1
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Sartoris S, Del Pozzo G. Exploring the HLA complex in autoimmunity: From the risk haplotypes to the modulation of expression. Clin Immunol 2024; 265:110266. [PMID: 38851519 DOI: 10.1016/j.clim.2024.110266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 06/10/2024]
Abstract
The genes mapping at the HLA region show high density, strong linkage disequilibrium and high polymorphism, which affect the association of HLA class I and class II genes with autoimmunity. We focused on the HLA haplotypes, genomic structures consisting of an array of specific alleles showing some degrees of genetic association with different autoimmune disorders. GWASs in many pathologies have identified variants in either the coding loci or the flanking regulatory regions, both in linkage disequilibrium in haplotypes, that are frequently associated with increased risk and may influence gene expression. We discuss the relevance of the HLA gene expression because the level of surface heterodimers determines the number of complexes presenting self-antigen and, thus, the strength of pathogenic autoreactive T cells immune response.
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Affiliation(s)
- Silvia Sartoris
- Dept. of Medicine, Section of Immunology University of Verona School of Medicine, Verona, Italy
| | - Giovanna Del Pozzo
- Institute of Genetics and Biophysics "Adriano Buzzati Traverso" National Research Council (CNR), Naples, Italy.
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2
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Burnham KL, Milind N, Lee W, Kwok AJ, Cano-Gamez K, Mi Y, Geoghegan CG, Zhang P, McKechnie S, Soranzo N, Hinds CJ, Knight JC, Davenport EE. eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis. CELL GENOMICS 2024; 4:100587. [PMID: 38897207 PMCID: PMC11293594 DOI: 10.1016/j.xgen.2024.100587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/27/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024]
Abstract
Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene-expression-based patient subgroups (sepsis response signatures [SRS]) informative for outcome and underlying pathophysiology. Here, we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-sequencing data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection.
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Affiliation(s)
- Katie L Burnham
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Nikhil Milind
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK; University of Cambridge, Cambridge, UK
| | - Wanseon Lee
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Andrew J Kwok
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Kiki Cano-Gamez
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK; Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Yuxin Mi
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Ping Zhang
- Centre for Human Genetics, University of Oxford, Oxford, UK; Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, UK
| | | | - Nicole Soranzo
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Charles J Hinds
- Centre for Translational Medicine & Therapeutics, William Harvey Research Institute, Faculty of Medicine & Dentistry, Queen Mary University of London, London, UK
| | - Julian C Knight
- Centre for Human Genetics, University of Oxford, Oxford, UK; Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, UK.
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3
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Pahkuri S, Katayama S, Valta M, Nygård L, Knip M, Kere J, Ilonen J, Lempainen J. The effect of type 1 diabetes protection and susceptibility associated HLA class II genotypes on DNA methylation in immune cells. HLA 2024; 103:e15548. [PMID: 38887913 DOI: 10.1111/tan.15548] [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: 01/02/2024] [Revised: 04/24/2024] [Accepted: 05/17/2024] [Indexed: 06/20/2024]
Abstract
The HLA region, especially HLA class I and II genes, which encode molecules for antigen presentation to T cells, plays a major role in the predisposition to autoimmune disorders. To clarify the mechanisms behind this association, we examined genome-wide DNA methylation by microarrays to cover over 850,000 CpG sites in the CD4+ T cells and CD19+ B cells of healthy subjects homozygous either for DRB1*15-DQA1*01-DQB1*06:02 (DR2-DQ6, n = 14), associated with a strongly decreased T1D risk, DRB1*03-DQA1*05-DQB1*02 (DR3-DQ2, n = 19), or DRB1*04:01-DQA1*03-DQB1*03:02 (DR4-DQ8, n = 17), associated with a moderately increased T1D risk. In total, we discovered 14 differentially methylated CpG probes, of which 10 were located in the HLA region and six in the HLA-DRB1 locus. The main differences were between the protective genotype DR2-DQ6 and the risk genotypes DR3-DQ2 and DR4-DQ8, where the DR2-DQ6 group was hypomethylated compared to the other groups in all but four of the differentially methylated probes. The differences between the risk genotypes DR3-DQ2 and DR4-DQ8 were small. Our results indicate that HLA variants have few systemic effects on methylation and that their effect on autoimmunity is conveyed directly by HLA molecules, possibly by differences in expression levels or function.
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Affiliation(s)
- Sirpa Pahkuri
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Shintaro Katayama
- Folkhälsan Research Center, Helsinki, Finland
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Milla Valta
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Lucas Nygård
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Clinical Microbiology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Mikael Knip
- Faculty of Medicine, Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
- Tampere Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Juha Kere
- Folkhälsan Research Center, Helsinki, Finland
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Jorma Ilonen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Johanna Lempainen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
- Clinical Microbiology, Turku University Hospital, Turku, Finland
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4
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Haplotype-aware pantranscriptome analyses using spliced pangenome graphs. Nat Methods 2023; 20:239-247. [PMID: 36646895 DOI: 10.1038/s41592-022-01731-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/28/2022] [Indexed: 01/18/2023]
Abstract
Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our toolchain, which consists of additions to the VG toolkit and a standalone tool, RPVG, can construct spliced pangenome graphs, map RNA sequencing data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. We show that this workflow improves accuracy over state-of-the-art RNA sequencing mapping methods, and that it can efficiently quantify haplotype-specific transcript expression without needing to characterize the haplotypes of a sample beforehand.
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5
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Sibbesen JA, Eizenga JM, Novak AM, Sirén J, Chang X, Garrison E, Paten B. Haplotype-aware pantranscriptome analyses using spliced pangenome graphs. Nat Methods 2023; 20:239-247. [PMID: 36646895 DOI: 10.1101/2021.03.26.437240] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/28/2022] [Indexed: 05/24/2023]
Abstract
Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our toolchain, which consists of additions to the VG toolkit and a standalone tool, RPVG, can construct spliced pangenome graphs, map RNA sequencing data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. We show that this workflow improves accuracy over state-of-the-art RNA sequencing mapping methods, and that it can efficiently quantify haplotype-specific transcript expression without needing to characterize the haplotypes of a sample beforehand.
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Affiliation(s)
| | | | - Adam M Novak
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Jouni Sirén
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Xian Chang
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Erik Garrison
- University of Tennessee Health Science Center, Memphis, TN, USA
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6
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Aguiar VRC, Castelli EC, Single RM, Bashirova A, Ramsuran V, Kulkarni S, Augusto DG, Martin MP, Gutierrez-Arcelus M, Carrington M, Meyer D. Comparison between qPCR and RNA-seq reveals challenges of quantifying HLA expression. Immunogenetics 2023; 75:249-262. [PMID: 36707444 PMCID: PMC9883133 DOI: 10.1007/s00251-023-01296-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/11/2023] [Indexed: 01/29/2023]
Abstract
Human leukocyte antigen (HLA) class I and II loci are essential elements of innate and acquired immunity. Their functions include antigen presentation to T cells leading to cellular and humoral immune responses, and modulation of NK cells. Their exceptional influence on disease outcome has now been made clear by genome-wide association studies. The exons encoding the peptide-binding groove have been the main focus for determining HLA effects on disease susceptibility/pathogenesis. However, HLA expression levels have also been implicated in disease outcome, adding another dimension to the extreme diversity of HLA that impacts variability in immune responses across individuals. To estimate HLA expression, immunogenetic studies traditionally rely on quantitative PCR (qPCR). Adoption of alternative high-throughput technologies such as RNA-seq has been hampered by technical issues due to the extreme polymorphism at HLA genes. Recently, however, multiple bioinformatic methods have been developed to accurately estimate HLA expression from RNA-seq data. This opens an exciting opportunity to quantify HLA expression in large datasets but also brings questions on whether RNA-seq results are comparable to those by qPCR. In this study, we analyze three classes of expression data for HLA class I genes for a matched set of individuals: (a) RNA-seq, (b) qPCR, and (c) cell surface HLA-C expression. We observed a moderate correlation between expression estimates from qPCR and RNA-seq for HLA-A, -B, and -C (0.2 ≤ rho ≤ 0.53). We discuss technical and biological factors which need to be accounted for when comparing quantifications for different molecular phenotypes or using different techniques.
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Affiliation(s)
- Vitor R. C. Aguiar
- Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, SP Brazil ,Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA ,Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Erick C. Castelli
- Molecular Genetics and Bioinformatics Laboratory, Experimental Research Unit, School of Medicine, São Paulo State University, Botucatu, SP Brazil
| | - Richard M. Single
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT USA
| | - Arman Bashirova
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA
| | - Veron Ramsuran
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA ,Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa ,School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Smita Kulkarni
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA ,Host-Pathogen Interactions Program, Texas Biomedical Research Institute, San Antonio, TX USA
| | - Danillo G. Augusto
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA ,Department of Biological Sciences, The University of North Carolina at Charlotte, Charlotte, NC USA ,Programa de Pós-Graduação em Genética, Universidade Federal do Paraná, Curitiba, PR Brazil
| | - Maureen P. Martin
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA
| | - Maria Gutierrez-Arcelus
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA ,Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Mary Carrington
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA ,Ragon Institute of MGH, MIT and Harvard, Cambridge, MA USA
| | - Diogo Meyer
- Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, SP Brazil
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7
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Li T, Du D, Zhang D, Lin Y, Ma J, Zhou M, Meng W, Jin Z, Chen Z, Yuan H, Wang J, Dong S, Sun S, Ye W, Li B, Liu H, Zhang Z, Jiao Y, Xie Z, Qiu W, Liu Y. CRISPR-based targeted haplotype-resolved assembly of a megabase region. Nat Commun 2023; 14:22. [PMID: 36596772 PMCID: PMC9810730 DOI: 10.1038/s41467-022-35389-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 11/29/2022] [Indexed: 01/04/2023] Open
Abstract
Constructing high-quality haplotype-resolved genome assemblies has substantially improved the ability to detect and characterize genetic variants. A targeted approach providing readily access to the rich information from haplotype-resolved genome assemblies will be appealing to groups of basic researchers and medical scientists focused on specific genomic regions. Here, using the 4.5 megabase, notoriously difficult-to-assemble major histocompatibility complex (MHC) region as an example, we demonstrated an approach to construct haplotype-resolved assembly of the targeted genomic region with the CRISPR-based enrichment. Compared to the results from haplotype-resolved genome assembly, our targeted approach achieved comparable completeness and accuracy with reduced computing complexity, sequencing cost, as well as the amount of starting materials. Moreover, using the targeted assembled personal MHC haplotypes as the reference both improves the quantification accuracy for sequencing data and enables allele-specific functional genomics analyses of the MHC region. Given its highly efficient use of resources, our approach can greatly facilitate population genetic studies of targeted regions, and may pave a new way to elucidate the molecular mechanisms in disease etiology.
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Affiliation(s)
- Taotao Li
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Duo Du
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Dandan Zhang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Yicheng Lin
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Jiakang Ma
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Mengyu Zhou
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Weida Meng
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Zelin Jin
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Ziqiang Chen
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Haozhe Yuan
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Jue Wang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Shulong Dong
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Shaoyang Sun
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Wenjing Ye
- Division of Rheumatology and Immunology, Huashan Hospital, Fudan University, Shanghai, China
| | - Bosen Li
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Houbao Liu
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhao Zhang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Yuchen Jiao
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Wenqing Qiu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China. .,Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China. .,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China.
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8
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Houtman M, Dzebisashvili A, Dubnovitsky A, Kozhukh G, Rönnblom L, Klareskog L, Malmström V, Padyukov L. Five commercially-available antibodies react differentially with allelic forms of human HLA-DR beta chain. Mol Immunol 2022; 152:106-110. [DOI: 10.1016/j.molimm.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 10/05/2022] [Accepted: 10/16/2022] [Indexed: 11/06/2022]
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9
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Yang A, Tang JYS, Troup M, Ho JWK. Scavenger: A pipeline for recovery of unaligned reads utilising similarity with aligned reads. F1000Res 2022; 8:1587. [PMID: 32913631 PMCID: PMC7459848 DOI: 10.12688/f1000research.19426.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/08/2022] [Indexed: 11/25/2022] Open
Abstract
Read alignment is an important step in RNA-seq analysis as the result of alignment forms the basis for downstream analyses. However, recent studies have shown that published alignment tools have variable mapping sensitivity and do not necessarily align all the reads which should have been aligned, a problem we termed as the false-negative non-alignment problem. Here we present Scavenger, a python-based bioinformatics pipeline for recovering unaligned reads using a novel mechanism in which a putative alignment location is discovered based on sequence similarity between aligned and unaligned reads. We showed that Scavenger could recover unaligned reads in a range of simulated and real RNA-seq datasets, including single-cell RNA-seq data. We found that recovered reads tend to contain more genetic variants with respect to the reference genome compared to previously aligned reads, indicating that divergence between personal and reference genomes plays a role in the false-negative non-alignment problem. Even when the number of recovered reads is relatively small compared to the total number of reads, the addition of these recovered reads can impact downstream analyses, especially in terms of estimating the expression and differential expression of lowly expressed genes, such as pseudogenes.
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Affiliation(s)
- Andrian Yang
- Victor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia
- St. Vincent’s Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Joshua Y. S. Tang
- Victor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia
- St. Vincent’s Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Michael Troup
- Victor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia
| | - Joshua W. K. Ho
- Victor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia
- St. Vincent’s Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
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10
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Johansson T, Partanen J, Saavalainen P. HLA allele-specific expression: Methods, disease associations, and relevance in hematopoietic stem cell transplantation. Front Immunol 2022; 13:1007425. [PMID: 36248878 PMCID: PMC9554311 DOI: 10.3389/fimmu.2022.1007425] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 09/09/2022] [Indexed: 11/27/2022] Open
Abstract
Varying HLA allele-specific expression levels are associated with human diseases, such as graft versus host disease (GvHD) in hematopoietic stem cell transplantation (HSCT), cytotoxic T cell response and viral load in HIV infection, and the risk of Crohn’s disease. Only recently, RNA-based next generation sequencing (NGS) methodologies with accompanying bioinformatics tools have emerged to quantify HLA allele-specific expression replacing the quantitative PCR (qPCR) -based methods. These novel NGS approaches enable the systematic analysis of the HLA allele-specific expression changes between individuals and between normal and disease phenotypes. Additionally, analyzing HLA allele-specific expression and allele-specific expression loss provide important information for predicting efficacies of novel immune cell therapies. Here, we review available RNA sequencing-based approaches and computational tools for NGS to quantify HLA allele-specific expression. Moreover, we explore recent studies reporting disease associations with differential HLA expression. Finally, we discuss the role of allele-specific expression in HSCT and how considering the expression quantification in recipient-donor matching could improve the outcome of HSCT.
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Affiliation(s)
- Tiira Johansson
- Translational Immunology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
- *Correspondence: Tiira Johansson,
| | - Jukka Partanen
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Päivi Saavalainen
- Translational Immunology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
- Genetics Research Program, Folkhälsan Research Center, Helsinki, Finland
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11
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Regulation of HLA class I expression by non-coding gene variations. PLoS Genet 2022; 18:e1010212. [PMID: 35666741 PMCID: PMC9170083 DOI: 10.1371/journal.pgen.1010212] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 04/20/2022] [Indexed: 11/19/2022] Open
Abstract
The Human Leukocyte Antigen (HLA) is a critical genetic system for different outcomes after solid organ and hematopoietic cell transplantation. Its polymorphism is usually determined by molecular technologies at the DNA level. A potential role of HLA allelic expression remains under investigation in the context of the allogenic immune response between donors and recipients. In this study, we quantified the allelic expression of all three HLA class I loci (HLA-A, B and C) by RNA sequencing and conducted an analysis of expression quantitative traits loci (eQTL) to investigate whether HLA expression regulation could be associated with non-coding gene variations. HLA-B alleles exhibited the highest expression levels followed by HLA-C and HLA-A alleles. The max fold expression variation was observed for HLA-C alleles. The expression of HLA class I loci of distinct individuals demonstrated a coordinated and paired expression of both alleles of the same locus. Expression of conserved HLA-A~B~C haplotypes differed in distinct PBMC's suggesting an individual regulated expression of both HLA class I alleles and haplotypes. Cytokines TNFα /IFNβ, which induced a very similar upregulation of HLA class I RNA and cell surface expression across alleles did not modify the individually coordinated expression at the three HLA class I loci. By identifying cis eQTLs for the HLA class I genes, we show that the non-coding eQTLs explain 29%, 13%, and 31% of the respective HLA-A, B, C expression variance in unstimulated cells, and 9%, 23%, and 50% of the variance in cytokine-stimulated cells. The eQTLs have significantly higher effect sizes in stimulated cells compared to unstimulated cells for HLA-B and HLA-C genes expression. Our data also suggest that the identified eQTLs are independent from the coding variation which defines HLA alleles and thus may be influential on intra-allele expression variability although they might not represent the causal eQTLs.
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12
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Aguiar VRC, Augusto DG, Castelli EC, Hollenbach JA, Meyer D, Nunes K, Petzl-Erler ML. An immunogenetic view of COVID-19. Genet Mol Biol 2021; 44:e20210036. [PMID: 34436508 PMCID: PMC8388242 DOI: 10.1590/1678-4685-gmb-2021-0036] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/12/2021] [Indexed: 02/06/2023] Open
Abstract
Meeting the challenges brought by the COVID-19 pandemic requires an interdisciplinary approach. In this context, integrating knowledge of immune function with an understanding of how genetic variation influences the nature of immunity is a key challenge. Immunogenetics can help explain the heterogeneity of susceptibility and protection to the viral infection and disease progression. Here, we review the knowledge developed so far, discussing fundamental genes for triggering the innate and adaptive immune responses associated with a viral infection, especially with the SARS-CoV-2 mechanisms. We emphasize the role of the HLA and KIR genes, discussing what has been uncovered about their role in COVID-19 and addressing methodological challenges of studying these genes. Finally, we comment on questions that arise when studying admixed populations, highlighting the case of Brazil. We argue that the interplay between immunology and an understanding of genetic associations can provide an important contribution to our knowledge of COVID-19.
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Affiliation(s)
- Vitor R. C. Aguiar
- Universidade de São Paulo, Departamento de Genética e Biologia
Evolutiva, São Paulo, SP, Brazil
| | - Danillo G. Augusto
- University of California, UCSF Weill Institute for Neurosciences,
Department of Neurology, San Francisco, CA, USA
- Universidade Federal do Paraná, Departamento de Genética, Curitiba,
PR, Brazil
| | - Erick C. Castelli
- Universidade Estadual Paulista, Faculdade de Medicina de Botucatu,
Departamento de Patologia, Botucatu, SP, Brazil
| | - Jill A. Hollenbach
- University of California, UCSF Weill Institute for Neurosciences,
Department of Neurology, San Francisco, CA, USA
| | - Diogo Meyer
- Universidade de São Paulo, Departamento de Genética e Biologia
Evolutiva, São Paulo, SP, Brazil
| | - Kelly Nunes
- Universidade de São Paulo, Departamento de Genética e Biologia
Evolutiva, São Paulo, SP, Brazil
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13
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Houtman M, Hesselberg E, Rönnblom L, Klareskog L, Malmström V, Padyukov L. Haplotype-Specific Expression Analysis of MHC Class II Genes in Healthy Individuals and Rheumatoid Arthritis Patients. Front Immunol 2021; 12:707217. [PMID: 34484204 PMCID: PMC8416041 DOI: 10.3389/fimmu.2021.707217] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 08/02/2021] [Indexed: 01/03/2023] Open
Abstract
HLA-DRB1 alleles have been associated with several autoimmune diseases. For anti-citrullinated protein antibody positive rheumatoid arthritis (RA), HLA-DRB1 shared epitope (SE) alleles are the major genetic risk factors. In order to study the genetic regulation of major histocompatibility complex (MHC) Class II gene expression in immune cells, we investigated transcriptomic profiles of a variety of immune cells from healthy individuals carrying different HLA-DRB1 alleles. Sequencing libraries from peripheral blood mononuclear cells, CD4+ T cells, CD8+ T cells, and CD14+ monocytes of 32 genetically pre-selected healthy female individuals were generated, sequenced and reads were aligned to the standard reference. For the MHC region, reads were mapped to available MHC reference haplotypes and AltHapAlignR was used to estimate gene expression. Using this method, HLA-DRB and HLA-DQ were found to be differentially expressed in different immune cells of healthy individuals as well as in whole blood samples of RA patients carrying HLA-DRB1 SE-positive versus SE-negative alleles. In contrast, no genes outside the MHC region were differentially expressed between individuals carrying HLA-DRB1 SE-positive and SE-negative alleles, thus HLA-DRB1 SE alleles have a strong cis effect on gene expression. Altogether, our findings suggest that immune effects associated with different allelic forms of HLA-DR and HLA-DQ may be associated not only with differences in the structure of these proteins, but also with differences in their expression levels.
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Affiliation(s)
- Miranda Houtman
- Division of Rheumatology, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Espen Hesselberg
- Division of Rheumatology, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Lars Rönnblom
- Department of Medical Sciences, Rheumatology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Lars Klareskog
- Division of Rheumatology, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Vivianne Malmström
- Division of Rheumatology, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Leonid Padyukov
- Division of Rheumatology, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
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14
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Vekemans X, Castric V, Hipperson H, Müller NA, Westerdahl H, Cronk Q. Whole-genome sequencing and genome regions of special interest: Lessons from major histocompatibility complex, sex determination, and plant self-incompatibility. Mol Ecol 2021; 30:6072-6086. [PMID: 34137092 PMCID: PMC9290700 DOI: 10.1111/mec.16020] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 05/31/2021] [Accepted: 06/07/2021] [Indexed: 11/27/2022]
Abstract
Whole‐genome sequencing of non‐model organisms is now widely accessible and has allowed a range of questions in the field of molecular ecology to be investigated with greater power. However, some genomic regions that are of high biological interest remain problematic for assembly and data‐handling. Three such regions are the major histocompatibility complex (MHC), sex‐determining regions (SDRs) and the plant self‐incompatibility locus (S‐locus). Using these as examples, we illustrate the challenges of both assembling and resequencing these highly polymorphic regions and how bioinformatic and technological developments are enabling new approaches to their study. Mapping short‐read sequences against multiple alternative references improves genotyping comprehensiveness at the S‐locus thereby contributing to more accurate assessments of allelic frequencies. Long‐read sequencing, producing reads of several tens to hundreds of kilobase pairs in length, facilitates the assembly of such regions as single sequences can span the multiple duplicated gene copies of the MHC region, and sequence through repetitive stretches and translocations in SDRs and S‐locus haplotypes. These advances are adding value to short‐read genome resequencing approaches by allowing, for example, more accurate haplotype phasing across longer regions. Finally, we assessed further technical improvements, such as nanopore adaptive sequencing and bioinformatic tools using pangenomes, which have the potential to further expand our knowledge of a number of genomic regions that remain challenging to study with classical resequencing approaches.
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Affiliation(s)
| | | | - Helen Hipperson
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK
| | - Niels A Müller
- Thünen Institute of Forest Genetics, Grosshansdorf, Germany
| | - Helena Westerdahl
- Molecular Ecology and Evolution Laboratory, Department of Biology, Lund University, Lund, Sweden
| | - Quentin Cronk
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada
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15
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Johansson T, Yohannes DA, Koskela S, Partanen J, Saavalainen P. HLA RNA Sequencing With Unique Molecular Identifiers Reveals High Allele-Specific Variability in mRNA Expression. Front Immunol 2021; 12:629059. [PMID: 33717155 PMCID: PMC7949471 DOI: 10.3389/fimmu.2021.629059] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 01/18/2021] [Indexed: 11/13/2022] Open
Abstract
The HLA gene complex is the most important single genetic factor in susceptibility to most diseases with autoimmune or autoinflammatory origin and in transplantation matching. Most studies have focused on the vast allelic variation in these genes; only a few studies have explored differences in the expression levels of HLA alleles. In this study, we quantified mRNA expression levels of HLA class I and II genes from peripheral blood samples of 50 healthy individuals. The gene- and allele-specific mRNA expression was assessed using unique molecular identifiers, which enabled PCR bias removal and calculation of the number of original mRNA transcripts. We identified differences in mRNA expression between different HLA genes and alleles. Our results suggest that HLA alleles are differentially expressed and these differences in expression levels are quantifiable using RNA sequencing technology. Our method provides novel insights into HLA research, and it can be applied to quantify expression differences of HLA alleles in various tissues and to evaluate the role of this type of variation in transplantation matching and susceptibility to autoimmune diseases.
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Affiliation(s)
- Tiira Johansson
- Research Programs Unit, Translational Immunology Program, University of Helsinki, Helsinki, Finland
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Dawit A. Yohannes
- Research Programs Unit, Translational Immunology Program, University of Helsinki, Helsinki, Finland
| | - Satu Koskela
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Jukka Partanen
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Päivi Saavalainen
- Research Programs Unit, Translational Immunology Program, University of Helsinki, Helsinki, Finland
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
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16
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Aguiar VRC, Masotti C, Camargo AA, Meyer D. HLApers: HLA Typing and Quantification of Expression with Personalized Index. Methods Mol Biol 2021; 2120:101-112. [PMID: 32124314 DOI: 10.1007/978-1-0716-0327-7_7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
The plethora of RNA-seq data which have been generated in the recent years constitutes an attractive resource to investigate HLA variation and its relationship with normal and disease phenotypes, such as cancer. However, next generation sequencing (NGS) brings new challenges to HLA analysis because of the mapping bias introduced by aligning short reads originated from polymorphic genes to a single reference genome. Here we describe HLApers, a pipeline which adapts widely used tools for analysis of standard RNA-seq data to infer HLA genotypes and estimate expression. By generating reliable expression estimates for each HLA allele that an individual carries, HLApers allows a better understanding of the relationship between HLA alleles and phenotypes manifested by an individual.
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Affiliation(s)
- Vitor R C Aguiar
- Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, Brazil.
| | - Cibele Masotti
- Molecular Oncology Center, Hospital Sírio Libanês, São Paulo, SP, Brazil
| | - Anamaria A Camargo
- Molecular Oncology Center, Hospital Sírio Libanês, São Paulo, SP, Brazil
| | - Diogo Meyer
- Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, Brazil
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17
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Darby CA, Stubbington MJT, Marks PJ, Martínez Barrio Á, Fiddes IT. scHLAcount: allele-specific HLA expression from single-cell gene expression data. Bioinformatics 2020; 36:3905-3906. [PMID: 32330223 PMCID: PMC7320622 DOI: 10.1093/bioinformatics/btaa264] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 04/08/2020] [Accepted: 04/16/2020] [Indexed: 11/13/2022] Open
Abstract
Summary Bulk RNA sequencing studies have demonstrated that human leukocyte antigen (HLA) genes may be expressed in a cell type-specific and allele-specific fashion. Single-cell gene expression assays have the potential to further resolve these expression patterns, but currently available methods do not perform allele-specific quantification at the molecule level. Here, we present scHLAcount, a post-processing workflow for single-cell RNA-seq data that computes allele-specific molecule counts of the HLA genes based on a personalized reference constructed from the sample’s HLA genotypes. Availability and implementation scHLAcount is available under the MIT license at https://github.com/10XGenomics/scHLAcount. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Charlotte A Darby
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
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18
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Dang H, Polineni D, Pace RG, Stonebraker JR, Corvol H, Cutting GR, Drumm ML, Strug LJ, O’Neal WK, Knowles MR. Mining GWAS and eQTL data for CF lung disease modifiers by gene expression imputation. PLoS One 2020; 15:e0239189. [PMID: 33253230 PMCID: PMC7703903 DOI: 10.1371/journal.pone.0239189] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 09/02/2020] [Indexed: 12/18/2022] Open
Abstract
Genome wide association studies (GWAS) have identified several genomic loci with candidate modifiers of cystic fibrosis (CF) lung disease, but only a small proportion of the expected genetic contribution is accounted for at these loci. We leveraged expression data from CF cohorts, and Genotype-Tissue Expression (GTEx) reference data sets from multiple human tissues to generate predictive models, which were used to impute transcriptional regulation from genetic variance in our GWAS population. The imputed gene expression was tested for association with CF lung disease severity. By comparing and combining results from alternative approaches, we identified 379 candidate modifier genes. We delved into 52 modifier candidates that showed consensus between approaches, and 28 of them were near known GWAS loci. A number of these genes are implicated in the pathophysiology of CF lung disease (e.g., immunity, infection, inflammation, HLA pathways, glycosylation, and mucociliary clearance) and the CFTR protein biology (e.g., cytoskeleton, microtubule, mitochondrial function, lipid metabolism, endoplasmic reticulum/Golgi, and ubiquitination). Gene set enrichment results are consistent with current knowledge of CF lung disease pathogenesis. HLA Class II genes on chr6, and CEP72, EXOC3, and TPPP near the GWAS peak on chr5 are most consistently associated with CF lung disease severity across the tissues tested. The results help to prioritize genes in the GWAS regions, predict direction of gene expression regulation, and identify new candidate modifiers throughout the genome for potential therapeutic development.
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Affiliation(s)
- Hong Dang
- Marsico Lung Institute, University of North Carolina at Chapel Hill School of Medicine Cystic Fibrosis/Pulmonary Research & Treatment Center, Chapel Hill, North Carolina, United States of America
| | - Deepika Polineni
- University of Kansas Medical Center, Kansas City, Kansas, United States of America
| | - Rhonda G. Pace
- Marsico Lung Institute, University of North Carolina at Chapel Hill School of Medicine Cystic Fibrosis/Pulmonary Research & Treatment Center, Chapel Hill, North Carolina, United States of America
| | - Jaclyn R. Stonebraker
- Marsico Lung Institute, University of North Carolina at Chapel Hill School of Medicine Cystic Fibrosis/Pulmonary Research & Treatment Center, Chapel Hill, North Carolina, United States of America
| | - Harriet Corvol
- Pediatric Pulmonary Department, Assistance Publique-Hôpitaux sde Paris (AP-HP), Hôpital Trousseau, Institut National de la Santé et la Recherche Médicale (INSERM) U938, Paris, France
- Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 6, Paris, France
| | - Garry R. Cutting
- McKusick-Nathans Institute of Genetic Medicine, Baltimore, Maryland, United States of America
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Mitchell L. Drumm
- Department of Pediatrics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Lisa J. Strug
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Wanda K. O’Neal
- Marsico Lung Institute, University of North Carolina at Chapel Hill School of Medicine Cystic Fibrosis/Pulmonary Research & Treatment Center, Chapel Hill, North Carolina, United States of America
| | - Michael R. Knowles
- Marsico Lung Institute, University of North Carolina at Chapel Hill School of Medicine Cystic Fibrosis/Pulmonary Research & Treatment Center, Chapel Hill, North Carolina, United States of America
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19
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Eizenga JM, Novak AM, Sibbesen JA, Heumos S, Ghaffaari A, Hickey G, Chang X, Seaman JD, Rounthwaite R, Ebler J, Rautiainen M, Garg S, Paten B, Marschall T, Sirén J, Garrison E. Pangenome Graphs. Annu Rev Genomics Hum Genet 2020; 21:139-162. [PMID: 32453966 DOI: 10.1146/annurev-genom-120219-080406] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Low-cost whole-genome assembly has enabled the collection of haplotype-resolved pangenomes for numerous organisms. In turn, this technological change is encouraging the development of methods that can precisely address the sequence and variation described in large collections of related genomes. These approaches often use graphical models of the pangenome to support algorithms for sequence alignment, visualization, functional genomics, and association studies. The additional information provided to these methods by the pangenome allows them to achieve superior performance on a variety of bioinformatic tasks, including read alignment, variant calling, and genotyping. Pangenome graphs stand to become a ubiquitous tool in genomics. Although it is unclear whether they will replace linearreference genomes, their ability to harmoniously relate multiple sequence and coordinate systems will make them useful irrespective of which pangenomic models become most common in the future.
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Affiliation(s)
- Jordan M Eizenga
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Adam M Novak
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Jonas A Sibbesen
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Simon Heumos
- Quantitative Biology Center, University of Tübingen, 72076 Tübingen, Germany
| | - Ali Ghaffaari
- Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.,Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.,Saarbrücken Graduate School for Computer Science, Saarland University, 66123 Saarbrücken, Germany
| | - Glenn Hickey
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Xian Chang
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Josiah D Seaman
- Royal Botanic Gardens, Kew, Richmond TW9 3AB, United Kingdom.,School of Biological and Chemical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Robin Rounthwaite
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Jana Ebler
- Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.,Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.,Saarbrücken Graduate School for Computer Science, Saarland University, 66123 Saarbrücken, Germany
| | - Mikko Rautiainen
- Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.,Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.,Saarbrücken Graduate School for Computer Science, Saarland University, 66123 Saarbrücken, Germany
| | - Shilpa Garg
- Departments of Genetics and Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02215, USA.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA
| | - Benedict Paten
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Tobias Marschall
- Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.,Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
| | - Jouni Sirén
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Erik Garrison
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
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20
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D'Antonio M, Reyna J, Jakubosky D, Donovan MKR, Bonder MJ, Matsui H, Stegle O, Nariai N, D'Antonio-Chronowska A, Frazer KA. Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease. eLife 2019; 8:e48476. [PMID: 31746734 PMCID: PMC6904215 DOI: 10.7554/elife.48476] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 11/19/2019] [Indexed: 02/06/2023] Open
Abstract
The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease. We create a comprehensive map of regulatory variation in the MHC region using WGS from 419 individuals to call eight-digit HLA types and RNA-seq data from matched iPSCs. Building on this regulatory map, we explored GWAS signals for 4083 traits, detecting colocalization for 180 disease loci with eQTLs. We show that eQTL analyses taking HLA type haplotypes into account have substantially greater power compared with only using single variants. We examined the association between the 8.1 ancestral haplotype and delayed colonization in Cystic Fibrosis, postulating that downregulation of RNF5 expression is the likely causal mechanism. Our study provides insights into the genetic architecture of the MHC region and pinpoints disease associations that are due to differential expression of HLA genes and non-HLA genes.
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Affiliation(s)
- Matteo D'Antonio
- Institute for Genomic MedicineUniversity of California, San DiegoSan DiegoUnited States
- Department of PediatricsRady Children’s Hospital, University of California, San DiegoSan DiegoUnited States
| | - Joaquin Reyna
- Department of PediatricsRady Children’s Hospital, University of California, San DiegoSan DiegoUnited States
- Biomedical Sciences Graduate ProgramUniversity of California, San DiegoLa JollaUnited States
| | - David Jakubosky
- Biomedical Sciences Graduate ProgramUniversity of California, San DiegoLa JollaUnited States
- Bioinformatics and Systems Biology Graduate ProgramUniversity of California, San DiegoSan DiegoUnited States
| | - Margaret KR Donovan
- Bioinformatics and Systems Biology Graduate ProgramUniversity of California, San DiegoSan DiegoUnited States
- Department of Biomedical InformaticsUniversity of California, San DiegoSan DiegoUnited States
| | - Marc-Jan Bonder
- European Molecular Biology Laboratory, European Bioinformatics InstituteCambridgeUnited Kingdom
| | - Hiroko Matsui
- Institute for Genomic MedicineUniversity of California, San DiegoSan DiegoUnited States
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics InstituteCambridgeUnited Kingdom
| | - Naoki Nariai
- Department of PediatricsRady Children’s Hospital, University of California, San DiegoSan DiegoUnited States
| | - Agnieszka D'Antonio-Chronowska
- Institute for Genomic MedicineUniversity of California, San DiegoSan DiegoUnited States
- Department of PediatricsRady Children’s Hospital, University of California, San DiegoSan DiegoUnited States
| | - Kelly A Frazer
- Institute for Genomic MedicineUniversity of California, San DiegoSan DiegoUnited States
- Department of PediatricsRady Children’s Hospital, University of California, San DiegoSan DiegoUnited States
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21
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Llamas B, Narzisi G, Schneider V, Audano PA, Biederstedt E, Blauvelt L, Bradbury P, Chang X, Chin CS, Fungtammasan A, Clarke WE, Cleary A, Ebler J, Eizenga J, Sibbesen JA, Markello CJ, Garrison E, Garg S, Hickey G, Lazo GR, Lin MF, Mahmoud M, Marschall T, Minkin I, Monlong J, Musunuri RL, Sagayaradj S, Novak AM, Rautiainen M, Regier A, Sedlazeck FJ, Siren J, Souilmi Y, Wagner J, Wrightsman T, Yokoyama TT, Zeng Q, Zook JM, Paten B, Busby B. A strategy for building and using a human reference pangenome. F1000Res 2019; 8:1751. [PMID: 34386196 PMCID: PMC8350888 DOI: 10.12688/f1000research.19630.1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/23/2021] [Indexed: 01/27/2024] Open
Abstract
In March 2019, 45 scientists and software engineers from around the world converged at the University of California, Santa Cruz for the first pangenomics codeathon. The purpose of the meeting was to propose technical specifications and standards for a usable human pangenome as well as to build relevant tools for genome graph infrastructures. During the meeting, the group held several intense and productive discussions covering a diverse set of topics, including advantages of graph genomes over a linear reference representation, design of new methods that can leverage graph-based data structures, and novel visualization and annotation approaches for pangenomes. Additionally, the participants self-organized themselves into teams that worked intensely over a three-day period to build a set of pipelines and tools for specific pangenomic applications. A summary of the questions raised and the tools developed are reported in this manuscript.
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Affiliation(s)
- Bastien Llamas
- Australian Centre for Ancient DNA, School of Biological Sciences, Environment Institute, The University of Adelaide, Adelaide, South Australia, 5005, Australia
| | | | - Valerie Schneider
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Peter A. Audano
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Evan Biederstedt
- Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02215, USA
| | - Lon Blauvelt
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Peter Bradbury
- Robert W. Holley Center, USDA-ARS, Ithaca, NY, 14853, USA
| | - Xian Chang
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | | | | | | | - Alan Cleary
- National Center for Genome Resources 87505, Santa Fe, NM, 87505, USA
| | - Jana Ebler
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Jordan Eizenga
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Jonas A. Sibbesen
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Charles J. Markello
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Erik Garrison
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Shilpa Garg
- Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA
| | - Glenn Hickey
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Gerard R. Lazo
- Western Regional Research Center, USDA-ARS, Albany, CA, 94710-1105, USA
| | | | - Medhat Mahmoud
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston TX, TX, 77030, USA
| | | | - Ilia Minkin
- Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Jean Monlong
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | | | - Sagayamary Sagayaradj
- Genome Center, University of California, Davis, Davis, CA, USA
- BASF, West Sacramento, CA, USA
| | - Adam M. Novak
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | | | - Allison Regier
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, 63108, USA
| | - Fritz J. Sedlazeck
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston TX, TX, 77030, USA
| | - Jouni Siren
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Yassine Souilmi
- Australian Centre for Ancient DNA, School of Biological Sciences, Environment Institute, The University of Adelaide, Adelaide, South Australia, 5005, Australia
| | - Justin Wagner
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
| | - Travis Wrightsman
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14853, USA
| | - Toshiyuki T. Yokoyama
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Qiandong Zeng
- Laboratory Corporation of America Holdings, Westborough, MA, 01581, USA
| | - Justin M. Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
| | - Benedict Paten
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Ben Busby
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
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Llamas B, Narzisi G, Schneider V, Audano PA, Biederstedt E, Blauvelt L, Bradbury P, Chang X, Chin CS, Fungtammasan A, Clarke WE, Cleary A, Ebler J, Eizenga J, Sibbesen JA, Markello CJ, Garrison E, Garg S, Hickey G, Lazo GR, Lin MF, Mahmoud M, Marschall T, Minkin I, Monlong J, Musunuri RL, Sagayaradj S, Novak AM, Rautiainen M, Regier A, Sedlazeck FJ, Siren J, Souilmi Y, Wagner J, Wrightsman T, Yokoyama TT, Zeng Q, Zook JM, Paten B, Busby B. A strategy for building and using a human reference pangenome. F1000Res 2019; 8:1751. [PMID: 34386196 PMCID: PMC8350888 DOI: 10.12688/f1000research.19630.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/23/2021] [Indexed: 11/20/2022] Open
Abstract
In March 2019, 45 scientists and software engineers from around the world converged at the University of California, Santa Cruz for the first pangenomics codeathon. The purpose of the meeting was to propose technical specifications and standards for a usable human pangenome as well as to build relevant tools for genome graph infrastructures. During the meeting, the group held several intense and productive discussions covering a diverse set of topics, including advantages of graph genomes over a linear reference representation, design of new methods that can leverage graph-based data structures, and novel visualization and annotation approaches for pangenomes. Additionally, the participants self-organized themselves into teams that worked intensely over a three-day period to build a set of pipelines and tools for specific pangenomic applications. A summary of the questions raised and the tools developed are reported in this manuscript.
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Affiliation(s)
- Bastien Llamas
- Australian Centre for Ancient DNA, School of Biological Sciences, Environment Institute, The University of Adelaide, Adelaide, South Australia, 5005, Australia
| | | | - Valerie Schneider
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Peter A Audano
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Evan Biederstedt
- Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02215, USA
| | - Lon Blauvelt
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Peter Bradbury
- Robert W. Holley Center, USDA-ARS, Ithaca, NY, 14853, USA
| | - Xian Chang
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | | | | | | | - Alan Cleary
- National Center for Genome Resources 87505, Santa Fe, NM, 87505, USA
| | - Jana Ebler
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Jordan Eizenga
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Jonas A Sibbesen
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Charles J Markello
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Erik Garrison
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Shilpa Garg
- Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA
| | - Glenn Hickey
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Gerard R Lazo
- Western Regional Research Center, USDA-ARS, Albany, CA, 94710-1105, USA
| | | | - Medhat Mahmoud
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston TX, TX, 77030, USA
| | | | - Ilia Minkin
- Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Jean Monlong
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | | | - Sagayamary Sagayaradj
- Genome Center, University of California, Davis, Davis, CA, USA.,BASF, West Sacramento, CA, USA
| | - Adam M Novak
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | | | - Allison Regier
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, 63108, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston TX, TX, 77030, USA
| | - Jouni Siren
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Yassine Souilmi
- Australian Centre for Ancient DNA, School of Biological Sciences, Environment Institute, The University of Adelaide, Adelaide, South Australia, 5005, Australia
| | - Justin Wagner
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
| | - Travis Wrightsman
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14853, USA
| | - Toshiyuki T Yokoyama
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Qiandong Zeng
- Laboratory Corporation of America Holdings, Westborough, MA, 01581, USA
| | - Justin M Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
| | - Benedict Paten
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Ben Busby
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
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Aguiar VRC, César J, Delaneau O, Dermitzakis ET, Meyer D. Expression estimation and eQTL mapping for HLA genes with a personalized pipeline. PLoS Genet 2019; 15:e1008091. [PMID: 31009447 PMCID: PMC6497317 DOI: 10.1371/journal.pgen.1008091] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 05/02/2019] [Accepted: 03/13/2019] [Indexed: 01/07/2023] Open
Abstract
The HLA (Human Leukocyte Antigens) genes are well-documented targets of balancing selection, and variation at these loci is associated with many disease phenotypes. Variation in expression levels also influences disease susceptibility and resistance, but little information exists about the regulation and population-level patterns of expression. This results from the difficulty in mapping short reads originated from these highly polymorphic loci, and in accounting for the existence of several paralogues. We developed a computational pipeline to accurately estimate expression for HLA genes based on RNA-seq, improving both locus-level and allele-level estimates. First, reads are aligned to all known HLA sequences in order to infer HLA genotypes, then quantification of expression is carried out using a personalized index. We use simulations to show that expression estimates obtained in this way are not biased due to divergence from the reference genome. We applied our pipeline to the GEUVADIS dataset, and compared the quantifications to those obtained with reference transcriptome. Although the personalized pipeline recovers more reads, we found that using the reference transcriptome produces estimates similar to the personalized pipeline (r ≥ 0.87) with the exception of HLA-DQA1. We describe the impact of the HLA-personalized approach on downstream analyses for nine classical HLA loci (HLA-A, HLA-C, HLA-B, HLA-DRA, HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1). Although the influence of the HLA-personalized approach is modest for eQTL mapping, the p-values and the causality of the eQTLs obtained are better than when the reference transcriptome is used. We investigate how the eQTLs we identified explain variation in expression among lineages of HLA alleles. Finally, we discuss possible causes underlying differences between expression estimates obtained using RNA-seq, antibody-based approaches and qPCR. The level at which a gene is expressed can have important influence on the phenotype of an organism, including its predisposition to develop diseases. One way to estimate gene expression is by quantifying the abundance of RNA. RNA-seq has become the method of choice to provide such estimates at the genomewide scale. However, the application of RNA-seq to HLA genes —key players in the immune adaptive response— has remained a rarely explored approach. This is due to the problem of mapping bias, which causes deficient read alignment at genes which are very polymorphic and different from the reference genome. This has motivated approaches that replace the single reference genome with personalized sequences, comprised of the individual’s specific HLA genotype. Here we explore the use of computational frameworks to obtain reliable expression levels for HLA genes from RNA-seq datasets. We present a pipeline in which the quantification of HLA expression is carried out using methods which account for HLA diversity, avoiding the biases of standard approaches. We then evaluate the impact of this form of quantifying HLA expression on downstream analyses. The pipeline also allows us to integrate information on eQTLs with expression levels at the HLA allele-level, which can help disentangle different contributions to disease phenotypes and help understand the regulatory architecture at the HLA region.
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Affiliation(s)
- Vitor R. C. Aguiar
- Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, Brazil
- * E-mail: (VRCA); (DM)
| | - Jônatas César
- Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, Brazil
| | - Olivier Delaneau
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Emmanouil T. Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Diogo Meyer
- Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, Brazil
- * E-mail: (VRCA); (DM)
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