Human Genomics of COVID-19 Pneumonia: Contributions of Rare and Common Variants.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Aurélie Cobat, Qian Zhang, Laurent Abel, Jean-Laurent Casanova, Jacques Fellay
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引用次数: 0

Abstract

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection is silent or benign in most infected individuals, but causes hypoxemic COVID-19 pneumonia in about 10% of cases. We review studies of the human genetics of life-threatening COVID-19 pneumonia, focusing on both rare and common variants. Large-scale genome-wide association studies have identified more than 20 common loci robustly associated with COVID-19 pneumonia with modest effect sizes, some implicating genes expressed in the lungs or leukocytes. The most robust association, on chromosome 3, concerns a haplotype inherited from Neanderthals. Sequencing studies focusing on rare variants with a strong effect have been particularly successful, identifying inborn errors of type I interferon (IFN) immunity in 1-5% of unvaccinated patients with critical pneumonia, and their autoimmune phenocopy, autoantibodies against type I IFN, in another 15-20% of cases. Our growing understanding of the impact of human genetic variation on immunity to SARS-CoV-2 is enabling health systems to improve protection for individuals and populations.

COVID-19 肺炎的人类基因组学:罕见和常见变异的贡献
SARS-CoV-2(严重急性呼吸系统综合征冠状病毒 2)感染在大多数感染者中是无症状或良性的,但在约 10% 的病例中会引起低氧血症 COVID-19 肺炎。我们回顾了危及生命的 COVID-19 肺炎的人类遗传学研究,重点关注罕见变异和常见变异。大规模的全基因组关联研究发现了 20 多个与 COVID-19 肺炎密切相关的常见基因位点,其效应大小适中,其中一些与肺部或白细胞中表达的基因有关。3号染色体上的一个单倍型与COVID-19肺炎关系最为密切。在未接种疫苗的重症肺炎患者中,有1%-5%的患者存在I型干扰素(IFN)免疫先天性错误,另有15%-20%的患者存在自身免疫表型,即抗I型干扰素的自身抗体。我们对人类基因变异对 SARS-CoV-2 免疫力的影响有了越来越多的了解,这使卫生系统能够改善对个人和人群的保护。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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