The Genetics of Symptom Severity in COVID-19 Infections

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。关于由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，也有大量关于常见CNV在人类各种特征中的作用的证据。到目前为止，利用外显子组或基因组序列数据进行大规模CNV相关性研究的进展受到方法学限制和/或技术限制。大多数CNV相关性研究都是使用SNP基因分型阵列进行的，这种阵列存在CNV分辨率低和剂量反应有限的问题。我们最近开发了一种方法，允许从英国生物库内的外显子序列进行大规模拷贝数关联测试。这些方法产生了高分辨率(外显子级别)的CNV信息，并具有良好的人类特征关联发现信号。我们已经对各种人类特征进行了拷贝数关联测试，并产生了稳健的结果，验证了以前已知的影响这些特征的基因中的一些重要区域。此外，这些分析还导致了以前没有描述过的新发现，但在例如基因功能方面显示出巨大的希望。我们正在利用来自英国生物库和英国基因组学的数据，对新冠肺炎患者进行SNPs和CNV的基因关联测试。我们的CNV关联方法可以扩大到在非常大的整个外显子组和全基因组测序数据集上生成结果。重要的是，我们能够在这两类遗传变异之间建立联系，在某些情况下，当某些SNP可以很好地标记特定的CNV时，这两种方法可以独立发现人类特征分布的差异。然而，有大量的CNV不能被单个或多个SNP很好地标记，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新冠肺炎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定位方法，以便我们可以建立整个基因组中CNV位置的稳健定位模型。这使我们能够将拷贝数信息归因于全球更多的SNP基因分型队列，为全球新冠肺炎的关联检测工作增加了相当大的价值。世界各地的许多群体正在积极开展新冠肺炎易感性的遗传学研究，使用来自SNP基因分型阵列的大规模GWA分析。然而，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信息来允许对CNV进行全基因组关联测试的人要少得多。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肯定是我们在对新冠肺炎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新冠肺炎病例症状严重程度差异的遗传基础。很可能存在大量基于人类人口中罕见变异的特定风险因素，这些变异会增加出现严重症状的风险。目前尚不清楚是否会有单个(或少数)具有大效应大小的高度显著的遗传变异，使个人容易出现严重症状的风险增加。然而，大量罕见或罕见和常见基因变异的组合很可能会降低个体对新冠肺炎感染的整体耐受性。普遍观察到的CNV完全有可能与新冠肺炎症状严重程度的差异有关，这是一个可能对全球产生重大影响的重要研究领域。

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.