Next Article in Journal
Current Management of Acute Severe Ulcerative Colitis: New Insights on the Surgical Approaches
Previous Article in Journal
Self-Reported Sleep Duration Is a Useful Tool to Predict Sarcopenia in Chilean Older Adults: Evidence from the ALEXANDROS Longitudinal Study
Previous Article in Special Issue
Deterioration in Quality of Life among COVID-19 Survivors: Population-Based Cohort Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JPM
Volume 14
Issue 6
10.3390/jpm14060579
jpm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Brief Report

Correlation between Genomic Variants and Worldwide COVID-19 Epidemiology

by
Ana Caroline Alves da Costa
1,
Laura Patrícia Albarello Gellen
1,
Marianne Rodrigues Fernandes
1,2,
Rita de Cássia Calderaro Coelho
1,
Natasha Monte
1,
Francisco Cezar Aquino de Moraes
1,
Maria Clara Leite Calderaro
1,
Lilian Marques de Freitas
1,
Juliana Aires Matos
1,
Thamara Fernanda da Silva Fernandes
1,
Kaio Evandro Cardoso Aguiar
1,
Lui Wallacy Morikawa Souza Vinagre
1,2,
Sidney Emanuel Batista dos Santos
1,3 and
Ney Pereira Carneiro dos Santos
1,*
1
Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil
2
Ophir Loyola Hospital, Pará State Departament of Health, Belém 66063-240, PA, Brazil
3
Laboratory of Human and Medical Genetics, Institute of Biological Science, Federal University of Pará, Belém 66077-830, PA, Brazil
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2024, 14(6), 579; https://doi.org/10.3390/jpm14060579
Submission received: 21 April 2024 / Revised: 16 May 2024 / Accepted: 21 May 2024 / Published: 28 May 2024
(This article belongs to the Special Issue Personalized Medicine for COVID-19)
Browse Figures
Versions Notes

Abstract

:
COVID-19 is a systemic disease caused by the etiologic agent SARS-CoV-2, first reported in Hubei Province in Wuhan, China, in late 2019. The SARS-CoV-2 virus has evolved over time with distinct transmissibility subvariants from ancestral lineages. The clinical manifestations of the disease vary according to their severity and can range from asymptomatic to severe. Due to the rapid evolution to a pandemic, epidemiological studies have become essential to understand and effectively combat COVID-19, as the incidence and mortality of this disease vary between territories and populations. This study correlated epidemiological data on the incidence and mortality of COVID-19 with frequencies of important SNPs in GWAS studies associated with the susceptibility and mortality of this disease in different populations. Our results indicated significant correlations for 11 genetic variants (rs117169628, rs2547438, rs2271616, rs12610495, rs12046291, rs35705950, rs2176724, rs10774671, rs1073165, rs4804803 and rs7528026). Of these 11 variants, 7 (rs12046291, rs117169628, rs1073165, rs2547438, rs2271616, rs12610495 and rs35705950) were positively correlated with the incidence rate, these variants were more frequent in EUR populations, suggesting that this population is more susceptible to COVID-19. The rs2176724 variant was inversely related to incidence rates; therefore, the higher the frequency of the allele is, the lower the incidence rate. This variant was more frequent in the AFR population, which suggests a protective factor against SARS-CoV-2 infection in this population. The variants rs10774671, rs4804803, and rs7528026 showed a significant relationship with mortality rates. SNPs rs10774671 and rs4804803 were inversely related to mortality rates and are more frequently present in the AFR population. The rs7528026 variant, which is more frequent in the AMR population, was positively related to mortality rates. The study has the potential to identify and correlate the genetic profile with epidemiological data, identify populations that are more susceptible to severe forms of COVID-19, and relate them to incidence and mortality.

1. Introduction

COVID-19 is a severe acute respiratory infection with systemic manifestation that was first reported in early December 2019 in Wuhan, Hubei Province, China [1]. SARS-CoV-2 is a virus of the genus Betacoronavirus that has similarities to SARS-CoV in tissue tropism, genome structure, and viral pathogenesis. SARS-CoV-2 is a shrouded, positive-sense single-stranded RNA virus. The genomic sequence of SARS-CoV-2 shares about 80% of the sequence identity with SARS-CoV and about 50% with MERS-CoV. However, despite the similarities with SARS-CoV and MERS-CoV, SARS-CoV-2 has been shown to be more transmissible and the diversity of immune responses of its hosts is still poorly understood [2].
The pandemic caused by SARS-CoV-2 infection has caused severe disruption to health systems and global economies, with significant variations in incidence and mortality rates. Critical COVID-19 develops in less than 10% of individuals infected with SARS-CoV-2; however, studies point to a high genetic risk (greater than 10%) in individuals who may or may not have clinical risk factors [3]. The global public health response to SARS-CoV-2 includes different degrees of interventions, according to the containment policies adopted and socioeconomic conditions [4].
The SARS-CoV-2 virus has evolved over time with subvariants of transmissibility distinct from ancestral lineages, predominant in successive epidemic waves. The clinical manifestations of the disease vary according to their severity and can range from asymptomatic to severe [5,6,7]. At the beginning of the pandemic, advanced age and comorbidities were consolidated as important risk factors [8]. However, in isolation, these factors cannot explain the clinical variability of the disease observed among individuals. Studies in genomics can potentially explain the greater predisposition to severe forms of COVID-19 and thus contribute to the discovery of related pathways and their probable therapeutic use [9].
As of 1 September 2023, a total of 539,873,221 cases of COVID-19 have been confirmed for the populations analyzed in this study: Europe (EUR), Africa (AFR), East Asia (EAS), South Asia (SAS), and the Americas (AMR). Data obtained by the World Health Organization (WHO) indicate that the population with the highest incidence of the disease is EUR (51.10%), followed by the AMR population (37.78%). On the other hand, the population of African origin had the lowest incidence rate (1.76%). Of the total number of cases registered, 6,814,976 individuals died, of which the highest mortality rate was observed in individuals from the American population (47%) and the lowest rate was observed in the AFR population of origin (2.83%) [10].
In this context, epidemiological studies that correlate genetic variants and global epidemiology are extremely important, as they help in the identification of genes and pathways related to mortality and COVID-19 susceptibility, establishing the possible relationship of genetic variants with epidemiological rates. Therefore, our study aims to correlate epidemiological data on COVID-19 incidence and mortality worldwide with the frequency of important variants related to disease susceptibility and mortality.

2. Materials and Methods

2.1. Determination of SNPs

For the SNPs associated with COVID-19 selection that were evaluated in the study, articles with variants associated with susceptibility and COVID-19 mortality were selected. For this, articles in English were selected from the Medline/PubMed databases, according to the keywords COVID-19, SARS-CoV-2, GWAS, risk and susceptibility. Inclusion criteria were based on the selection of studies of genomic variants associated with susceptibility and severity to COVID-19 in the period 2019 to 2023.
A total of 100 articles were found, of which, those that did not contain variants associated with susceptibility or more severe forms of COVID-19 were excluded, leaving only 25 articles (Supplementary Tables). After reading the studies, we selected 96 variants related to susceptibility or severity to COVID-19 that had their frequency described in the 1000 Genomes database (Figure 1).

2.2. Epidemiological and Genetic Data

The incidence and mortality rates of COVID-19 for the population were obtained from the World Health Organization platform for Coronavirus (COVID-19), available on the WHO Coronavirus (COVID-19) Dashboard|WHO Coronavirus (COVID-19) Dashboard with Vaccination Data [10].
In the 1000 Genomes Project, the allele frequencies of genetic variants in the following continental populations were evaluated: Europe (EUR), Africa (AFR), East Asia (EAS), South Asia (SAS) and Americas (AMR).
We compared WHO populations with continental populations available in phase 3 of the 1000 Genomes URL www.1000genomes.org database (accessed on 20 August 2023) [11]. According to the Consortium of the 1000 Genomes Project, populations are grouped by the predominant component of ancestry, so our study correlated the epidemiological and genetic data of the populations of EUR, AFR, EAS, SAS, and AMR. For the statistical analyses, the East and South Asian populations were grouped due to their genetic similarities.

2.3. Statistical Analysis

The incidence and severity of COVID-19, as well as the frequencies of variant alleles, were assessed using Pearson’s correlation test. The data were evaluated with the groups described above (AFR, AMR, EUR, EAS, and SAS), using the “color.test” function of the “stats” package of the R program. After this procedure, r, r2 and p-values were obtained. All graphs were created using the “ggplot2” graphics package. Results lower than p ≤ 0.05 were considered statistically significant.

3. Results

A total of 539,873,221 cases of COVID-19 were confirmed worldwide as of 1 September 2023 for the populations analyzed in this study. Of these, 51.10% of the cases were in EUR, 37.78% in AMR, 11.33% in SAS and EAS and 1.76% in AFR. Of the total cases, 6,814,976 individuals died, 47% in AMR, 36% in EUR, 13% in SAS and EAS, and 2.83% in Africa. Figure 2 shows the incidence and mortality rates from COVID-19 in 2023. The EUR population had the highest incidence rates; however, the AMR population stood out with the highest number of deaths [10].
A total of 96 variants (Supplementary Tables) associated with susceptibility and severity to COVID-19 were selected as described in Figure 1. Among these 96 analyzed variants, 11 (rs117169628, rs2547438, rs2271616, rs12610495, rs12046291, rs35705950, rs2176724, rs10774671, rs1073165, rs4804803 and rs7528026) were significant in Pearson’s correlation analysis and for epidemiological data (Table 1). Of these variants, two (rs1073165 and rs2176724) are in intergenic regions, these regions, despite not being encoded by proteins, regulate the expression of nearby genes (Intergenic Regions (genome.gov/, accessed on 1 November 2023)) [12].
Of these 11, 7 (rs12046291, rs117169628, rs1073165, rs2547438, rs2271616, rs12610495 and rs35705950) were positively correlated with the incidence rate (Figure 3). The estimated data denote that the higher the frequency of the variant allele of these variants, the higher the numbers of new cases estimated in the world’s populations. We observed that all variants had a higher frequency in the population of European origin (EUR) and lower frequency in Africans. This analysis is confirmed by numerical data on global epidemiology (WHO data). According to our analyses, the European population is more susceptible to COVID-19 infection.
In contrast, one variant (rs2176724) was inversely correlated with incidence rates, so the higher the frequency of the variant allele, the lower the incidence rate (Figure 4). The variant was more frequent in the African population (AFR). In the global numerical data, the African population had the lowest incidence rates for COVID-19 when compared to other populations (Figure 2). In view of this, it is possible to suggest the protective factor of these variants against SARS-CoV-2 infection in African ancestry.
Figure 5 shows the two variants (rs10774671 and rs4804803) that showed a statistically significant correlation with the mortality rate. Both were inversely correlated with mortality rates, so that the higher the frequency of the variant allele, the lower the mortality rate. The two variants are more frequent in the African population and may be a protective factor against the disease caused by SARS-CoV-2 infection. This fact is confirmed by the graph in Figure 2, which shows the lowest mortality rates in the African population.
rs7528026 was positively correlated with mortality rates (Figure 6), i.e., the higher the frequency of the variant allele, the higher the number of deaths. The highest mortality rates were in the American population, and this analysis is justified by the world’s epidemiology.

4. Discussion

COVID-19 incidence and mortality rates vary significantly by ethnicity, showing a wide fluctuation in epidemiological rates (WHO data). Our results demonstrated that the most genetically important variants related to the incidence of COVID-19 are positively correlated with populations of European origin, this has been demonstrated in epidemiological data from the World Health Organization. Regarding mortality, global epidemiological data show that Latino populations have higher mortality rates for COVID-19. Three variants (rs10774671, rs4804803, and rs7528026) were important to relate genetic influence to the estimated data for the AMR population, two (rs10774671 and rs4804803) were inversely related to mortality, and one (rs7528026) was positively related.
This study correlated epidemiological data on COVID-19 incidence and mortality with frequencies of important SNPs in GWA studies associated with the susceptibility and mortality of this disease in different populations. Our results indicated significant correlations for 11 genetic variants (rs117169628, rs2547438, rs2271616, rs12610495, rs12046291, rs35705950, rs2176724, rs10774671, rs1073165, rs4804803 and rs7528026), 2 of them in intergenic regions and 9 in 9 genes (C3, SLC6A20, DPP9, MUC5B, OAS1, CD209, JAK1, SLC22A31 and TRIM46) with the incidence and mortality rates of the disease. Our results show that variants related to high incidence are more frequent in the European population and less frequent in the African population.
Of the variants studied, two (rs1073165 and rs2176724) are in intergenic regions. The rs1073165 SNP was associated with the incidence of COVID-19 and the rs2176724 variant was shown to be an important protective factor against infection (incidence), being inversely related to the incidence of the disease, we observed a higher frequency of this variant in the African population. This analysis is confirmed with data from World Epidemiology. In addition, we identified important localized variants in genes responsible for regulating the immune system, such as JAK1, DPP9, MUC5B, CD209, OAS1, and TRIM46. In our results, the JAK1, DPP9, and MUC5B genes had their variants (rs12046291, rs12610495, and rs3570595) correlated with the risk of developing the disease/incidence and the CD209, OAS1, and TRIM46 genes had their variants (rs4804803, rs10774671 and rs7528026) associated with mortality.
The JAK1 gene is a gene that encodes a protein that is inhibited by baricitinib and other inhibitors of JAK—the intracellular signaling kinase [13]. The literature has revealed a significant positive association between gene expression and disease severity [7,9,14,15,16]. In our study, we found a positive correlation in the rs12046291 variant present in this gene with COVID-19 incidence rates, this variant was more frequent in the European population. In addition, the rs12046291 variant had significant associations with critical COVID-19 in GWAs studies [13]. This suggests that inhibiting the signaling of this gene may be an effective therapy for severe COVID-19.
DPP9 belongs to the serine protease family S9B/DPPIV and is responsible for regulating the inflammatory response. A recent study demonstrated that patients with severe stage COVID-19 expressed higher levels of DPP9 compared to healthy control cases [8]. In our analyses, the variant (rs12610495) located in this gene was associated with COVID-19 incidence, this SNP was found more frequently in the EUR population, which reinforces that this population is more susceptible to SARS-CoV-2 infection.
Mucins belong to the first defense against pathogens in the airways and are essential for mucociliary correction; high expression of MUC5B may be related to worse prognosis of SARS-CoV-2 infection [8,14,17]. The rs35705950 SNP present in this gene is described as an important variant associated with hospitalization and idiopathic pulmonary fibrosis [14]. In our results, the rs35705950 variant was more frequently present in the European population and was positively related to COVID-19 incidence rates.
SLC620 is a sodium transporter that can functionally interact with angiotensin-converting enzyme 2, the cell surface receptor of SARS-CoV-2 [18,19]. The rs2271616 found in our study suggests the potential involvement of this variant in the gene SLC6A20’s susceptibility to COVID-19, which is represented by the incidence of this disease. The same observation was made in the rs11719628 variant found in the SLC22A31 gene, this gene is part of the transmembrane transporter family and the variant described in this gene (rs11719628) was associated with severity to COVID-19 in genomic sequencing studies conducted on COVID-19 patients [7,20,21].
In our study, we identified genetic variants associated with mortality and located in genes that are fundamental for the regulation of immune responses, an example is rs10774671 present in the gene Oligoadenilato Sintetase 1 (OAS1). The genetic variants present in this gene have been strongly related to susceptibility to viral infections such as SARS-CoV-2 [20,22,23]. In our study, the mutant variant present in this gene (rs10774671) was inversely associated with COVID-19 mortality, which suggests that this variant is a protective factor against COVID-19, this SNP was more frequent in the AFR population.
CD209 is described as an important encoder of C-type lectin, which is also related to the recognition of several viral pathogens including the SARS virus [9,24,25]. In our findings, rs4804803 demonstrated an inverse relationship with mortality rates from the disease, which is mainly present in the African population, suggesting a protective factor against SARS-CoV-2 infection in this population. This fact reinforces what we observed in the epidemiological data of the WHO (Figure 2), where it is possible to observe that the AFR population had lower mortality rates from the disease.
The rs7528026 located in the TRIM46 gene was positively associated with mortality, this variant was more frequent in the AMR population. This gene is an important signaler of interferon gamma pathways and cytokine signaling in the immune system, and is associated with inflammatory bowel disease, lung cancer chemoresistance, and susceptibility to severe influenza virus infections [26,27,28]. In addition, the rs7528026 variant was significantly associated with critical forms of COVID-19 in GWAs studies [13].
COVID-19 incidence and mortality rates vary significantly by ethnicity, showing a wide fluctuation in epidemiological rates (WHO data). This study had limitations in relation to information on certain ethnic groups, such as Africans, due to the lack of homogeneity in socioeconomic levels in the populations analyzed. Despite these limitations, this study contributes important findings to the literature and may help to choose genetic markers that are more globally homogeneous in relation to COVID-19 incidence and prognosis.
Our results demonstrated that the most important variants directly correlated with the incidence/susceptibility of COVID-19 are more frequently present in populations of European origin, which may suggest that this population is potentially more susceptible to infection by the SARS-COV-2 virus. Epidemiological data have shown that the highest mortality rates worldwide were found in populations of Latino origin, our results found three important variants related to the immune response that may contribute in part to the increase in case fatality rates in these populations.
The present study may contribute to a homogenization of data from studies with GWAs around the world, since we often find different results between these variants and their correlation with epidemiological data regarding susceptibility/incidence and prognosis/mortality of COVID-19.

5. Conclusions

Our study demonstrated that 11 SNPs (rs117169628, rs2547438, rs2271616, rs12610495, rs12046291, rs35705950, rs2176724, rs10774671, rs1073165, rs4804803 and rs7528026) were correlated with epidemiological data in different ethnic groups. Seven SNPs (rs12046291, rs117169628, rs1073165, rs2547438, rs2271616, rs12610495 and rs35705950) were positively correlated with the incidence rate. One variant was inversely associated with incidence rates (rs2176724). Two variants (rs10774671 and rs4804803) were inversely correlated with mortality rates. In contrast, one variant (rs7528026) was positively related to mortality rates.
Our study has the potential to identify and correlate the genetic profile with epidemiological data, to identify populations that are more susceptible to severe forms of COVID-19 and relate them to incidence and mortality. This study shows promise for the implementation of personalized strategies for molecular screening of COVID-19 in diverse populations around the world, a fact that can contribute to the identification of susceptible individuals and more effectively manage the evolution of the disease.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jpm14060579/s1: Table S1: SNPs correlated with the incidence of COVID-19 in different populations. Table S2: SNPs that correlated with COVID-19 mortality in different populations. Table S3: 25 studies included in the review [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47].

Author Contributions

Conceptualization, A.C.A.d.C., L.P.A.G. and N.P.C.d.S.; methodology, A.C.A.d.C. and L.P.A.G.; formal analysis, N.M.; investigation, A.C.A.d.C. and R.d.C.C.C.; Validation, M.R.F.; resources, M.R.F. and S.E.B.d.S.; writing—original draft preparation, A.C.A.d.C., L.P.A.G., L.W.M.S.V. and K.E.C.A.; writing—review and editing, A.C.A.d.C., L.P.A.G., F.C.A.d.M., M.C.L.C., J.A.M., T.F.d.S.F., L.W.M.S.V. and L.M.d.F.; supervision, N.P.C.d.S. and M.R.F.; project administration, S.E.B.d.S. and N.P.C.d.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by CNPq (National Council for Scientific and Technological Development), FAPESPA (Amazonian Foundation for the Support of Studies and Research), CAPES (Coordination for the Improvement of Higher Education Personnel) and UFPA (Federal University of Pará). These funding agencies played no role in the study design, data collection and analysis, or the decision to publish, or the preparation of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable for this study. All data used were obtained from secondary sources available on the 1000 Genomes Project platform/database and GBD Compare website.

Data Availability Statement

All relevant data will be shared as Supplementary Materials if the manuscript is accepted for publication.

Acknowledgments

We acknowledge funding from CNPq (National Council for Scientific and Technological Development), CAPES (Coordination for the Improvement of Higher Education Personnel), FAPESPA (Amazon Foundation for the Support of Studies and Research), UFPA (Federal University of Pará) and PROPESP (Dean of Research and Graduate Studies) from UFPA.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Hu, J.; Li, C.; Wang, S.; Li, T.; Zhang, H. Genetic variants are identified to increase risk of COVID-19 related mortality from UK Biobank data. Hum. Genom. 2021, 15, 10. [Google Scholar] [CrossRef] [PubMed]
  2. Harrison, A.G.; Lin, T.; Wang, P. Mechanisms of SARS-CoV-2 Transmission and Pathogenesis. Immunol. Tendencies 2020, 41, 1100–1115. [Google Scholar] [CrossRef] [PubMed]
  3. Karlsen, T.H. Understanding COVID-19 through genome-wide association studies. Nat. Genet. 2022, 54, 368–369. [Google Scholar] [CrossRef] [PubMed]
  4. Vetter, P.; Vu, D.L.; L’Huillier, A.G.; Schibler, M.; Kaiser, L.; Jacquerioz, F. Clinical features of COVID-19. BMJ 2020, 369, m1470. [Google Scholar] [CrossRef] [PubMed]
  5. Zhou, F.; Yu, T.; Du, R.; Fan, G.; Liu, Y.; Liu, Z.; Xiang, J.; Wang, Y.; Song, B.; Gu, X.; et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020, 395, 1054–1062. [Google Scholar] [CrossRef] [PubMed]
  6. Leung, K.; Lau, E.H.Y.; Wong, C.K.H.; Leung, G.M.; Wu, J.T. Estimating the transmission dynamics of SARS-CoV-2 Omicron BF.7 in Beijing after adjustment of the zero-COVID policy in November–December 2022. Nat. Med. 2023, 29, 579–582. [Google Scholar] [CrossRef] [PubMed]
  7. Kousathanas, A.; Pairo-Castineira, E.; Rawlik, K.; Stuckey, A.; Odhams, C.A.; Walker, S.; Russell, C.D.; Malinauskas, T.; Wu, Y.; Millar, J.; et al. Whole-genome sequencing reveals host factors underlying critical COVID-19. Nature 2022, 607, 97–103. [Google Scholar] [PubMed]
  8. Sharif-Zak, M.; Abbasi-Jorjandi, M.; Asadikaram, G.; Ghoreshi, Z.-A.-S.; Rezazadeh-Jabalbarzi, M.; Afsharipur, A.; Rashidinejad, H.; Khajepour, F.; Jafarzadeh, A.; Arefinia, N.; et al. CCR2 and DPP9 expression in the peripheral blood of COVID-19 patients: Influences of the disease severity and gender. Immunobiology 2022, 227, 152184. [Google Scholar] [CrossRef] [PubMed]
  9. Rahimi, A.; Mirzazadeh, A.; Tavakolpour, S. Genetics and genomics of SARS-CoV-2: A review of the literature with the special focus on genetic diversity and SARS-CoV-2 genome detection. Genomics 2021, 113, 1221–1232. [Google Scholar] [CrossRef] [PubMed]
  10. WHO Coronavirus (COVID-19) Dashboard. Available online: https://covid19.who.int (accessed on 25 September 2023).
  11. 1000 Genomes|A Deep Catalog of Human Genetic Variation. Available online: https://www.internationalgenome.org/ (accessed on 20 August 2023).
  12. Intergenic Regions Genome.gov. Available online: https://www.genome.gov/genetics-glossary/Intergenic-Regions (accessed on 1 November 2023).
  13. Pairo-Castineira, E.; Rawlik, K.; Bretherick, A.D.; Qi, T.; Wu, Y.; Nassiri, I.; McConkey, G.A.; Zechner, M.; Klaric, L.; Griffiths, F.; et al. GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19. Nature 2023, 617, 764–768. [Google Scholar] [CrossRef] [PubMed]
  14. COVID-19 Host Genetics Initiative. A second update on mapping the human genetic architecture of COVID-19. Nature 2023, 621, E7–E26. [Google Scholar] [CrossRef] [PubMed]
  15. Li, G.; Hilgenfeld, R.; Whitley, R.; De Clercq, E. Therapeutic strategies for COVID-19: Progress and lessons learned. Nat. Rev. Drug Discov. 2023, 22, 449–475. [Google Scholar] [CrossRef] [PubMed]
  16. Ferreira, L.C.; Gomes, C.E.M.; Rodrigues-Neto, J.F.; Jeronimo, S.M.B. Genome-wide association studies of COVID-19: Connecting the dots. Infection. Genet. Evol. 2022, 106, 105379. [Google Scholar] [CrossRef] [PubMed]
  17. Qin, S.; Yao, X.; Li, W.; Wang, C.; Xu, W.; Gan, Z.; Yang, Y.; Zhong, A.; Wang, B.; He, Z.; et al. Novel insight into the underlying dysregulation mechanisms of immune cell-to-cell communication by analyzing multitissue single-cell atlas of two COVID-19 patients. Cell Death Dis. 2023, 14, 286. [Google Scholar] [CrossRef] [PubMed]
  18. Yao, Y.; Ye, F.; Li, K.; Xu, P.; Tan, W.; Feng, Q.; Rao, S. Genome and epigenome editing identify CCR9 and SLC6A20 as target genes at the 3p21.31 locus associated with severe COVID-19. Signal Transduct. Target. Ther. 2021, 6, 85. [Google Scholar] [CrossRef] [PubMed]
  19. Acar, A. Pan-Cancer Analysis of the COVID-19 Causal Gene SLC6A20. ACS Omega 2023, 8, 13153–13161. [Google Scholar] [CrossRef] [PubMed]
  20. 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 2021, 591, 92–98. [Google Scholar] [CrossRef]
  21. SLC22A31 Solute Carrier Family 22 Member 31 [Homo Sapiens (Human)]—Gene—NCBI. Available online: https://www.ncbi.nlm.nih.gov/gene?Db=gene&Cmd=DetailsSearch&Term=146429 (accessed on 2 December 2023).
  22. Luo, S.; Liang, Y.; Wong, T.H.T.; Schooling, C.M.; Au Yeung, S.L. Identifying factors contributing to increased susceptibility to COVID-19 risk: A systematic review of Mendelian randomization studies. Int. J. Epidemiol. 2022, 51, 1088–1105. [Google Scholar] [CrossRef] [PubMed]
  23. Parikh, V.N.; Ioannidis, A.G.; Jimenez-Morales, D.; Gorzynski, J.E.; De Jong, H.N.; Liu, X.; Roque, J.; Cepeda-Espinoza, V.P.; Osoegawa, K.; Hughes, C.; et al. Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy. Nat. Commun. 2022, 13, 5107. [Google Scholar] [CrossRef]
  24. Barturen, G.; Carnero-Montoro, E.; Martínez-Bueno, M.; Rojo-Rello, S.; Sobrino, B.; Porras-Perales, Ó.; Alcántara-Domínguez, C.; Bernardo, D.; Alarcón-Riquelme, M.E. Whole blood DNA methylation analysis reveals respiratory environmental traits involved in COVID-19 severity following SARS-CoV-2 infection. Nat. Commun. 2022, 13, 4597. [Google Scholar] [CrossRef]
  25. Amraei, R.; Yin, W.; Napoleon, M.A.; Suder, E.L.; Berrigan, J.; Zhao, Q.; Olejnik, J.; Chandler, K.B.; Xia, C.; Feldman, J.; et al. CD209L/L-SIGN and CD209/DC-SIGN act as receptors for SARS-CoV-2. bioRxiv 2021. 2020.06.22.165803. [Google Scholar] [CrossRef] [PubMed]
  26. Su, W.; Lin, X.-T.; Zhao, S.; Zheng, X.-Q.; Zhou, Y.-Q.; Xiao, L.-L.; Chen, H.; Zhang, Z.-Y.; Zhang, L.-J.; Wu, X.-X. Tripartite motif-containing protein 46 accelerates influenza A H7N9 virus infection by promoting K48-linked ubiquitination of TBK1. Virol. J. 2022, 19, 176. [Google Scholar] [CrossRef] [PubMed]
  27. Tantai, J.; Pan, X.; Chen, Y.; Shen, Y.; Ji, C. TRIM46 activates AKT/HK2 signaling by modifying PHLPP2 ubiquitylation to promote glycolysis and chemoresistance of lung cancer cells. Cell Death Dis. 2022, 13, 285. [Google Scholar] [CrossRef]
  28. Anisul, M.; Shilts, J.; Schwartzentruber, J.; Hayhurst, J.; Buniello, A.; Mohammed, E.S.E.; Zheng, J.; Holmes, M.; Ochoa, D.; Carmona, M.; et al. A proteome-wide genetic investigation identifies several SARS-CoV-2-exploited host targets of clinical relevance. eLife 2021, 10, e69719. [Google Scholar] [CrossRef] [PubMed]
  29. Horowitz, J.E.; Kosmicki, J.A.; Damask, A.; Sharma, D.; Roberts, G.H.L.; Justice, A.E.; Banerjee, N.; Coignet, M.; Yadav, A.; Leader, J.B.; et al. Genome-wide analysis provides genetic evidence that ACE2 influences COVID-19 risk and yields risk scores associated with severe disease. Nat. Genet. 2022, 54, 382–392. [Google Scholar] [CrossRef] [PubMed]
  30. Li, Y.; Ke, Y.; Xia, X.; Wang, Y.; Cheng, F.; Liu, X.; Jin, X.; Li, B.; Xie, C.; Liu, S.; et al. Genome-wide association study of COVID-19 severity among the Chinese population. Cell Discov. 2021, 7, 1–16. [Google Scholar] [CrossRef]
  31. Smatti, M.K.; Al-Sarraj, Y.A.; Albagha, O.; Yassine, H.M. Host Genetic Variants Potentially Associated With SARS-CoV-2: A Multi-Population Analysis. Front. Genet. 2020, 11, 578523. [Google Scholar] [CrossRef] [PubMed]
  32. Cruz, R.; Almeida, S.D.-D.; de Heredia, M.L.; Quintela, I.; Ceballos, F.C.; Pita, G.; Lorenzo-Salazar, J.M.; González-Montelongo, R.; Gago-Domínguez, M.; Porras, M.S.; et al. Novel genes and sex differences in COVID-19 severity. Hum. Mol. Genet. 2022, 31, 3789–3806. [Google Scholar] [CrossRef] [PubMed]
  33. Eshetie, S.; Jullian, P.; Benyamin, B.; Lee, S.H. Host genetic determinants of COVID-19 susceptibility and severity: A systematic review and meta-analysis. Rev. Med Virol. 2023, 33, e2466. [Google Scholar] [CrossRef] [PubMed]
  34. Chamnanphon, M.; Pongpanich, M.; Suttichet, T.B.; Jantarabenjakul, W.; Torvorapanit, P.; Putcharoen, O.; Sodsai, P.; Phokaew, C.; Hirankarn, N.; Chariyavilaskul, P.; et al. Host genetic factors of COVID-19 susceptibility and disease severity in a Thai population. J. Hum. Genet. 2022, 67, 295–301. [Google Scholar] [CrossRef] [PubMed]
  35. Wang, S.; Peng, H.; Chen, F.; Liu, C.; Zheng, Q.; Wang, M.; Wang, J.; Yu, H.; Xue, E.; Chen, X.; et al. Identification of genetic loci jointly influencing COVID-19 and coronary heart diseases. Hum. Genom. 2023, 17, 1–11. [Google Scholar] [CrossRef] [PubMed]
  36. Asteris, P.G.; Gavriilaki, E.; Touloumenidou, T.; Koravou, E.; Koutra, M.; Papayanni, P.G.; Pouleres, A.; Karali, V.; Lemonis, M.E.; Mamou, A.; et al. Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks. J. Cell. Mol. Med. 2022, 26, 1445–1455. [Google Scholar] [CrossRef] [PubMed]
  37. Degenhardt, F.; Ellinghaus, D.; Juzenas, S.; Lerga-Jaso, J.; Wendorff, M.; Maya-Miles, D.; Uellendahl-Werth, F.; ElAbd, H.; Ruehlemann, M.C.; Arora, J.; et al. Detailed stratified GWAS analysis for severe COVID-19 in four European populations. Hum. Mol. Genet. 2022, 31, 3945–3966. [Google Scholar] [CrossRef] [PubMed]
  38. Li, P.; Ke, Y.; Shen, W.; Shi, S.; Wang, Y.; Lin, K.; Guo, X.; Wang, C.; Zhang, Y.; Zhao, Z. Targeted screening of genetic associations with COVID-19 susceptibility and severity. Front. Genet. 2022, 13, 1073880. [Google Scholar] [CrossRef] [PubMed]
  39. Fadista, J.; Kraven, L.M.; Karjalainen, J.; Andrews, S.J.; Geller, F.; Baillie, J.K.; Wain, L.; Jenkins, R.G.; Feenstra, B. Shared genetic etiology between idiopathic pulmonary fibrosis and COVID-19 severity. EBioMedicine 2021, 65, 103277. [Google Scholar] [CrossRef] [PubMed]
  40. Du, M.; Garcia, J.G.N.; Christie, J.D.; Xin, J.; Cai, G.; Meyer, N.J.; Zhu, Z.; Yuan, Q.; Zhang, Z.; Su, L.; et al. Integrative omics provide biological and clinical insights into acute respiratory distress syndrome. Intensiv. Care Med. 2021, 47, 761–771. [Google Scholar] [CrossRef] [PubMed]
  41. Gavriilaki, E.; Asteris, P.G.; Touloumenidou, T.; Koravou, E.-E.; Koutra, M.; Papayanni, P.G.; Karali, V.; Papalexandri, A.; Varelas, C.; Chatzopoulou, F.; et al. Genetic justification of severe COVID-19 using a rigorous algorithm. Clin. Immunol. 2021, 226, 108726. [Google Scholar] [CrossRef] [PubMed]
  42. di Flora, D.C.; Dionizio, A.; Pereira, H.A.B.S.; Garbieri, T.F.; Grizzo, L.T.; Dionisio, T.J.; Leite, A.d.L.; Silva-Costa, L.C.; Buzalaf, N.R.; Reis, F.N.; et al. Analysis of Plasma Proteins Involved in Inflammation, Immune Response/Complement System, and Blood Coagulation upon Admission of COVID-19 Patients to Hospital May Help to Predict the Prognosis of the Disease. Cells 2023, 12, 1601. [Google Scholar] [CrossRef] [PubMed]
  43. Udomsinprasert, W.; Nontawong, N.; Saengsiwaritt, W.; Panthan, B.; Jiaranai, P.; Thongchompoo, N.; Santon, S.; Runcharoen, C.; Sensorn, I.; Jittikoon, J.; et al. Host genetic polymorphisms involved in long-term symptoms of COVID-19. Emerg. Microbes Infect. 2023, 12, 2239952. [Google Scholar] [CrossRef] [PubMed]
  44. Verma, A.; Minnier, J.; Wan, E.S.; Huffman, J.E.; Gao, L.; Joseph, J.; Ho, Y.-L.; Wu, W.-C.; Cho, K.; Gorman, B.R.; et al. A MUC5B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality. Am. J. Respir. Crit. Care Med. 2022, 206, 1220–1229. [Google Scholar] [CrossRef] [PubMed]
  45. Velavan, T.P.; Pallerla, S.R.; Rüter, J.; Augustin, Y.; Kremsner, P.G.; Krishna, S.; Meyer, C.G. Host genetic factors determining COVID-19 susceptibility and severity. EBioMedicine 2021, 72, 103629. [Google Scholar] [CrossRef] [PubMed]
  46. Pathak, G.A.; Polimanti, R.; Karjalainen, J.; Daly, M.; Ganna, A.; Daly, M.J.; Stevens, C.; Kanai, M.; Liao, R.G.; Trankiem, A.; et al. A first update on mapping the human genetic architecture of COVID-19. Nature 2022, 608, E1–E10. [Google Scholar] [CrossRef] [PubMed]
  47. Huffman, J.E.; Butler-Laporte, G.; Khan, A.; Pairo-Castineira, E.; Drivas, T.G.; Peloso, G.M.; Nakanishi, T.; Ganna, A.; Verma, A.; Baillie, J.K.; et al. Multi-ancestry fine mapping implicates OAS1 splicing in risk of severe COVID-19. Nat. Genet. 2022, 54, 125–127. [Google Scholar] [CrossRef] [PubMed]
Jpm 14 00579 g001
Figure 1. SNPs selected flowchart.
Figure 1. SNPs selected flowchart.
Jpm 14 00579 g001
Jpm 14 00579 g002
Figure 2. COVID-19 incidence and mortality in 2023 from World Health Organization registries, in continental populations EUR, AFR, EAS, SAS, and AMR. AFR: African population; AMR: American population; EUR: European population; SAS: South Asian population; EAS: East Asian population.
Figure 2. COVID-19 incidence and mortality in 2023 from World Health Organization registries, in continental populations EUR, AFR, EAS, SAS, and AMR. AFR: African population; AMR: American population; EUR: European population; SAS: South Asian population; EAS: East Asian population.
Jpm 14 00579 g002
Jpm 14 00579 g003aJpm 14 00579 g003b
Figure 3. SNPs positively associated with COVID-19 incidence in different populations. (a) SNP rs12046291. (b) SNP rs117169628. (c) SNP rs1073165. (d) SNP rs2547438. (e) SNP rs2271616. (f) SNP rs12610495. (g) SNP rs35705950. AFR: African population; AMR: American population; EUR: European population; SAS: South Asian population; EAS: East Asian population.
Figure 3. SNPs positively associated with COVID-19 incidence in different populations. (a) SNP rs12046291. (b) SNP rs117169628. (c) SNP rs1073165. (d) SNP rs2547438. (e) SNP rs2271616. (f) SNP rs12610495. (g) SNP rs35705950. AFR: African population; AMR: American population; EUR: European population; SAS: South Asian population; EAS: East Asian population.
Jpm 14 00579 g003aJpm 14 00579 g003b
Jpm 14 00579 g004
Figure 4. SNP inversely associated with COVID-19 incidence in different populations. AFR: African population; AMR: American population; EUR: European population; SAS: South Asian population; EAS: East Asian population.
Figure 4. SNP inversely associated with COVID-19 incidence in different populations. AFR: African population; AMR: American population; EUR: European population; SAS: South Asian population; EAS: East Asian population.
Jpm 14 00579 g004
Jpm 14 00579 g005
Figure 5. SNPs inversely associated with COVID-19 mortality rates in different populations. (a) SNP rs4804803. (b) SNP rs10774671. AFR: African population; AMR: American population; EUR: European population; SAS: South Asian population; EAS: East Asian population.
Figure 5. SNPs inversely associated with COVID-19 mortality rates in different populations. (a) SNP rs4804803. (b) SNP rs10774671. AFR: African population; AMR: American population; EUR: European population; SAS: South Asian population; EAS: East Asian population.
Jpm 14 00579 g005
Jpm 14 00579 g006
Figure 6. SNPs positively associated with COVID-19 mortality in different populations. AFR: African population; AMR: American population; EUR: European population; SAS: South Asian population; EAS: East Asian population.
Figure 6. SNPs positively associated with COVID-19 mortality in different populations. AFR: African population; AMR: American population; EUR: European population; SAS: South Asian population; EAS: East Asian population.
Jpm 14 00579 g006
Table 1. Description of variants that have been significantly correlated with COVID-19 incidence and/or mortality in continental (African (AFR), American (AMR), European (EUR), and East Asian (EAS) + South Asian (SAS)) populations described in the 1000 Genomes database and p-values.
Table 1. Description of variants that have been significantly correlated with COVID-19 incidence and/or mortality in continental (African (AFR), American (AMR), European (EUR), and East Asian (EAS) + South Asian (SAS)) populations described in the 1000 Genomes database and p-values.
GeneSNP ID/AssociationMost Severe ConsequenceAllelesAncestralClinical Impactp ValueFrequences
AFRAMREURSAS + EAS
C3rs2547438/
Incidence		Intron VariantT/C/GGBenign0.04360.0420.2050.2570.166
SLC6A20rs2271616/
Incidence		5′ UTR VariantG/TGBenign0.04970.0110.1080.1350.09
DPP9rs12610495/
Incidence		Intron VariantA/G/TANot Reported in ClinVar0.04980.1280.2030.2940.157
MUC5B rs35705950/
Incidence		NoneG/A/TGBenign; risk factor0.02210.0030.0560.1070.0425
- *rs2176724/
Incidence		Intergenic VariantG/AGNot Reported in ClinVar0.04940.4110.1020.110.046
OAS1rs10774671/
Mortality		Splice Acceptor VariantG/A/CGBenign0.01050.640.2740.3520.272
CD209rs4804803/
Mortality		2 KB Upstream
Variant		A/GGprotective; risk factor0.0460.4450.1570.2160.129
JAK1rs12046291/
Incidence		Intron VariantA/GANot Reported in ClinVar0.0210.1260.4510.7100.379
SLC22A31rs117169628/
Incidence		Missense VariantG/AGBenign0.0420.0060.1150.1610.057
- *rs1073165/
Incidence		Intergenic VariantA/GANot Reported in ClinVar0.0190.2030.3760.4030.2165
TRIM46rs7528026/
Mortality		Intron VariantG/AGNot Reported in ClinVar0.0490.0030.0300.0200.009
- *: No annotation.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Costa, A.C.A.d.; Albarello Gellen, L.P.; Fernandes, M.R.; Coelho, R.d.C.C.; Monte, N.; Moraes, F.C.A.d.; Calderaro, M.C.L.; Freitas, L.M.d.; Matos, J.A.; Fernandes, T.F.d.S.; et al. Correlation between Genomic Variants and Worldwide COVID-19 Epidemiology. J. Pers. Med. 2024, 14, 579. https://doi.org/10.3390/jpm14060579

AMA Style

Costa ACAd, Albarello Gellen LP, Fernandes MR, Coelho RdCC, Monte N, Moraes FCAd, Calderaro MCL, Freitas LMd, Matos JA, Fernandes TFdS, et al. Correlation between Genomic Variants and Worldwide COVID-19 Epidemiology. Journal of Personalized Medicine. 2024; 14(6):579. https://doi.org/10.3390/jpm14060579

Chicago/Turabian Style

Costa, Ana Caroline Alves da, Laura Patrícia Albarello Gellen, Marianne Rodrigues Fernandes, Rita de Cássia Calderaro Coelho, Natasha Monte, Francisco Cezar Aquino de Moraes, Maria Clara Leite Calderaro, Lilian Marques de Freitas, Juliana Aires Matos, Thamara Fernanda da Silva Fernandes, and et al. 2024. "Correlation between Genomic Variants and Worldwide COVID-19 Epidemiology" Journal of Personalized Medicine 14, no. 6: 579. https://doi.org/10.3390/jpm14060579

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop