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Parikh VN, Ioannidis AG, Jimenez-Morales D, Gorzynski JE, De Jong HN, Liu X, Roque J, Cepeda-Espinoza VP, Osoegawa K, Hughes C, Sutton SC, Youlton N, Joshi R, Amar D, Tanigawa Y, Russo D, Wong J, Lauzon JT, Edelson J, Mas Montserrat D, Kwon Y, Rubinacci S, Delaneau O, Cappello L, Kim J, Shoura MJ, Raja AN, Watson N, Hammond N, Spiteri E, Mallempati KC, Montero-Martín G, Christle J, Kim J, Kirillova A, Seo K, Huang Y, Zhao C, Moreno-Grau S, Hershman SG, Dalton KP, Zhen J, Kamm J, Bhatt KD, Isakova A, Morri M, Ranganath T, Blish CA, Rogers AJ, Nadeau K, Yang S, Blomkalns A, O’Hara R, Neff NF, DeBoever C, Szalma S, Wheeler MT, Gates CM, Farh K, Schroth GP, Febbo P, deSouza F, Cornejo OE, Fernandez-Vina M, Kistler A, Palacios JA, Pinsky BA, Bustamante CD, Rivas MA, Ashley EA. Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy. Nat Commun 2022; 13:5107. [PMID: 36042219 PMCID: PMC9426371 DOI: 10.1038/s41467-022-32397-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 07/28/2022] [Indexed: 02/05/2023] Open
Abstract
The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in Northern California. Genome-wide association disaggregated by admixture mapping reveals novel COVID-19-severity-associated regions containing previously reported markers of neurologic, pulmonary and viral disease susceptibility. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. Summary data from multiomic investigation reveals metagenomic and HLA associations with severe COVID-19. The wealth of data available from residual nasopharyngeal swabs in combination with clinical data abstracted automatically at scale highlights a powerful strategy for pandemic tracking, and reveals distinct epidemiologic, genetic, and biological associations for those at the highest risk.
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Affiliation(s)
- Victoria N. Parikh
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Alexander G. Ioannidis
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA ,grid.168010.e0000000419368956Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA USA
| | - David Jimenez-Morales
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - John E. Gorzynski
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Genetics, Stanford University School of Medicine, Stanford, CA USA
| | - Hannah N. De Jong
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Genetics, Stanford University School of Medicine, Stanford, CA USA
| | - Xiran Liu
- grid.168010.e0000000419368956Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA USA
| | - Jonasel Roque
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | | | - Kazutoyo Osoegawa
- grid.490568.60000 0004 5997 482XHistocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Stanford Health Care, Stanford, USA
| | - Chris Hughes
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Genetics, Stanford University School of Medicine, Stanford, CA USA
| | - Shirley C. Sutton
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Genetics, Stanford University School of Medicine, Stanford, CA USA
| | - Nathan Youlton
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Genetics, Stanford University School of Medicine, Stanford, CA USA
| | - Ruchi Joshi
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - David Amar
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Yosuke Tanigawa
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA
| | - Douglas Russo
- grid.168010.e0000000419368956Department of Statistics, Stanford University, Stanford, CA USA
| | - Justin Wong
- grid.168010.e0000000419368956Department of Statistics, Stanford University, Stanford, CA USA
| | - Jessie T. Lauzon
- grid.168010.e0000000419368956Department of Aeronautics and Astronautics, Stanford University, Stanford, CA USA
| | - Jacob Edelson
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA
| | - Daniel Mas Montserrat
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA
| | - Yongchan Kwon
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA
| | - Simone Rubinacci
- grid.9851.50000 0001 2165 4204Department of Computational Biology and Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - Olivier Delaneau
- grid.9851.50000 0001 2165 4204Department of Computational Biology and Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - Lorenzo Cappello
- grid.168010.e0000000419368956Department of Statistics, Stanford University, Stanford, CA USA
| | - Jaehee Kim
- grid.5386.8000000041936877XDepartment of Computational Biology, Cornell University, Ithaca, NY USA
| | - Massa J. Shoura
- grid.168010.e0000000419368956Department of Genetics, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, Stanford, CA USA
| | - Archana N. Raja
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Nathaniel Watson
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, Stanford, CA USA
| | - Nathan Hammond
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, Stanford, CA USA
| | - Elizabeth Spiteri
- grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, Stanford, CA USA
| | - Kalyan C. Mallempati
- grid.490568.60000 0004 5997 482XHistocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Stanford Health Care, Stanford, USA
| | - Gonzalo Montero-Martín
- grid.490568.60000 0004 5997 482XHistocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Stanford Health Care, Stanford, USA
| | - Jeffrey Christle
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Jennifer Kim
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Anna Kirillova
- grid.21925.3d0000 0004 1936 9000Medical Scientist Training Program, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA USA
| | - Kinya Seo
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Yong Huang
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Chunli Zhao
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Sonia Moreno-Grau
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA
| | - Steven G. Hershman
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Karen P. Dalton
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Jimmy Zhen
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Jack Kamm
- grid.499295.a0000 0004 9234 0175Chan Zuckerburg Biohub, San Francisco, CA USA
| | - Karan D. Bhatt
- grid.499295.a0000 0004 9234 0175Chan Zuckerburg Biohub, San Francisco, CA USA
| | - Alina Isakova
- grid.168010.e0000000419368956Department of Bioengineering, Stanford University, Stanford, CA USA
| | - Maurizio Morri
- grid.499295.a0000 0004 9234 0175Chan Zuckerburg Biohub, San Francisco, CA USA
| | - Thanmayi Ranganath
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Catherine A. Blish
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Angela J. Rogers
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Kari Nadeau
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA USA
| | - Samuel Yang
- grid.168010.e0000000419368956Department of Emergency Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Andra Blomkalns
- grid.168010.e0000000419368956Department of Emergency Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Ruth O’Hara
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA USA
| | - Norma F. Neff
- grid.499295.a0000 0004 9234 0175Chan Zuckerburg Biohub, San Francisco, CA USA
| | | | - Sándor Szalma
- Takeda Development Center, Americas, Inc, San Diego, CA USA
| | - Matthew T. Wheeler
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | | | - Kyle Farh
- grid.185669.50000 0004 0507 3954Illumina, Inc, San Diego, CA USA
| | - Gary P. Schroth
- grid.185669.50000 0004 0507 3954Illumina, Inc, San Diego, CA USA
| | - Phil Febbo
- grid.185669.50000 0004 0507 3954Illumina, Inc, San Diego, CA USA
| | - Francis deSouza
- grid.185669.50000 0004 0507 3954Illumina, Inc, San Diego, CA USA
| | - Omar E. Cornejo
- grid.30064.310000 0001 2157 6568School of Biological Sciences, Washington State University, Pullman, WA USA
| | - Marcelo Fernandez-Vina
- grid.490568.60000 0004 5997 482XHistocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Stanford Health Care, Stanford, USA ,grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, Stanford, CA USA
| | - Amy Kistler
- grid.499295.a0000 0004 9234 0175Chan Zuckerburg Biohub, San Francisco, CA USA
| | - Julia A. Palacios
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA ,grid.168010.e0000000419368956Department of Statistics, Stanford University, Stanford, CA USA
| | - Benjamin A. Pinsky
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Pathology, Stanford University School of Medicine, Stanford, CA USA
| | - Carlos D. Bustamante
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA
| | - Manuel A. Rivas
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA
| | - Euan A. Ashley
- grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Genetics, Stanford University School of Medicine, Stanford, CA USA
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Osoegawa K, Creary LE, Montero-Martín G, Mallempati KC, Gangavarapu S, Caillier SJ, Santaniello A, Isobe N, Hollenbach JA, Hauser SL, Oksenberg JR, Fernández-Viňa MA. High Resolution Haplotype Analyses of Classical HLA Genes in Families With Multiple Sclerosis Highlights the Role of HLA-DP Alleles in Disease Susceptibility. Front Immunol 2021; 12:644838. [PMID: 34211458 PMCID: PMC8240666 DOI: 10.3389/fimmu.2021.644838] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 05/10/2021] [Indexed: 11/13/2022] Open
Abstract
Multiple sclerosis (MS) susceptibility shows strong genetic associations with HLA alleles and haplotypes. We genotyped 11 HLA genes in 477 non-Hispanic European MS patients and their 954 unaffected parents using a validated next-generation sequencing (NGS) methodology. HLA haplotypes were assigned unequivocally by tracing HLA allele transmissions. We explored HLA haplotype/allele associations with MS using the genotypic transmission disequilibrium test (gTDT) and multiallelic TDT (mTDT). We also conducted a case-control (CC) study with all patients and 2029 healthy unrelated ethnically matched controls. We performed separate analyses of 54 extended multi-case families by reviewing transmission of haplotype blocks. The haplotype fragment including DRB5*01:01:01~DRB1*15:01:01:01 was significantly associated with predisposition (gTDT: p < 2.20e-16; mTDT: p =1.61e-07; CC: p < 2.22e-16) as reported previously. A second risk allele, DPB1*104:01 (gTDT: p = 3.69e-03; mTDT: p = 2.99e-03; CC: p = 1.00e-02), independent from the haplotype bearing DRB1*15:01 was newly identified. The allele DRB1*01:01:01 showed significant protection (gTDT: p = 8.68e-06; mTDT: p = 4.50e-03; CC: p = 1.96e-06). Two DQB1 alleles, DQB1*03:01 (gTDT: p = 2.86e-03; mTDT: p = 5.56e-02; CC: p = 4.08e-05) and DQB1*03:03 (gTDT: p = 1.17e-02; mTDT: p = 1.16e-02; CC: p = 1.21e-02), defined at two-field level also showed protective effects. The HLA class I block, A*02:01:01:01~C*03:04:01:01~B*40:01:02 (gTDT: p = 5.86e-03; mTDT: p = 3.65e-02; CC: p = 9.69e-03) and the alleles B*27:05 (gTDT: p = 6.28e-04; mTDT: p = 2.15e-03; CC: p = 1.47e-02) and B*38:01 (gTDT: p = 3.20e-03; mTDT: p = 6.14e-03; CC: p = 1.70e-02) showed moderately protective effects independently from each other and from the class II associated factors. By comparing statistical significance of 11 HLA loci and 19 haplotype segments with both untruncated and two-field allele names, we precisely mapped MS candidate alleles/haplotypes while eliminating false signals resulting from ‘hitchhiking’ alleles. We assessed genetic burden for the HLA allele/haplotype identified in this study. This family-based study including the highest-resolution of HLA alleles proved to be powerful and efficient for precise identification of HLA genotypes associated with both, susceptibility and protection to development of MS.
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Affiliation(s)
- Kazutoyo Osoegawa
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States
| | - Lisa E Creary
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States.,Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Gonzalo Montero-Martín
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States.,Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Kalyan C Mallempati
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States
| | - Sridevi Gangavarapu
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States
| | - Stacy J Caillier
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Noriko Isobe
- Department of Neurology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Jill A Hollenbach
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Marcelo A Fernández-Viňa
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States.,Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, United States
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3
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Gorzynski JE, De Jong HN, Amar D, Hughes CR, Ioannidis A, Bierman R, Liu D, Tanigawa Y, Kistler A, Kamm J, Kim J, Cappello L, Neff NF, Rubinacci S, Delaneau O, Shoura MJ, Seo K, Kirillova A, Raja A, Sutton S, Huang C, Sahoo MK, Mallempati KC, Montero-Martin G, Osoegawa K, Jimenez-Morales D, Watson N, Hammond N, Joshi R, Fernandez-Vina M, Christle JW, Wheeler MT, Febbo P, Farh K, Schroth G, Desouza F, Palacios J, Salzman J, Pinsky BA, Rivas MA, Bustamante CD, Ashley EA, Parikh VN. High-throughput SARS-CoV-2 and host genome sequencing from single nasopharyngeal swabs. medRxiv 2020. [PMID: 32766602 DOI: 10.1101/2020.07.27.20163147] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
During COVID19 and other viral pandemics, rapid generation of host and pathogen genomic data is critical to tracking infection and informing therapies. There is an urgent need for efficient approaches to this data generation at scale. We have developed a scalable, high throughput approach to generate high fidelity low pass whole genome and HLA sequencing, viral genomes, and representation of human transcriptome from single nasopharyngeal swabs of COVID19 patients.
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4
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Barquera R, Zuniga J, Flores-Rivera J, Corona T, Penman BS, Hernández-Zaragoza DI, Soler M, Jonapá-Gómez L, Mallempati KC, Yescas P, Ochoa-Morales A, Barsakis K, Aguilar-Vázquez JA, García-Lechuga M, Mindrinos M, Yunis M, Jiménez-Alvarez L, Mena-Hernández L, Ortega E, Cruz-Lagunas A, Tovar-Méndez VH, Granados J, Fernández-Viña M, Yunis E. Diversity of HLA Class I and Class II blocks and conserved extended haplotypes in Lacandon Mayans. Sci Rep 2020; 10:3248. [PMID: 32094421 PMCID: PMC7039995 DOI: 10.1038/s41598-020-58897-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 01/22/2020] [Indexed: 12/18/2022] Open
Abstract
Here we studied HLA blocks and haplotypes in a group of 218 Lacandon Maya Native American using a high-resolution next generation sequencing (NGS) method. We assessed the genetic diversity of HLA class I and class II in this population, and determined the most probable ancestry of Lacandon Maya HLA class I and class II haplotypes. Importantly, this Native American group showed a high degree of both HLA homozygosity and linkage disequilibrium across the HLA region and also lower class II HLA allelic diversity than most previously reported populations (including other Native American groups). Distinctive alleles present in the Lacandon population include HLA-A*24:14 and HLA-B*40:08. Furthermore, in Lacandons we observed a high frequency of haplotypes containing the allele HLA-DRB1*04:11, a relatively frequent allele in comparison with other neighboring indigenous groups. The specific demographic history of the Lacandon population including inbreeding, as well as pathogen selection, may have elevated the frequencies of a small number of HLA class II alleles and DNA blocks. To assess the possible role of different selective pressures in determining Native American HLA diversity, we evaluated the relationship between genetic diversity at HLA-A, HLA-B and HLA-DRB1 and pathogen richness for a global dataset and for Native American populations alone. In keeping with previous studies of such relationships we included distance from Africa as a covariate. After correction for multiple comparisons we did not find any significant relationship between pathogen diversity and HLA genetic diversity (as measured by polymorphism information content) in either our global dataset or the Native American subset of the dataset. We found the expected negative relationship between genetic diversity and distance from Africa in the global dataset, but no relationship between HLA genetic diversity and distance from Africa when Native American populations were considered alone.
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Affiliation(s)
- Rodrigo Barquera
- Department of Archaeogenetics, Max Planck Institute for the Science of Human History (MPI-SHH), Jena, Germany
- Laboratory of Molecular Genetics, Escuela Nacional de Antropología e Historia (ENAH), Mexico City, Mexico
| | - Joaquin Zuniga
- Department of Immunology, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas (INER), Mexico City, Mexico
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Mexico City, Mexico
| | - José Flores-Rivera
- Clinical Laboratory of Neurodegenerative Diseases, Instituto Nacional de Neurología y Neurocirugía "Manuel Velasco Suárez", Mexico City, Mexico
| | - Teresa Corona
- Clinical Laboratory of Neurodegenerative Diseases, Instituto Nacional de Neurología y Neurocirugía "Manuel Velasco Suárez", Mexico City, Mexico
| | - Bridget S Penman
- University of Warwick, School of Life Sciences, Coventry, United Kingdom
| | - Diana Iraíz Hernández-Zaragoza
- Laboratory of Molecular Genetics, Escuela Nacional de Antropología e Historia (ENAH), Mexico City, Mexico
- Immunogenetics Unit, Técnicas Genéticas Aplicadas a la Clínica (TGAC), Mexico City, Mexico
| | - Manuel Soler
- Department of Transplantation, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMSZ), Mexico City, Mexico
| | | | - Kalyan C Mallempati
- Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
- Biology Department, University of Crete, Heraklion, Greece
| | - Petra Yescas
- Department of Neurogenetics and Molecular Biology, Instituto Nacional de Neurología y Neurocirugía "Manuel Velasco Suárez", Mexico City, Mexico
| | - Adriana Ochoa-Morales
- Department of Neurogenetics and Molecular Biology, Instituto Nacional de Neurología y Neurocirugía "Manuel Velasco Suárez", Mexico City, Mexico
| | - Konstantinos Barsakis
- Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
- Department of Pathology, Stanford University, CA, USA
| | - José Artemio Aguilar-Vázquez
- Clinical Analysis Laboratory, Unidad Médica Familiar (UMF) No. 23, Instituto Mexicano del Seguro Social (IMSS), Tuxtla Gutiérrez, Chiapas, Mexico
| | - Maricela García-Lechuga
- Department of Transplantation, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMSZ), Mexico City, Mexico
| | | | - María Yunis
- Department of Cancer Immunology and Virology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Luis Jiménez-Alvarez
- Department of Immunology, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas (INER), Mexico City, Mexico
| | - Lourdes Mena-Hernández
- Department of Transplantation, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMSZ), Mexico City, Mexico
| | - Esteban Ortega
- The William Harvey Research Institute, Barts and London School of Medicine, Queen Mary University of London, London, United Kingdom
| | - Alfredo Cruz-Lagunas
- Department of Immunology, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas (INER), Mexico City, Mexico
| | - Víctor Hugo Tovar-Méndez
- Department of Transplantation, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMSZ), Mexico City, Mexico
| | - Julio Granados
- Department of Transplantation, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMSZ), Mexico City, Mexico.
| | | | - Edmond Yunis
- Department of Cancer Immunology and Virology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
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5
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Osoegawa K, Mallempati KC, Gangavarapu S, Oki A, Gendzekhadze K, Marino SR, Brown NK, Bettinotti MP, Weimer ET, Montero-Martín G, Creary LE, Vayntrub TA, Chang CJ, Askar M, Mack SJ, Fernández-Viña MA. HLA alleles and haplotypes observed in 263 US families. Hum Immunol 2019; 80:644-660. [PMID: 31256909 DOI: 10.1016/j.humimm.2019.05.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Revised: 05/29/2019] [Accepted: 05/31/2019] [Indexed: 11/17/2022]
Abstract
The 17th International HLA and Immunogenetics Workshop (IHIW) conducted a project entitled "The Study of Haplotypes in Families by NGS HLA". We investigated the HLA haplotypes of 1017 subjects in 263 nuclear families sourced from five US clinical immunogenetics laboratories, primarily as part of the evaluation of related donor candidates for hematopoietic stem cell and solid organ transplantation. The parents in these families belonged to five broad groups - African (72 parents), Asian (115), European (210), Hispanic (118) and "Other" (11). High-resolution HLA genotypes were generated for each subject using next-generation sequencing (NGS) HLA typing systems. We identified the HLA haplotypes in each family using HaplObserve, software that builds haplotypes in families by reviewing HLA allele segregation from parents to children. We calculated haplotype frequencies within each broad group, by treating the parents in each family as unrelated individuals. We also calculated standard measures of global linkage disequilibrium (LD) and conditional asymmetric LD for each ethnic group, and used untruncated and two-field allele names to investigate LD patterns. Finally we demonstrated the utility of consensus DNA sequences in identifying novel variants, confirming them using HLA allele segregation at the DNA sequence level.
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Affiliation(s)
- Kazutoyo Osoegawa
- Histocompatibility, Immunogenetics & Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA.
| | - Kalyan C Mallempati
- Histocompatibility, Immunogenetics & Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Sridevi Gangavarapu
- Histocompatibility, Immunogenetics & Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Arisa Oki
- HLA Laboratory, City of Hope, Duarte, CA, USA
| | | | - Susana R Marino
- Transplant Immunology Laboratory, The University of Chicago Medicine, Chicago, IL, USA
| | - Nicholas K Brown
- Transplant Immunology Laboratory, The University of Chicago Medicine, Chicago, IL, USA
| | | | - Eric T Weimer
- Department of Pathology & Laboratory Medicine, UNC Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Gonzalo Montero-Martín
- Histocompatibility, Immunogenetics & Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA; Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Lisa E Creary
- Histocompatibility, Immunogenetics & Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA; Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Tamara A Vayntrub
- Histocompatibility, Immunogenetics & Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | | | - Medhat Askar
- Baylor University Medical Center, Dallas, TX, USA
| | - Steven J Mack
- Center for Genetics, Children's Hospital Oakland Research Institute, Oakland, CA, USA
| | - Marcelo A Fernández-Viña
- Histocompatibility, Immunogenetics & Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA; Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
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6
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Osoegawa K, Vayntrub TA, Wenda S, De Santis D, Barsakis K, Ivanova M, Hsu S, Barone J, Holdsworth R, Diviney M, Askar M, Willis A, Railton D, Laflin S, Gendzekhadze K, Oki A, Sacchi N, Mazzocco M, Andreani M, Ameen R, Stavropoulos-Giokas C, Dinou A, Torres M, Dos Santos Francisco R, Serra-Pages C, Goodridge D, Balladares S, Bettinotti MP, Iglehart B, Kashi Z, Martin R, Saw CL, Ragoussis J, Downing J, Navarrete C, Chong W, Saito K, Petrek M, Tokic S, Padros K, Beatriz Rodriguez M, Zakharova V, Shragina O, Marino SR, Brown NK, Shiina T, Suzuki S, Spierings E, Zhang Q, Yin Y, Morris GP, Hernandez A, Ruiz P, Khor SS, Tokunaga K, Geretz A, Thomas R, Yamamoto F, Mallempati KC, Gangavarapu S, Kanga U, Tyagi S, Marsh SGE, Bultitude WP, Liu X, Cao D, Penning M, Hurley CK, Cesbron A, Mueller C, Mytilineos J, Weimer ET, Bengtsson M, Fischer G, Hansen JA, Chang CJ, Mack SJ, Creary LE, Fernandez-Viña MA. Quality control project of NGS HLA genotyping for the 17th International HLA and Immunogenetics Workshop. Hum Immunol 2019; 80:228-236. [PMID: 30738112 PMCID: PMC6446570 DOI: 10.1016/j.humimm.2019.01.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 01/10/2019] [Accepted: 01/30/2019] [Indexed: 11/24/2022]
Abstract
The 17th International HLA and Immunogenetics Workshop (IHIW) organizers conducted a Pilot Study (PS) in which 13 laboratories (15 groups) participated to assess the performance of the various sequencing library preparation protocols, NGS platforms and software in use prior to the workshop. The organizers sent 50 cell lines to each of the 15 groups, scored the 15 independently generated sets of NGS HLA genotyping data, and generated "consensus" HLA genotypes for each of the 50 cell lines. Proficiency Testing (PT) was subsequently organized using four sets of 24 cell lines, selected from 48 of 50 PS cell lines, to validate the quality of NGS HLA typing data from the 34 participating IHIW laboratories. Completion of the PT program with a minimum score of 95% concordance at the HLA-A, HLA-B, HLA-C, HLA-DRB1 and HLA-DQB1 loci satisfied the requirements to submit NGS HLA typing data for the 17th IHIW projects. Together, these PS and PT efforts constituted the 17th IHIW Quality Control project. Overall PT concordance rates for HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-DRB3, HLA-DRB4 and HLA-DRB5 were 98.1%, 97.0% and 98.1%, 99.0%, 98.6%, 98.8%, 97.6%, 96.0%, 99.1%, 90.0% and 91.7%, respectively. Across all loci, the majority of the discordance was due to allele dropout. The high cost of NGS HLA genotyping per experiment likely prevented the retyping of initially failed HLA loci. Despite the high HLA genotype concordance rates of the software, there remains room for improvement in the assembly of more accurate consensus DNA sequences by NGS HLA genotyping software.
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Affiliation(s)
- Kazutoyo Osoegawa
- Histocompatibility, Immunogenetics, and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA.
| | - Tamara A Vayntrub
- Histocompatibility, Immunogenetics, and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Sabine Wenda
- Department of Blood Group Serology and Transfusion Medicine, Medical University of Vienna, Vienna, Austria
| | | | - Konstantinos Barsakis
- Histocompatibility, Immunogenetics, and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA; University of Crete, Biology Department, Heraklion, Greece; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Susan Hsu
- Histocompatibility/Molecular Genetics, American Red Cross, Philadelphia, PA, USA
| | - Jonathan Barone
- Histocompatibility/Molecular Genetics, American Red Cross, Philadelphia, PA, USA
| | | | - Mary Diviney
- Australian Red Cross Blood Services, Melbourne, Australia
| | - Medhat Askar
- Baylor University Medical Center, Dallas, TX, USA
| | | | - Dawn Railton
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Sophie Laflin
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | | | - Arisa Oki
- City of Hope National Medical Center, Duarte, CA, USA
| | | | | | - Marco Andreani
- Fondazione I.M.E. Istituto Mediterraneo Di Ematologia, Rome, Italy
| | - Reem Ameen
- Health Sciences Center, Kuwait University, Jabriya, Kuwait
| | | | | | | | | | - Carles Serra-Pages
- Centro de Diagonóstico Biomédico, Hospital Clínic de Barcelona, Barcelona, Spain
| | | | | | | | - Brian Iglehart
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zahra Kashi
- Kashi Clinical Laboratories, Inc., Portland, OR, USA
| | | | | | - Jiannis Ragoussis
- McGill University Health Centre, Montreal, QC, Canada; Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | | | - Cristina Navarrete
- National H&I Service Development Laboratory NHS Blood and Transplant, London, UK
| | - Winnie Chong
- National H&I Service Development Laboratory NHS Blood and Transplant, London, UK
| | | | - Martin Petrek
- Palacky University, Faculty of Medicine and Dentistry, Olomouc, Czech Republic
| | - Stana Tokic
- Palacky University, Faculty of Medicine and Dentistry, Olomouc, Czech Republic
| | - Karin Padros
- Primer Centro Argentino de Immunogenetica (PRICAI), Fundación Favaloro, CABA, Argentina
| | - Ma Beatriz Rodriguez
- Primer Centro Argentino de Immunogenetica (PRICAI), Fundación Favaloro, CABA, Argentina
| | - Viktoria Zakharova
- Rogachev Federal Research Centre of Pediatric Hematology,Oncology and Immunology, Moscow, Russian Federation
| | - Olga Shragina
- Rogachev Federal Research Centre of Pediatric Hematology,Oncology and Immunology, Moscow, Russian Federation
| | | | | | | | - Shingo Suzuki
- Tokai University School of Medicine, Kanagawa, Japan
| | | | - Qiuheng Zhang
- University of California, Los Angeles, Immunogenetics Center, Los Angeles, CA, USA
| | - Yuxin Yin
- University of California, Los Angeles, Immunogenetics Center, Los Angeles, CA, USA
| | | | | | - Phillip Ruiz
- University of Miami Miller School of Medicine, USA
| | | | | | - Aviva Geretz
- Walter Reed Army Institute of Research, Silver Spring, MD, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Rasmi Thomas
- Walter Reed Army Institute of Research, Silver Spring, MD, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Fumiko Yamamoto
- Histocompatibility, Immunogenetics, and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Kalyan C Mallempati
- Histocompatibility, Immunogenetics, and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Sridevi Gangavarapu
- Histocompatibility, Immunogenetics, and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Uma Kanga
- All India Institute of Medical Sciences, New Delhi, India
| | - Shweta Tyagi
- All India Institute of Medical Sciences, New Delhi, India
| | - Steven G E Marsh
- Anthony Nolan Research Institute and UCL Cancer Institute, Royal Free Campus, London, UK
| | - Will P Bultitude
- Anthony Nolan Research Institute and UCL Cancer Institute, Royal Free Campus, London, UK
| | - Xiangjun Liu
- Bo Fu Rui (BFR) Transplant Diagnostics, Beijing, China
| | - Dajiang Cao
- Bo Fu Rui (BFR) Transplant Diagnostics, Beijing, China
| | | | | | - Anne Cesbron
- Histocompatibility and Immunogenetics Laboratory, Nantes, France
| | - Claudia Mueller
- Transplantation and Immunology, Universitat Tuebingen, Germany
| | | | - Eric T Weimer
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill School of Medicine, NC, USA
| | - Mats Bengtsson
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Gottfried Fischer
- Department of Blood Group Serology and Transfusion Medicine, Medical University of Vienna, Vienna, Austria
| | - John A Hansen
- Division of Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Steven J Mack
- Center for Genetics, Children's Hospital Oakland Research Institute, Oakland, CA, USA
| | - Lisa E Creary
- Histocompatibility, Immunogenetics, and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Marcelo A Fernandez-Viña
- Histocompatibility, Immunogenetics, and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
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Creary LE, Guerra SG, Chong W, Brown CJ, Turner TR, Robinson J, Bultitude WP, Mayor NP, Marsh SGE, Saito K, Lam K, Duke JL, Mosbruger TL, Ferriola D, Monos D, Willis A, Askar M, Fischer G, Saw CL, Ragoussis J, Petrek M, Serra-Pagés C, Juan M, Stavropoulos-Giokas C, Dinou A, Ameen R, Al Shemmari S, Spierings E, Gendzekhadze K, Morris GP, Zhang Q, Kashi Z, Hsu S, Gangavarapu S, Mallempati KC, Yamamoto F, Osoegawa K, Vayntrub T, Chang CJ, Hansen JA, Fernández-Viňa MA. Next-generation HLA typing of 382 International Histocompatibility Working Group reference B-lymphoblastoid cell lines: Report from the 17th International HLA and Immunogenetics Workshop. Hum Immunol 2019; 80:449-460. [PMID: 30844424 DOI: 10.1016/j.humimm.2019.03.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 02/09/2019] [Accepted: 03/01/2019] [Indexed: 10/27/2022]
Abstract
Extended molecular characterization of HLA genes in the IHWG reference B-lymphoblastoid cell lines (B-LCLs) was one of the major goals for the 17th International HLA and Immunogenetics Workshop (IHIW). Although reference B-LCLs have been examined extensively in previous workshops complete high-resolution typing was not completed for all the classical class I and class II HLA genes. To address this, we conducted a single-blind study where select panels of B-LCL genomic DNA samples were distributed to multiple laboratories for HLA genotyping by next-generation sequencing methods. Identical cell panels comprised of 24 and 346 samples were distributed and typed by at least four laboratories in order to derive accurate consensus HLA genotypes. Overall concordance rates calculated at both 2- and 4-field allele-level resolutions ranged from 90.4% to 100%. Concordance for the class I genes ranged from 91.7 to 100%, whereas concordance for class II genes was variable; the lowest observed at HLA-DRB3 (84.2%). At the maximum allele-resolution 78 B-LCLs were defined as homozygous for all 11 loci. We identified 11 novel exon polymorphisms in the entire cell panel. A comparison of the B-LCLs NGS HLA genotypes with the HLA genotypes catalogued in the IPD-IMGT/HLA Database Cell Repository, revealed an overall allele match at 68.4%. Typing discrepancies between the two datasets were mostly due to the lower-resolution historical typing methods resulting in incomplete HLA genotypes for some samples listed in the IPD-IMGT/HLA Database Cell Repository. Our approach of multiple-laboratory NGS HLA typing of the B-LCLs has provided accurate genotyping data. The data generated by the tremendous collaborative efforts of the 17th IHIW participants is useful for updating the current cell and sequence databases and will be a valuable resource for future studies.
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Affiliation(s)
- Lisa E Creary
- Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA; Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA.
| | - Sandra G Guerra
- Histocompatibility and Immunogenetics Service Development Laboratory, NHS Blood and Transplant, London, UK
| | - Winnie Chong
- Histocompatibility and Immunogenetics Service Development Laboratory, NHS Blood and Transplant, London, UK
| | - Colin J Brown
- Department of Histocompatibility and Immunogenetics, NHS Blood and Transplant, London, UK
| | - Thomas R Turner
- Anthony Nolan Research Institute, Royal Free Hospital, London, UK; UCL Cancer Institute, Royal Free Campus, London, UK
| | - James Robinson
- Anthony Nolan Research Institute, Royal Free Hospital, London, UK; UCL Cancer Institute, Royal Free Campus, London, UK
| | - Will P Bultitude
- Anthony Nolan Research Institute, Royal Free Hospital, London, UK; UCL Cancer Institute, Royal Free Campus, London, UK
| | - Neema P Mayor
- Anthony Nolan Research Institute, Royal Free Hospital, London, UK; UCL Cancer Institute, Royal Free Campus, London, UK
| | - Steven G E Marsh
- Anthony Nolan Research Institute, Royal Free Hospital, London, UK; UCL Cancer Institute, Royal Free Campus, London, UK
| | - Katsuyuki Saito
- Molecular Biology Research Department, One Lambda, Thermo Fisher Scientific, Canoga Park, CA, USA
| | - Kevin Lam
- Molecular Biology Research Department, One Lambda, Thermo Fisher Scientific, Canoga Park, CA, USA
| | - Jamie L Duke
- Immunogenetics Laboratory, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Timothy L Mosbruger
- Immunogenetics Laboratory, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Deborah Ferriola
- Immunogenetics Laboratory, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Dimitrios Monos
- Immunogenetics Laboratory, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pathology and Lab Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amanda Willis
- Department of Pathology and Laboratory Medicine, Baylor University Medical Center, Dallas, USA
| | - Medhat Askar
- Department of Pathology and Laboratory Medicine, Baylor University Medical Center, Dallas, USA
| | - Gottfried Fischer
- Department for Blood Group Serology and Transfusion Medicine, Medical University of Vienna, Vienna, Austria
| | - Chee Loong Saw
- HLA Laboratory, Division of Haematology, McGill University Health Centre, Montreal, Canada
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University & McGill University and Genome Quèbec Innovation Centre, Montreal, Canada
| | - Martin Petrek
- Department of Pathological Physiology and Immunogenomics, IMTM, Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
| | - Carles Serra-Pagés
- Immunology Department, Hospital Clinic de Barcelona, University of Barcelona, IDIBAPS, Barcelona, Spain
| | - Manel Juan
- Immunology Department, Hospital Clinic de Barcelona, University of Barcelona, IDIBAPS, Barcelona, Spain
| | | | - Amalia Dinou
- Biomedical Research Foundation Academy of Athens, Hellenic Cord Blood Bank, Athens, Greece
| | - Reem Ameen
- Health Sciences Center, Kuwait University, Kuwait
| | | | - Eric Spierings
- Laboratory of Translational Immunology, UMC Utrecht, Utrecht, Netherlands
| | | | - Gerald P Morris
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Qiuheng Zhang
- Department of Pathology and Laboratory Medicine, UCLA Immunogenetics Center, Los Angeles, CA, USA
| | - Zahra Kashi
- HLA Department, Kashi Clinical Laboratories, Inc., Portland, OR, USA
| | - Susan Hsu
- HLA Laboratory, American Red Cross, Philadelphia, PA, USA
| | - Sridevi Gangavarapu
- Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Kalyan C Mallempati
- Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Fumiko Yamamoto
- Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Kazutoyo Osoegawa
- Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Tamara Vayntrub
- Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | | | - John A Hansen
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Marcelo A Fernández-Viňa
- Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA; Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
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Montero-Martín G, Mallempati KC, Gangavarapu S, Sánchez-Gordo F, Herrero-Mata MJ, Balas A, Vicario JL, Sánchez-García F, González-Escribano MF, Muro M, Moya-Quiles MR, González-Fernández R, Ocejo-Vinyals JG, Marín L, Creary LE, Osoegawa K, Vayntrub T, Caro-Oleas JL, Vilches C, Planelles D, Fernández-Viña MA. High-resolution characterization of allelic and haplotypic HLA frequency distribution in a Spanish population using high-throughput next-generation sequencing. Hum Immunol 2019; 80:429-436. [PMID: 30763600 DOI: 10.1016/j.humimm.2019.02.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 02/07/2019] [Accepted: 02/09/2019] [Indexed: 12/25/2022]
Abstract
Next-generation sequencing (NGS) at the HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1 and -DRB3/4/5 loci was performed on 282 healthy unrelated individuals from different major regions of Spain. High-resolution HLA genotypes defined by full sequencing of class I loci and extended coverage of class II loci were obtained to determine allele frequencies and also to estimate extended haplotype frequencies. HLA alleles were typed at the highest resolution level (4-field level, 4FL); with exception of a minor deviation in HLA-DPA1, no statistically significant deviations from expected Hardy Weinberg Equilibrium (HWE) proportions were observed for all other HLA loci. This study provides new 4FL-allele and -haplotype frequencies estimated for the first time in the Spanish population. Furthermore, our results describe extended haplotypes (including the less frequently typed HLA-DPA1 and HLA-DQA1 loci) and show distinctive haplotype associations found at 4FL-allele definition in this Spanish population study. The distinctive allelic and haplotypic diversity found at the 4FL reveals the high level of heterozygosity and specific haplotypic associations displayed that were not apparent at 2-field level (2FL). Overall, these results may contribute as a useful reference source for future population studies, for HLA-disease association studies as a healthy control group dataset and for improving donor recruitment strategies of bone marrow registries. HLA genotyping data of this Spanish population cohort was also included in the 17th International Histocompatibility and Immunogenetics Workshop (IHIW) as part of the study of HLA diversity in unrelated worldwide populations using NGS.
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Affiliation(s)
| | - Kalyan C Mallempati
- Stanford Blood Center, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Sridevi Gangavarapu
- Stanford Blood Center, Stanford University School of Medicine, Palo Alto, CA, USA
| | | | | | - Antonio Balas
- Histocompatibility, Centro de Transfusión de la Comunidad de Madrid, Madrid, Spain
| | - Jose L Vicario
- Histocompatibility, Centro de Transfusión de la Comunidad de Madrid, Madrid, Spain
| | | | | | - Manuel Muro
- Immunology, Hospital Clínico Universitario Virgen de la Arrixaca, Murcia, Spain
| | - Maria R Moya-Quiles
- Immunology, Hospital Clínico Universitario Virgen de la Arrixaca, Murcia, Spain
| | | | | | - Luis Marín
- Molecular Biology-Hematology, Hospital Clínico Universitario, Salamanca, Spain
| | - Lisa E Creary
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kazutoyo Osoegawa
- Stanford Blood Center, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Tamara Vayntrub
- Stanford Blood Center, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Jose L Caro-Oleas
- Histocompatibility and Immunogenetics, Banc de Sang i Teixits, Barcelona, Spain
| | - Carlos Vilches
- Immunogenetics and Histocompatibility, Instituto de Investigación Sanitaria Puerta de Hierro, Madrid, Spain
| | - Dolores Planelles
- Histocompatibility, Centro de Transfusión de la Comunidad Valenciana, Valencia, Spain
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Creary LE, Mallempati KC, Gangavarapu S, Caillier SJ, Oksenberg JR, Fernández-Viňa MA. Deconstruction of HLA-DRB1*04:01:01 and HLA-DRB1*15:01:01 class II haplotypes using next-generation sequencing in European-Americans with multiple sclerosis. Mult Scler 2018; 25:772-782. [PMID: 29683085 DOI: 10.1177/1352458518770019] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND The association between HLA-DRB1*15:01 with multiple sclerosis (MS) susceptibility is well established, but the contribution of the tightly associated HLA-DRB5*01:01 allele has not yet been completely ascertained. Similarly, the effects of HLA-DRB1*04:01 alleles and haplotypes, defined at the full-gene resolution level with MS risk remains to be elucidated. OBJECTIVES To characterize the molecular architecture of class II HLA-DR15 and HLA-DR4 haplotypes associated with MS. METHODS Next-generation sequencing was used to determine HLA-DQB1, HLA-DQA1, and HLA-DRB1/4/5 alleles in 1403 unrelated European-American patients and 1425 healthy unrelated controls. Effect sizes of HLA alleles and haplotypes on MS risk were measured by odds ratio (OR) with 95% confidence intervals. RESULTS HLA-DRB1*15:01:01:01SG (OR = 3.20, p < 2.2E-16), HLA-DRB5*01:01:01 (OR = 2.96, p < 2.2E-16), and HLA-DRB5*01:01:01v1_STR1 (OR = 8.18, p = 4.3E-05) alleles all occurred at significantly higher frequencies in MS patients compared to controls. The most significant predis-posing haplotypes were HLA-DQB1*06:02:01~ HLA-DQA1*01:02:01:01SG~HLA-DRB1*15:01:01:01SG~HLA-DRB5*01:01:01 and HLA-DQB1*06:02:01~HLA-DQA1*01:02:01:01SG~HLA-DRB1*15:01:01:01SG~HLA-DRB5*01:01:01v1_STR1 (OR = 3.19, p < 2.2E-16; OR = 9.30, p = 9.7E-05, respectively). Analyses of the HLA-DRB1*04 cohort in the absence of HLA-DRB1*15:01 haplotypes revealed that the HLA-DQB1*03:01:01:01~HLA-DQA1*03:03:01:01~HLA-DRB1*04:01:01:01SG~HLA-DRB4*01:03:01:01 haplotype was protective (OR = 0.64, p = 0.028), whereas the HLA-DQB1*03:02:01~HLA-DQA1*03:01:01~HLA-DRB1*04:01:01:01SG~HLA-DRB4*01:03:01:01 haplotype was associated with MS susceptibility (OR = 1.66, p = 4.9E-03). CONCLUSION HLA-DR15 haplotypes, including genomic variants of HLA-DRB5, and HLA-DR4 haplotypes affect MS risk.
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Affiliation(s)
- Lisa E Creary
- Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Kalyan C Mallempati
- Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Sridevi Gangavarapu
- Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Stacy J Caillier
- Department of Neurology, University of California, San Francisco, CA, USA
| | - Jorge R Oksenberg
- Department of Neurology, University of California, San Francisco, CA, USA
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Durtschi J, Margraf RL, Coonrod EM, Mallempati KC, Voelkerding KV. VarBin, a novel method for classifying true and false positive variants in NGS data. BMC Bioinformatics 2013; 14 Suppl 13:S2. [PMID: 24266885 PMCID: PMC3849648 DOI: 10.1186/1471-2105-14-s13-s2] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Background Variant discovery for rare genetic diseases using Illumina genome or exome sequencing involves screening of up to millions of variants to find only the one or few causative variant(s). Sequencing or alignment errors create "false positive" variants, which are often retained in the variant screening process. Methods to remove false positive variants often retain many false positive variants. This report presents VarBin, a method to prioritize variants based on a false positive variant likelihood prediction. Methods VarBin uses the Genome Analysis Toolkit variant calling software to calculate the variant-to-wild type genotype likelihood ratio at each variant change and position divided by read depth. The resulting Phred-scaled, likelihood-ratio by depth (PLRD) was used to segregate variants into 4 Bins with Bin 1 variants most likely true and Bin 4 most likely false positive. PLRD values were calculated for a proband of interest and 41 additional Illumina HiSeq, exome and whole genome samples (proband's family or unrelated samples). At variant sites without apparent sequencing or alignment error, wild type/non-variant calls cluster near -3 PLRD and variant calls typically cluster above 10 PLRD. Sites with systematic variant calling problems (evident by variant quality scores and biases as well as displayed on the iGV viewer) tend to have higher and more variable wild type/non-variant PLRD values. Depending on the separation of a proband's variant PLRD value from the cluster of wild type/non-variant PLRD values for background samples at the same variant change and position, the VarBin method's classification is assigned to each proband variant (Bin 1 to Bin 4). Results To assess VarBin performance, Sanger sequencing was performed on 98 variants in the proband and background samples. True variants were confirmed in 97% of Bin 1 variants, 30% of Bin 2, and 0% of Bin 3/Bin 4. Conclusions These data indicate that VarBin correctly classifies the majority of true variants as Bin 1 and Bin 3/4 contained only false positive variants. The "uncertain" Bin 2 contained both true and false positive variants. Future work will further differentiate the variants in Bin 2.
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11
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Shetty S, Siady M, Mallempati KC, Wilson A, Poarch J, Chandler B, Gray J, Salama ME. Utility of a column-free cell sorting system for separation of plasma cells in multiple myeloma FISH testing in clinical laboratories. Int J Hematol 2012; 95:274-81. [PMID: 22328174 DOI: 10.1007/s12185-012-1021-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2011] [Revised: 01/22/2012] [Accepted: 01/24/2012] [Indexed: 12/15/2022]
Abstract
Targeted FISH analysis is an essential component of the management of plasma cell myeloma for identification of cytogenetic abnormalities. The purpose of this study was to evaluate the column-free method, RoboSep® (RS), for sorting CD138-expressing cells in bone marrow aspirates. Comparative analysis of column-based and RS methodologies was carried out on 54 paired bone marrow aspirate validation samples from patients undergoing work-up for plasma cell dyscrasia. Abnormalities detected by FISH analysis using an IGH@/CCND1 probe set were seen in 54% with RS, and 44% with column-based. We found a statistically significant difference between the yield of abnormalities detected in paired positive cases (p = 0.0001). An additional 183 consecutive post-validation samples sorted by RS showed recurrent genetic abnormalities in 85/120 (71%) of successfully sorted samples with ≥ 1% plasma cells but in none of 63 samples in which FISH analysis was completed on samples that could not be sorted due to insufficient plasma cells upon cell sorting. The column-free method successfully sorted PC, when present in ≥ 1% of cells, for detection of abnormalities by FISH. Furthermore, our data suggest that FISH analysis should not be performed on samples with an inadequate yield at the cell selection step.
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Affiliation(s)
- Shashirekha Shetty
- Cleveland Clinic, 9500 Euclid Avenue, Mail Code LL2-2, Cleveland, OH 44195, USA.
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