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1
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AlOraibi S, Taurin S, Alshammary S. Advancements in Umbilical Cord Biobanking: A Comprehensive Review of Current Trends and Future Prospects. Stem Cells Cloning 2024; 17:41-58. [PMID: 39655226 PMCID: PMC11626973 DOI: 10.2147/sccaa.s481072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Accepted: 11/01/2024] [Indexed: 12/12/2024] Open
Abstract
Biobanking has emerged as a transformative concept in advancing the medical field, particularly with the exponential growth of umbilical cord (UC) biobanking in recent decades. UC blood and tissue provide a rich source of primitive hematopoietic stem cells (HSCs) and mesenchymal stem cells (MSCs) for clinical transplantation, offering distinct advantages over alternative adult stem cell sources. However, to fully realize the therapeutic potential of UC-derived stem cells and establish a comprehensive global UC-biobanking network, it is imperative to optimize and standardize UC processing, cryopreservation methods, quality control protocols, and regulatory frameworks, alongside developing effective consent provisions. This review aims to comprehensively explore recent advancements in UC biobanking, focusing on the establishment of rigorous safety and quality control procedures, the standardization of biobanking operations, and the optimization and automation of UC processing and cryopreservation techniques. Additionally, the review examines the expanded clinical applications of UC stem cells, addresses the challenges associated with umbilical cord biobanking and UC-derived stem cell therapies, and discusses the promising role of artificial intelligence (AI) in enhancing various operational aspects of biobanking, streamlining data processing, and improving data analysis accuracy while ensuring compliance with safety and quality standards. By addressing these critical areas, this review seeks to provide insights into the future direction of UC biobanking and its potential to significantly impact regenerative medicine.
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Affiliation(s)
- Sahar AlOraibi
- Molecular Medicine Department, Princess Al Jawhara Center for Molecular Medicine, Genetics, and Hereditary Diseases, College of Medicine and Health Sciences, Arabian Gulf University, Manama, Bahrain
| | - Sebastien Taurin
- Molecular Medicine Department, Princess Al Jawhara Center for Molecular Medicine, Genetics, and Hereditary Diseases, College of Medicine and Health Sciences, Arabian Gulf University, Manama, Bahrain
| | - Sfoug Alshammary
- Molecular Medicine Department, Princess Al Jawhara Center for Molecular Medicine, Genetics, and Hereditary Diseases, College of Medicine and Health Sciences, Arabian Gulf University, Manama, Bahrain
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2
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Huang J, Tengvall K, Lima IB, Hedström AK, Butt J, Brenner N, Gyllenberg A, Stridh P, Khademi M, Ernberg I, Al Nimer F, Manouchehrinia A, Hillert J, Alfredsson L, Andersen O, Sundström P, Waterboer T, Olsson T, Kockum I. Genetics of immune response to Epstein-Barr virus: prospects for multiple sclerosis pathogenesis. Brain 2024; 147:3573-3582. [PMID: 38630618 PMCID: PMC11449136 DOI: 10.1093/brain/awae110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 02/20/2024] [Accepted: 02/29/2024] [Indexed: 04/19/2024] Open
Abstract
Epstein-Barr virus (EBV) infection has been advocated as a prerequisite for developing multiple sclerosis (MS) and possibly the propagation of the disease. However, the precise mechanisms for such influences are still unclear. A large-scale study investigating the host genetics of EBV serology and related clinical manifestations, such as infectious mononucleosis (IM), may help us better understand the role of EBV in MS pathogenesis. This study evaluates the host genetic factors that influence serological response against EBV and history of IM and cross-evaluates them with MS risk and genetic susceptibility in the Swedish population. Plasma IgG antibody levels against EBV nuclear antigen-1 [EBNA-1, truncated = amino acids (aa) (325-641), peptide = aa(385-420)] and viral capsid antigen p18 (VCAp18) were measured using bead-based multiplex serology for 8744 MS cases and 7229 population-matched control subjects. The MS risk association for high/low EBV antibody levels and history of IM was compared to relevant clinical measures along with sex, age at sampling, and associated HLA allele variants. Genome-wide and HLA allele association analyses were also performed to identify genetic risk factors for EBV antibody response and IM history. Higher antibody levels against VCAp18 [odds ratio (OR) = 1.74, 95% confidence interval (CI) = 1.60-1.88] and EBNA-1, particularly the peptide (OR = 3.13, 95% CI = 2.93-3.35), were associated with an increased risk for MS. The risk increased with higher anti-EBNA-1 IgG levels up to 12× the reference risk. We also identified several independent HLA haplotypes associated with EBV serology overlapping with known MS risk alleles (e.g. DRB1*15:01). Although there were several candidates, no variants outside the HLA region reached genome-wide significance. Cumulative HLA risk for anti-EBNA-1 IgG levels, particularly the peptide fragment, was strongly associated with MS. In contrast, the genetic risk for high anti-VCAp18 IgG levels was not as strongly associated with MS risk. IM history was not associated with class II HLA genes but negatively associated with A*02:01, which is protective against MS. Our findings emphasize that the risk association between anti-EBNA-1 IgG levels and MS may be partly due to overlapping HLA associations. Additionally, the increasing MS risk with increasing anti-EBNA-1 levels would be consistent with a pathogenic role of the EBNA-1 immune response, perhaps through molecular mimicry. Given that high anti-EBNA-1 antibodies may reflect a poorly controlled T-cell defence against the virus, our findings would be consistent with DRB1*15:01 being a poor class II antigen in the immune defence against EBV. Last, the difference in genetic control of IM supports the independent roles of EBNA-1 and IM in MS susceptibility.
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Affiliation(s)
- Jesse Huang
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Centrum for Molecular Medicine, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Katarina Tengvall
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Centrum for Molecular Medicine, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, SE 751 23 Uppsala, Sweden
| | - Izaura Bomfim Lima
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Centrum for Molecular Medicine, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Anna Karin Hedström
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Julia Butt
- Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), DE-69120 Heidelberg, Germany
| | - Nicole Brenner
- Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), DE-69120 Heidelberg, Germany
| | - Alexandra Gyllenberg
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Centrum for Molecular Medicine, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Pernilla Stridh
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Centrum for Molecular Medicine, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Mohsen Khademi
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Centrum for Molecular Medicine, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Ingemar Ernberg
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Faiez Al Nimer
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Centrum for Molecular Medicine, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Ali Manouchehrinia
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Centrum for Molecular Medicine, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Lars Alfredsson
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Centre for Occupational and Environmental Medicine, Stockholm County Council, SE-171 77 Stockholm, Sweden
| | - Oluf Andersen
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburgh, Sweden
| | - Peter Sundström
- Department of Clinical Science, Neurosciences, Umeå University, SE-901 85 Umeå, Sweden
| | - Tim Waterboer
- Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), DE-69120 Heidelberg, Germany
| | - Tomas Olsson
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Centrum for Molecular Medicine, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Ingrid Kockum
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Centrum for Molecular Medicine, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
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3
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Naito T, Okada Y. Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology. J Hum Genet 2024; 69:481-486. [PMID: 38225263 PMCID: PMC11422162 DOI: 10.1038/s10038-023-01213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/23/2023] [Accepted: 12/04/2023] [Indexed: 01/17/2024]
Abstract
The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation methods for genome-wide variants with the capability of learning complex linkage disequilibrium patterns have been developed. Additionally, deep learning-based imputation has been applied to a distinct genomic region known as the major histocompatibility complex, referred to as HLA imputation. Despite their various advantages, the current deep learning-based genotype imputation methods do have certain limitations and have not yet become standard. These limitations include the modest accuracy improvement over statistical and conventional machine learning-based methods. However, their benefits include other aspects, such as their "reference-free" nature, which ensures complete privacy protection, and their higher computational efficiency. Furthermore, the continuing evolution of deep learning technologies is expected to contribute to further improvements in prediction accuracy and usability in the future.
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Affiliation(s)
- Tatsuhiko Naito
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan.
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
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4
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Tanaka K, Kato K, Nonaka N, Seita J. Efficient HLA imputation from sequential SNPs data by transformer. J Hum Genet 2024; 69:533-540. [PMID: 39095607 PMCID: PMC11422163 DOI: 10.1038/s10038-024-01278-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 07/18/2024] [Accepted: 07/19/2024] [Indexed: 08/04/2024]
Abstract
Human leukocyte antigen (HLA) genes are associated with a variety of diseases, yet the direct typing of HLA alleles is both time-consuming and costly. Consequently, various imputation methods leveraging sequential single nucleotide polymorphisms (SNPs) data have been proposed, employing either statistical or deep learning models, such as the convolutional neural network (CNN)-based model, DEEP*HLA. However, these methods exhibit limited imputation efficiency for infrequent alleles and necessitate a large size of reference dataset. In this context, we have developed a Transformer-based model to HLA allele imputation, named "HLA Reliable IMpuatioN by Transformer (HLARIMNT)" designed to exploit the sequential nature of SNPs data. We evaluated HLARIMNT's performance using two distinct reference panels; Pan-Asian reference panel (n = 530) and Type 1 Diabetes genetics Consortium (T1DGC) reference panel (n = 5225), alongside a combined panel (n = 1060). HLARIMNT demonstrated superior accuracy to DEEP*HLA across several indices, particularly for infrequent alleles. Furthermore, we explored the impact of varying training data sizes on imputation accuracy, finding that HLARIMNT consistently outperformed across all data size. These findings suggest that Transformer-based models can efficiently impute not only HLA types but potentially other gene types from sequential SNPs data.
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Affiliation(s)
- Kaho Tanaka
- Faculty of Engineering, Kyoto University, Kyoto, Japan
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Tokyo, Japan
| | - Kosuke Kato
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Tokyo, Japan
| | - Naoki Nonaka
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Tokyo, Japan
| | - Jun Seita
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Tokyo, Japan.
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5
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Ingvarsson J, Grut V, Biström M, Berg LP, Stridh P, Huang J, Hillert J, Alfredsson L, Kockum I, Olsson T, Waterboer T, Nilsson S, Sundström P. Rubella virus seropositivity after infection or vaccination as a risk factor for multiple sclerosis. Eur J Neurol 2024; 31:e16387. [PMID: 39023088 DOI: 10.1111/ene.16387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/27/2024] [Accepted: 05/31/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND Multiple sclerosis (MS) is a demyelinating disease affecting millions of people worldwide. Hereditary susceptibility and environmental factors contribute to disease risk. Infection with Epstein-Barr virus (EBV) and human herpesvirus 6A (HHV-6A) have previously been associated with MS risk. Other neurotropic viruses, such as rubella virus (RV), are possible candidates in MS aetiopathogenesis, but previous results are limited and conflicting. METHODS In this nested case-control study of biobank samples in a Swedish cohort, we analysed the serological response towards RV before the clinical onset of MS with a bead-based multiplex assay in subjects vaccinated and unvaccinated towards RV. The association between RV seropositivity and MS risk was analysed with conditional logistic regression. RESULTS Seropositivity towards RV was associated with an increased risk of MS for unvaccinated subjects, even when adjusting for plausible confounders including EBV, HHV-6A, cytomegalovirus and vitamin D (adjusted odds ratio [AOR] = 4.0, 95% confidence interval [CI] 1.8-8.8). Cases also had stronger antibody reactivity towards rubella than controls, which was not seen for other neurotropic viruses such as herpes simplex or varicella zoster. Furthermore, we observed an association between RV seropositivity and MS in vaccinated subjects. However, this association was not significant when adjusting for the aforementioned confounders (AOR = 1.7, 95% CI 1.0-2.9). CONCLUSIONS To our knowledge, these are the first reported associations between early RV seropositivity and later MS development. This suggests a broadening of the virus hypothesis in MS aetiology, where molecular mimicry between rubella epitopes and human central nervous system molecules could be an attractive possible mechanism.
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Affiliation(s)
- Jens Ingvarsson
- Department of Clinical Sciences, Neurosciences, Umeå University, Umeå, Sweden
| | - Viktor Grut
- Department of Clinical Sciences, Neurosciences, Umeå University, Umeå, Sweden
| | - Martin Biström
- Department of Clinical Sciences, Neurosciences, Umeå University, Umeå, Sweden
| | - Linn Persson Berg
- Department of Infectious Diseases, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Microbiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Pernilla Stridh
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Jesse Huang
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Lars Alfredsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ingrid Kockum
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Tomas Olsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Tim Waterboer
- Division of Infections and Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
| | - Staffan Nilsson
- Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Peter Sundström
- Department of Clinical Sciences, Neurosciences, Umeå University, Umeå, Sweden
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6
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Tammi S, Koskela S, Hyvärinen K, Partanen J, Ritari J. Accurate multi-population imputation of MICA, MICB, HLA-E, HLA-F and HLA-G alleles from genome SNP data. PLoS Comput Biol 2024; 20:e1011718. [PMID: 39283896 PMCID: PMC11426482 DOI: 10.1371/journal.pcbi.1011718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 09/26/2024] [Accepted: 08/31/2024] [Indexed: 09/27/2024] Open
Abstract
In addition to the classical HLA genes, the major histocompatibility complex (MHC) harbors a high number of other polymorphic genes with less established roles in disease associations and transplantation matching. To facilitate studies of the non-classical and non-HLA genes in large patient and biobank cohorts, we trained imputation models for MICA, MICB, HLA-E, HLA-F and HLA-G alleles on genome SNP array data. We show, using both population-specific and multi-population 1000 Genomes references, that the alleles of these genes can be accurately imputed for screening and research purposes. The best imputation model for MICA, MICB, HLA-E, -F and -G achieved a mean accuracy of 99.3% (min, max: 98.6, 99.9). Furthermore, validation of the 1000 Genomes exome short-read sequencing-based allele calling against a clinical-grade reference data showed an average accuracy of 99.8%, testifying for the quality of the 1000 Genomes data as an imputation reference. We also fitted the models for Infinium Global Screening Array (GSA, Illumina, Inc.) and Axiom Precision Medicine Research Array (PMRA, Thermo Fisher Scientific Inc.) SNP content, with mean accuracies of 99.1% (97.2, 100) and 98.9% (97.4, 100), respectively.
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Affiliation(s)
- Silja Tammi
- Finnish Red Cross Blood Service, Research and Development, Helsinki, Finland
| | - Satu Koskela
- Finnish Red Cross Blood Service, Research and Development, Helsinki, Finland
- Finnish Red Cross Blood Service, Blood Service Biobank, Vantaa, Finland
| | | | - Kati Hyvärinen
- Finnish Red Cross Blood Service, Research and Development, Helsinki, Finland
| | - Jukka Partanen
- Finnish Red Cross Blood Service, Research and Development, Helsinki, Finland
- Finnish Red Cross Blood Service, Blood Service Biobank, Vantaa, Finland
| | - Jarmo Ritari
- Finnish Red Cross Blood Service, Research and Development, Helsinki, Finland
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7
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Sakaue S, Gurajala S, Curtis M, Luo Y, Choi W, Ishigaki K, Kang JB, Rumker L, Deutsch AJ, Schönherr S, Forer L, LeFaive J, Fuchsberger C, Han B, Lenz TL, de Bakker PIW, Okada Y, Smith AV, Raychaudhuri S. Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease. Nat Protoc 2023; 18:2625-2641. [PMID: 37495751 PMCID: PMC10786448 DOI: 10.1038/s41596-023-00853-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 04/27/2023] [Indexed: 07/28/2023]
Abstract
The human leukocyte antigen (HLA) locus is associated with more complex diseases than any other locus in the human genome. In many diseases, HLA explains more heritability than all other known loci combined. In silico HLA imputation methods enable rapid and accurate estimation of HLA alleles in the millions of individuals that are already genotyped on microarrays. HLA imputation has been used to define causal variation in autoimmune diseases, such as type I diabetes, and in human immunodeficiency virus infection control. However, there are few guidelines on performing HLA imputation, association testing, and fine mapping. Here, we present a comprehensive tutorial to impute HLA alleles from genotype data. We provide detailed guidance on performing standard quality control measures for input genotyping data and describe options to impute HLA alleles and amino acids either locally or using the web-based Michigan Imputation Server, which hosts a multi-ancestry HLA imputation reference panel. We also offer best practice recommendations to conduct association tests to define the alleles, amino acids, and haplotypes that affect human traits. Along with the pipeline, we provide a step-by-step online guide with scripts and available software ( https://github.com/immunogenomics/HLA_analyses_tutorial ). This tutorial will be broadly applicable to large-scale genotyping data and will contribute to defining the role of HLA in human diseases across global populations.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saisriram Gurajala
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michelle Curtis
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Wanson Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Joyce B Kang
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Laurie Rumker
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Aaron J Deutsch
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - Lukas Forer
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - Jonathon LeFaive
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Christian Fuchsberger
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Buhm Han
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea
| | - Tobias L Lenz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany
| | - Paul I W de Bakker
- Data and Computational Sciences, Vertex Pharmaceuticals, Boston, MA, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Albert V Smith
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, UK.
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8
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Clancy J, Hyvärinen K, Ritari J, Wahlfors T, Partanen J, Koskela S. Blood donor biobank and HLA imputation as a resource for HLA homozygous cells for therapeutic and research use. STEM CELL RESEARCH & THERAPY 2022; 13:502. [PMID: 36210465 PMCID: PMC9549658 DOI: 10.1186/s13287-022-03182-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/15/2022] [Indexed: 11/10/2022]
Abstract
Abstract
Background
Allogeneic therapeutic cells may be rejected if they express HLA alleles not found in the recipient. As finding cell donors with a full HLA match to a recipient requires vast donor pools, the use of HLA homozygous cells has been suggested as an alternative. HLA homozygous cells should be well tolerated by those who carry at least one copy of donor HLA alleles. HLA-A-B homozygotes could be valuable for HLA-matched thrombocyte products. We evaluated the feasibility of blood donor biobank and HLA imputation for the identification of potential cell donors homozygous for HLA alleles.
Methods
We imputed HLA-A, -B, -C, -DRB1, -DQA1, -DQB1 and -DPB1 alleles from genotypes of 20,737 Finnish blood donors in the Blood Service Biobank. We confirmed homozygosity by sequencing HLA alleles in 30 samples and by examining 36,161 MHC-located polymorphic DNA markers.
Results
Three hundred and seventeen individuals (1.5%), representing 41 different haplotypes, were found to be homozygous for HLA-A, -B, -C, -DRB1, -DQA1 and -DQB1 alleles. Ten most frequent haplotypes homozygous for HLA-A to -DQB1 were HLA-compatible with 49.5%, and three most frequent homozygotes to 30.4% of the Finnish population. Ten most frequent HLA-A-B homozygotes were compatible with 75.3%, and three most frequent haplotypes to 42.6% of the Finnish population. HLA homozygotes had a low level of heterozygosity in MHC-located DNA markers, in particular in HLA haplotypes enriched in Finland.
Conclusions
The present study shows that HLA imputation in a blood donor biobank of reasonable size can be used to identify HLA homozygous blood donors suitable for cell therapy, HLA-typed thrombocytes and research. The homozygotes were HLA-compatible with a large fraction of the Finnish population. Regular blood donors reported to have positive attitude to research donation appear a good option for these purposes. Differences in population frequencies of HLA haplotypes emphasize the need for population-specific collections of HLA homozygous samples.
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9
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Naito T, Okada Y. HLA imputation and its application to genetic and molecular fine-mapping of the MHC region in autoimmune diseases. Semin Immunopathol 2022; 44:15-28. [PMID: 34786601 PMCID: PMC8837514 DOI: 10.1007/s00281-021-00901-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 10/22/2021] [Indexed: 12/19/2022]
Abstract
Variations of human leukocyte antigen (HLA) genes in the major histocompatibility complex region (MHC) significantly affect the risk of various diseases, especially autoimmune diseases. Fine-mapping of causal variants in this region was challenging due to the difficulty in sequencing and its inapplicability to large cohorts. Thus, HLA imputation, a method to infer HLA types from regional single nucleotide polymorphisms, has been developed and has successfully contributed to MHC fine-mapping of various diseases. Different HLA imputation methods have been developed, each with its own advantages, and recent methods have been improved in terms of accuracy and computational performance. Additionally, advances in HLA reference panels by next-generation sequencing technologies have enabled higher resolution and a more reliable imputation, allowing a finer-grained evaluation of the association between sequence variations and disease risk. Risk-associated variants in the MHC region would affect disease susceptibility through complicated mechanisms including alterations in peripheral responses and central thymic selection of T cells. The cooperation of reliable HLA imputation methods, informative fine-mapping, and experimental validation of the functional significance of MHC variations would be essential for further understanding of the role of the MHC in the immunopathology of autoimmune diseases.
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Affiliation(s)
- Tatsuhiko Naito
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Osaka, Suita, 565-0871, Japan.
- Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Osaka, Suita, 565-0871, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
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10
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Suarez-Pajes E, Díaz-García C, Rodríguez-Pérez H, Lorenzo-Salazar JM, Marcelino-Rodríguez I, Corrales A, Zheng X, Callero A, Perez-Rodriguez E, Garcia-Robaina JC, González-Montelongo R, Flores C, Guillen-Guio B. Targeted analysis of genomic regions enriched in African ancestry reveals novel classical HLA alleles associated with asthma in Southwestern Europeans. Sci Rep 2021; 11:23686. [PMID: 34880287 PMCID: PMC8654850 DOI: 10.1038/s41598-021-02893-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/24/2021] [Indexed: 12/30/2022] Open
Abstract
Despite asthma has a considerable genetic component, an important proportion of genetic risks remain unknown, especially for non-European populations. Canary Islanders have the largest African genetic ancestry observed among Southwestern Europeans and the highest asthma prevalence in Spain. Here we examined broad chromosomal regions previously associated with an excess of African genetic ancestry in Canary Islanders, with the aim of identifying novel risk variants associated with asthma susceptibility. In a two-stage cases-control study, we revealed a variant within HLA-DQB1 significantly associated with asthma risk (rs1049213, meta-analysis p = 1.30 × 10-7, OR [95% CI] = 1.74 [1.41-2.13]) previously associated with asthma and broad allergic phenotype. Subsequent fine-mapping analyses of classical HLA alleles revealed a novel allele significantly associated with asthma protection (HLA-DQA1*01:02, meta-analysis p = 3.98 × 10-4, OR [95% CI] = 0.64 [0.50-0.82]) that had been linked to infectious and autoimmune diseases, and peanut allergy. HLA haplotype analyses revealed a novel haplotype DQA1*01:02-DQB1*06:04 conferring asthma protection (meta-analysis p = 4.71 × 10-4, OR [95% CI] = 0.47 [0.29- 0.73]).
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Affiliation(s)
- Eva Suarez-Pajes
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | - Claudio Díaz-García
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | - Héctor Rodríguez-Pérez
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | - Jose M Lorenzo-Salazar
- Genomics Division, Instituto Tecnológico Y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain
| | - Itahisa Marcelino-Rodríguez
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | - Almudena Corrales
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Xiuwen Zheng
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ariel Callero
- Allergy Unit, Hospital Universitario N.S. de Candelaria, Santa Cruz de Tenerife, Spain
| | - Eva Perez-Rodriguez
- Allergy Unit, Hospital Universitario N.S. de Candelaria, Santa Cruz de Tenerife, Spain
| | - Jose C Garcia-Robaina
- Allergy Unit, Hospital Universitario N.S. de Candelaria, Santa Cruz de Tenerife, Spain
| | | | - Carlos Flores
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.
- Genomics Division, Instituto Tecnológico Y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.
| | - Beatriz Guillen-Guio
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.
- Department of Health Sciences, University of Leicester, Leicester, UK.
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11
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Douillard V, Castelli EC, Mack SJ, Hollenbach JA, Gourraud PA, Vince N, Limou S. Approaching Genetics Through the MHC Lens: Tools and Methods for HLA Research. Front Genet 2021; 12:774916. [PMID: 34925459 PMCID: PMC8677840 DOI: 10.3389/fgene.2021.774916] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/08/2021] [Indexed: 01/11/2023] Open
Abstract
The current SARS-CoV-2 pandemic era launched an immediate and broad response of the research community with studies both about the virus and host genetics. Research in genetics investigated HLA association with COVID-19 based on in silico, population, and individual data. However, they were conducted with variable scale and success; convincing results were mostly obtained with broader whole-genome association studies. Here, we propose a technical review of HLA analysis, including basic HLA knowledge as well as available tools and advice. We notably describe recent algorithms to infer and call HLA genotypes from GWAS SNPs and NGS data, respectively, which opens the possibility to investigate HLA from large datasets without a specific initial focus on this region. We thus hope this overview will empower geneticists who were unfamiliar with HLA to run MHC-focused analyses following the footsteps of the Covid-19|HLA & Immunogenetics Consortium.
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Affiliation(s)
- Venceslas Douillard
- Centre de Recherche en Transplantation et Immunologie, CHU Nantes, Inserm, Centre de Recherche en Transplantation et Immunologie, Université de Nantes, Nantes, France
| | | | - Steven J. Mack
- Division of Allergy, Immunology and Bone Marrow Transplantation, Department of Pediatrics, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Jill A. Hollenbach
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States
| | - Pierre-Antoine Gourraud
- Centre de Recherche en Transplantation et Immunologie, CHU Nantes, Inserm, Centre de Recherche en Transplantation et Immunologie, Université de Nantes, Nantes, France
| | - Nicolas Vince
- Centre de Recherche en Transplantation et Immunologie, CHU Nantes, Inserm, Centre de Recherche en Transplantation et Immunologie, Université de Nantes, Nantes, France
| | - Sophie Limou
- Centre de Recherche en Transplantation et Immunologie, CHU Nantes, Inserm, Centre de Recherche en Transplantation et Immunologie, Université de Nantes, Nantes, France
- Ecole Centrale de Nantes, Department of Computer Sciences and Mathematics in Biology, Nantes, France
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12
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Ha E, Bae SC, Kim K. Recent advances in understanding the genetic basis of systemic lupus erythematosus. Semin Immunopathol 2021; 44:29-46. [PMID: 34731289 DOI: 10.1007/s00281-021-00900-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/14/2021] [Indexed: 12/22/2022]
Abstract
Systemic lupus erythematosus (SLE) is a polygenic chronic autoimmune disease leading to multiple organ damage. A large heritability of up to 66% is estimated in SLE, with roughly 180 reported susceptibility loci that have been identified mostly by genome-wide association studies (GWASs) and account for approximately 30% of genetic heritability. A vast majority of risk variants reside in non-coding regions, which makes it quite challenging to interpret their functional implications in the SLE-affected immune system, suggesting the importance of understanding cell type-specific epigenetic regulation around SLE GWAS variants. The latest genetic studies have been highly fruitful as several dozens of SLE loci were newly discovered in the last few years and many loci have come to be understood in systemic approaches integrating GWAS signals with other biological resources. In this review, we summarize SLE-associated genetic variants in both the major histocompatibility complex (MHC) and non-MHC loci, examining polygenetic risk scores for SLE and their associations with clinical features. Finally, variant-driven pathogenetic functions underlying genetic associations are described, coupled with discussion about challenges and future directions in genetic studies on SLE.
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Affiliation(s)
- Eunji Ha
- Department of Biology, Kyung Hee University, Seoul, Republic of Korea.,Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Seoul, Republic of Korea
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea. .,Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea.
| | - Kwangwoo Kim
- Department of Biology, Kyung Hee University, Seoul, Republic of Korea. .,Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Seoul, Republic of Korea.
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13
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Barizzone N, Cagliani R, Basagni C, Clarelli F, Mendozzi L, Agliardi C, Forni D, Tosi M, Mascia E, Favero F, Corà D, Corrado L, Sorosina M, Esposito F, Zuccalà M, Vecchio D, Liguori M, Comi C, Comi G, Martinelli V, Filippi M, Leone M, Martinelli-Boneschi F, Caputo D, Sironi M, Guerini FR, D’Alfonso S. An Investigation of the Role of Common and Rare Variants in a Large Italian Multiplex Family of Multiple Sclerosis Patients. Genes (Basel) 2021; 12:1607. [PMID: 34681001 PMCID: PMC8535321 DOI: 10.3390/genes12101607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 09/26/2021] [Accepted: 10/01/2021] [Indexed: 12/30/2022] Open
Abstract
Known multiple sclerosis (MS) susceptibility variants can only explain half of the disease's estimated heritability, whereas low-frequency and rare variants may partly account for the missing heritability. Thus, here we sought to determine the occurrence of rare functional variants in a large Italian MS multiplex family with five affected members. For this purpose, we combined linkage analysis and next-generation sequencing (NGS)-based whole exome and whole genome sequencing (WES and WGS, respectively). The genetic burden attributable to known common MS variants was also assessed by weighted genetic risk score (wGRS). We found a significantly higher burden of common variants in the affected family members compared to that observed among sporadic MS patients and healthy controls (HCs). We also identified 34 genes containing at least one low-frequency functional variant shared among all affected family members, showing a significant enrichment in genes involved in specific biological processes-particularly mRNA transport-or neurodegenerative diseases. Altogether, our findings point to a possible pathogenic role of different low-frequency functional MS variants belonging to shared pathways. We propose that these rare variants, together with other known common MS variants, may account for the high number of affected family members within this MS multiplex family.
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Affiliation(s)
- Nadia Barizzone
- Department of Health Sciences, CAAD (Center for Translational Research on Autoimmune and Allergic Diseases), University of Eastern Piedmont, 28100 Novara, Italy; (C.B.); (M.T.); (L.C.); (M.Z.)
| | - Rachele Cagliani
- Bioinformatics, Scientific Institute IRCCS E.MEDEA, 23842 Bosisio Parini, Italy; (R.C.); (D.F.); (M.S.)
| | - Chiara Basagni
- Department of Health Sciences, CAAD (Center for Translational Research on Autoimmune and Allergic Diseases), University of Eastern Piedmont, 28100 Novara, Italy; (C.B.); (M.T.); (L.C.); (M.Z.)
| | - Ferdinando Clarelli
- Laboratory of Genetics of Neurological Complex Disorders, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (F.C.); (E.M.); (M.S.); (F.E.)
| | - Laura Mendozzi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148 Milan, Italy; (L.M.); (C.A.); (D.C.); (F.R.G.)
| | - Cristina Agliardi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148 Milan, Italy; (L.M.); (C.A.); (D.C.); (F.R.G.)
| | - Diego Forni
- Bioinformatics, Scientific Institute IRCCS E.MEDEA, 23842 Bosisio Parini, Italy; (R.C.); (D.F.); (M.S.)
| | - Martina Tosi
- Department of Health Sciences, CAAD (Center for Translational Research on Autoimmune and Allergic Diseases), University of Eastern Piedmont, 28100 Novara, Italy; (C.B.); (M.T.); (L.C.); (M.Z.)
| | - Elisabetta Mascia
- Laboratory of Genetics of Neurological Complex Disorders, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (F.C.); (E.M.); (M.S.); (F.E.)
| | - Francesco Favero
- Department of Translational Medicine, CAAD (Center for Translational Research on Autoimmune and Allergic Diseases), University of Eastern Piedmont, 28100 Novara, Italy; (F.F.); (D.C.)
| | - Davide Corà
- Department of Translational Medicine, CAAD (Center for Translational Research on Autoimmune and Allergic Diseases), University of Eastern Piedmont, 28100 Novara, Italy; (F.F.); (D.C.)
| | - Lucia Corrado
- Department of Health Sciences, CAAD (Center for Translational Research on Autoimmune and Allergic Diseases), University of Eastern Piedmont, 28100 Novara, Italy; (C.B.); (M.T.); (L.C.); (M.Z.)
| | - Melissa Sorosina
- Laboratory of Genetics of Neurological Complex Disorders, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (F.C.); (E.M.); (M.S.); (F.E.)
| | - Federica Esposito
- Laboratory of Genetics of Neurological Complex Disorders, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (F.C.); (E.M.); (M.S.); (F.E.)
| | - Miriam Zuccalà
- Department of Health Sciences, CAAD (Center for Translational Research on Autoimmune and Allergic Diseases), University of Eastern Piedmont, 28100 Novara, Italy; (C.B.); (M.T.); (L.C.); (M.Z.)
| | - Domizia Vecchio
- Department of Translational Medicine, IRCAD (Interdisciplinary Research Center of Autoimmune Diseases), University of Eastern Piedmont, 28100 Novara, Italy; (D.V.); (C.C.)
| | - Maria Liguori
- Institute of Biomedical Technologies, Bari Unit, National Research Council, 70126 Bari, Italy;
| | - Cristoforo Comi
- Department of Translational Medicine, IRCAD (Interdisciplinary Research Center of Autoimmune Diseases), University of Eastern Piedmont, 28100 Novara, Italy; (D.V.); (C.C.)
| | - Giancarlo Comi
- Vita-Salute San Raffaele University, 20132 Milan, Italy; (G.C.); (M.F.)
| | - Vittorio Martinelli
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;
| | - Massimo Filippi
- Vita-Salute San Raffaele University, 20132 Milan, Italy; (G.C.); (M.F.)
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Maurizio Leone
- Dipartimento di Emergenza e Area Critica, UO Neurologia, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013 Foggia, Italy;
| | - Filippo Martinelli-Boneschi
- Department of Pathophysiology and Transplantation (DEPT), Dino Ferrari Centre, Neuroscience Section, University of Milan, 20122 Milan, Italy;
- Neurology Unit and MS Centre, Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Domenico Caputo
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148 Milan, Italy; (L.M.); (C.A.); (D.C.); (F.R.G.)
| | - Manuela Sironi
- Bioinformatics, Scientific Institute IRCCS E.MEDEA, 23842 Bosisio Parini, Italy; (R.C.); (D.F.); (M.S.)
| | - Franca Rosa Guerini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148 Milan, Italy; (L.M.); (C.A.); (D.C.); (F.R.G.)
| | - Sandra D’Alfonso
- Department of Health Sciences, CAAD (Center for Translational Research on Autoimmune and Allergic Diseases), University of Eastern Piedmont, 28100 Novara, Italy; (C.B.); (M.T.); (L.C.); (M.Z.)
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14
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Luo Y, Kanai M, Choi W, Li X, Sakaue S, Yamamoto K, Ogawa K, Gutierrez-Arcelus M, Gregersen PK, Stuart PE, Elder JT, Forer L, Schönherr S, Fuchsberger C, Smith AV, Fellay J, Carrington M, Haas DW, Guo X, Palmer ND, Chen YDI, Rotter JI, Taylor KD, Rich SS, Correa A, Wilson JG, Kathiresan S, Cho MH, Metspalu A, Esko T, Okada Y, Han B, McLaren PJ, Raychaudhuri S. A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response. Nat Genet 2021; 53:1504-1516. [PMID: 34611364 PMCID: PMC8959399 DOI: 10.1038/s41588-021-00935-7] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 08/02/2021] [Indexed: 02/08/2023]
Abstract
Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.
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Affiliation(s)
- Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Immunology, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Masahiro Kanai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Wanson Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Xinyi Li
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kotaro Ogawa
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Neurology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Maria Gutierrez-Arcelus
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Peter K Gregersen
- The Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research,North Short LIJ Health System, Manhasset, NY, USA
| | - Philip E Stuart
- Department of Dermatology, University of Michigan, Ann Arbor, MI, USA
| | - James T Elder
- Department of Dermatology, University of Michigan, Ann Arbor, MI, USA
- Ann Arbor Veterans Affairs Hospital, Ann Arbor, MI, USA
| | - Lukas Forer
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Christian Fuchsberger
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Albert V Smith
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jacques Fellay
- Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- School of Life Sciences, EPFL, Lausanne, Switzerland
| | - Mary Carrington
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
- Ragon Institute of MGH, MIT and Harvard, Boston, MA, USA
| | - David W Haas
- Vanderbilt University Medical Center, Nashville, TN, USA
- Meharry Medical College, Nashville, TN, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Adolfo Correa
- Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - James G Wilson
- Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Sekar Kathiresan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiology Division of the Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tonu Esko
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Buhm Han
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea
| | - Paul J McLaren
- J.C. Wilt Infectious Diseases Research Centre, National Microbiology Laboratories, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Immunology, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, UK.
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15
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Dicks KL, Pemberton JM, Ballingall KT, Johnston SE. MHC class IIa haplotypes derived by high-throughput SNP screening in an isolated sheep population. G3-GENES GENOMES GENETICS 2021; 11:6298591. [PMID: 34568908 PMCID: PMC8496268 DOI: 10.1093/g3journal/jkab200] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 05/12/2021] [Indexed: 12/01/2022]
Abstract
Investigating the current evolutionary processes acting on a highly polymorphic gene region, such as the major histocompatibility complex (MHC), requires extensive population data for both genotypes and phenotypes. The MHC consists of several tightly linked loci with both allelic and gene content variation, making it challenging to genotype. Eight class IIa haplotypes have previously been identified in the Soay sheep (Ovis aries) of St. Kilda using Sanger sequencing and cloning, but no single locus is representative of all haplotypes. Here, we exploit the closed nature of the island population of Soay sheep and its limited haplotypic variation to identify a panel of SNPs that enable imputation of MHC haplotypes. We compared MHC class IIa haplotypes determined by Sanger sequence-based genotyping of 135 individuals to their SNP profiles generated using the Ovine Infinium HD BeadChip. A panel of 11 SNPs could reliably determine MHC diplotypes, and two additional SNPs within the DQA1 gene enabled detection of a recombinant haplotype affecting only the SNPs downstream of the expressed genes. The panel of 13 SNPs was genotyped in 5951 Soay sheep, of which 5349 passed quality control. Using the Soay sheep pedigree, we were able to trace the origin and inheritance of the recombinant SNP haplotype. This SNP-based method has enabled the rapid generation of locus-specific MHC genotypes for large numbers of Soay sheep. This volume of high-quality genotypes in a well-characterized population of free-living sheep will be valuable for investigating the mechanisms maintaining diversity at the MHC.
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Affiliation(s)
- Kara L Dicks
- School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Josephine M Pemberton
- School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Keith T Ballingall
- Moredun Research Institute, Pentlands Science Park, Edinburgh EH26 0PZ, UK
| | - Susan E Johnston
- School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
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16
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Kherreh N, Cleary S, Seoighe C. No evidence that HLA genotype influences the driver mutations that occur in cancer patients. Cancer Immunol Immunother 2021; 71:819-827. [PMID: 34417841 PMCID: PMC8921139 DOI: 10.1007/s00262-021-03028-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 07/30/2021] [Indexed: 01/15/2023]
Abstract
The major histocompatibility (MHC) molecules are capable of presenting neoantigens resulting from somatic mutations on cell surfaces, potentially directing immune responses against cancer. This led to the hypothesis that cancer driver mutations may occur in gaps in the capacity to present neoantigens that are dependent on MHC genotype. If this is correct, it has important implications for understanding oncogenesis and may help to predict driver mutations based on genotype data. In support of this hypothesis, it has been reported that driver mutations that occur frequently tend to be poorly presented by common MHC alleles and that the capacity of a patient’s MHC alleles to present the resulting neoantigens is predictive of the driver mutations that are observed in their tumor. Here we show that these reports of a strong relationship between driver mutation occurrence and patient MHC alleles are a consequence of unjustified statistical assumptions. Our reanalysis of the data provides no evidence of an effect of MHC genotype on the oncogenic mutation landscape.
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Affiliation(s)
- Noor Kherreh
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland
| | - Siobhán Cleary
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland
| | - Cathal Seoighe
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland.
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17
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Prins BP, Leitsalu L, Pärna K, Fischer K, Metspalu A, Haller T, Snieder H. Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example. J Pers Med 2021; 11:jpm11050358. [PMID: 33946982 PMCID: PMC8145318 DOI: 10.3390/jpm11050358] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/19/2021] [Accepted: 04/25/2021] [Indexed: 02/07/2023] Open
Abstract
The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.
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Affiliation(s)
- Bram Peter Prins
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Correspondence: (B.P.P.); (H.S.)
| | - Liis Leitsalu
- Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; (L.L.); (K.P.); (K.F.); (A.M.); (T.H.)
| | - Katri Pärna
- Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; (L.L.); (K.P.); (K.F.); (A.M.); (T.H.)
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
- Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia
| | - Krista Fischer
- Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; (L.L.); (K.P.); (K.F.); (A.M.); (T.H.)
- Institute of Mathematics and Statistics, University of Tartu, 50409 Tartu, Estonia
| | - Andres Metspalu
- Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; (L.L.); (K.P.); (K.F.); (A.M.); (T.H.)
| | - Toomas Haller
- Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; (L.L.); (K.P.); (K.F.); (A.M.); (T.H.)
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
- Correspondence: (B.P.P.); (H.S.)
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18
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Choi W, Luo Y, Raychaudhuri S, Han B. HATK: HLA analysis toolkit. Bioinformatics 2021; 37:416-418. [PMID: 32735319 PMCID: PMC8058762 DOI: 10.1093/bioinformatics/btaa684] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 07/01/2020] [Accepted: 07/24/2020] [Indexed: 01/19/2023] Open
Abstract
Summary Fine-mapping human leukocyte antigen (HLA) genes involved in disease susceptibility to individual alleles or amino acid residues has been challenging. Using information regarding HLA alleles obtained from HLA typing, HLA imputation or HLA inference, our software expands the alleles to amino acid sequences using the most recent IMGT/HLA database and prepares a dataset suitable for fine-mapping analysis. Our software also provides useful functionalities, such as various association tests, visualization tools and nomenclature conversion. Availability and implementation https://github.com/WansonChoi/HATK.
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Affiliation(s)
- Wanson Choi
- Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Yang Luo
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Soumya Raychaudhuri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.,Centre for Genetics and Genomics Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PL, UK
| | - Buhm Han
- Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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19
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Naito T, Suzuki K, Hirata J, Kamatani Y, Matsuda K, Toda T, Okada Y. A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes. Nat Commun 2021; 12:1639. [PMID: 33712626 PMCID: PMC7955122 DOI: 10.1038/s41467-021-21975-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/19/2021] [Indexed: 01/31/2023] Open
Abstract
Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), DEEP*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. DEEP*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply DEEP*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10-120). Our study illustrates the value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping.
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Affiliation(s)
- Tatsuhiko Naito
- grid.136593.b0000 0004 0373 3971Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan ,grid.26999.3d0000 0001 2151 536XDepartment of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ken Suzuki
- grid.136593.b0000 0004 0373 3971Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Jun Hirata
- grid.136593.b0000 0004 0373 3971Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan ,grid.419889.50000 0004 1779 3502Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited, Hino, Japan
| | - Yoichiro Kamatani
- grid.26999.3d0000 0001 2151 536XLaboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Koichi Matsuda
- grid.26999.3d0000 0001 2151 536XLaboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Tatsushi Toda
- grid.26999.3d0000 0001 2151 536XDepartment of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- grid.136593.b0000 0004 0373 3971Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan ,grid.136593.b0000 0004 0373 3971Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan ,grid.136593.b0000 0004 0373 3971Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
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20
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Cook S, Choi W, Lim H, Luo Y, Kim K, Jia X, Raychaudhuri S, Han B. Accurate imputation of human leukocyte antigens with CookHLA. Nat Commun 2021; 12:1264. [PMID: 33627654 PMCID: PMC7904773 DOI: 10.1038/s41467-021-21541-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 02/01/2021] [Indexed: 01/31/2023] Open
Abstract
The recent development of imputation methods enabled the prediction of human leukocyte antigen (HLA) alleles from intergenic SNP data, allowing studies to fine-map HLA for immune phenotypes. Here we report an accurate HLA imputation method, CookHLA, which has superior imputation accuracy compared to previous methods. CookHLA differs from other approaches in that it locally embeds prediction markers into highly polymorphic exons to account for exonic variability, and in that it adaptively learns the genetic map within MHC from the data to facilitate imputation. Our benchmarking with real datasets shows that our method achieves high imputation accuracy in a wide range of scenarios, including situations where the reference panel is small or ethnically unmatched.
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Affiliation(s)
- Seungho Cook
- Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul, South Korea
- Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Wanson Choi
- Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyunjoon Lim
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, South Korea
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kunhee Kim
- Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul, South Korea
- Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Xiaoming Jia
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Centre for Genetics and Genomics Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Buhm Han
- Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul, South Korea.
- Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea.
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, South Korea.
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21
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Dilthey AT. State-of-the-art genome inference in the human MHC. Int J Biochem Cell Biol 2021; 131:105882. [PMID: 33189874 DOI: 10.1016/j.biocel.2020.105882] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 10/29/2020] [Accepted: 11/04/2020] [Indexed: 12/20/2022]
Abstract
The Major Histocompatibility Complex (MHC) on the short arm of chromosome 6 is associated with more diseases than any other region of the genome; it encodes the antigen-presenting Human Leukocyte Antigen (HLA) proteins and is one of the key immunogenetic regions of the genome. Accurate genome inference and interpretation of MHC association signals have traditionally been hampered by the region's uniquely complex features, such as high levels of polymorphism; inter-gene sequence homologies; structural variation; and long-range haplotype structures. Recent algorithmic and technological advances have, however, significantly increased the accessibility of genetic variation in the MHC; these developments include (i) accurate SNP-based HLA type imputation; (ii) genome graph approaches for variation-aware genome inference from next-generation sequencing data; (iii) long-read-based diploid de novo assembly of the MHC; (iv) cost-effective targeted MHC sequencing methods. Applied to hundreds of thousands of samples over the last years, these technologies have already enabled significant biological discoveries, for example in the field of autoimmune disease genetics. Remaining challenges concern the development of integrated methods that leverage haplotype-resolved de novo assembly of the MHC for the development of improved MHC genotyping methods for short reads and the construction of improved reference panels for SNP-based imputation. Improved genome inference in the MHC can crucially contribute to an improved genetic and functional understanding of many immune-related phenotypes and diseases.
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Affiliation(s)
- Alexander T Dilthey
- Institute of Medical Statistics and Computational Biology, University of Cologne, Cologne, Germany; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany; Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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22
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Current insights into the genetics of food allergy. J Allergy Clin Immunol 2021; 147:15-28. [PMID: 33436162 DOI: 10.1016/j.jaci.2020.10.039] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 10/02/2020] [Accepted: 10/30/2020] [Indexed: 12/19/2022]
Abstract
Food allergy (FA), a growing public health burden in the United States, and familial aggregation studies support strong roles for both genes and environment in FA risk. Deepening our understanding of the molecular and cellular mechanisms driving FAs is paramount to improving its prevention, diagnosis, and clinical management. In this review, we document lessons learned from the genetics of FA that have aided our understanding of these mechanisms. Although current genetic association studies suffer from low power, heterogeneity in definition of FA, and difficulty in our ability to truly disentangle FA from food sensitization (FS) and general atopy genetics, they reveal a set of genetic loci, genes, and variants that continue to implicate the importance of barrier and immune function genes across the atopic march, and FA in particular. The largest reported effects on FA are from MALT1 (odds ratio, 10.99), FLG (average odds ratio, ∼2.9), and HLA (average odds ratio, ∼2.03). The biggest challenge in the field of FA genetics is to elucidate the specific mechanism of action on FA risk and pathogenesis for these loci, and integrative approaches including genetics/genomics with transcriptomics, proteomics, and metabolomics will be critical next steps to translating these genetic insights into practice.
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23
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Castro-Santos P, Olloquequi J, Verdugo RA, Gutiérrez MA, Pinochet C, Quiñones LA, Díaz-Peña R. HLA-DRB1*07:01 and * 08:02 Alleles Confer a Protective Effect Against ACPA-Positive Rheumatoid Arthritis in a Latin American Admixed Population. BIOLOGY 2020; 9:biology9120467. [PMID: 33327594 PMCID: PMC7765073 DOI: 10.3390/biology9120467] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 12/11/2020] [Accepted: 12/13/2020] [Indexed: 06/12/2023]
Abstract
HLA-DRB1 shared epitope (SE) alleles are important genetic contributors for the risk of developing anti-citrullinated protein antibodies (ACPA)-positive rheumatoid arthritis (RA), particularly in Caucasians. We aimed to analyze the contribution of HLA-DRB1 alleles and single nucleotide polymorphisms (SNPs) within the major histocompatibility complex (MHC) region to the susceptibility to develop ACPA-positive RA in a Latin American (LA) population with admixed ancestry. A total of 289 ACPA-positive RA patients and 510 controls were enrolled in this study. The presence of HLA-DRB1*04:01, *09:01 and *10:01 was increased in ACPA-positive RA patients compared with healthy controls (p < 0.0001, p < 0.001 and p < 0.01, respectively), whereas DRB1*07:01 and *08:02 was associated with a decreased risk of ACPA-positive RA (p < 0.001 and p < 0.01, respectively). These results showed a strong correlation with estimates from studies in Asians but not in Caucasian populations. The present study describes the protective effects of the HLA-DRB1*07:01 and *08:02 alleles in ACPA-positive RA patients in a LA population for the first time. Identifying relationships between HLA-DRB1 alleles and RA is important for identifying disease associations in different ethnic groups in order to reach a better understanding of RA worldwide.
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Affiliation(s)
- Patricia Castro-Santos
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca 3460000, Chile; (P.C.-S.); (J.O.)
- Inmunología, Centro de Investigaciones Biomédicas (CINBIO), Universidad de Vigo, 36310 Vigo, Spain
| | - Jordi Olloquequi
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca 3460000, Chile; (P.C.-S.); (J.O.)
| | - Ricardo A. Verdugo
- Programa de Genética Humana, ICBM, Facultad de Medicina, Universidad de Chile, Santiago 8389100, Chile;
- Departamento de Oncología Básico-Clínico, Facultad de Medicina, Universidad de Chile, Santiago 8389100, Chile
| | - Miguel A. Gutiérrez
- Rheumatology, Almirante Nef Naval Hospital, Viña del Mar, Valparaíso 2340000, Chile;
- School of Medicine, Valparaíso University, Valparaíso 2340000, Chile
| | | | - Luis A. Quiñones
- Laboratory of Chemical Carcinogenesis and Pharmacogenetics, Department of Basic-Clinical Oncology, Faculty of Medicine, University of Chile, Santiago 8320000, Chile
- Latin American Network for Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), 28015 Madrid, Spain
| | - Roberto Díaz-Peña
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca 3460000, Chile; (P.C.-S.); (J.O.)
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24
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Wang J, Jelcic I, Mühlenbruch L, Haunerdinger V, Toussaint NC, Zhao Y, Cruciani C, Faigle W, Naghavian R, Foege M, Binder TMC, Eiermann T, Opitz L, Fuentes-Font L, Reynolds R, Kwok WW, Nguyen JT, Lee JH, Lutterotti A, Münz C, Rammensee HG, Hauri-Hohl M, Sospedra M, Stevanovic S, Martin R. HLA-DR15 Molecules Jointly Shape an Autoreactive T Cell Repertoire in Multiple Sclerosis. Cell 2020; 183:1264-1281.e20. [PMID: 33091337 PMCID: PMC7707104 DOI: 10.1016/j.cell.2020.09.054] [Citation(s) in RCA: 150] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 08/04/2020] [Accepted: 09/18/2020] [Indexed: 12/16/2022]
Abstract
The HLA-DR15 haplotype is the strongest genetic risk factor for multiple sclerosis (MS), but our understanding of how it contributes to MS is limited. Because autoreactive CD4+ T cells and B cells as antigen-presenting cells are involved in MS pathogenesis, we characterized the immunopeptidomes of the two HLA-DR15 allomorphs DR2a and DR2b of human primary B cells and monocytes, thymus, and MS brain tissue. Self-peptides from HLA-DR molecules, particularly from DR2a and DR2b themselves, are abundant on B cells and thymic antigen-presenting cells. Furthermore, we identified autoreactive CD4+ T cell clones that can cross-react with HLA-DR-derived self-peptides (HLA-DR-SPs), peptides from MS-associated foreign agents (Epstein-Barr virus and Akkermansia muciniphila), and autoantigens presented by DR2a and DR2b. Thus, both HLA-DR15 allomorphs jointly shape an autoreactive T cell repertoire by serving as antigen-presenting structures and epitope sources and by presenting the same foreign peptides and autoantigens to autoreactive CD4+ T cells in MS.
HLA-DR15 present abundant HLA-DR-derived self-peptides on B cells Autoreactive T cells in MS recognize HLA-DR-derived self-peptides/DR15 complexes Foreign peptides/DR15 complexes trigger potential autoreactive T cells in MS HLA-DR15 shape an autoreactive T cell repertoire by cross-reactivity/restriction
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Affiliation(s)
- Jian Wang
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland
| | - Ivan Jelcic
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland
| | - Lena Mühlenbruch
- Department of Immunology, Institute of Cell Biology, University of Tübingen, Tübingen 72076, Germany; German Cancer Consortium (DKTK), Partner Site Tübingen, Tübingen 72076, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies," University of Tübingen, Tübingen 72076, Germany
| | - Veronika Haunerdinger
- Pediatric Stem Cell Transplantation, University Children's Hospital Zurich, Zurich 8032, Switzerland
| | - Nora C Toussaint
- NEXUS Personalized Health Technologies, ETH Zurich, Zurich 8093, Switzerland; Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Yingdong Zhao
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, NCI, NIH, Rockville, MD 20850, USA
| | - Carolina Cruciani
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland
| | - Wolfgang Faigle
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland
| | - Reza Naghavian
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland
| | - Magdalena Foege
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland
| | - Thomas M C Binder
- HLA Laboratory of the Stefan Morsch Foundation (SMS), Birkenfeld 55765, Germany
| | - Thomas Eiermann
- Department of Transfusion Medicine, University Medical Center Hamburg-Eppendorf, Hamburg 20251, Germany
| | - Lennart Opitz
- Functional Genomics Center Zurich, Swiss Federal Institute of Technology and University of Zurich, Zurich 8057, Switzerland
| | - Laura Fuentes-Font
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, London, UK
| | - Richard Reynolds
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, London, UK
| | - William W Kwok
- Benaroya Research Institute at Virginia Mason, Seattle, WA 98101, USA
| | - Julie T Nguyen
- One Lambda, Inc., a part of Transplant Diagnostics Thermo Fisher Scientific, 22801 Roscoe Blvd., West Hills, CA 91304, USA
| | - Jar-How Lee
- One Lambda, Inc., a part of Transplant Diagnostics Thermo Fisher Scientific, 22801 Roscoe Blvd., West Hills, CA 91304, USA
| | - Andreas Lutterotti
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland
| | - Christian Münz
- Viral Immunobiology, Institute of Experimental Immunology, University of Zurich, Zurich 8057, Switzerland
| | - Hans-Georg Rammensee
- Department of Immunology, Institute of Cell Biology, University of Tübingen, Tübingen 72076, Germany; German Cancer Consortium (DKTK), Partner Site Tübingen, Tübingen 72076, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies," University of Tübingen, Tübingen 72076, Germany
| | - Mathias Hauri-Hohl
- Pediatric Stem Cell Transplantation, University Children's Hospital Zurich, Zurich 8032, Switzerland
| | - Mireia Sospedra
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland
| | - Stefan Stevanovic
- Department of Immunology, Institute of Cell Biology, University of Tübingen, Tübingen 72076, Germany; German Cancer Consortium (DKTK), Partner Site Tübingen, Tübingen 72076, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies," University of Tübingen, Tübingen 72076, Germany
| | - Roland Martin
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland.
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25
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Burkill S, Smith KA, Stridh P, Kockum I, Hillert J, Lindahl H, Alfredsson L, Olsson T, Piehl F, Montgomery S, Bahmanyar S. The DQB1 *03:02 Genotype and Treatment for Pain in People With and Without Multiple Sclerosis. Front Neurol 2020; 11:993. [PMID: 33013655 PMCID: PMC7500133 DOI: 10.3389/fneur.2020.00993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 07/29/2020] [Indexed: 11/13/2022] Open
Abstract
Murine models have demonstrated that the major histocompatibility complex (MHC) is associated with pain-like behavior in peripheral nerve injury, however, the same association has not been shown when considering injury to the central nervous system (CNS), which more closely mimics the damage to the CNS experienced by MS patients. Previous research has indicated the DQB1*03:02 allele of the class II HLA genes as being associated with development of neuropathic pain in persons undergoing inguinal hernia surgery or with lumbar spinal disk herniation. Whether this HLA allele plays a part in susceptibility to pain, has not, as far as we are aware, been previously investigated. This study utilizes information on DQB1*03:02 alleles as part of the EIMS, GEMS, and IMSE studies in Sweden. It also uses register data for 3,877 MS patients, and 4,548 matched comparators without MS, to assess whether the DQB1*03:02 allele is associated with prescribed pain medication use, and whether associations with this genotype differ depending on MS status. Our results showed no association between the DQB1*03:02 genotype and pain medication in MS patients, with an adjusted odds ratio (OR) of 1.02 (95% CI 0.85-1.24). In contrast, there was a statistically significant association of low magnitude in individuals without MS [adjusted OR 1.18 (95% CI 1.03-1.35)], which provides support for HLA influence on susceptibility to pain in the general population. Additionally, the effect of zygosity was evident for the non-MS cohort, but not among MS patients, suggesting the DQB1*03:02 allele effect is modified by the presence of MS.
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Affiliation(s)
- Sarah Burkill
- Department of Medicine Solna, Centre for Pharmacoepidemiology, Karolinska Institutet, Solna, Sweden.,Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Solna, Sweden.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Kelsi A Smith
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Solna, Sweden.,Institute of Environmental Medicine, Karolinska Institutet, Solna, Sweden
| | - Pernilla Stridh
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden.,Centre for Molecular Medicine, Karolinska University Hospital Solna, Solna, Sweden
| | - Ingrid Kockum
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden.,Centre for Molecular Medicine, Karolinska University Hospital Solna, Solna, Sweden
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Hannes Lindahl
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden.,Centre for Molecular Medicine, Karolinska University Hospital Solna, Solna, Sweden
| | - Lars Alfredsson
- Institute of Environmental Medicine, Karolinska Institutet, Solna, Sweden.,Centre for Occupational and Environmental Medicine, Stockholm County Council, Stockholm, Sweden
| | - Tomas Olsson
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden.,Centre for Molecular Medicine, Karolinska University Hospital Solna, Solna, Sweden
| | - Fredrik Piehl
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden.,Centre for Molecular Medicine, Karolinska University Hospital Solna, Solna, Sweden
| | - Scott Montgomery
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Solna, Sweden.,Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden.,Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Shahram Bahmanyar
- Department of Medicine Solna, Centre for Pharmacoepidemiology, Karolinska Institutet, Solna, Sweden.,Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Solna, Sweden
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26
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O'Connor D, Png E, Khor CC, Snape MD, Hill AVS, van der Klis F, Hoggart C, Levin M, Hibberd ML, Pollard AJ. Common Genetic Variations Associated with the Persistence of Immunity following Childhood Immunization. Cell Rep 2020; 27:3241-3253.e4. [PMID: 31189108 DOI: 10.1016/j.celrep.2019.05.053] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 01/25/2019] [Accepted: 05/15/2019] [Indexed: 12/22/2022] Open
Abstract
Vaccines have revolutionized public health, preventing millions of deaths each year, particularly in childhood. Yet, there is considerable variability in the magnitude and persistence of vaccine-induced immunity. Maintenance of specific antibody is essential for continuity of vaccine-induced serological protection. We conducted a genome-wide association study into the persistence of immunity to three childhood vaccines: capsular group C meningococcal (MenC), Haemophilus influenzae type b, and tetanus toxoid (TT) vaccines. We detail associations between variants in a locus containing a family of signal-regulatory proteins and the persistence MenC immunity. We postulate a regulatory role for the lead SNP, with supporting epigenetic and expression quantitative trait loci data. Furthermore, we define associations between SNPs in the human leukocyte antigen (HLA) locus and the persistence of TT-specific immunity. Moreover, we describe four classical HLA alleles, HLA DRB1∗0301, HLA DQB1∗0201, HLA DQB1∗0602, and HLA DRB1∗1501, associated with TT-specific immunity, independent of the lead SNP association.
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Affiliation(s)
- Daniel O'Connor
- Department of Paediatrics, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK.
| | - Eileen Png
- Infectious Diseases, Genome Institute of Singapore, Singapore, Singapore
| | - Chiea Chuen Khor
- Infectious Diseases, Genome Institute of Singapore, Singapore, Singapore
| | - Matthew D Snape
- Department of Paediatrics, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Adrian V S Hill
- NIHR Oxford Biomedical Research Centre, Oxford, UK; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Fiona van der Klis
- Centre for Infectious Disease Control Netherlands, RIVM, Bilthoven, the Netherlands
| | - Clive Hoggart
- Division of Infectious Diseases, Department of Medicine, Imperial College London, London, UK
| | - Michael Levin
- Division of Infectious Diseases, Department of Medicine, Imperial College London, London, UK
| | - Martin L Hibberd
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Pathogen Molecular Biology, London School of Hygiene & Tropical Medicine, London, UK
| | - Andrew J Pollard
- Department of Paediatrics, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK
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27
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Sallah N, Miley W, Labo N, Carstensen T, Fatumo S, Gurdasani D, Pollard MO, Dilthey AT, Mentzer AJ, Marshall V, Cornejo Castro EM, Pomilla C, Young EH, Asiki G, Hibberd ML, Sandhu M, Kellam P, Newton R, Whitby D, Barroso I. Distinct genetic architectures and environmental factors associate with host response to the γ2-herpesvirus infections. Nat Commun 2020; 11:3849. [PMID: 32737300 PMCID: PMC7395761 DOI: 10.1038/s41467-020-17696-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 07/13/2020] [Indexed: 01/05/2023] Open
Abstract
Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr Virus (EBV) establish life-long infections and are associated with malignancies. Striking geographic variation in incidence and the fact that virus alone is insufficient to cause disease, suggests other co-factors are involved. Here we present epidemiological analysis and genome-wide association study (GWAS) in 4365 individuals from an African population cohort, to assess the influence of host genetic and non-genetic factors on virus antibody responses. EBV/KSHV co-infection (OR = 5.71(1.58-7.12)), HIV positivity (OR = 2.22(1.32-3.73)) and living in a more rural area (OR = 1.38(1.01-1.89)) are strongly associated with immunogenicity. GWAS reveals associations with KSHV antibody response in the HLA-B/C region (p = 6.64 × 10-09). For EBV, associations are identified for VCA (rs71542439, p = 1.15 × 10-12). Human leucocyte antigen (HLA) and trans-ancestry fine-mapping substantiate that distinct variants in HLA-DQA1 (p = 5.24 × 10-44) are driving associations for EBNA-1 in Africa. This study highlights complex interactions between KSHV and EBV, in addition to distinct genetic architectures resulting in important differences in pathogenesis and transmission.
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MESH Headings
- Adolescent
- Adult
- Antibodies, Viral/biosynthesis
- Antigens, Viral/genetics
- Antigens, Viral/immunology
- Capsid Proteins/genetics
- Capsid Proteins/immunology
- Coinfection
- Disease Resistance/genetics
- Epstein-Barr Virus Infections/epidemiology
- Epstein-Barr Virus Infections/genetics
- Epstein-Barr Virus Infections/immunology
- Epstein-Barr Virus Infections/virology
- Epstein-Barr Virus Nuclear Antigens/genetics
- Epstein-Barr Virus Nuclear Antigens/immunology
- Female
- Gene Expression
- Genome-Wide Association Study
- HIV/genetics
- HIV/immunology
- HIV/pathogenicity
- HLA-DQ alpha-Chains/genetics
- HLA-DQ alpha-Chains/immunology
- Henipavirus Infections/epidemiology
- Henipavirus Infections/genetics
- Henipavirus Infections/immunology
- Henipavirus Infections/virology
- Herpesvirus 4, Human/genetics
- Herpesvirus 4, Human/immunology
- Herpesvirus 4, Human/pathogenicity
- Herpesvirus 8, Human/genetics
- Herpesvirus 8, Human/immunology
- Herpesvirus 8, Human/pathogenicity
- Host-Pathogen Interactions/genetics
- Host-Pathogen Interactions/immunology
- Humans
- Incidence
- Male
- Middle Aged
- Rural Population
- Sarcoma, Kaposi/epidemiology
- Sarcoma, Kaposi/genetics
- Sarcoma, Kaposi/immunology
- Sarcoma, Kaposi/virology
- Uganda/epidemiology
- Urban Population
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Affiliation(s)
- Neneh Sallah
- The Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- London School of Hygiene & Tropical Medicine, London, UK.
- London School of Hygiene & Tropical Medicine, London, UK.
| | - Wendell Miley
- Viral Oncology Section, AIDS and Cancer Virus Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, USA
| | - Nazzarena Labo
- Viral Oncology Section, AIDS and Cancer Virus Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, USA
| | - Tommy Carstensen
- The Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Segun Fatumo
- The Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- London School of Hygiene & Tropical Medicine, London, UK
- MRC/UVRI at the London School of Hygiene & Tropical Medicine, Entebbe, Uganda
| | - Deepti Gurdasani
- The Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Queen Mary University London, London, UK
| | - Martin O Pollard
- The Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Alexander T Dilthey
- Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Alexander J Mentzer
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Vickie Marshall
- Viral Oncology Section, AIDS and Cancer Virus Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, USA
| | - Elena M Cornejo Castro
- Viral Oncology Section, AIDS and Cancer Virus Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, USA
| | - Cristina Pomilla
- The Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Elizabeth H Young
- The Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Gershim Asiki
- African Population and Health Research Center, Nairobi, Kenya
| | | | | | - Paul Kellam
- Department of Infectious Diseases, Imperial College London, London, UK
- Kymab Ltd, Babraham Research Complex, Cambridge, UK
| | - Robert Newton
- MRC/UVRI at the London School of Hygiene & Tropical Medicine, Entebbe, Uganda
| | - Denise Whitby
- Viral Oncology Section, AIDS and Cancer Virus Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, USA
| | - Inês Barroso
- The Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Exeter Centre of ExcEllence in Diabetes (ExCEED), University of Exeter Medical School, Exeter, UK.
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28
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Ritari J, Hyvärinen K, Clancy J, Partanen J, Koskela S. Increasing accuracy of HLA imputation by a population-specific reference panel in a FinnGen biobank cohort. NAR Genom Bioinform 2020; 2:lqaa030. [PMID: 33575586 PMCID: PMC7671345 DOI: 10.1093/nargab/lqaa030] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 04/20/2020] [Accepted: 04/26/2020] [Indexed: 01/02/2023] Open
Abstract
The HLA genes, the most polymorphic genes in the human genome, constitute the strongest single genetic susceptibility factor for autoimmune diseases, transplantation alloimmunity and infections. HLA imputation via statistical inference of alleles based on single-nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD) with alleles is a powerful first-step screening tool. Due to different LD structures between populations, the accuracy of HLA imputation may benefit from matching the imputation reference with the study population. To evaluate the potential advantage of using population-specific reference in HLA imputation, we constructed an HLA reference panel consisting of 1150 Finns with 5365 major histocompatibility complex region SNPs consistent between genome builds. We evaluated the accuracy of the panel against a European panel in an independent test set of 213 Finnish subjects. We show that the Finnish panel yields a lower imputation error rate (1.24% versus 1.79%). More than 30% of imputation errors occurred in haplotypes enriched in Finland. The frequencies of imputed HLA alleles were highly correlated with clinical-grade HLA allele frequencies and allowed accurate replication of established HLA–disease associations in ∼102 000 biobank participants. The results show that a population-specific reference increases imputation accuracy in a relatively isolated population within Europe and can be successfully applied to biobank-scale genome data collections.
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Affiliation(s)
- Jarmo Ritari
- Research and Development, Finnish Red Cross Blood Service, Kivihaantie 7, 00310 Helsinki, Finland
| | - Kati Hyvärinen
- Research and Development, Finnish Red Cross Blood Service, Kivihaantie 7, 00310 Helsinki, Finland
| | - Jonna Clancy
- Research and Development, Finnish Red Cross Blood Service, Kivihaantie 7, 00310 Helsinki, Finland
| | | | - Jukka Partanen
- Research and Development, Finnish Red Cross Blood Service, Kivihaantie 7, 00310 Helsinki, Finland
| | - Satu Koskela
- Research and Development, Finnish Red Cross Blood Service, Kivihaantie 7, 00310 Helsinki, Finland
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29
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Abstract
Genotype imputation infers missing genotypes in silico using haplotype information from reference samples with genotypes from denser genotyping arrays or sequencing. This approach can confer a number of improvements on genome-wide association studies: it can improve statistical power to detect associations by reducing the number of missing genotypes; it can simplify data harmonization for meta-analyses by improving overlap of genomic variants between differently-genotyped sample sets; and it can increase the overall number and density of genomic variants available for association testing. This article reviews the general concepts behind imputation, describes imputation approaches and methods for various types of genotype data, including family-based data, and identifies web-based resources that can be used in different steps of the imputation process. For practical application, it provides a step-by-step guide to implementation of a two-step imputation process consisting of phasing of the study genotypes and the imputation of reference panel genotypes into the study haplotypes. In addition, this review describes recently developed haplotype reference panel resources and online imputation servers that are capable of remotely and securely implementing an imputation workflow on uploaded genotype array data. © 2019 by John Wiley & Sons, Inc.
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Affiliation(s)
- Adam C Naj
- Department of Biostatistics, Epidemiology, and Informatics and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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30
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Green HD, Beaumont RN, Thomas A, Hamilton B, Wood AR, Sharp S, Jones SE, Tyrrell J, Walker G, Goodhand J, Kennedy NA, Ahmad T, Weedon MN. Genome-Wide Association Study of Microscopic Colitis in the UK Biobank Confirms Immune-Related Pathogenesis. J Crohns Colitis 2019; 13:1578-1582. [PMID: 31125052 PMCID: PMC6903793 DOI: 10.1093/ecco-jcc/jjz104] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND AND AIMS The causes of microscopic colitis are currently poorly understood. Previous reports have found clinical associations with coeliac disease and genetic associations at the human leukocyte antigen [HLA] locus on the ancestral 8.1 haplotype. We investigated pharmacological and genetic factors associated with microscopic colitis in the UK Biobank. METHODS In total, 483 European UK Biobank participants were identified by ICD10 coding, and a genome-wide association study was performed using BOLT-LMM, with a sensitivity analysis performed excluding potential confounders. The HLA*IMP:02 algorithm was used to estimate allele frequency at 11 classical HLA genes, and downstream analysis was performed using FUMA. Genetic overlap with inflammatory bowel disease [Crohn's disease and ulcerative colitis] was investigated using genetic risk scores. RESULTS We found significant phenotypic associations with smoking status, coeliac disease and the use of proton-pump inhibitors but not with other commonly reported pharmacological risk factors. Using the largest sample size to date, we confirmed a recently reported association with the MHC Ancestral 8.1 Haplotype. Downstream analysis suggests association with digestive tract morphogenesis. By calculating genetic risk scores, we also report suggestive evidence of shared genetic risk with Crohn's disease, but not with ulcerative colitis. CONCLUSIONS This report confirms the role of genetic determinants in the HLA in the pathogenesis of microscopic colitis. The genetic overlap with Crohn's disease suggests a common underlying mechanism of disease.
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Affiliation(s)
- Harry D Green
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
- IBD Pharmacogenetics, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Robin N Beaumont
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Amanda Thomas
- IBD Pharmacogenetics, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Benjamin Hamilton
- IBD Pharmacogenetics, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Seth Sharp
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Samuel E Jones
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Jessica Tyrrell
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Gareth Walker
- IBD Pharmacogenetics, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - James Goodhand
- IBD Pharmacogenetics, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Nicholas A Kennedy
- IBD Pharmacogenetics, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Tariq Ahmad
- IBD Pharmacogenetics, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Michael N Weedon
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
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31
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Amariuta T, Luo Y, Knevel R, Okada Y, Raychaudhuri S. Advances in genetics toward identifying pathogenic cell states of rheumatoid arthritis. Immunol Rev 2019; 294:188-204. [PMID: 31782165 DOI: 10.1111/imr.12827] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 11/07/2019] [Indexed: 12/11/2022]
Abstract
Rheumatoid arthritis (RA) risk has a large genetic component (~60%) that is still not fully understood. This has hampered the design of effective treatments that could promise lifelong remission. RA is a polygenic disease with 106 known genome-wide significant associated loci and thousands of small effect causal variants. Our current understanding of RA risk has suggested cell-type-specific contexts for causal variants, implicating CD4 + effector memory T cells, as well as monocytes, B cells and stromal fibroblasts. While these cellular states and categories are still mechanistically broad, future studies may identify causal cell subpopulations. These efforts are propelled by advances in single cell profiling. Identification of causal cell subpopulations may accelerate therapeutic intervention to achieve lifelong remission.
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Affiliation(s)
- Tiffany Amariuta
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Graduate School of Arts and Sciences, Harvard University, Boston, MA, USA
| | - Yang Luo
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Rachel Knevel
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Rheumatology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Yukinori Okada
- Division of Medicine, Osaka University, Osaka, Japan.,Osaka University Graduate School of Medicine, Osaka, Japan
| | - Soumya Raychaudhuri
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
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32
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Engdahl E, Gustafsson R, Huang J, Biström M, Lima Bomfim I, Stridh P, Khademi M, Brenner N, Butt J, Michel A, Jons D, Hortlund M, Alonso-Magdalena L, Hedström AK, Flamand L, Ihira M, Yoshikawa T, Andersen O, Hillert J, Alfredsson L, Waterboer T, Sundström P, Olsson T, Kockum I, Fogdell-Hahn A. Increased Serological Response Against Human Herpesvirus 6A Is Associated With Risk for Multiple Sclerosis. Front Immunol 2019; 10:2715. [PMID: 32038605 PMCID: PMC6988796 DOI: 10.3389/fimmu.2019.02715] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 11/05/2019] [Indexed: 11/26/2022] Open
Abstract
Human herpesvirus (HHV)-6A or HHV-6B involvement in multiple sclerosis (MS) etiology has remained controversial mainly due to the lack of serological methods that can distinguish the two viruses. A novel multiplex serological assay measuring IgG reactivity against the immediate-early protein 1 from HHV-6A (IE1A) and HHV-6B (IE1B) was used in a MS cohort (8,742 persons with MS and 7,215 matched controls), and a pre-MS cohort (478 individuals and 476 matched controls) to investigate this further. The IgG response against IE1A was positively associated with MS (OR = 1.55, p = 9 × 10-22), and increased risk of future MS (OR = 2.22, p = 2 × 10-5). An interaction was observed between IE1A and Epstein-Barr virus (EBV) antibody responses for MS risk (attributable proportion = 0.24, p = 6 × 10-6). In contrast, the IgG response against IE1B was negatively associated with MS (OR = 0.74, p = 6 × 10-11). The association did not differ between MS subtypes or vary with severity of disease. The genetic control of HHV-6A/B antibody responses were located to the Human Leukocyte Antigen (HLA) region and the strongest association for IE1A was the DRB1*13:01-DQA1*01:03-DQB1*06:03 haplotype while the main association for IE1B was DRB1*13:02-DQA1*01:02-DQB1*06:04. In conclusion a role for HHV-6A in MS etiology is supported by an increased serological response against HHV-6A IE1 protein, an interaction with EBV, and an association to HLA genes.
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Affiliation(s)
- Elin Engdahl
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Stockholm, Sweden
| | - Rasmus Gustafsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Stockholm, Sweden
| | - Jesse Huang
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Stockholm, Sweden
| | - Martin Biström
- Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå, Sweden
| | - Izaura Lima Bomfim
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Stockholm, Sweden
| | - Pernilla Stridh
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Stockholm, Sweden
| | - Mohsen Khademi
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Stockholm, Sweden
| | - Nicole Brenner
- Infections and Cancer Epidemiology, German Cancer Research Center (Deutsches Krebsforschungszentrum), Heidelberg, Germany
| | - Julia Butt
- Infections and Cancer Epidemiology, German Cancer Research Center (Deutsches Krebsforschungszentrum), Heidelberg, Germany
| | - Angelika Michel
- Infections and Cancer Epidemiology, German Cancer Research Center (Deutsches Krebsforschungszentrum), Heidelberg, Germany
| | - Daniel Jons
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Maria Hortlund
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Anna Karin Hedström
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Stockholm, Sweden
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Louis Flamand
- Department of Microbiology, Infectious Disease and Immunology, Laval University, Quebec City, QC, Canada
| | - Masaru Ihira
- Clinical Engineering Technology, Fujita Health University School of Medical Sciences, Toyoake, Japan
| | - Tetsushi Yoshikawa
- Department of Pediatrics, Fujita Health University School of Medicine, Toyoake, Japan
| | - Oluf Andersen
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Stockholm, Sweden
| | - Lars Alfredsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Centre for Occupational and Environmental Medicine, Stockholm County Council, Stockholm, Sweden
| | - Tim Waterboer
- Infections and Cancer Epidemiology, German Cancer Research Center (Deutsches Krebsforschungszentrum), Heidelberg, Germany
| | - Peter Sundström
- Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå, Sweden
| | - Tomas Olsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Stockholm, Sweden
| | - Ingrid Kockum
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Stockholm, Sweden
| | - Anna Fogdell-Hahn
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Stockholm, Sweden
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33
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Potts ND, Bichet C, Merat L, Guitton E, Krupa AP, Burke TA, Kennedy LJ, Sorci G, Kaufman J. Development and optimization of a hybridization technique to type the classical class I and class II B genes of the chicken MHC. Immunogenetics 2019; 71:647-663. [PMID: 31761978 PMCID: PMC6900278 DOI: 10.1007/s00251-019-01149-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Accepted: 11/17/2019] [Indexed: 01/02/2023]
Abstract
The classical class I and class II molecules of the major histocompatibility complex (MHC) play crucial roles in immune responses to infectious pathogens and vaccines as well as being important for autoimmunity, allergy, cancer and reproduction. These classical MHC genes are the most polymorphic known, with roughly 10,000 alleles in humans. In chickens, the MHC (also known as the BF-BL region) determines decisive resistance and susceptibility to infectious pathogens, but relatively few MHC alleles and haplotypes have been described in any detail. We describe a typing protocol for classical chicken class I (BF) and class II B (BLB) genes based on a hybridization method called reference strand-mediated conformational analysis (RSCA). We optimize the various steps, validate the analysis using well-characterized chicken MHC haplotypes, apply the system to type some experimental lines and discover a new chicken class I allele. This work establishes a basis for typing the MHC genes of chickens worldwide and provides an opportunity to correlate with microsatellite and with single nucleotide polymorphism (SNP) typing for approaches involving imputation.
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Affiliation(s)
- Nicola D Potts
- Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK.,LGC Ltd., Newmarket Road, Fordham, Ely, CB7 5WW, UK
| | - Coraline Bichet
- BioGéoSciences, CNRS UMR 5561, Université de Bourgogne Franche-Comté, 6 Boulevard Gabriel, 21000, Dijon, France.,Institute of Avian Research, An der Vogelwarte 21, 26386, Wilhelmshaven, Germany
| | - Laurence Merat
- Plate-Forme d'Infectiologie Expérimentale (PFIE), UE-1277, INRA Centre Val de Loire, 37380, Nouzilly, France
| | - Edouard Guitton
- Plate-Forme d'Infectiologie Expérimentale (PFIE), UE-1277, INRA Centre Val de Loire, 37380, Nouzilly, France
| | - Andrew P Krupa
- Department of Animal and Plant Sciences, University of Sheffield, Western Bank, S10 2TN, Sheffield, UK
| | - Terry A Burke
- Department of Animal and Plant Sciences, University of Sheffield, Western Bank, S10 2TN, Sheffield, UK
| | - Lorna J Kennedy
- Division of Population Health, Health Services Research & Primary Care, University of Manchester, Oxford Road, M13 9PL, Manchester, UK
| | - Gabriele Sorci
- BioGéoSciences, CNRS UMR 5561, Université de Bourgogne Franche-Comté, 6 Boulevard Gabriel, 21000, Dijon, France
| | - Jim Kaufman
- Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK. .,Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge, CB3 0ES, UK.
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34
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Kwon YC, Chun S, Kim K, Mak A. Update on the Genetics of Systemic Lupus Erythematosus: Genome-Wide Association Studies and Beyond. Cells 2019; 8:cells8101180. [PMID: 31575058 PMCID: PMC6829439 DOI: 10.3390/cells8101180] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 09/20/2019] [Accepted: 09/28/2019] [Indexed: 12/11/2022] Open
Abstract
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of complex etiology that primarily affects women of childbearing age. The development of SLE is attributed to the breach of immunological tolerance and the interaction between SLE-susceptibility genes and various environmental factors, resulting in the production of pathogenic autoantibodies. Working in concert with the innate and adaptive arms of the immune system, lupus-related autoantibodies mediate immune-complex deposition in various tissues and organs, leading to acute and chronic inflammation and consequent end-organ damage. Over the past two decades or so, the impact of genetic susceptibility on the development of SLE has been well demonstrated in a number of large-scale genetic association studies which have uncovered a large fraction of genetic heritability of SLE by recognizing about a hundred SLE-susceptibility loci. Integration of genetic variant data with various omics data such as transcriptomic and epigenomic data potentially provides a unique opportunity to further understand the roles of SLE risk variants in regulating the molecular phenotypes by various disease-relevant cell types and in shaping the immune systems with high inter-individual variances in disease susceptibility. In this review, the catalogue of SLE susceptibility loci will be updated, and biological signatures implicated by the SLE-risk variants will be critically discussed. It is optimistically hoped that identification of SLE risk variants will enable the prognostic and therapeutic biomarker armamentarium of SLE to be strengthened, a major leap towards precision medicine in the management of the condition.
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Affiliation(s)
- Young-Chang Kwon
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, 222–1 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea;
| | - Sehwan Chun
- Department of Biology, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea;
| | - Kwangwoo Kim
- Department of Biology, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea;
- Correspondence: (K.K.); (A.M.); Tel.: +82-29610604 (K.K.); +65-82338216 (A.M.)
| | - Anselm Mak
- Division of Rheumatology, University Medicine Cluster, National University Health System, Singapore 119228, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Correspondence: (K.K.); (A.M.); Tel.: +82-29610604 (K.K.); +65-82338216 (A.M.)
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35
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Jang CS, Choi W, Cook S, Han B. Analysis of differences in human leukocyte antigen between the two Wellcome Trust Case Control Consortium control datasets. Genomics Inform 2019; 17:e29. [PMID: 31610625 PMCID: PMC6808636 DOI: 10.5808/gi.2019.17.3.e29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 08/30/2019] [Indexed: 11/20/2022] Open
Abstract
The Wellcome Trust Case Control Consortium (WTCCC) study was a large genome-wide association study that aimed to identify common variants associated with seven diseases. That study combined two control datasets (58C and UK Blood Services) as shared controls. Prior to using the combined controls, the WTCCC performed analyses to show that the genomic content of the control datasets was not significantly different. Recently, the analysis of human leukocyte antigen (HLA) genes has become prevalent due to the development of HLA imputation technology. In this project, we extended the between-control homogeneity analysis of the WTCCC to HLA. We imputed HLA information in the WTCCC control dataset and showed that the HLA content was not significantly different between the two control datasets, suggesting that the combined controls can be used as controls for HLA fine-mapping analysis based on HLA imputation.
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36
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Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 2019; 365:365/6460/eaav7188. [PMID: 31604244 PMCID: PMC7241648 DOI: 10.1126/science.aav7188] [Citation(s) in RCA: 713] [Impact Index Per Article: 118.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Accepted: 08/06/2019] [Indexed: 02/02/2023]
Abstract
We analyzed genetic data of 47,429 multiple sclerosis (MS) and 68,374 control subjects and established a reference map of the genetic architecture of MS that includes 200 autosomal susceptibility variants outside the major histocompatibility complex (MHC), one chromosome X variant, and 32 variants within the extended MHC. We used an ensemble of methods to prioritize 551 putative susceptibility genes that implicate multiple innate and adaptive pathways distributed across the cellular components of the immune system. Using expression profiles from purified human microglia, we observed enrichment for MS genes in these brain-resident immune cells, suggesting that these may have a role in targeting an autoimmune process to the central nervous system, although MS is most likely initially triggered by perturbation of peripheral immune responses.
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Affiliation(s)
- International Multiple Sclerosis Genetics Consortium
- Correspondence to: Philip L. De Jager, MD PhD, Center for Translational & Computational Neuroimmunology, Multiple Sclerosis Center, Department of Neurology, Columbia University Medical Center, 630 W 168th Street P&S Box 16, New York, NY 10032, T: 212.305.3609,
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37
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Han B, Akiyama M, Kim KK, Oh H, Choi H, Lee CH, Jung S, Lee HS, Kim EE, Cook S, Haritunians T, Yamazaki K, Park SH, Ye BD, McGovern DPB, Esaki M, Kawaguchi T, Khor SS, Taylor KD, Rotter JI, Suzuki Y, Matsui T, Motoya S, Bang SY, Kim TH, Momozawa Y, Kamatani Y, Tokunaga K, Kubo M, Okada Y, Yang SK, Song K. Amino acid position 37 of HLA-DRβ1 affects susceptibility to Crohn's disease in Asians. Hum Mol Genet 2019; 27:3901-3910. [PMID: 30084967 DOI: 10.1093/hmg/ddy285] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 07/24/2018] [Indexed: 12/19/2022] Open
Abstract
Crohn's disease (CD) and ulcerative colitis (UC) are the major types of chronic inflammatory bowel disease (IBD) characterized by recurring episodes of inflammation of the gastrointestinal tract. Although it is well established that human leukocyte antigen (HLA) is a major risk factor for IBD, it is yet to be determined which HLA alleles or amino acids drive the risks of CD and UC in Asians. To define the roles of HLA for IBD in Asians, we fine-mapped HLA in 12 568 individuals from Korea and Japan (3294 patients with CD, 1522 patients with UC and 7752 controls). We identified that the amino acid position 37 of HLA-DRβ1 plays a key role in the susceptibility to CD (presence of serine being protective, P = 3.6 × 10-67, OR = 0.48 [0.45-0.52]). For UC, we confirmed the known association of the haplotype spanning HLA-C*12:02, HLA-B*52:01 and HLA-DRB1*1502 (P = 1.2 × 10-28, OR = 4.01 [3.14-5.12]).
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Affiliation(s)
- Buhm Han
- Department of Medicine, Seoul National University College of Medicine, Seoul, Korea.,Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kyung-Kon Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.,Convergence Medicine Research Center and Biomedical Research Center, AILS, Asan Medical Center, Seoul, Korea
| | - Hyunjung Oh
- Department of Biochemistry and Molecular Biology, University of Ulsan College of Medicine, Seoul, Korea
| | - Hyunchul Choi
- Department of Biochemistry and Molecular Biology, University of Ulsan College of Medicine, Seoul, Korea
| | - Cue Hyunkyu Lee
- Department of Medicine, Seoul National University College of Medicine, Seoul, Korea.,Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Seulgi Jung
- Department of Biochemistry and Molecular Biology, University of Ulsan College of Medicine, Seoul, Korea
| | - Ho-Su Lee
- Department of Biochemistry and Molecular Biology, University of Ulsan College of Medicine, Seoul, Korea
| | - Emma E Kim
- Department of Medicine, Seoul National University College of Medicine, Seoul, Korea.,Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.,Department of Chemistry, Seoul National University, Seoul, Korea
| | - Seungho Cook
- Department of Medicine, Seoul National University College of Medicine, Seoul, Korea.,Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Talin Haritunians
- The F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Keiko Yamazaki
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan.,Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Sang Hyoung Park
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Byong Duk Ye
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Dermot P B McGovern
- The F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Motohiro Esaki
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Takaaki Kawaguchi
- Division of Gastroenterology, Department of Medicine, TokyoYamate Medical Center, Tokyo, Japan.,Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Seik-Soon Khor
- Human Genetics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences,Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical CenterTorrance, CA, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences,Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical CenterTorrance, CA, USA
| | - Yasuo Suzuki
- Department of Internal Medicine, Faculty of Medicine, Toho University, Chiba, Japan
| | - Toshiyuki Matsui
- Department of Gastroenterology, Fukuoka University,Chikushi Hospital, Fukuoka, Japan
| | - Satoshi Motoya
- Department of Gastroenterology, Sapporo-Kosei General Hospital, Sapporo, Japan
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
| | - Tae-Hwan Kim
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Center for Genomic Medicine, Kyoto University Graduate School of Medicine,Sakyo-ku, Kyoto, Japan
| | - Katsushi Tokunaga
- Human Genetics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yukinori Okada
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan
| | - Suk-Kyun Yang
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kyuyoung Song
- Department of Biochemistry and Molecular Biology, University of Ulsan College of Medicine, Seoul, Korea
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38
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Lim J, Bae SC, Kim K. Understanding HLA associations from SNP summary association statistics. Sci Rep 2019; 9:1337. [PMID: 30718717 PMCID: PMC6362191 DOI: 10.1038/s41598-018-37840-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 12/11/2018] [Indexed: 12/13/2022] Open
Abstract
Strong genetic associations in the region containing human leukocyte antigen (HLA) genes have been well-documented in various human immune disorders. Imputation methods to infer HLA variants from single nucleotide polymorphism (SNP) genotypes are currently used to understand HLA associations with a trait of interest. However, it is challenging for some researchers to obtain individual-level SNP genotype data or reference haplotype data. In this study, we developed and evaluated a new method, DISH (direct imputing summary association statistics of HLA variants), for imputing summary association statistics of HLA variants from SNP summary association statistics based on linkage disequilibria in Asian and European populations. Disease association Z scores in DISH were highly correlated with those from imputed HLA genotypes in null model datasets (r = 0.934 in Asians; r = 0.960 in Europeans). We applied DISH to two previous GWAS datasets in Asian systemic lupus erythematosus and European rheumatoid arthritis populations. There was a high correlation between Z scores in the DISH and HLA genotype imputations, showing the same disease-susceptible and protective alleles. This study illustrated the usefulness of the DISH method in understanding and identifying disease-associated HLA variants in human diseases while maintaining individual-level data security.
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Affiliation(s)
- Jiwoo Lim
- Department of Biology, Kyung Hee University, Seoul, Republic of Korea
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Kwangwoo Kim
- Department of Biology, Kyung Hee University, Seoul, Republic of Korea.
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39
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Petersdorf EW, O'hUigin C. The MHC in the era of next-generation sequencing: Implications for bridging structure with function. Hum Immunol 2019; 80:67-78. [PMID: 30321633 PMCID: PMC6542361 DOI: 10.1016/j.humimm.2018.10.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 09/24/2018] [Accepted: 10/01/2018] [Indexed: 12/19/2022]
Abstract
The MHC continues to have the most disease-associations compared to other regions of the human genome, even in the genome-wide association study (GWAS) and single nucleotide polymorphism (SNP) era. Analysis of non-coding variation and their impact on the level of expression of HLA allotypes has shed new light on the potential mechanisms underlying HLA disease associations and alloreactivity in transplantation. Next-generation sequencing (NGS) technology has the capability of delineating the phase of variants in the HLA antigen-recognition site (ARS) with non-coding regulatory polymorphisms. These relationships are critical for understanding the qualitative and quantitative implications of HLA gene diversity. This article summarizes current understanding of non-coding region variation of HLA loci, the consequences of regulatory variation on HLA expression, the role for evolution in shaping lineage-specific expression, and the impact of HLA expression on disease susceptibility and transplantation outcomes. A role for phased sequencing methods for the MHC, and perspectives for future directions in basic and applied immunogenetic studies of the MHC are presented.
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Affiliation(s)
- Effie W Petersdorf
- University of Washington, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, D4-115, Seattle, WA 98109, United States.
| | - Colm O'hUigin
- Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Microbiome and Genetics Core, Building 37, Room 4140B, Bethesda, MD 20852, United States.
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40
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Darlay R, Ayers KL, Mells GF, Hall LS, Liu JZ, Almarri MA, Alexander GJ, Jones DE, Sandford RN, Anderson CA, Cordell HJ. Amino acid residues in five separate HLA genes can explain most of the known associations between the MHC and primary biliary cholangitis. PLoS Genet 2018; 14:e1007833. [PMID: 30507971 PMCID: PMC6292650 DOI: 10.1371/journal.pgen.1007833] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 12/13/2018] [Accepted: 11/13/2018] [Indexed: 12/15/2022] Open
Abstract
Primary Biliary Cholangitis (PBC) is a chronic autoimmune liver disease characterised by progressive destruction of intrahepatic bile ducts. The strongest genetic association is with HLA-DQA1*04:01, but at least three additional independent HLA haplotypes contribute to susceptibility. We used dense single nucleotide polymorphism (SNP) data in 2861 PBC cases and 8514 controls to impute classical HLA alleles and amino acid polymorphisms using state-of-the-art methodologies. We then demonstrated through stepwise regression that association in the HLA region can be largely explained by variation at five separate amino acid positions. Three-dimensional modelling of protein structures and calculation of electrostatic potentials for the implicated HLA alleles/amino acid substitutions demonstrated a correlation between the electrostatic potential of pocket P6 in HLA-DP molecules and the HLA-DPB1 alleles/amino acid substitutions conferring PBC susceptibility/protection, highlighting potential new avenues for future functional investigation.
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Affiliation(s)
- Rebecca Darlay
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Kristin L. Ayers
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - George F. Mells
- Academic Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Lynsey S. Hall
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Jimmy Z. Liu
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Mohamed A. Almarri
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
- Department of Forensic Science and Criminology, Dubai Police HQ, Dubai, United Arab Emirates
| | - Graeme J. Alexander
- Department of Hepatology, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, Cambridge, United Kingdom
| | - David E. Jones
- Institute of Cellular Medicine, Medical School, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Richard N. Sandford
- Academic Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Carl A. Anderson
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Heather J. Cordell
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
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41
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42
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Hernandez‐Fuentes MP, Franklin C, Rebollo‐Mesa I, Mollon J, Delaney F, Perucha E, Stapleton C, Borrows R, Byrne C, Cavalleri G, Clarke B, Clatworthy M, Feehally J, Fuggle S, Gagliano SA, Griffin S, Hammad A, Higgins R, Jardine A, Keogan M, Leach T, MacPhee I, Mark PB, Marsh J, Maxwell P, McKane W, McLean A, Newstead C, Augustine T, Phelan P, Powis S, Rowe P, Sheerin N, Solomon E, Stephens H, Thuraisingham R, Trembath R, Topham P, Vaughan R, Sacks SH, Conlon P, Opelz G, Soranzo N, Weale ME, Lord GM. Long- and short-term outcomes in renal allografts with deceased donors: A large recipient and donor genome-wide association study. Am J Transplant 2018; 18:1370-1379. [PMID: 29392897 PMCID: PMC6001640 DOI: 10.1111/ajt.14594] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 10/28/2017] [Accepted: 11/13/2017] [Indexed: 01/25/2023]
Abstract
Improvements in immunosuppression have modified short-term survival of deceased-donor allografts, but not their rate of long-term failure. Mismatches between donor and recipient HLA play an important role in the acute and chronic allogeneic immune response against the graft. Perfect matching at clinically relevant HLA loci does not obviate the need for immunosuppression, suggesting that additional genetic variation plays a critical role in both short- and long-term graft outcomes. By combining patient data and samples from supranational cohorts across the United Kingdom and European Union, we performed the first large-scale genome-wide association study analyzing both donor and recipient DNA in 2094 complete renal transplant-pairs with replication in 5866 complete pairs. We studied deceased-donor grafts allocated on the basis of preferential HLA matching, which provided some control for HLA genetic effects. No strong donor or recipient genetic effects contributing to long- or short-term allograft survival were found outside the HLA region. We discuss the implications for future research and clinical application.
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Affiliation(s)
| | | | | | - Jennifer Mollon
- King's College LondonMRC Centre for TransplantationLondonUK,Department of HaematologyUniversity of Cambridge, Cambridge, UK
| | - Florence Delaney
- King's College LondonMRC Centre for TransplantationLondonUK,NIHR Biomedical Research Centre at Guy's and St Thomas’NHS Foundation Trust and King's College LondonLondonUK
| | | | | | - Richard Borrows
- Renal Institute of BirminghamDepartment of Nephrology and TransplantationBirminghamUK
| | - Catherine Byrne
- Nottingham Renal and Transplant UnitNottingham University Hospitals NHS TrustNottinghamUK
| | | | - Brendan Clarke
- Transplant and Cellular ImmunologyLeeds Teaching Hospitals NHS TrustLeedsUK
| | | | | | - Susan Fuggle
- Transplant Immunology & ImmunogeneticsChurchill HospitalOxfordUK
| | - Sarah A. Gagliano
- Center for Statistical GeneticsDepartment of BiostatisticsUniversity of MichiganAnn ArborMIUSA
| | - Sian Griffin
- Cardiff & Vale University Health BoardCardiff UniversityCardiffUK
| | - Abdul Hammad
- The Royal Liverpool and Broadgreen University HospitalsLiverpoolUK
| | - Robert Higgins
- University Hospitals Coventry and Warwickshire NHS TrustCoventryUK
| | - Alan Jardine
- School of MedicineDentistry and NursingUniversity of GlasgowGlasgowUK
| | | | | | | | - Patrick B. Mark
- School of MedicineDentistry and NursingUniversity of GlasgowGlasgowUK
| | - James Marsh
- Epsom and St Helier University Hospitals TrustCarshaltonUK
| | - Peter Maxwell
- School of MedicineDentistry and Biomedical SciencesQueens University BelfastBelfastUK
| | - William McKane
- Sheffield Kidney InstituteSheffield Teaching Hospitals NHS Foundation TrustSheffieldUK
| | - Adam McLean
- Kidney and TransplantImperial College Healthcare NHS TrustLondonUK
| | | | - Titus Augustine
- Central Manchester University Hospitals NHS TrustManchesterUK
| | | | - Steve Powis
- Division of MedicineUniversity College LondonLondonUK
| | | | - Neil Sheerin
- The Medical SchoolNewcastle University NewcastleNewcastle upon TyneUK
| | - Ellen Solomon
- Division of Genetics& Molecular MedicineKing's College LondonLondonUK
| | | | | | - Richard Trembath
- Division of Genetics& Molecular MedicineKing's College LondonLondonUK
| | | | - Robert Vaughan
- Clinical Transplantation Laboratory at Guy's HospitalGuy's and St Thomas’ NHS TrustLondonUK
| | - Steven H. Sacks
- King's College LondonMRC Centre for TransplantationLondonUK,NIHR Biomedical Research Centre at Guy's and St Thomas’NHS Foundation Trust and King's College LondonLondonUK
| | - Peter Conlon
- Royal College of Surgeons in IrelandDublinIreland,Beaumont HospitalDublinIreland
| | - Gerhard Opelz
- University of HeidelbergTransplantation ImmunologyHeidelbergGermany
| | - Nicole Soranzo
- Welcome Trust Sanger InstituteHuman GeneticsCambridgeUK,Department of HaematologyUniversity of Cambridge, Cambridge, UK
| | - Michael E. Weale
- Division of Genetics& Molecular MedicineKing's College LondonLondonUK,Present address:
Genomics plcOxfordUK
| | - Graham M. Lord
- King's College LondonMRC Centre for TransplantationLondonUK,NIHR Biomedical Research Centre at Guy's and St Thomas’NHS Foundation Trust and King's College LondonLondonUK
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43
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Kennedy AE, Ozbek U, Dorak MT. What has GWAS done for HLA and disease associations? Int J Immunogenet 2018; 44:195-211. [PMID: 28877428 DOI: 10.1111/iji.12332] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 06/16/2017] [Accepted: 07/20/2017] [Indexed: 12/14/2022]
Abstract
The major histocompatibility complex (MHC) is located in chromosome 6p21 and contains crucial regulators of immune response, including human leucocyte antigen (HLA) genes, alongside other genes with nonimmunological roles. More recently, a repertoire of noncoding RNA genes, including expressed pseudogenes, has also been identified. The MHC is the most gene dense and most polymorphic part of the human genome. The region exhibits haplotype-specific linkage disequilibrium patterns, contains the strongest cis- and trans-eQTLs/meQTLs in the genome and is known as a hot spot for disease associations. Another layer of complexity is provided to the region by the extreme structural variation and copy number variations. While the HLA-B gene has the highest number of alleles, the HLA-DR/DQ subregion is structurally most variable and shows the highest number of disease associations. Reliance on a single reference sequence has complicated the design, execution and analysis of GWAS for the MHC region and not infrequently, the MHC region has even been excluded from the analysis of GWAS data. Here, we contrast features of the MHC region with the rest of the genome and highlight its complexities, including its functional polymorphisms beyond those determined by single nucleotide polymorphisms or single amino acid residues. One of the several issues with customary GWAS analysis is that it does not address this additional layer of polymorphisms unique to the MHC region. We highlight alternative approaches that may assist with the analysis of GWAS data from the MHC region and unravel associations with all functional polymorphisms beyond single SNPs. We suggest that despite already showing the highest number of disease associations, the true extent of the involvement of the MHC region in disease genetics may not have been uncovered.
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Affiliation(s)
- A E Kennedy
- Center for Research Strategy, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - U Ozbek
- Department of Population Health Science and Policy, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - M T Dorak
- Head of School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Kingston-upon-Thames, UK
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44
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Keating BJ, Pereira AC, Snyder M, Piening BD. Applying genomics in heart transplantation. Transpl Int 2018; 31:278-290. [PMID: 29363220 PMCID: PMC5990370 DOI: 10.1111/tri.13119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 11/18/2017] [Accepted: 01/17/2018] [Indexed: 12/13/2022]
Abstract
While advances in patient care and immunosuppressive pharmacotherapies have increased the lifespan of heart allograft recipients, there are still significant comorbidities post-transplantation and 5-year survival rates are still significant, at approximately 70%. The last decade has seen massive strides in genomics and other omics fields, including transcriptomics, with many of these advances now starting to impact heart transplant clinical care. This review summarizes a number of the key advances in genomics which are relevant for heart transplant outcomes, and we highlight the translational potential that such knowledge may bring to patient care within the next decade.
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Affiliation(s)
- Brendan J. Keating
- Division of Transplantation, Department of Surgery, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA
| | - Alexandre C. Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School Hospital, São Paulo, Brazil
| | - Michael Snyder
- Department of Genetics, Stanford University, Stanford, CA, USA
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45
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Seielstad M, Page GP, Gaddis N, Lanteri M, Lee TH, Kakaiya R, Barcellos LF, Criswell LA, Triulzi D, Norris PJ, Busch MP. Genomewide association study of HLA alloimmunization in previously pregnant blood donors. Transfusion 2018; 58:402-412. [PMID: 29168253 PMCID: PMC5803399 DOI: 10.1111/trf.14402] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 07/21/2017] [Accepted: 09/27/2017] [Indexed: 02/05/2023]
Abstract
BACKGROUND Alloimmunization through blood transfusion, transplantation, or circulating fetal cells during pregnancy is a significant concern. Some exposed individuals make alloantibodies while others do not, implying variation in genetic risk factors. STUDY DESIGN AND METHODS We conducted a genomewide association study (GWAS) of 9,427,497 single-nucleotide polymorphisms (SNPs) to identify genetic variants for HLA alloimmunization in previously pregnant blood donors with (n = 752) and without (n = 753) HLA Class I or II alloantibodies. RESULTS A SNP in the neurexophilin 2 (NXPH2) gene surpassed genome-wide significance (p = 2.06 × 10-8 ), with multiple adjacent markers p < 10-6 , for women with anti-Class I alloantibodies only. Little is currently known about the function of NXPH2, although gene family members have been shown to impact immunity. SNPs in the E2F7 gene, a transcription factor related to cell cycle control and cellular proliferation, also approached genomewide significance (p = 2.5 × 10-7 ). CONCLUSION Further work to extend the GWAS approach and to characterize variants in NXPH2 and E2F7 in the context of alloantibody formation is warranted.
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Affiliation(s)
- Mark Seielstad
- Blood Systems Research Institute, San Francisco CA 94118
- Institute for Human Genetics, University of California San Francisco, San Francisco CA 94143
- Department of Laboratory Medicine, University of California San Francisco, San Francisco CA 94143
| | | | | | - Marion Lanteri
- Blood Systems Research Institute, San Francisco CA 94118
| | - Tzong-Hae Lee
- Blood Systems Research Institute, San Francisco CA 94118
| | | | - Lisa F. Barcellos
- Division of Epidemiology, School of Public Health, University of California Berkeley, Berkeley, CA 94720
| | - Lindsey A. Criswell
- Rosalind Russell/Ephraim P Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA 94143
| | - Darrell Triulzi
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15213
| | - Philip J. Norris
- Blood Systems Research Institute, San Francisco CA 94118
- Department of Laboratory Medicine, University of California San Francisco, San Francisco CA 94143
- Rosalind Russell/Ephraim P Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA 94143
| | - Michael P. Busch
- Blood Systems Research Institute, San Francisco CA 94118
- Department of Laboratory Medicine, University of California San Francisco, San Francisco CA 94143
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46
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Abstract
The MHC/HLA region has been consistently associated with a large number of complex traits, including but not limited to, most immune-mediated ones. Efforts to pinpoint drivers of this commonly encountered association peak at the short arm of chromosome 6, however, have been challenging, owing to the high density of genes and the long and extended linkage disequilibrium that are characteristic of this region.The development of methods to impute classical HLA alleles and amino acids from SNP genotyping data has offered an important additional layer of information to the investigators seeking to fine map the signal in the region. As a result, imputation-aided association analyses are now typically employed to shed light on the relationship of this locus with disease susceptibility and response to drugs.In this chapter we discuss how the signal in the HLA region can be interrogated in practice, from performing the imputation to understanding its output and to incorporating it into downstream analysis. In addition, we recount some of the analytical approaches that are commonly used and suggest ways in which the findings from such imputation-aided analyses can be interpreted.
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47
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Meyer D, C Aguiar VR, Bitarello BD, C Brandt DY, Nunes K. A genomic perspective on HLA evolution. Immunogenetics 2018; 70:5-27. [PMID: 28687858 PMCID: PMC5748415 DOI: 10.1007/s00251-017-1017-3] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 06/16/2017] [Indexed: 12/20/2022]
Abstract
Several decades of research have convincingly shown that classical human leukocyte antigen (HLA) loci bear signatures of natural selection. Despite this conclusion, many questions remain regarding the type of selective regime acting on these loci, the time frame at which selection acts, and the functional connections between genetic variability and natural selection. In this review, we argue that genomic datasets, in particular those generated by next-generation sequencing (NGS) at the population scale, are transforming our understanding of HLA evolution. We show that genomewide data can be used to perform robust and powerful tests for selection, capable of identifying both positive and balancing selection at HLA genes. Importantly, these tests have shown that natural selection can be identified at both recent and ancient timescales. We discuss how findings from genomewide association studies impact the evolutionary study of HLA genes, and how genomic data can be used to survey adaptive change involving interaction at multiple loci. We discuss the methodological developments which are necessary to correctly interpret genomic analyses involving the HLA region. These developments include adapting the NGS analysis framework so as to deal with the highly polymorphic HLA data, as well as developing tools and theory to search for signatures of selection, quantify differentiation, and measure admixture within the HLA region. Finally, we show that high throughput analysis of molecular phenotypes for HLA genes-namely transcription levels-is now a feasible approach and can add another dimension to the study of genetic variation.
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Affiliation(s)
- Diogo Meyer
- Department of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil.
| | - Vitor R C Aguiar
- Department of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil
| | - Bárbara D Bitarello
- Department of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Débora Y C Brandt
- Department of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil
- Department of Integrative Biology, University of California, Berkeley, CA, USA
| | - Kelly Nunes
- Department of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil
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48
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Cook S, Han B. MergeReference: A Tool for Merging Reference Panels for HLA Imputation. Genomics Inform 2017; 15:108-111. [PMID: 29020726 PMCID: PMC5637342 DOI: 10.5808/gi.2017.15.3.108] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 08/29/2017] [Accepted: 08/30/2017] [Indexed: 01/16/2023] Open
Abstract
Recently developed computational methods allow the imputation of human leukocyte antigen (HLA) genes using intergenic single nucleotide polymorphism markers. To improve the imputation accuracy in HLA imputation, it is essential to increase the sample size and the diversity of alleles in the reference panel. Our software, MergeReference, helps achieve this goal by providing a streamlined pipeline for combining multiple reference panels into one.
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Affiliation(s)
- Seungho Cook
- Department of Convergence Medicine, University of Ulsan College of Medicine and Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea
| | - Buhm Han
- Department of Convergence Medicine, University of Ulsan College of Medicine and Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea
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49
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Jeanmougin M, Noirel J, Coulonges C, Zagury JF. HLA-check: evaluating HLA data from SNP information. BMC Bioinformatics 2017; 18:334. [PMID: 28697761 PMCID: PMC5504728 DOI: 10.1186/s12859-017-1746-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 06/26/2017] [Indexed: 02/08/2023] Open
Abstract
Background The major histocompatibility complex (MHC) region of the human genome, and specifically the human leukocyte antigen (HLA) genes, play a major role in numerous human diseases. With the recent progress of sequencing methods (eg, Next-Generation Sequencing, NGS), the accurate genotyping of this region has become possible but remains relatively costly. In order to obtain the HLA information for the millions of samples already genotyped by chips in the past ten years, efficient bioinformatics tools, such as SNP2HLA or HIBAG, have been developed that infer HLA information from the linkage disequilibrium existing between HLA alleles and SNP markers in the MHC region. Results In this study, we first used ShapeIT and Impute2 to implement an imputation method akin to SNP2HLA and found a comparable quality of imputation on a European dataset. More importantly, we developed a new tool, HLA-check, that allows for the detection of aberrant HLA allele calling with regard to the SNP genotypes in the region. Adding this tool to the HLA imputation software increases dramatically their accuracy, especially for HLA class I genes. Conclusion Overall, HLA-check was able to identify a limited number of implausible HLA typings (less than 10%) in a population, and these samples can then either be removed or be retyped by NGS for HLA association analysis.
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Affiliation(s)
- Marc Jeanmougin
- Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, Paris, 75003, France
| | - Josselin Noirel
- Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, Paris, 75003, France
| | - Cédric Coulonges
- Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, Paris, 75003, France
| | - Jean-François Zagury
- Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, Paris, 75003, France.
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50
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Karnes JH, Bastarache L, Shaffer CM, Gaudieri S, Xu Y, Glazer AM, Mosley JD, Zhao S, Raychaudhuri S, Mallal S, Ye Z, Mayer JG, Brilliant MH, Hebbring SJ, Roden DM, Phillips EJ, Denny JC. Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants. Sci Transl Med 2017; 9:eaai8708. [PMID: 28490672 PMCID: PMC5563969 DOI: 10.1126/scitranslmed.aai8708] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 03/27/2017] [Indexed: 12/22/2022]
Abstract
Although many phenotypes have been associated with variants in human leukocyte antigen (HLA) genes, the full phenotypic impact of HLA variants across all diseases is unknown. We imputed HLA genomic variation from two populations of 28,839 and 8431 European ancestry individuals and tested association of HLA variation with 1368 phenotypes. A total of 104 four-digit and 92 two-digit HLA allele phenotype associations were significant in both discovery and replication cohorts, the strongest being HLA-DQB1*03:02 and type 1 diabetes. Four previously unidentified associations were identified across the spectrum of disease with two- and four-digit HLA alleles and 10 with nonsynonymous variants. Some conditions associated with multiple HLA variants and stronger associations with more severe disease manifestations were identified. A comprehensive, publicly available catalog of clinical phenotypes associated with HLA variation is provided. Examining HLA variant disease associations in this large data set allows comprehensive definition of disease associations to drive further mechanistic insights.
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Affiliation(s)
- Jason H Karnes
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, AZ 85721, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Christian M Shaffer
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Silvana Gaudieri
- School of Anatomy, Physiology and Human Biology, University of Western Australia, Nedlands, Western Australia, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Andrew M Glazer
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Shilin Zhao
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Soumya Raychaudhuri
- Division of Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA 02115, USA
- Institute of Inflammation and Repair, University of Manchester, Manchester, UK
- Department of Medicine, Karolinska Institutet and Karolinska University Hospital Solna, Stockholm, Sweden
| | - Simon Mallal
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Zhan Ye
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - John G Mayer
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Murray H Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Scott J Hebbring
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Elizabeth J Phillips
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
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