SARS-CoV-2 genomic variations associated with mortality rate of COVID-19

Abstract

The coronavirus disease 2019 (COVID-19) outbreak, caused by SARS-CoV-2, has rapidly expanded to a global pandemic. However, numbers of infected cases, deaths, and mortality rates related to COVID-19 vary from country to country. Although many studies were conducted, the reasons of these differences have not been clarified. In this study, we comprehensively investigated 12,343 SARS-CoV-2 genome sequences isolated from patients/individuals in six geographic areas and identified a total of 1234 mutations by comparing with the reference SARS-CoV-2 sequence. Through a hierarchical clustering based on the mutant frequencies, we classified the 28 countries into three clusters showing different fatality rates of COVID-19. In correlation analyses, we identified that ORF1ab 4715L and S protein 614G variants, which are in a strong linkage disequilibrium, showed significant positive correlations with fatality rates (r = 0.41, P = 0.029 and r = 0.43, P = 0.022, respectively). We found that BCG-vaccination status significantly associated with the fatality rates as well as number of infected cases. In BCG-vaccinated countries, the frequency of the S 614G variant had a trend of association with the higher fatality rate. We also found that the frequency of several HLA alleles, including HLA-A*11:01, were significantly associated with the fatality rates, although these factors were associated with number of infected cases and not an independent factor to affect fatality rate in each country. Our findings suggest that SARS-CoV-2 mutations as well as BCG-vaccination status and a host genetic factor, HLA genotypes might affect the susceptibility to SARS-CoV-2 infection or severity of COVID-19.

Fig. 1

Clustering analysis of SARS-CoV-2 among 28 countries. a Heatmap for the frequencies of SARS-CoV-2 mutants. The 28 countries were classified into three clusters based on the mutational signature by a hierarchical clustering. Protein sequence based on the SARS-CoV-2_Wuhan-Hu-1 sequence (GenBank accession number MN908947) is used as a reference. Ref; amino acid in reference SARS-CoV-2 sequence, Mut, amino acid in mutant SARS-CoV-2. b A global mapping of the three clusters. c Fatality rates according to the clusters. Horizontal lines represent the means. The Student’s t test was used to evaluate statistical significance

Fig. 2

Correlation analysis of variant frequencies of SARS-CoV-2 ORF1ab 4715L (a) or S 614G (b) with fatality rates of COVID-19 among 28 countries. Pearson’s correlation coefficients (r) were calculated. Colors of each dot were corresponding to the mutational clusters shown in Fig. 1a

Fig. 3

Association of variant frequencies of SARS-CoV-2 with fatality rates of COVID-19 among 17 states in the United States. a Fatality rates in three different areas in the United States, Western, Central, and Eastern. Horizontal lines represent the means. The Student’s t test was used to evaluate statistical significance. b, c Correlation analysis between frequencies of SARS-CoV-2 ORF1ab 4715L (b) or S 614G variants (c) and fatality rates. Pearson’s correlation coefficients (r) were calculated

Fig. 4

Association of BCG-vaccination status with fatality rates and infected cases of COVID-19 among 28 countries. a Fatality rates in BCG-vaccinated (BCG+) and BCG-non-vaccinated countries (BCG−). Horizontal lines represent the means. The Student’s t test was used to evaluate statistical significance. b Correlation analysis between frequencies of S 614G variant of SARS-CoV-2 and fatality rates in BCG+ and BCG− countries. Pearson’s correlation coefficients (r) were calculated. c Number of infected cases in BCG+ and BCG− countries. Horizontal lines represent the means. The Student’s t test was used to evaluate statistical significance

Fig. 5

Association of HLA allele frequency with fatality rates and infected cases of COVID-19 among countries. a–c Correlation between HLA-A*11:01 (a), HLA-A*02:06 (b), and HLA-B*54:01 (c) allelic frequencies and fatality rates of COVID-19. Numbers of analyzed countries are 21, 16, and 15, respectively, for HLA-A*11:01, HLA-A*02:06, and HLA-B*54:01. Pearson’s correlation coefficient (r) was calculated. d–f Correlation between HLA-A*11:01 (d), HLA-A*02:06 (e), and HLA-B*54:01 (f) allelic frequency and number of infected cases of COVID-19. Pearson’s correlation coefficient (r) was calculated