Genome-wide association analysis of COVID-19 mortality risk in SARS-CoV-2 genomes identifies mutation in the SARS-CoV-2 spike protein that colocalizes with P.1 of the Brazilian strain
Genome-wide association analysis of COVID-19 mortality risk in SARS-CoV-2 genomes identifies mutation in the SARS-CoV-2 spike protein that colocalizes with P.1 of the Brazilian strain
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DOI：
10.1002/gepi.22421
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发表时间：
2021-06-22
影响因子：
2.1
通讯作者：
Lange, Christoph
中科院分区：
文献类型：
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作者：
Hahn, Georg;Wu, Chloe M.;Lange, Christoph
SARS-CoV-2死亡率与宿主易感性的关系已得到广泛研究。SARS-CoV-2基因组中的序列变异如何影响致病性尚不清楚。从2020年10月开始，我们使用全基因组关联研究（GWAS）的方法，研究了病毒的全基因组测序（WGS）数据与COVID-19死亡率之间的关联，作为早期识别高致病性菌株以进行遏制的潜在方法。尽管不断更新我们的分析，但在2020年12月，我们分析了GISAID数据库中7548个COVID-19患者的单链SARS-CoV-2基因组，并使用逻辑回归分析了与死亡率相关的变异。总体而言，评估了病毒基因组的29，891个测序位点与患者/宿主死亡率的相关性，12，053和25，088 bp的两个位点实现了全基因组显著性（p值分别为4.09e-09和4.41e-23），尽管只有25，088 bp在随访分析中保持显著性。我们的关联发现完全由巴西提交的样本驱动（25，088 bp的p值为4.90e-13）。GISAID上巴西样本的25，088 bp突变频率从2020年10月/12月的约0. 4迅速增加到2021年3月的0. 77。尽管GWAS方法适用于不同地理区域之间突变频率不同的样本，但它无法解释随时间迅速变化的突变频率，因此GWAS对2020年12月之后提交的GISAID样本的后续分析无效。25，088 bp的基因座位于P.1菌株中，该菌株后来（2021年4月）成为疾病控制中心定义的巴西菌株的区别基因座之一（准确地说，取代V1176 F）。具体而言，25，088 bp处的突变发生在SARS-CoV-2刺突蛋白的S2亚基中，该亚基在病毒进入靶宿主细胞中起关键作用。由于突变改变了氨基酸编码序列，它们可能会造成结构变化，从而增强病毒的感染性和症状的严重程度。我们的分析表明，GWAS方法可以提供合适的分析工具，用于实时检测数据库（如GISAID）中新的更具传播性和致病性的病毒株，尽管需要新的方法来适应随着时间的推移而快速变化的突变频率，同时改变病例/对照比例。相关元数据/患者信息在质量和可用性方面的改进对于充分利用GWAS方法在该领域的潜力也很重要。
SARS-CoV-2 mortality has been extensively studied in relation to host susceptibility. How sequence variations in the SARS-CoV-2 genome affect pathogenicity is poorly understood. Starting in October 2020, using the methodology of genome-wide association studies (GWAS), we looked at the association between whole-genome sequencing (WGS) data of the virus and COVID-19 mortality as a potential method of early identification of highly pathogenic strains to target for containment. Although continuously updating our analysis, in December 2020, we analyzed 7548 single-stranded SARS-CoV-2 genomes of COVID-19 patients in the GISAID database and associated variants with mortality using a logistic regression. In total, evaluating 29,891 sequenced loci of the viral genome for association with patient/host mortality, two loci, at 12,053 and 25,088 bp, achieved genome-wide significance (p values of 4.09e-09 and 4.41e-23, respectively), though only 25,088 bp remained significant in follow-up analyses. Our association findings were exclusively driven by the samples that were submitted from Brazil (p value of 4.90e-13 for 25,088 bp). The mutation frequency of 25,088 bp in the Brazilian samples on GISAID has rapidly increased from about 0.4 in October/December 2020 to 0.77 in March 2021. Although GWAS methodology is suitable for samples in which mutation frequencies varies between geographical regions, it cannot account for mutation frequencies that change rapidly overtime, rendering a GWAS follow-up analysis of the GISAID samples that have been submitted after December 2020 as invalid. The locus at 25,088 bp is located in the P.1 strain, which later (April 2021) became one of the distinguishing loci (precisely, substitution V1176F) of the Brazilian strain as defined by the Centers for Disease Control. Specifically, the mutations at 25,088 bp occur in the S2 subunit of the SARS-CoV-2 spike protein, which plays a key role in viral entry of target host cells. Since the mutations alter amino acid coding sequences, they potentially imposing structural changes that could enhance viral infectivity and symptom severity. Our analysis suggests that GWAS methodology can provide suitable analysis tools for the real-time detection of new more transmissible and pathogenic viral strains in databases such as GISAID, though new approaches are needed to accommodate rapidly changing mutation frequencies over time, in the presence of simultaneously changing case/control ratios. Improvements of the associated metadata/patient information in terms of quality and availability will also be important to fully utilize the potential of GWAS methodology in this field.