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SNP‐HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC‐centric analyses in genomics
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) polymorphis...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7540691/ https://www.ncbi.nlm.nih.gov/pubmed/32681667 http://dx.doi.org/10.1002/gepi.22334 |
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author | Vince, Nicolas Douillard, Venceslas Geffard, Estelle Meyer, Diogo Castelli, Erick C. Mack, Steven J. Limou, Sophie Gourraud, Pierre‐Antoine |
author_facet | Vince, Nicolas Douillard, Venceslas Geffard, Estelle Meyer, Diogo Castelli, Erick C. Mack, Steven J. Limou, Sophie Gourraud, Pierre‐Antoine |
author_sort | Vince, Nicolas |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7540691 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75406912020-10-15 SNP‐HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC‐centric analyses in genomics Vince, Nicolas Douillard, Venceslas Geffard, Estelle Meyer, Diogo Castelli, Erick C. Mack, Steven J. Limou, Sophie Gourraud, Pierre‐Antoine Genet Epidemiol Research Articles 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. John Wiley and Sons Inc. 2020-07-18 2020-10 /pmc/articles/PMC7540691/ /pubmed/32681667 http://dx.doi.org/10.1002/gepi.22334 Text en © 2020 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Research Articles Vince, Nicolas Douillard, Venceslas Geffard, Estelle Meyer, Diogo Castelli, Erick C. Mack, Steven J. Limou, Sophie Gourraud, Pierre‐Antoine SNP‐HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC‐centric analyses in genomics |
title | SNP‐HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC‐centric analyses in genomics |
title_full | SNP‐HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC‐centric analyses in genomics |
title_fullStr | SNP‐HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC‐centric analyses in genomics |
title_full_unstemmed | SNP‐HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC‐centric analyses in genomics |
title_short | SNP‐HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC‐centric analyses in genomics |
title_sort | snp‐hla reference consortium (shlarc): hla and snp data sharing for promoting mhc‐centric analyses in genomics |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7540691/ https://www.ncbi.nlm.nih.gov/pubmed/32681667 http://dx.doi.org/10.1002/gepi.22334 |
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