiley (John Wiley & Sons)
Publication Date
Oct 01, 2020
Volume
44
Issue
7
Pages
733–740
Identifiers
DOI: 10.1002/gepi.22334
PMID: 32681667
Source
Medline
Keywords
Language
English
License
Unknown

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. © 2020 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.

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