Correlation of Angiotensin-converting Enzyme 2 Gene (rs1514283) Polymorphism with the Incidence of COVID-19

Hams Hussain Hashim Al-Fattli1*, Tabarek Salam Abd-Alraoof2

1Faculty of Medicine, University of Al-Qadisiyah, Al Diwaniyah, Qadisiyyah Province, Iraq

2Department of Science, College of Basic Education, Al-Muthanna University, Samawah, Al Muthanna Province, Iraq


It has been a busy year for coronaviruses, with the most recent one causing severe coronavirus illness in 2019 (COVID-19). It is broadly distributed in many human tissues and organs as the potential SARS-CoV-2 receptor angiotensin-converting enzyme 2 (ACE2). ACE2 provides homeostatic modulation of circulation angiotensin II levels by acting as a physiological counterbalance to ACE. They have been linked to COVID-19 disease acquisition, progression, and severity. As a result, we investigated how ACE2 variations and epigenetic variables affect SARS-CoV-2 infection susceptibility and infection outcomes in terms of age, gender, and ethnicity. Debates raged over the etiology of this occurrence. It is important to note that further research is required to demonstrate the efficacy of human recombinant ACE2 and ACE2-derived peptides in fighting SARSCoV-2 variants. Better recognition of a host genetic, as well as the function of the properties of ACE2 variations, would assist in explaining clinical disparities of infection between individuals and contribute to the development of remedies and managing future SARS-CoV-2 epidemics, an essential function for ACE2 in essential hypertension (EH). We wanted to see how ACE2 gene polymorphisms and enzyme activity correlated with COVID-19 incidence in the Iraqi province of Al-Diwaniya. A total of 63 COVID-19 patients and 70 (NT) controls were genotyped using Sequenom Mass-ARRAY RS1000 for ACE2. Participants’ ACE2 rs1514283 SNP was linked to COVID-19.

Key words: ACE2, COVID-19, hypertension, rs1514283 SNP

*Corresponding author: Hams Hussain Hashim Al-Fattli, Faculty of Medicine, University of Al-Qadisiyah, Al Diwaniyah, Qadisiyyah Province, Iraq. Email:

Submitted: 2 March 2022; Accepted: 8 April 2022; Published: 8 June 2022

DOI: 10.47750/jptcp.2022.922

©2022 Al-Fattli HHH and Abd-Alraoof TS
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). License (


COVID-19 is a major health hazard, say, physicians. By August 4, 2021, the disease had killed almost 4 million people. SARS-CoV-2 has 85% DNA of SARS-CoV. COVID-19 is SARS 2 (SARS-CoV-2).1 COVID-19 causes SARS (SARS-CoV-2). SARS-CoV enters a cell via the spike (S) glycoprotein. It unites with the membrane as well as the receptor.2 The S1/S2 interdomain protease site is thought to be exposed by ACE2’s S1 receptor binding protein (RBD) and program death (PD). Proprotein convertase furoate (S1/S2) then cleaves the S protein (TMPRSS2). Somatic S2-fusion peptide injection into the host membrane, to fuse the host membrane, utilizes the S2. The SARS-CoV-2 virus replicates and transcribes RNA.3 A new infection cycle occurs when new viruses grow within the cell. ACE2 is a 120 kDa ZMP1 member and SARS-CoV-2 is a virus receptor. Collecting-like domain and HEXXH + E consensus zinc-binding domain. The zinc-binding domain (PD) consensus sequence is HEXXH + E which may be open or closed. Ligand has a free path away from the active site,4 such as testicles (including the adrenal gland), thyroid, heart, and adipose (including the adrenal gland). Acyl cysteine is found in the brain and spinal cord ADP (ACE2). Also, the body has 554 amino acid waste product ACE2.5

Due to the metalloproteinase 17 enzyme, these ACP2 protein variations may be anti-inflammatory (ADAM17). SARS-CoV-2 binds to sACE2 and inhibits COVID-19. ACE2 has three main roles: hepatic neutral amino acid absorption is aided by ACE2.69 This gene’s variations include protein, transcriptional, and post-transcriptional changes. Scientists are becoming interested in ACE2 single nucleotide polymorphisms (SNPs).1012 Several databases found 298 protein-altering variations in human ACE2 (hACE2).13 COVID-19 symptoms are acute respiratory distress syndrome (ARDS) and death. This mutation may be linked to the differential transmission. For starters, the ACE2 rs1514283 polymorphism must be fully understood as ACE2 mutations1418 that may affect SARS-CoV-2 and COVID-19 infection outcomes. Using the enzyme ACE2 in novel therapies is trendy now. The ACE2 peptidase domain is to blame. S1/S2 and TMPRSS2 break the S protein here.19

This helps in getting the S2-fusion peptide into the host membrane. After assembling the host membrane, SARS-CoV-2 RNA is released for viral replication and translation. A new infection cycle begins with an entry and exit of a new virus.20,21 ACE2 is a 120 kDa ZMP type I and is a SARS-CoV-2 virus.2229 It has zinc-binding and collecting domains. Its zinc-binding domain is HEXXH + E and may be open or closed. Distant from the active site, such as the thyroid, small intestine, kidneys, and fat (including the fat around the waist), it accepts ligands.21,30 The ligand binds to ACE2 and disables it: endocrine and liver (ACE2); the brain and spine (ACE2).11,31 sACE2 is a 554 amino acid digestible byproduct. Anti-inflammatory ACP2 proteins are shed by metalloproteinase 179,21 (ADAM17). sACE2 binds SARS-CoV-2 preventing COVID-19. ACE2 has three main roles: it not only protects the heart but also aids in intestine neutral amino acid absorption and regulate blood pressure and blood glucose level.10,32,33

The ACE2 gene has SNPs, transcriptional changes, and potential protein mutations. SNPs are variations in the DNA sequence.22,23,34,35 The human ACE2 protein has 298 protein-altering variants spread across 256 codons. Pneumonia, ARDS, and death are all COVID-19 symptoms.2226,36 Researchers in this study suspect ACE2 rs1514283 mutations as a reason. The ACE2 rs1514283 polymorphism needs more research. ACE2 polymorphisms with SARS-CoV-2 and COVID-19 infection risk. These remedies are now being studied and COVID-19 rs1514283 is examined in Al-Diwaniya patients.

Material and Methods

Study design

When it came to this study, which involved participants, the investigators employed a prospective observational technique. According to the Ethical Committee of Al-Qadisiya University, which is located in the province of Al-Qadisiya, the procedure in the province of Al-Diwaniya was carried out with the agreement of the Ethical Committee of the Faculty of Medicine at Al-Diwaniya University. The Al-Qadisiya University Faculty of Medicine’s Ethical Committee is located in the province of Al-Qadisiya, and it is comprised of medical students and faculty members. Participants and their families were invited to submit written informed consent before taking part in the study. Several hours were spent deliberating on the goals and objectives of the study before this choice was reached.

Study population

In this research endeavor, participants were placed in a range of contexts. A total of 133 blood samples were taken from patients at the Diwaniyah Educational Hospital (85 males and 48 females), and each sample was examined for a variety of variables. In this study, blood samples were collected from 62 patients (22 men and 40 women) who were being monitored at Al-Qadisiya University’s Faculty of Medicine and provided to 71 healthy persons (45 men and 26 women) who served as a control group for the study. Those who took part in the study ranged in age 32.05 ± 15.9 years and were from various backgrounds. During the steps of the study, each patient underwent routine data collection procedures that included recording their age, weight, BMI, and the polymorphism under investigation, as well as other demographic data.

Living region

This study examined the ACE2 gene polymorphism rs1514283 in all samples, which were dispersed according to dwelling region, to establish if there is a relationship between environmental factors and the frequency of mutations. In order to conduct the demographic research, the study group was classified into two groups based on where they live: in cities and rural areas.

History of respiratory disease

Every sample was checked to see if there were any evidence of respiratory disease in order to further refine the association between the presence of COVID-19 and a single nucleotide mutation in the ACE2 gene. Cases and controls were classified into two subgroups based on whether or not they had a history of previous respiratory disease.

History of hypertension

This study included both hypertensive patients and healthy controls who were categorized as having either positive or negative hypertension for the sake of future data analysis and investigation.

Severity of COVID-19

According to the study’s design, the patients with COVID-19 were separated into two groups depending on the severity of their infection status, which were classified as mild and severe, respectively, based on the infection states of the patients.


The blood samples were obtained from each patient and the healthy controls, and they were collected in sodium citrate tubes for further analysis. The plasma separation from the blood samples is carried out by centrifuging them at 3000 x g for 10 min. It was possible to separate genomic DNA from peripheral lymphocytes using a Macherey–Nagel genomic DNA purification kit purchased from the company (Qiagen, Germany). When it came to determining the ACE2 gene rs1514283 polymorphism, the polymerase chain reaction tetra-primer amplification refractory mutation system (ARMS) technique was applied. Real-time polymerase chain reaction (RT-PCR) was employed to corroborate the results.


As part of this investigation, ARMS-PCR primers were produced for polymorphism rs1514283 in the ACE2 gene, using assistance from the NCBI-SNP database and the ARMS-PCR primers generation tool, both of which may be obtained from the National Center for Biotechnology Information (NCBI). These primers have been given by Scientific Researcher Co. Ltd., Iraq for your convenience and for individual use only (Table 1).

TABLE 1. Specific primers for ACE2 gene rs1514283 polymorphism.

Primer Sequence (5’-3’) Product size
Common reverse primer CTACCTCCAAATGCCAATAC

Statistical analysis

The findings were analyzed by the Statistical Package for Social Sciences (SPSS) program, which was installed on a Windows 11 machine running the SPSS software. The alleles discovered in the wild-type, heterozygote, and mutant groups were compared to one another using a one-way analysis of variance (ANOVA). To determine the relationship between continuous variables and their corresponding discrete variables, the correlation data analysis method was used. It was determined that a p-value greater than 0.05 was statistically significant based on the findings of this inquiry.


Correlation of genotype with the age groups

The study group comprised of different age groups, and there were 18 subgroups based on age (Table 2). The mean age was 32.04 ± 15.9 years (Figure 1). Genotype correlation with the frequency of age did not show a significant relationship.

TABLE 2. Genotype correlation with age, weight, and body mass index.

Variable Mean Std. Deviation
Age 32.05 15.904
Weight 28.74 2.98
BMI 81.16 11.437

FIGURE 1. Correlation of genotype with age distribution.

Correlation of genotype with the sex groups

The collection was according to the distribution of patients and controls in this study (Tables 3 and 4). The study consisted of 62 patients and 71 healthy controls, 85 males and 48 females. The data were analyzed with descriptive statistics to evaluate the correlation between gene polymorphism and sex distribution. There were variable genotypes in each group, and the resulting values indicated no significant correlation between them (Figure 2).

TABLE 3. Distribution of sex according to age in each group.

Sex Statistics Std. Error
Age Female Mean 30.58 2.170
95% Confidence interval for mean Lower bound 26.22  
Upper bound 34.95  
5% Trimmed mean 30.01  
Median 25.00  
Variance 225.993  
Std. Deviation 15.033  
Minimum 10  
Maximum 62  
Range 52  
Interquartile range 25  
Skewness 0.517 0.343
Kurtosis –0.975 0.674
  Male Mean 32.87 1.779
95% Confidence interval for mean Lower bound 29.33  
Upper bound 36.41  
5% Trimmed mean 32.11  
Median 31.00  
Variance 269.138  
Std. Deviation 16.405  
Minimum 10  
Maximum 80  
Range 70  
Interquartile range 28  
Skewness 0.422 0.261
Kurtosis –0.678 0.517

TABLE 4. Genotype and sex groups.

Sex Genotype
C/T CC TT Total
Female Group Case Count 7 13 2 22
% within Group 31.8% 59.1% 9.1% 100.0%
Control Count 5 10 11 26
% within Group 19.2% 38.5% 42.3% 100.0%
Total Count 12 23 13 48
% within Group 25.0% 47.9% 27.1% 100.0%
Male Group Case Count 12 22 6 40
% within Group 30.0% 55.0% 15.0% 100.0%
Control Count 9 24 12 45
% within Group 20.0% 53.3% 26.7% 100.0%
Total Count 21 46 18 85
% within Group 24.7% 54.1% 21.2% 100.0%

FIGURE 2. Distribution of genotype in the sex.

Correlation of genotype with BMI groups

The correlation of BMI with the genotype is given in Table 1 and Figures 3 and 4, and did not show a significant correlation between them.

FIGURE 3. Genotype and BMI in the case group.

FIGURE 4. Genotype and BMI in the control group.

Correlation of genotype with weight groups

The results did not show a significant correlation between the weight of participants and genotype allele mutation (Tables 5 and 6; Figures 5, 6, 7 and 8).

TABLE 5. Correlation of genotype with weight groups.

Genotype Total
Weight 62 0 3 2 5
  63 0 4 1 5
  67 6 8 2 16
  71 0 3 2 5
  73 0 5 1 6
  74 2 3 1 6
  75 1 3 2 6
  77 2 3 1 6
  78 3 6 3 12
  81 2 1 2 5
  85 3 1 1 5
  87 5 4 2 11
  88 1 5 0 6
  89 0 4 2 6
  90 4 0 2 6
  91 2 1 2 5
  95 1 3 2 6
  98 1 7 2 10
  103 0 5 1 6
Total 33 69 31 133

TABLE 6. Significancy of data analysis.

Group Value df Asymptotic significance (2-sided)
Case Pearson chi-square 42.464a 36 0.212
Likelihood ratio 52.664 36 0.036
Number of valid cases 62    
Control Pearson chi-square 39.698b 36 0.309
Likelihood ratio 48.819 36 0.075
Number of valid cases 71c    

a53 cells (93.0%) have an expected count of less than 5. The minimum expected count is 1.17.

b57 cells (100.0%) have an expected count of less than 5. The minimum expected count is 0.26.

c57 cells (100.0%) have an expected count of less than 5. The minimum expected count is 0.39.

FIGURE 5. Frequency of BMI in the genotypes of groups.

FIGURE 6. Correlation of genotype with weight of the case group.

FIGURE 7. Correlation of genotype with weight of the control group.

FIGURE 8. Frequency of weight in the genotype of each group.

Correlation of genotype with case and control groups

Results show a significant correlation between genotype and group of cases. CC alleles of polymorphism rs1514283 in the ACE2 gene were correlated with the incidence of this disease (Table 4; Figure 9).

FIGURE 9. Correlation of genotype with case and control groups.

Correlation of genotype with the region of living

The correlation of genotype was clear in the SNP variation of the ACE2 gene. The correlation shows variation in alleles mutation in the city rather than in the countryside (Table 7; Figure 10).

TABLE 7. Genotype and the region of living.

Group Region Total
City Countryside
Case Genotype C/T Count 11 8 19
% within region 28.2% 34.8% 30.6%
CC Count 23 12 35
% within region 59.0% 52.2% 56.5%
TT Count 5 3 8
% within region 12.8% 13.0% 12.9%
Total Count 39 23 62
% within region 100.0% 100.0% 100.0%
Control Genotype C/T Count 6 8 14
% within region 20.0% 19.5% 19.7%
CC Count 16 18 34
% within region 53.3% 43.9% 47.9%
TT Count 8 15 23
% within region 26.7% 36.6% 32.4%
Total Count 30 41 71
% within region 100.0% 100.0% 100.0%
Total Genotype C/T Count 17 16 33
% within region 24.6% 25.0% 24.8%
CC Count 39 30 69
% within region 56.5% 46.9% 51.9%
TT Count 13 18 31
% within region 18.8% 28.1% 23.3%
Total Count 69 64 133
% within region 100.0% 100.0% 100.0%

FIGURE 10. Genotype and the region of living.

Correlation of genotype with hypertension of participants

A significant correlation is shown between cases of hypertension and genotype alterations. While there is no significance shown regarding the sex (Tables 8 and 9; Figure 11).

TABLE 8. Correlation of genotype with hypertension of participants.

Group Hypertension Total
Negative Positive
Case Genotype C/T Count 4 15 19
% within hypertension 25.0% 32.6% 30.6%
CC Count 9 26 35
% within hypertension 56.3% 56.5% 56.5%
TT Count 3 5 8
% within hypertension 18.8% 10.9% 12.9%
Total Count 16 46 62
% within hypertension 100.0% 100.0% 100.0%
Control Genotype C/T Count 9 5 14
% within hypertension 19.6% 20.0% 19.7%
CC Count 22 12 34
% within hypertension 47.8% 48.0% 47.9%
TT Count 15 8 23
% within hypertension 32.6% 32.0% 32.4%
Total Count 46 25 71
% within hypertension 100.0% 100.0% 100.0%
Total Genotype C/T Count 13 20 33
% within hypertension 21.0% 28.2% 24.8%
CC Count 31 38 69
% within hypertension 50.0% 53.5% 51.9%
TT Count 18 13 31
% within hypertension 29.0% 18.3% 23.3%
Total Count 62 71 133
% within hypertension 100.0% 100.0% 100.0%

TABLE 9. Correlation of genotype with hypertension of male and female groups.

Sex Hypertension Total
Negative Positive
Female Genotype C/T Count 4 8 12
% within hypertension 18.2% 30.8% 25.0%
CC Count 9 14 23
% within hypertension 40.9% 53.8% 47.9%
TT Count 9 4 13
% within hypertension 40.9% 15.4% 27.1%
Total Count 22 26 48
% within hypertension 100.0% 100.0% 100.0%
Male Genotype C/T Count 9 12 21
% within hypertension 22.5% 26.7% 24.7%
CC Count 22 24 46
% within hypertension 55.0% 53.3% 54.1%
TT Count 9 9 18
% within hypertension 22.5% 20.0% 21.2%
Total Count 40 45 85
% within hypertension 100.0% 100.0% 100.0%
Total Genotype C/T Count 13 20 33
% within hypertension 21.0% 28.2% 24.8%
CC Count 31 38 69
% within hypertension 50.0% 53.5% 51.9%
TT Count 18 13 31
% within hypertension 29.0% 18.3% 23.3%
Total Count 62 71 133
% within hypertension 100.0% 100.0% 100.0%

FIGURE 11. Correlation of genotype with hypertension of participants.

Correlation of genotype with history of respiratory disease of participants

A significant correlation between cases of hypertension and genotype alterations is shown in Table 10 and Figure 12.

TABLE 10. Correlation of genotype with history of respiratory disease of participants.

Group History of respiratory disease Total
Absence Presence
Case Genotype C/T Count 4 15 19
% within history of respiratory disease 26.7% 31.9% 30.6%
CC Count 10 25 35
% within history of respiratory disease 66.7% 53.2% 56.5%
TT Count 1 7 8
% within history of respiratory disease 6.7% 14.9% 12.9%
Total Count 15 47 62
% within history of respiratory disease 100.0% 100.0% 100.0%
Control Genotype C/T Count 8 6 14
% within history of respiratory disease 16.0% 28.6% 19.7%
CC Count 25 9 34
% within history of respiratory disease 50.0% 42.9% 47.9%
TT Count 17 6 23
% within history of respiratory disease 34.0% 28.6% 32.4%
Total Count 50 21 71
% within history of respiratory disease 100.0% 100.0% 100.0%
Total Genotype C/T Count 12 21 33
% within history of respiratory disease 18.5% 30.9% 24.8%
CC Count 35 34 69
% within history of respiratory disease 53.8% 50.0% 51.9%
TT Count 18 13 31
% within history of respiratory disease 27.7% 19.1% 23.3%
Total Count 65 68 133
% within history of respiratory disease 100.0% 100.0% 100.0%

FIGURE 12. Correlation of genotype with history of respiratory disease of participants.

Correlation of genotype with COVID-19 severity disease

Results show a significant correlation between the severity of COVID-19 with alteration of alleles in the SNP of ACE2 (Table 11; Figure 13).

TABLE 11. Correlation of genotype with COVID-19 severity disease.

Group COVID-19 Severity
Moderate Severe Total
Case Genotype C/T Count 6 13 19
% within COVID-19 severity 28.6% 31.7% 30.6%
CC Count 11 24 35
% within COVID-19 severity 52.4% 58.5% 56.5%
TT Count 4 4 8
% within COVID-19 severity 19.0% 9.8% 12.9%
Total Count 21 41 62
% within COVID-19 severity 100.0% 100.0% 100.0%

FIGURE 13. Correlation of genotype with COVID-19 severity disease.


This study investigates ACE2 rs1514283 SNP variants and epigenetic factors to improve our understanding of the COVID-19 disease. Specifically, we were interested in learning how the history of respiratory disease, the severity of COVID-19, gender, BMI, weight, and age affects the susceptibility of individuals to SARS-CoV-2 disease and its outcomes in relation to one’s overall susceptibility, as well as how these factors interact with one another. Also, we investigated how they influence the overall outcome of the disease.26 Some of the COVID-19 genetic variations that are currently available in the research investigation and is based on the rs1514283 SNP of the ACE2 gene, as well as those that are in the development stage and are based on other genes, were also covered in detail.25

As a result of the presence of SARS-CoV-2, changes in the ACE2 rs1514283 SNP at the binding interface, among other factors, may have an impact on the virus’s expression and affinity for SARS-CoV-2. To compare allele frequencies and expression patterns between two different populations, the ACE2 rs1514283 SNP was investigated in different populations. According to our findings, some variations in the prevalence of COVID-19 and rates of mortality that have been observed between two populations may be explained by these variants.31

It has resulted in many different publications, including one in Nature, that some variants of the ACE2 variant-S1 proteins have increased affinity, but the findings have not been confirmed by systems biology studies, even though some variants have been reported to increase the affinity in several different publications. Our study includes one immediate follow-up to determine the role played by population-specific rs1514283 SNP of ACE2 and host factor in the susceptibility of humans to SARS-CoV-2 are required. As a result of the publication of this finding, these will be conducted as soon as possible.22,23

Also important is the promotion of the use of multidisciplinary tools and cutting-edge “omics” technologies to define a comprehensive extent of interindividual differences in immune response and subsequent susceptibility affected by genetic polymorphism. Meanwhile, we should conduct a large-scale sequencing project in severely diseased patients in each population, with a particular emphasis on ACE2 rs1514283 SNP sequencing, while we wait for this to happen. The findings of this research should allow them in identifying highly specific sensitive markers for different populations within each population.27

Polymorphic variants in the rs1514283 SNP ACE2 gene, which have been linked to a number of infections such as cardiomyopathy, type 2 diabetes, and hypertension are associated with a poor prognosis in the COVID-19 study population, may be responsible for these diseases, according to the researchers. Overexpression of the ACE2 gene investigated by rs1514283 SNP is particularly noteworthy, according to the findings, because it may explain why older populations, including children, are greatly susceptible to SARS-CoV-2 disease when compared to younger individuals.28

As a result of having lower levels of sACE2 andvitamin D in their blood, previous research has suggested that they are less protected from ARDS. Because it is believed that promoting specific DNA methylation of the ACE2 gene may reduce susceptibility to infection and severity of disease in the elderly, early zinc and vitamin D supplementation, in particular, may be beneficial. Our study investigates the correlation of severity of infection as a marker for analysis.21,37

According to the correlation of history of respiratory disease with the severity of COVID-19, this study discussed the age-related inflammation that has been shown to reduce the expression of ACE2 in type II pneumocytes and infection of damaged alveolar epithelia, both of which are detrimental to lung function; therefore, it has been demonstrated that decreasing age-related inflammation improves lung function.24,38


The finding in this study refers to the accurate correlation between the ACE2 rs1514283 SNP and the incidence of COVID-19 in this population. These ACE2 rs1514283 SNP variants and epigenetic factors help improve our understanding of the COVID-19 disease. In particular, we were interested in findings on how a person’s susceptibility to SARS-CoV-2 infection is influenced by their history of respiratory disease, the severity of COVID-19 infection, their gender, BMI, weight, and age. We concluded that the severity of COVID-19 and the area of residence were related to allele mutations of ACE2 rs1514283 SNP.

Ethical approval

The study protocol was approved by the Iraqi Ethical Committee at the Departments of Microbiology, College of Medicine, University of Al-Qadisiyah, Iraq.

Conflicts of interest

The authors declare no conflicts of interest.


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