Lack of association between HLA and asymptomatic SARS-CoV-2 infection

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Abstract

Human genetic studies of critical COVID-19 pneumonia have revealed the essential role of type I interferon-dependent innate immunity to SARS-CoV-2 infection. Conversely, an association between the HLA-B*15:01 allele and asymptomatic SARS-CoV-2 infection in unvaccinated individuals was recently reported, suggesting a contribution of pre-existing T cell-dependent adaptive immunity. We report a lack of association of classical HLA alleles, including HLA-B*15:01, with pre-omicron asymptomatic SARS-CoV-2 infection in unvaccinated participants in a prospective population-based study in the US (191 asymptomatic vs. 945 symptomatic COVID-19 cases). Moreover, we found no such association in the international COVID Human Genetic Effort cohort (206 asymptomatic vs. 574 mild or moderate COVID-19 cases and 1,625 severe or critical COVID-19 cases). Finally, in the Human Challenge Characterisation study, the three HLA-B*15:01 individuals infected with SARS-CoV-2 developed symptoms. As with other acute primary infections, no classical HLA alleles favoring an asymptomatic course of SARS-CoV-2 infection were identified. These findings suggest that memory T-cell immunity to seasonal coronaviruses does not strongly influence the outcome of SARS-CoV-2 infection in unvaccinated individuals.

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The mutational constraint spectrum quantified from variation in 141,456 humans

Konrad J. Karczewski, Laurent C. Francioli, Grace Tiao … (2021)

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.

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METAL: fast and efficient meta-analysis of genomewide association scans

Cristen Willer, Yun Li, Gonçalo R Abecasis (2010)

Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu

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Mapping the human genetic architecture of COVID-19

David Torrents, George Davey Smith, Mark Peters … (2021)

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19 1 , 2 , host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases 3 – 7 . They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease. A global network of researchers was formed to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity; this paper reports 13 genome-wide significant loci and potentially actionable mechanisms in response to infection.

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Journal

Journal ID (nlm-ta): medRxiv

Journal ID (publisher-id): MEDRXIV

Title: medRxiv

Publisher: Cold Spring Harbor Laboratory

Publication date (Electronic): 08 December 2023

Electronic Location Identifier: 2023.12.06.23299623

Affiliations

[1 ]Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Paris, France, EU.

[2 ]University Paris Cité, Imagine Institute, Paris, France, EU.

[3 ]Helix, San Mateo, CA, USA.

[4 ]Department of Internal Medicine, University of Nevada School of Medicine, Reno, NV, USA.

[5 ]Department of Infectious Diseases, Imperial College London, London, United Kingdom.

[6 ]National Heart and Lung Institute, Imperial College London, London, United Kingdom.

[7 ]Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, NIAID, Bethesda, MD, USA.

[8 ]hVIVO Services Ltd., London, UK.

[9 ]Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.

[10 ]St Vincent’s Clinical School, Faculty of Medicine, UNSW Sydney, NSW, Australia.

[11 ]St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA.

[12 ]Department of Medical Microbiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands, EU.

[13 ]NIAID Collaborative Bioinformatics Resource, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, USA.

[14 ]Infection, Antimicrobials, Modelling, Evolution (IAME), INSERM, UMR1137, University of Paris, Paris, France, EU.

[15 ]AP-HP, Bichat Claude Bernard Hospital, Infectious and Tropical Diseases Department, Paris, France, EU.

[16 ]Epidémiologie clinique du Centre d’Investigation Clinique (CIC-EP), INSERM CIC 1425, Hôpital Bichat, 75018 Paris, France, EU.

[17 ]Département Epidémiologie, Biostatistiques et Recherche Clinique, Hôpital Bichat, Assistance Publique-Hôpitaux de Paris, 75018 Paris, France, EU.

[18 ]Sorbonne Université, INSERM Centre d’Immunologie et des Maladies Infectieuses, CIMI-Paris, Département d’immunologie Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France, EU.

[19 ]Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, Unité de Recherche Clinique PSL-CFX, CIC-1901, Paris, France, EU.

[20 ]Emergency Department, Hôpital Pitié-Salpêtrière, APHP-Sorbonne Université, Paris, France, EU.

[21 ]GRC-14 BIOFAST Sorbonne Université, UMR INSERM 1135, CIMI, Sorbonne Université, Paris, France, EU.

[22 ]Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.

[23 ]School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

[24 ]Swiss Institute of Bioinformatics, Lausanne, Switzerland.

[25 ]Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.

[26 ]Renown Health, Reno, NV, USA.

[27 ]Centre for Paediatrics and Child Health, Faculty of Medicine, Imperial College London, London, UK.

[28 ]Howard Hughes Medical Institute, New York, NY, USA.

Author notes

A full list of Consortia collaborators is provided at the end of the manuscript.

Author contributions

AM, A Cobat, and AB performed computational analysis. AM, ETC, IN, EB, RT, KMSB, YZ, INB, MK, A Catchpole, JBL, CLD, VSS, A Cobat and AB performed or supervised experiments, generated and analyzed data, and contributed to the manuscript by providing figures and tables. SGT, ANS, JG, CB, GG, FT, PH, SYZ, QZ, CC, JF, JJG, VSS and the consortium collaborators evaluated and recruited patients and /or controls. AM, LA, JLC, A Cobat, and AB wrote the manuscript. CC, JJG, LA, JLC, A Cobat, and AB supervised the project. All authors edited the manuscript. All authors read and approved the final manuscript.

[* ]Correspondence: aurelie.cobat@ 123456inserm.fr

[** ] alexandre.bolze@ 123456gmail.com

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Aurélie Cobat http://orcid.org/0000-0001-7209-6257

Article

DOI: 10.1101/2023.12.06.23299623

PMC ID: 10760282

PubMed ID: 38168184

SO-VID: 1dfc75cf-1cb4-46de-9561-49c0cab17bd1

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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.

Lack of association between HLA and asymptomatic SARS-CoV-2 infection

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Novel Coronavirus Disease COVID-19

Most cited references 37

The mutational constraint spectrum quantified from variation in 141,456 humans

METAL: fast and efficient meta-analysis of genomewide association scans

Mapping the human genetic architecture of COVID-19

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