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Lemieux W, Richard L, Nunes JM, Sanchez-Mazas A, Renaud C, Sapir-Pichhadze R, Lewin A. A registry-based population study of the HLA in Québec, Canada. HLA 2023; 102:671-689. [PMID: 37439270 DOI: 10.1111/tan.15154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 06/15/2023] [Accepted: 06/28/2023] [Indexed: 07/14/2023]
Abstract
As part of the worldwide effort to better characterize HLA diversity in populations, we have studied the population of Québec in Canada. This province has been defined by a complex history with multiple founder effects and migration patterns. We analyzed the typing data of 3806 individuals registered in Héma-Québec's Registry, which covered most administrative regions in Québec. Typing information was resolved at the second field level of resolution by next-generation sequencing (NGS) or by Sanger sequencing. We used the HLA-net.eu GENE[RATE] tools to estimate allele and two-locus haplotype frequencies for HLA-A, -B, -C, -DRB1, -DQB1, and -DPB1, as well as Hardy-Weinberg equilibrium (HWE), selective neutrality, and linkage disequilibrium. The chord genetic distance was also calculated between administrative regions and was visualized using non-metric multidimensional scaling (NMDS) analysis. While most individual regions were in HWE, HWE was rejected for the province considered as a whole. Some regions exhibited signatures of selection, mostly toward an excess of heterozygotes. Allele and haplotype frequencies revealed outlier regions that strongly differed from the other regions. NMDS plots also showed differences between regions. The administrative regions of the province of Québec displayed heterogeneity in their HLA profiles. This heterogeneity was attributable to differing allele and haplotype specificities by region. In particular, regions 02-Saguenay-Lac-Saint-Jean and 01-Bas-St-Laurent diverged from the rest of the regions. The urban regions 06-Montréal and 13-Laval were very diversified in their HLA profiles. Together, these results will help optimize donor recruitment strategies in Québec.
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Affiliation(s)
- William Lemieux
- Medical Affairs & Innovation, Héma-Québec, Montréal, Quebec, Canada
- Centre for Outcomes Research and Evaluation (CORE), Research Institute of McGill University Health Centre, Montréal, Quebec, Canada
| | - Lucie Richard
- Transfusion Medicine/Reference Laboratory, Héma-Québec, Montréal, Quebec, Canada
| | - José Manuel Nunes
- Laboratory of Anthropology, Genetics and Peopling history, Department of Genetics and Evolution, University of Geneva and Institute of Genetics and Genomics in Geneva (IGE3), Geneva, Switzerland
| | - Alicia Sanchez-Mazas
- Laboratory of Anthropology, Genetics and Peopling history, Department of Genetics and Evolution, University of Geneva and Institute of Genetics and Genomics in Geneva (IGE3), Geneva, Switzerland
| | - Christian Renaud
- Medical Affairs & Innovation, Héma-Québec, Montréal, Quebec, Canada
| | - Ruth Sapir-Pichhadze
- Centre for Outcomes Research and Evaluation (CORE), Research Institute of McGill University Health Centre, Montréal, Quebec, Canada
- Division of Nephrology and the Multi-Organ Transplant Program, Royal Victoria Hospital, McGill University Health Centre, Montréal, Quebec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada
| | - Antoine Lewin
- Medical Affairs & Innovation, Héma-Québec, Montréal, Quebec, Canada
- Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, Quebec, Canada
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Jung K, Kim JG, Shin S, Roh EY, Hong YJ, Song EY. Allele and haplotype frequencies of 11 HLA loci in Koreans by next-generation sequencing. HLA 2023; 101:602-612. [PMID: 36719349 DOI: 10.1111/tan.14980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 12/24/2022] [Accepted: 01/27/2023] [Indexed: 02/01/2023]
Abstract
Data on HLA genotype distribution, including DQA1 and DPA1, in the Korean population are limited. We aimed to investigate the allele and haplotype frequencies of 11 HLA loci in 339 Korean subjects using next-generation sequencing (NGS)-based HLA typing. A total of 339 samples from unrelated healthy subjects were genotyped for HLA-A, -B, -C, -DRB1, -DRB3, -DRB4, -DRB5, -DQB1, -DQA1, -DPB1, and -DPA1 using two different NGS-based HLA typing kits (166 tested using the NGSgo-MX11-3 kit [GenDx, Netherlands] and 173 by the AllType NGS 11 Loci Amplification kit [One Lambda, USA]). PyPop software was used to estimate allele and haplotype frequencies and linkage disequilibrium between the loci. Additionally, a principal component analysis was performed to compare the allele distribution of Koreans with that of other populations. A total of 214 HLA alleles (97 class I and 117 class II alleles) were assigned. The most frequent alleles for each locus were A*24:02:01 (24.78%), B*15:01:01 (10.18%), C*01:02:01 (18.44%), DRB1*04:05:01 (9.59%), DRB3*02:02:01 (13.72%), DRB4*01:03:01 (25.81%), DRB5*01:01:01 (9.0%), DQA1*01:02:01 (16.96%), DQB1*03:01:01 (14.31%), DPA1*01:03:01 (44.4%), and DPB1*05:01:01 (35.1%), respectively. The most frequent haplotypes were A*33:03:01-C*03:02:02-B*58:01:01 for HLA class I (5.01%) and DRB1*04:05:01-DQA1*03:03:01-DQB1*04:01:01-DPA1*02:02:02-DPB1*05:01:01 for HLA class II (6.23%). The total allelic ambiguities by NGS were estimated to be minimal and considerably decreased compared with those by Sanger sequencing. The Japanese population had the most similar allele distribution to Koreans, followed by the Chinese population. Frequency data of 11 HLA loci in Koreans can provide essential data for population genetics and disease association studies.
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Affiliation(s)
- Kiwook Jung
- Department of Laboratory Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Laboratory Medicine, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Jisoo G Kim
- Department of Laboratory Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sue Shin
- Department of Laboratory Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Laboratory Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Eun Youn Roh
- Department of Laboratory Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Laboratory Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Yun Ji Hong
- Department of Laboratory Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Laboratory Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Eun Young Song
- Department of Laboratory Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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Barquera R, Collen E, Di D, Buhler S, Teixeira J, Llamas B, Nunes JM, Sanchez-Mazas A. Binding affinities of 438 HLA proteins to complete proteomes of seven pandemic viruses and distributions of strongest and weakest HLA peptide binders in populations worldwide. HLA 2020; 96:277-298. [PMID: 32475052 PMCID: PMC7300650 DOI: 10.1111/tan.13956] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 05/19/2020] [Accepted: 05/26/2020] [Indexed: 12/11/2022]
Abstract
We report detailed peptide‐binding affinities between 438 HLA Class I and Class II proteins and complete proteomes of seven pandemic human viruses, including coronaviruses, influenza viruses and HIV‐1. We contrast these affinities with HLA allele frequencies across hundreds of human populations worldwide. Statistical modelling shows that peptide‐binding affinities classified into four distinct categories depend on the HLA locus but that the type of virus is only a weak predictor, except in the case of HIV‐1. Among the strong HLA binders (IC50 ≤ 50), we uncovered 16 alleles (the top ones being A*02:02, B*15:03 and DRB1*01:02) binding more than 1% of peptides derived from all viruses, 9 (top ones including HLA‐A*68:01, B*15:25, C*03:02 and DRB1*07:01) binding all viruses except HIV‐1, and 15 (top ones A*02:01 and C*14:02) only binding coronaviruses. The frequencies of strongest and weakest HLA peptide binders differ significantly among populations from different geographic regions. In particular, Indigenous peoples of America show both higher frequencies of strongest and lower frequencies of weakest HLA binders. As many HLA proteins are found to be strong binders of peptides derived from distinct viral families, and are hence promiscuous (or generalist), we discuss this result in relation to possible signatures of natural selection on HLA promiscuous alleles due to past pathogenic infections. Our findings are highly relevant for both evolutionary genetics and the development of vaccine therapies. However they should not lead to forget that individual resistance and vulnerability to diseases go beyond the sole HLA allelic affinity and depend on multiple, complex and often unknown biological, environmental and other variables.
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Affiliation(s)
- Rodrigo Barquera
- Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, Germany
| | - Evelyn Collen
- Australian Centre for Ancient DNA (ACAD), Department of Genetics and Evolution, The University of Adelaide, Adelaide, South Australia, Australia
| | - Da Di
- Anthropology Unit, Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland
| | - Stéphane Buhler
- Anthropology Unit, Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland.,Transplantation Immunology Unit and National Reference Laboratory for Histocompatibility, Department of Diagnostic, Geneva University Hospitals, Geneva, Switzerland
| | - João Teixeira
- Australian Centre for Ancient DNA (ACAD), Department of Genetics and Evolution, The University of Adelaide, Adelaide, South Australia, Australia.,School of Biological Sciences, Centre of Excellence for Australian Biodiversity and Heritage, The University of Adelaide, Adelaide, South Australia, Australia
| | - Bastien Llamas
- School of Biological Sciences, Centre of Excellence for Australian Biodiversity and Heritage, The University of Adelaide, Adelaide, South Australia, Australia.,The Environment Institute, The University of Adelaide, Adelaide, South Australia, Australia
| | - José M Nunes
- Anthropology Unit, Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland.,Institute of Genetics and Genomics in Geneva (IGE3), University of Geneva, Geneva, Switzerland
| | - Alicia Sanchez-Mazas
- Anthropology Unit, Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland.,Institute of Genetics and Genomics in Geneva (IGE3), University of Geneva, Geneva, Switzerland
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