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Brief Report

Strong Correlation between the Case Fatality Rate of COVID-19 and the rs6598045 Single Nucleotide Polymorphism (SNP) of the Interferon-Induced Transmembrane Protein 3 (IFITM3) Gene at the Population-Level

by 1,2 and 1,2,*
1
Korea Zoonosis Research Institute, Jeonbuk National University, Iksan 54531, Jeonbuk, Korea
2
Department of Bioactive Material Sciences and Institute for Molecular Biology and Genetics, Jeonbuk National University, Jeonju 54896, Jeonbuk, Korea
*
Author to whom correspondence should be addressed.
Genes 2021, 12(1), 42; https://doi.org/10.3390/genes12010042
Received: 23 November 2020 / Revised: 24 December 2020 / Accepted: 29 December 2020 / Published: 30 December 2020
(This article belongs to the Section Molecular Genetics and Genomics)

Abstract

Coronavirus disease 2019 (COVID-19) is a fatal pandemic disease that is caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of 13 December, 2020, over 70,000,000 cases and 1,500,000 deaths have been reported over a period of several months; however, the mechanism underlying the pathogenesis of COVID-19 has not been elucidated. To identify the novel risk genetic biomarker for COVID-19, we evaluated the correlation between the case fatality rate of COVID-19 and the genetic polymorphisms of several potential COVID-19-related genes, including interferon-induced transmembrane protein 3 (IFITM3), the angiotensin I converting enzyme 2 (ACE2) gene, transmembrane protease, serine 2 (TMPRSS2), interleukin 6 (IL6), leucine zipper transcription factor-like protein 1 (LZTFL1), and the ABO genes, in various ethnic groups. We obtained the number of COVID-19 cases and deaths from the World Health Organization (WHO) COVID-19 dashboard and calculated the case fatality rate of each ethnic group. In addition, we obtained the allele distribution of the polymorphisms of the IFITM3, ACE2, TMPRSS2, IL6, LZTFL1, and ABO genes from the 1000 Genomes Project and performed Log-linear regression analysis using SAS version 9.4. We found different COVID-19 case fatality rates in each ethnic group. Notably, we identified a strong correlation between the case fatality rate of COVID-19 and the allele frequency of the rs6598045 single nucleotide polymorphism (SNP) of the IFITM3 gene. To the best of our knowledge, this report is the first to describe a strong correlation between the COVID-19 case fatality rate and the rs6598045 SNP of the IFITM3 gene at the population-level.
Keywords: COVID-19; case fatality rate; SARS-CoV-2; IFITM3; ACE2; TMPRSS2; IL6; LZTFL1; ABO; SNP COVID-19; case fatality rate; SARS-CoV-2; IFITM3; ACE2; TMPRSS2; IL6; LZTFL1; ABO; SNP

1. Introduction

Coronavirus disease 2019 (COVID-19) is a fatal acute respiratory disease caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1,2]. COVID19-infected patients have high fever, dry cough, dyspnea, and pneumonia [3]. As of 13 December, 2020, over 70,000,000 cases and 1,500,000 deaths (case fatality rate, over 2%) have been reported to the World Health Organization (WHO) from the WHO COVID-19 dashboard. Since the global threat posed by COVID-19 has been increasing due to shortages of adequate medical resources, it is very important to investigate the information regarding the mechanisms underlying the pathogenesis of COVID-19.
Previous studies have reported that the angiotensin I converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) genes play a pivotal role in the entry of SARS-CoV-2 into host cells. ACE2 is a major receptor of the spike protein of SARS-CoV-2 and polymorphisms of the ACE2 gene modulates the susceptibility of SARS-CoV-2 infection via elevation in expression level of ACE2. In addition, TMPRSS2 is a serine protease and plays a role in the spike protein priming of SARS-CoV-2 for viral invasion [4,5]. Thus, in a previous study, the genetic polymorphisms affecting the expression level of the TMPRSS2 gene have been suggested as novel candidates in the severity of COVID-19 in Italy [6]. However, a previous study did not observe an association between the genetic polymorphisms of the ACE2 and TMPRSS2 genes and SARS-CoV-2 infection [7]. In addition, the rs12252 single nucleotide polymorphism (SNP) of the interferon-induced transmembrane protein 3 (IFITM3) gene is related to the severity of COVID-19 in the Han Chinese population [8]. Furthermore, since the rs180079 SNP of the interleukin 6 (IL6) gene has been affected by the severity of several types of lung diseases, including chronic obstructive pulmonary disease (COPD) and pneumonia, the IL6 polymorphism is presumed to confer susceptibility to COVID-19 [9]. A recent genome-wide association study (GWAS) reported that two polymorphisms, the rs11385942 insertion/deletion polymorphism of the leucine zipper transcription factor-like protein 1 (LZTFL1) gene and the rs657152 SNP of the ABO gene, are related to severe COVID-19 cases with respiratory failure [10]. However, an association between genetic variants and the case fatality of COVID-19 has not been determined.
In the present study, to find the novel genetic biomarker for the severity of COVID-19, we evaluated a correlation at the population-level between the case fatality rate of COVID-19 and genetic polymorphisms of several potential COVID-19-related genes, including IFITM3, ACE2, TMPRSS2, IL6, LZTFL1, and the ABO genes. To investigate this correlation, we obtained the number of cases and deaths of COVID-19 from the WHO COVID-19 dashboard and calculated the case fatality rate of each ethnic group. In addition, we obtained allele frequencies of the polymorphisms of the IFITM3, ACE2, TMPRSS2 IL6, LZTFL1, and ABO genes from the 1000 Genomes Project and performed log-linear regression analysis at the population-level.

2. Results

2.1. Information on the Polymorphisms of COVID-19-Related Genes

We selected a total of 26 polymorphisms, which have been previously reported for relationship with SARS-CoV-2, influenza A H1N1 pandemic 2009 virus and COPD [6,8,9,10,11,12,13]. Three SNPs of the IFITM3 gene, rs12252, rs34481144 and rs6598045, were analyzed in this study. Four SNPs of the ACE2 gene, rs2285666, rs35803318, rs2074192 and rs2106809, were analyzed in this study. Fifteen SNPs of the TMPRSS2 gene and 1 insertion/deletion polymorphism, that is, rs2070788, rs2298659, rs17854725, rs12329760, rs3787950, rs463727, rs9974589, rs34624090, rs7364083, rs55964536, rs734056, rs4290734, rs34783969, rs11702475, rs35899679, and rs35041537, were analyzed in this study. One SNP of the IL6 gene, rs1800795, was analyzed in this study. One insertion/deletion polymorphism of the LZTFL1 gene, rs11385942, was analyzed in this study. One SNP of the ABO gene, rs657152, was analyzed in this study. Detailed information regarding these genes is presented in Supplementary Table S1.

2.2. Ethnic Differences of Case Fatality Rates among COVID-19 Patients

We obtained the number of cases and deaths of COVID-19 patients according to their ethnic groups, including African, European, American, East Asian, and South Asian groups, from the WHO COVID-19 dashboard (as of 29 June, 2020). Detailed information on worldwide case fatality rates of COVID-19 was described in Table 1. Interestingly, the case fatality rate of COVID-19 varies according to ethnic background.

2.3. Regression Analysis between the Case Fatality Rate of COVID-19 and the Polymorphisms of COVID-19-Related Genes

To identify a correlation between the case fatality rate and the minor allele frequencies (MAFs) of the polymorphisms of the COVID-19-related genes, we performed log-linear regression analysis. Detailed values of r2 and p-values are presented in Supplementary Table S2. Notably, only the MAF of the rs6598045 SNP of the IFITM3 gene showed a strong correlation (r2 = 0.8901, p = 0.0047) with the case fatality rate of COVID-19 (Figure 1A). Among the SNPs of the ACE2 gene, the rs2074192 SNP showed the highest correlation (r2 = 0.6503, p = 0.0526) with the case fatality rate of COVID-19 (Figure 1B). Among the SNPs of the TMPRSS2 gene, the rs2298659 SNP showed the strongest correlation (r2 = 0.7236, p = 0.0318) with the case fatality rate of COVID-19 (Figure 1C). In the IL6 gene, the rs1800795 SNP showed a weak correlation (r2 = 0.0136, p = 0.8260) with the case fatality rate of COVID-19 (Figure 1D). In the LZTFL1 gene, the rs11385942 polymorphism showed a weak correlation (r2 = 0.1691, p = 0.4180) with the case fatality rate of COVID-19 (Figure 1E). In the ABO gene, the rs657152 SNP showed a weak correlation (r2 = 0.1016, p = 0.5380) with the case fatality rate of COVID-19 (Figure 1F).

3. Discussion

In the present study, we identified a strong correlation between the case fatality rate of COVID-19 and the allele frequency of the rs6598045 SNP of the IFITM3 gene. Previous studies have reported that the IFITM3 protein showed potent antiviral capacity to a wide range of viruses, including influenza A viruses (IAVs), Ebola virus (EBOV), Marburg virus (MARV), SARS-CoV, dengue virus (DEV), West Nile virus (WNV), Zika virus (ZIKV), and foot-and-mouth disease virus (FMDV) [14,15,16,17,18,19]. The IFITM3 protein has been presumed to physically inhibit the endocytosis of several viruses by constructing a chain-like structure on the cell membrane between IFITM3 protein monomers. Thus, polymorphisms that affect the function and expression level of the IFITM3 protein play a crucial role in the antiviral capacity of the IFITM3 protein. The rs12252 SNP, which is located on the splicing receptor, is related to the truncated form of the IFITM3 protein and is associated with the severity of the 2009 H1N1 influenza A pandemic and COVID-19 [13,20,21]. However, previous studies using RNA sequencing did not find a splicing form of the IFITM3 protein induced by the rs12252 SNP and the correlation between the rs12252 SNP and the severity of 2009 pandemic H1N1 influenza A in several ethnic groups [22,23]. The rs34481144 SNP is located on the regulatory region of the IFITM3 gene and the binding site of CTCF. According to the allele of the rs34481144 SNP, transcriptional up/down regulation via binding affinity of the transcription factor of the IFITM3 gene is modified and is associated with the severity of 2009 pandemic H1N1 influenza A [12,24]. However, East Asian showed a notably low MAF of the rs34481144 SNP (0.006), and the Korean population did not show polymorphisms in the rs34481144 SNP (0) [11]. In addition, the Korean population did not show association between the rs34481144 SNP and the susceptibility of 2009 pandemic H1N1 influenza A. The rs6598045 SNP, which is located on the proximal promoter of the IFITM3 gene, is related to transcriptional efficiency via the binding ability of the transcription factor TFII-I and is a novel candidate SNP associated with the susceptibility to 2009 pandemic H1N1 influenza A infection [11]. Although viral receptor and binding protein are different between SARS-CoV-2 and 2009 pandemic H1N1 influenza A virus, both viruses showed several commonalities including RNA genome, identical target cell, similar respiratory symptoms and co-morbidities related to severe illness [25,26]. Thus, we selected polymorphisms, which have been previously reported for an association with SARS-CoV-2 and influenza A H1N1 pandemic 2009 virus [6,8,9,10,11,12,13].
In the present study, we performed regression analysis and found a strong correlation between the rs6598045 SNP and the case fatality rate of COVID-19 (Figure 1A, Supplementary Table S2). Interestingly, three SNPs of the IFITM3 gene showed different p-values (Supplementary Table S2). Although those SNPs are located close within 1 kb (Supplementary Table S1), the mechanism related to innate immune response is very different among them. Thus, rs6598045 SNP seems likely to play a more dominant role in COVID-19 severity than other two SNPs. In addition, the rs2074192 SNP of the ACE2 gene and rs2298659 SNP of the TMPRSS2 gene also showed the highest correlation among other ACE2 and TMPRSS2 polymorphisms, respectively (Figure 1B,C, Supplementary Table S2). However, we carried out a simplified analysis with exclusion of various factors, including the medical environment, average age, and quarantine system. Notably, the saturation of the medical system and population age played a pivotal role in case fatality rate in COVID-19 [27]. In addition, active quarantine measure, including prompt implement of large-scale viral testing and restrictions on rallies and crowd gatherings contributed to a remarkable decline of the case fatality rate in the early days of the COVID-19 outbreak in the Republic of Korea [28]. Since medical environment, average age and quarantine system had an impact on case fatality of COVID-19 and were quite different among countries, it must be considered as a limitation in this study. Furthermore, since case fatality rate depends on the amount of testing conducted in a community, there is the possibility of bias. Although population-level regression analysis is a useful and convenient tool in a fast-moving pandemic for identifying the relationship between genetic factors and clinical outcomes, there is the possibility of ecological fallacy, which is an error of making conclusions about individuals through only interpretation of statistical data at the population-level [29,30]. Since not all of the genetic data of each country was available, we collected genetic data in five major groups from 1000 genome project. However, since these large multinational groups are very heterogenous populations, which contain diverse population substructure, different minor allele frequencies, and various case fatality rates via different level of COVID-19 control, there is limitation of this simplified population-level analysis. In addition, we only analyzed using minor allele frequency in the present study. However, the heterozygote and homozygote of genotype frequency may possibly have different effects, and further investigation based on the genotype frequency is highly desirable in the future [31]. Furthermore, to validate the findings of the current study, we will investigate the correlation of these SNPs with phenotype of COVID-19, including asymptomatic, mild, severe, and fatal cases using a case-control study at the individual level in the future. Finally, since our analysis was based on the data up to June 2020, there is a gap to reflect the fast-moving current status of COVID-19. However, because the data up to June 2020 reflected the initial situation in which there was not enough the global preparation for COVID-19, these data are also very meaningful. In recent studies, several GWAS studies have been carried out to identify novel genetic biomarkers for severity of COVID-19. One study has been performed in Italian and Spanish COVID-19 patients with respiratory failure. This study indicated that solute carrier family 6 member 20 gene (SLC6A20), LZTFL1, C-C chemokine receptor type 9 gene (CCR9), FYVE and coiled-coil domain autophagy adaptor 1 gene (FYCO1), C-X-C chemokine receptor type 6 gene (CXCR6), X-C Motif Chemokine Receptor 1 gene (XCR1), and ABO genes were involved in severity of COVID-19 [10]. The other study has been done in British COVID-19 patients admitted to the intensive care unit. This study showed that interferon α and β receptor subunit 2 gene (IFNAR2), tyrosine kinase 2 gene (TYK2) and chemokine receptor type 2 gene (CCR2) gene were associated with severity of COVID-19 [32]. Although the same genes were not identified as biomarkers in both studies, immune-related genes were commonly associated with severity of COVID-19. Since our study also suggested that IFITM3 gene, downstream effector of innate immune system could also be involved in the case fatality rate of COVID-19, further GWAS analysis in death patients with COVID-19 in various ethnic groups is highly required to validate genetic biomarkers for the case fatality rate of COVID-19 in the future.

4. Materials and Methods

4.1. Data Collection

The number of confirmed cases and deaths of COVID-19 was obtained from the WHO COVID-19 dashboard. The worldwide distribution of the allele frequencies on polymorphisms of the IFITM3, ACE2, TMPRSS2, IL6, and ABO genes was obtained from the 1000 Genomes Project. The polymorphisms analyzed in this study were selected from polymorphisms, which have been reported for an association with SARS-CoV-2, influenza A H1N1 pandemic 2009 virus, and COPD in previous studies [6,8,9,10,11,12,13,33].

4.2. Statistical Analysis

All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Case fatality rate was calculated as follows: case fatality rate (%) = (number of cases)/(number of deaths) × 100. The relationships between the case fatality rate of COVID-19 and the MAF of the polymorphisms of the COVID-19-related genes were evaluated using log-linear regression (Poisson model) analysis. The regression analysis was performed on each polymorphism for a total of 26 times. Adjusted p-values < 0.002 using Bonferroni correction were considered statistically significant.

5. Conclusions

In conclusion, we obtained the worldwide case fatality rates of COVID-19 and genetic information on the IFITM3, ACE2, TMPRSS2, and IL6 genes. We performed log-linear regression analysis between the case fatality rate of COVID-19 and allele frequencies of the polymorphisms of the IFITM3, ACE2, TMPRSS2, and IL6 genes in several ethnic groups. We identified a strong correlation between the case fatality rate of COVID-19 and the allele frequency of the rs6598045 SNP IFITM3 gene. To the best of our knowledge, this report is the first to describe a strong correlation between COVID-19 and the rs6598045 SNP of the IFITM3 gene at the population-level.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4425/12/1/42/s1, Supplementary Table S1: Detailed information on polymorphisms of the COVID-19-related genes analyzed in this study, Supplementary Table S2: Summary of correlation analysis between the case fatality rate of COVID-19 and allele frequencies of the polymorphisms of the IFITM3, ACE2, TMPRSS2, IL6, LZTFL1, and ABO genes in several ethnic groups.

Author Contributions

Y.-C.K. and B.-H.J. conceived and designed the experiments. Y.-C.K. and B.-H.J. analyzed the data. Y.-C.K. and B.-H.J. wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Basic Science Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education, Science, and Technology (2018R1D1A1B07048711). This research was supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (2017R1A6A1A03015876).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are available from the corresponding author on reasonable request.

Acknowledgments

This work was supported by NRF (National Research Foundation of Korea) Grant funded by the Korean Government (NRF-2019-Fostering Core Leaders of the Future Basic Science Program/Global Ph.D. Fellowship Program).

Conflicts of Interest

The authors declare no conflict of interest, financial or otherwise.

Abbreviations

SNPSingle nucleotide polymorphism
IFITM3Interferon-induced transmembrane protein 3 gene
COVID-19Coronavirus disease 2019
SARS-CoV-2Severe acute respiratory syndrome coronavirus 2
ACE2Angiotensin I converting enzyme 2 gene
TMPRSS2Transmembrane protease, serine 2 gene
IL6Interleukin 6 gene
LZTFL1Leucine zipper transcription factor-like protein 1 gene
WHOWorld Health Organization
COPDChronic obstructive pulmonary disease
GWASGenome-wide association study
MAFMinor allele frequency
SLC6A20Solute carrier family 6 member 20 gene
CCR9C-C chemokine receptor type 9 gene
FYCO1FYVE and coiled-coil domain autophagy adaptor 1 gene
CXCR6C-X-C chemokine receptor type 6 gene
XCR1X-C Motif Chemokine Receptor 1 gene
IFNAR2Interferon α and β receptor subunit 2 gene
TYK2Tyrosine kinase 2 gene
CCR2Chemokine receptor type 2 gene

References

  1. Rothan, H.A.; Byrareddy, S.N. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J. Autoimmun. 2020, 109, 102433. [Google Scholar] [CrossRef] [PubMed]
  2. Contini, C.; Di Nuzzo, M.; Barp, N.; Bonazza, A.; De Giorgio, R.; Tognon, M.; Rubino, S. The novel zoonotic COVID-19 pandemic: An expected global health concern. J. Infect. Dev. Ctries. 2020, 14, 254–264. [Google Scholar] [CrossRef] [PubMed]
  3. Halaji, M.; Farahani, A.; Ranjbar, R.; Heiat, M.; Dehkordi, F.S. Emerging coronaviruses: First SARS, second MERS and third SARS-CoV-2: Epidemiological updates of COVID-19. Le Infez. Med. 2020, 28, 6–17. [Google Scholar]
  4. Bourgonje, A.R.; Abdulle, A.E.; Timens, W.; Hillebrands, J.L.; Navis, G.J.; Gordijn, S.J.; Bolling, M.C.; Dijkstra, G.; Voors, A.A.; Osterhaus, A.D.; et al. Angiotensin-converting enzyme 2 (ACE2), SARS-CoV-2 and the pathophysiology of coronavirus disease 2019 (COVID-19). J. Pathol. 2020. [Google Scholar] [CrossRef] [PubMed]
  5. Hoffmann, M.; Kleine-Weber, H.; Schroeder, S.; Kruger, N.; Herrler, T.; Erichsen, S.; Schiergens, T.S.; Herrler, G.; Wu, N.H.; Nitsche, A.; et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell 2020, 181, 271–280.e8. [Google Scholar] [CrossRef] [PubMed]
  6. Asselta, R.; Paraboschi, E.M.; Mantovani, A.; Duga, S. ACE2 and TMPRSS2 variants and expression as candidates to sex and country differences in COVID-19 severity in Italy. Aging (Albany NY) 2020, 12, 10087–10098. [Google Scholar] [CrossRef]
  7. Lopera Maya, E.A.; van der Graaf, A.; Lanting, P.; van der Geest, M.; Fu, J.; Swertz, M.; Franke, L.; Wijmenga, C.; Deelen, P.; Zhernakova, A.; et al. Lack of Association Between Genetic Variants at ACE2 and TMPRSS2 Genes Involved in SARS-CoV-2 Infection and Human Quantitative Phenotypes. Front. Genet. 2020, 11, 613. [Google Scholar] [CrossRef]
  8. Zhang, Y.; Qin, L.; Zhao, Y.; Zhang, P.; Xu, B.; Li, K.; Liang, L.; Zhang, C.; Dai, Y.; Feng, Y.; et al. Interferon-Induced Transmembrane Protein 3 Genetic Variant rs12252-C Associated With Disease Severity in Coronavirus Disease 2019. J. Infect. Dis. 2020, 222, 34–37. [Google Scholar] [CrossRef]
  9. Kirtipal, N.; Bharadwaj, S. Interleukin 6 polymorphisms as an indicator of COVID-19 severity in humans. J. Biomol. Struct. Dyn. 2020, 1–3. [Google Scholar] [CrossRef]
  10. Ellinghaus, D.; Degenhardt, F.; Bujanda, L.; Buti, M.; Albillos, A.; Invernizzi, P.; Fernandez, J.; Prati, D.; Baselli, G.; Asselta, R.; et al. Genomewide Association Study of Severe Covid-19 with Respiratory Failure. N. Engl. J. Med. 2020, 383, 1522–1534. [Google Scholar] [CrossRef]
  11. Kim, Y.C.; Jeong, M.J.; Jeong, B.H. Strong association of regulatory single nucleotide polymorphisms (SNPs) of the IFITM3 gene with influenza H1N1 2009 pandemic virus infection. Cell. Mol. Immunol. 2020, 17, 662–664. [Google Scholar] [CrossRef] [PubMed]
  12. Allen, E.K.; Randolph, A.G.; Bhangale, T.; Dogra, P.; Ohlson, M.; Oshansky, C.M.; Zamora, A.E.; Shannon, J.P.; Finkelstein, D.; Dressen, A.; et al. SNP-mediated disruption of CTCF binding at the IFITM3 promoter is associated with risk of severe influenza in humans. Nat. Med. 2017, 23, 975–983. [Google Scholar] [CrossRef] [PubMed]
  13. Zhang, Y.H.; Zhao, Y.; Li, N.; Peng, Y.C.; Giannoulatou, E.; Jin, R.H.; Yan, H.P.; Wu, H.; Liu, J.H.; Liu, N.; et al. Interferon-induced transmembrane protein-3 genetic variant rs12252-C is associated with severe influenza in Chinese individuals. Nat. Commun. 2013, 4, 1418. [Google Scholar] [CrossRef] [PubMed]
  14. Bailey, C.C.; Zhong, G.; Huang, I.C.; Farzan, M. IFITM-Family Proteins: The Cell’s First Line of Antiviral Defense. Annu. Rev. Virol. 2014, 1, 261–283. [Google Scholar] [CrossRef] [PubMed]
  15. Zani, A.; Yount, J.S. Antiviral Protection by IFITM3 In Vivo. Curr. Clin. Microbiol. Rep. 2018, 5, 229–237. [Google Scholar] [CrossRef]
  16. Smith, S.; Weston, S.; Kellam, P.; Marsh, M. IFITM proteins-cellular inhibitors of viral entry. Curr. Opin. Virol. 2014, 4, 71–77. [Google Scholar] [CrossRef]
  17. Bailey, C.C.; Huang, I.C.; Kam, C.; Farzan, M. Ifitm3 limits the severity of acute influenza in mice. PLoS Pathog. 2012, 8, e1002909. [Google Scholar] [CrossRef]
  18. John, S.P.; Chin, C.R.; Perreira, J.M.; Feeley, E.M.; Aker, A.M.; Savidis, G.; Smith, S.E.; Elia, A.E.; Everitt, A.R.; Vora, M.; et al. The CD225 domain of IFITM3 is required for both IFITM protein association and inhibition of influenza A virus and dengue virus replication. J. Virol. 2013, 87, 7837–7852. [Google Scholar] [CrossRef]
  19. Li, K.; Markosyan, R.M.; Zheng, Y.M.; Golfetto, O.; Bungart, B.; Li, M.; Ding, S.; He, Y.; Liang, C.; Lee, J.C.; et al. IFITM proteins restrict viral membrane hemifusion. PLoS Pathog. 2013, 9, e1003124. [Google Scholar] [CrossRef]
  20. Everitt, A.R.; Clare, S.; Pertel, T.; John, S.P.; Wash, R.S.; Smith, S.E.; Chin, C.R.; Feeley, E.M.; Sims, J.S.; Adams, D.J.; et al. IFITM3 restricts the morbidity and mortality associated with influenza. Nature 2012, 484, 519–523. [Google Scholar] [CrossRef]
  21. Prabhu, S.S.; Chakraborty, T.T.; Kumar, N.; Banerjee, I. Association between IFITM3 rs12252 polymorphism and influenza susceptibility and severity: A meta-analysis. Gene 2018, 674, 70–79. [Google Scholar] [CrossRef] [PubMed]
  22. Makvandi-Nejad, S.; Laurenson-Schafer, H.; Wang, L.; Wellington, D.; Zhao, Y.; Jin, B.; Qin, L.; Kite, K.; Moghadam, H.K.; Song, C.; et al. Lack of Truncated IFITM3 Transcripts in Cells Homozygous for the rs12252-C Variant That is Associated With Severe Influenza Infection. J. Infect. Dis. 2018, 217, 257–262. [Google Scholar] [CrossRef] [PubMed]
  23. Kim, Y.C.; Jeong, B.H. No Correlation of the Disease Severity of Influenza A Virus Infection with the rs12252 Polymorphism of the Interferon-Induced Transmembrane Protein 3 Gene. Intervirology 2017, 60, 69–74. [Google Scholar] [CrossRef] [PubMed]
  24. David, S.; Correia, V.; Antunes, L.; Faria, R.; Ferrao, J.; Faustino, P.; Nunes, B.; Maltez, F.; Lavinha, J.; Rebelo de Andrade, H. Population genetics of IFITM3 in Portugal and Central Africa reveals a potential modifier of influenza severity. Immunogenetics 2018, 70, 169–177. [Google Scholar] [CrossRef] [PubMed]
  25. Hartshorn, K.L. Innate Immunity and Influenza A Virus Pathogenesis: Lessons for COVID-19. Front. Cell. Infect. Microbiol. 2020, 10, 563850. [Google Scholar] [CrossRef]
  26. Abdelrahman, Z.; Li, M.; Wang, X. Comparative Review of SARS-CoV-2, SARS-CoV, MERS-CoV, and Influenza A Respiratory Viruses. Front. Immunol. 2020, 11, 552909. [Google Scholar] [CrossRef]
  27. Perone, G. The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: An analysis of environmental, demographic, and healthcare factors. Sci. Total Environ. 2020, 755, 142523. [Google Scholar] [CrossRef]
  28. Wang, Z.H.T.; Zhu, L.; Sheng, H.; Huang, S.; Hu, J. Active quarantine measures are the primary means to reduce the fatality rate of COVID-19. Bull. World Health Organ. 2020, 1–12. [Google Scholar] [CrossRef]
  29. Cote, M.L. Study designs in genetic epidemiology. Methods Mol. Biol. 2009, 520, 247–257. [Google Scholar] [CrossRef]
  30. Schwartz, S. The fallacy of the ecological fallacy: The potential misuse of a concept and the consequences. Am. J. Public Health 1994, 84, 819–824. [Google Scholar] [CrossRef]
  31. Parsons, P.A.; Bodmer, W.F. The evolution of overdominance: Natural selection and heterozygote advantage. Nature 1961, 190, 7–12. [Google Scholar] [CrossRef] [PubMed]
  32. Pairo-Castineira, E.; Clohisey, S.; Klaric, L.; Bretherick, A.D.; Rawlik, K.; Pasko, D.; Walker, S.; Parkinson, N.; Fourman, M.H.; Russell, C.D.; et al. Genetic mechanisms of critical illness in Covid-19. Nature 2020. In press. [Google Scholar] [CrossRef] [PubMed]
  33. Kim, Y.C.; Jeong, B.H. Ethnic variation in risk genotypes based on single nucleotide polymorphisms (SNPs) of the interferon-inducible transmembrane 3 (IFITM3) gene, a susceptibility factor for pandemic 2009 H1N1 influenza A virus. Immunogenetics 2020, 72, 447–453. [Google Scholar] [CrossRef] [PubMed]
Figure 1. (A) Log-linear regression relationships between the case fatality rate of coronavirus disease-2019 (COVID-19) and minor allele frequency (MAF) of the rs6598045 single nucleotide polymorphism (SNP) of the interferon-induced transmembrane protein 3 (IFITM3) gene. (B) Log-linear regression relationships between the case fatality rate of COVID-19 and MAF of the rs2074192 SNP of the angiotensin I converting enzyme 2 (ACE2) gene. (C) Log-linear regression relationships between the case fatality rate of COVID-19 and MAF of the rs2298659 SNP of the transmembrane protease serine 2 (TMPRSS2) gene. (D) Log-linear regression relationships between the case fatality rate of COVID-19 and MAF of the rs1800795 SNP of the interleukin 6 (IL6) gene. (E) Log-linear regression relationships between the case fatality rate of COVID-19 and MAF of the rs11385942 insertion/deletion polymorphism of the leucine zipper transcription factor-like protein 1 (LZTFL1) gene. (F) Log-linear regression relationships between the case fatality rate of COVID-19 and MAF of the rs657152 SNP of the ABO gene.
Figure 1. (A) Log-linear regression relationships between the case fatality rate of coronavirus disease-2019 (COVID-19) and minor allele frequency (MAF) of the rs6598045 single nucleotide polymorphism (SNP) of the interferon-induced transmembrane protein 3 (IFITM3) gene. (B) Log-linear regression relationships between the case fatality rate of COVID-19 and MAF of the rs2074192 SNP of the angiotensin I converting enzyme 2 (ACE2) gene. (C) Log-linear regression relationships between the case fatality rate of COVID-19 and MAF of the rs2298659 SNP of the transmembrane protease serine 2 (TMPRSS2) gene. (D) Log-linear regression relationships between the case fatality rate of COVID-19 and MAF of the rs1800795 SNP of the interleukin 6 (IL6) gene. (E) Log-linear regression relationships between the case fatality rate of COVID-19 and MAF of the rs11385942 insertion/deletion polymorphism of the leucine zipper transcription factor-like protein 1 (LZTFL1) gene. (F) Log-linear regression relationships between the case fatality rate of COVID-19 and MAF of the rs657152 SNP of the ABO gene.
Genes 12 00042 g001
Table 1. Information on cases and deaths of coronavirus disease 2019 (COVID-19) in several ethnic groups (29 June, 2020).
Table 1. Information on cases and deaths of coronavirus disease 2019 (COVID-19) in several ethnic groups (29 June, 2020).
Subject GroupsMatching Groups in1000 Genome ProjectCaseDeathCase Fatality Rate (%)
African aAfrican (AFR)278,81557852.07
European bEuropean (EUR)2,656,437196,5417.40
American cAmerican (AMR)4,933,972241,9314.90
East Asian dEast Asian (EAS)104,03556205.40
South Asian eSouth Asian (SAS)336,93358131.73
African a: Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Cote d’Ivoire, Democratic Republic of Congo, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, South Africa, South Sudan, Togo, Uganda, United Republic of Tanzania, Zambia and Zimbabwe. European b: Albania, Andorra, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Luxembourg, Malta, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Republic of Moldova, Romania, Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Tajikistan, Turkey, Turkmenistan, Ukraine, United Kingdom of Great Britain, and Northern Ireland, and Uzbekistan. American c: Antigua, Barbuda, Argentina, Bahamas, Barbados, Belize, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, United States of America, Uruguay and Venezuela. East Asian d: China, Japan; South Asian e: Sri Lanka, Bangladesh, and India.
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