The Genetics of Symptom Severity in COVID-19 Infections

COVID-19 感染症状严重程度的遗传学

• 批准号: BB/V011448/1
• 负责人: Ewan Birney
• 金额: $ 35.81万
• 依托单位: European Bioinformatics Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FV011448%2F1
• 关键词: Genetics; Symptom; Severity; COVID; 19

Copy number variation (CNV) is an important class of genetic variation that can have large impacts on human health. There is a significant amount of knowledge on genomic disorders caused by rare CNVs at specific locations in the genome and a good amount of evidence into the role of common CNVs across a variety of human traits. To date progress on large scale CNV association studies from Exome or Genome sequence data has been limited by methodological constraints and/or technological limitations. The majority of CNV associations studies have been performed using SNP genotyping arrays which suffer from low CNV resolution and limited dose response. We have recently developed methods to allow large scale copy number association tests from Exome sequences within the UK Biobank. These methods result in high resolution (exon level) CNV information with good association discovery signal for human traits. We have run copy number association testing for a variety of human traits and have generated robust results verifying some important regions within genes previously known to impact these traits. Furthermore, these analyses have resulted in new findings that have not be described previously but show great promise in terms of, for example, gene function. We are undertaking research involving genetic association testing for SNPs and CNVs in COVID-19 patients using data from the UK Biobank and Genomics England. Our CNV association methods can be scaled up to generate results across extremely large whole Exome and whole Genome sequencing datasets. Importantly, we are able to correlate GWAS findings between these two classes of genetic variation where, in some cases, when certain SNPs can 'well tag' specific CNVs the differences in human trait distributions could be discovered by the two approaches independently. However, there are a large number of CNVs that cannot be well tagged by single or multiple SNPs and it is these associations that would not be found using other approaches. Additionally, the interplay between SNPs and CNVs adds important information into the understanding of human traits and to the genetics of differences in symptom severity seen in covid-19 infection. We are actively developing SNP-CNV imputation methods such that we can built robust imputation models for CNV locations across the genome. This allows us to impute copy number information into further SNP genotyping cohorts worldwide adding considerable value to the global association testing efforts for covid-19. Many groups across the world are actively undertaking genetic research into covid-19 susceptibility using large scale GWAS analysis from SNP genotyping arrays. However, there are far fewer who can quickly and effectively leverage the CNV information available from whole Exome and Genome sequence data to allow genome wide association testing of CNVs. It is clear that differences in copy number can cause big differences in human traits and influence health. CNVs are certain to be one of the genetic sources of differences that we observe across the wide range of responses to covid-19 infection. Results from this project will contribute to an improved understanding of the genetic basis of differences in symptom severity of covid-19 cases. There are likely to be a large number of specific risk factors based on rare variants in the human population that confer an increased risk of severe symptoms. It is unclear whether there will be a single (or small number) of highly significant genetic variants with large effect sizes that predispose individuals to an increased risk of severe symptoms. It is however likely that a large number of rare or combinations of rare and common genetic variants may lower an individual's robustness to covid-19 infection overall. It is entirely possible that commonly observed CNVs may associate with differences in covid-19 symptom severity and this is an important research area that could have a high impact globally.

拷贝数变异（CNV）是一类重要的遗传变异，对人类健康有很大影响。关于基因组中特定位置的罕见CNV引起的基因组疾病有大量的知识，并且有大量证据表明常见CNV在各种人类特征中的作用。迄今为止，从外显子组或基因组序列数据进行的大规模CNV关联研究的进展受到方法学约束和/或技术限制的限制。大多数CNV相关性研究都是使用SNP基因分型阵列进行的，其CNV分辨率低且剂量反应有限。我们最近开发的方法，允许大规模的拷贝数关联测试从英国生物库内的外显子组序列。这些方法产生高分辨率（外显子水平）CNV信息，具有针对人类性状的良好关联发现信号。我们已经对各种人类特征进行了拷贝数关联测试，并产生了可靠的结果，验证了先前已知影响这些特征的基因内的一些重要区域。此外，这些分析导致了以前没有描述过的新发现，但在基因功能等方面显示出巨大的希望。我们正在进行的研究涉及使用英国生物银行和英国基因组学的数据对COVID-19患者的SNP和CNV进行遗传关联检测。我们的CNV关联方法可以扩大规模，以在非常大的整个外显子组和整个基因组测序数据集上生成结果。重要的是，我们能够将这两类遗传变异之间的GWAS发现关联起来，在某些情况下，当某些SNP可以“很好地标记”特定的CNV时，人类性状分布的差异可以通过两种方法独立地发现。然而，有大量的CNV不能被单个或多个SNP很好地标记，并且使用其他方法不会发现这些关联。此外，SNPs和CNV之间的相互作用为理解人类特征和COVID-19感染中症状严重程度差异的遗传学提供了重要信息。我们正在积极开发SNP-CNV插补方法，以便我们可以为整个基因组中的CNV位置建立强大的插补模型。这使我们能够将拷贝数信息输入到全球范围内的进一步SNP基因分型队列中，为全球Covid-19相关性测试工作增加了相当大的价值。世界各地的许多团体正在积极开展新冠病毒易感性的遗传研究，使用SNP基因分型阵列的大规模GWAS分析。然而，很少有人能够快速有效地利用从整个外显子组和基因组序列数据中获得的CNV信息来进行CNV的全基因组关联测试。很明显，拷贝数的差异会导致人类特征的巨大差异，并影响健康。CNV肯定是我们在对COVID-19感染的广泛反应中观察到的差异的遗传来源之一。该项目的结果将有助于更好地理解COVID-19病例症状严重程度差异的遗传基础。可能存在大量基于人群中罕见变异的特定风险因素，这些风险因素会增加严重症状的风险。目前还不清楚是否会有一个（或少量）具有大效应大小的高度显著的遗传变异使个体患严重症状的风险增加。然而，大量罕见或罕见和常见遗传变异的组合可能会降低个体对COVID-19感染的整体稳健性。通常观察到的CNV完全有可能与COVID-19症状严重程度的差异有关，这是一个重要的研究领域，可能在全球产生很大影响。

项目成果

CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank.

• DOI: 10.1016/j.xgen.2022.100167
• 发表时间: 2022-08-10
• 期刊: CELL GENOMICS
• 作者: Fitzgerald, Tomas;Birney, Ewan
• 通讯作者: Birney, Ewan