Out of 2956 papers searched initially, 21 academic papers were selected for the systematic review. (Fig. 1) 21 papers discussing and analyzing genetic factors related to infection with SARS-CoV-2 were reviewed. 18 of them were published in 2020, and 3 were published in 2021. Out of the 21 papers, 5 papers [6–10] recruited patients from specific regions or hospitals. The other 16 papers used large databases, such as the 1000 Genomes Project, gnomAD, National Center for Biotechnology Information (NCBI), Global Initiative on Sharing Avian Flu Data (GISAID) Illumina, and the World Health Organization (WHO) dashboard. All papers reviewed were assessed with Newcastle Ottawa Scale, and scored 8 out of 8 equivalently. In total, the mean ± standard deviation number of patients per paper was 63496 ± 13889.90; the numbers of patients in the databases mentioned above were not taken into account. The average age of the subjects was 55 years old; however, this value is not accurate because multiple studies did not report age or only recorded the age range and not the average age. The genes investigated in these papers were mainly ACE2 and TMPRSS2. IFITM3, CD147, IFIH1, IL6, LZTFL1, and ACE1 were also mentioned in some papers. (Table 1)
Table 1
Characteristics of the included studies
First author (last name)
|
publication year
|
Country
|
Journal
|
Database name
|
number of subjects
|
mean age (years)
mean ± SD
|
Gene
|
SNP
|
Maiti et al. [25]
|
2020
|
USA
|
Immunogenetics
|
1000 genome project
|
N/A
|
N/A
|
IFIH1
|
N/A
|
Torre-Fuentes et al. [6]
|
2021
|
Spain
|
Journal of Medical Virology
|
MS family cohort
|
120
|
N/A
|
ACE2, TMPRSS2
|
rs61735794, rs61735792, rs75603675, rs41303171, rs35803318
|
Gomez et al. [7]
|
2021
|
Spain
|
Cytokine
|
Hospital Univ. Central Asturias, Spain
|
311
|
65.23 ± 15.16
|
IFITM3
|
rs12252-C
|
Zhang et al. [8]
|
2020
|
China
|
The Journal of Infectious Diseases
|
Patients were recruited from Beijing Youan Hospital, Capital Medical University, Beijing, between January 2020 and February 2020
|
80
|
49.5
|
IFITM3
|
rs12252-C
|
Hussain et al. [11]
|
2020
|
Pakistan
|
Journal of Medical Virology
|
Ensembl, Genome Browser12, gnomAD.
|
N/A
|
N/A
|
ACE2
|
rs73635825 (S19P), rs143936283 (E329G)
|
Gomez et al. [9]
|
2020
|
Spain
|
elsvier
|
Hospital Univ. Central Asturias, Spain
|
740
|
67.44
|
ACE, ACE2
|
rs2285666(rs879922)
|
Wang et al. [12]
|
2020
|
China
|
Journal of General Virology
|
dbSNP, National Genomics Data Center
|
N/A
|
N/A
|
ACE2
|
rs143936283 rs267606406 rs4646116
|
Fujikura et al. [15]
|
2020
|
Japan
|
Journal of clinical pathology
|
1000G, NHLBI, gnomAD, ToMMo, UK10K
|
669
|
N/A
|
ACE2, TMPRSS2
|
N/A
|
Yamamoto et al. [16]
|
2020
|
Japan
|
elsvier
|
high-coverage sequenced data of the phase 3 panel of the international 1000 Genomes Project (1000Genomes) and the Korean Personal Genome Project (KPGP)
|
N/A
|
N/A
|
ACE, ACE2
|
N/A
|
Sienko et al. [17]
|
2020
|
Poland
|
Clinical Interventions in Aging
|
N/A
|
6272
|
N/A
|
ACE2, TMPRSS2, CD147
|
N/A
|
Paniri et al. [18]
|
2021
|
Iran
|
Gene Rep
|
NCBI, UniProtKB, PANTHER
|
52,456
|
N/A
|
ACE2
|
rs149039346, rs147311723, rs714205, rs1514283, rs4646175, rs3746444, rs113808830, rs3751304
|
Nguyen et al. [10]
|
2020
|
Vietnam
|
PLoS One
|
A hospital in Vietnam
|
44
|
15–74
|
hACE2
|
N/A
|
Senapati et al. [19]
|
2020
|
India
|
J Ganet
|
GTEx, Uniprot
|
26
|
60 or more
|
ACE2, TMPRSS2, CD26
|
rs112657409, rs11910678, rs77675406 and rs713400, rs13015258
|
Novelli et al. [20]
|
2020
|
Italy
|
Human Genomics
|
GnomAD
|
131
|
N/A
|
ACE2
|
N/A
|
Vargas-Alarcón et al. [21]
|
2020
|
Mexico
|
ELSEVIER
|
dbSNPs, Ensembl Genome Browser, and 1000 Genome Project
databases
|
N/A
|
N/A
|
ACE2, TMPRSS2, TMPRSS11A, ELANE, CTSL
|
rs12329760
|
Benetti et al. [22]
|
2020
|
Italy
|
European Journal of Human Genetics
|
NIG-db, LOVD, gnomAD,,
|
389
|
N/A
|
ACE2
|
rs775181355, rs762890235
|
Strafella et al. [13]
|
2020
|
Italy
|
MDPI
|
Ensembl, 1000 Genomes, GnomAD
|
268
|
46 ± 15
|
ACE2
|
rs35803318, rs41303171, rs774469453,
rs773676270, rs2285666
|
Shikov et al. [23]
|
2020
|
Russia
|
Front Genet
|
gnomAD
|
58
|
N/A
|
ACE2
|
rs146598386, rs73195521, rs755766792
|
Srivastava et al. [14]
|
2020
|
India
|
Front. Genet
|
1,000 genome project
|
N/A
|
N/A
|
ACE2
|
rs2285666
|
Yang et al. [26]
|
2020
|
Taiwan
|
PNAS
|
GISAID Illumi,na
|
1932
|
N/A
|
N/A
|
N/A
|
Kim et al. [24]
|
2020
|
South Korea
|
MDPI
|
World Health Organization (WHO) COVID-19 dashboard
|
N/A
|
N/A
|
IFITM3, ACE2, TMPRSS2, IL6, LZTFL1
|
rs6598045
|
Quality assessment
All papers had equal quality assessment scores. (Table 2) The papers varied in terms of the representativeness of the cohort. Specifically, the papers that used databases were categorized as “truly representative.” The remaining papers, which were classified as “somewhat representative,” collected genomic data from patients from a single hospital or region. Since the aim of this systematic review was to identify SNPs associated with infection with SARS-CoV-2 and the severity of COVID-19 regardless of other health factors, papers that were relevant to the purpose of the review were mostly assessed as being appropriate.
Table 2
Newcastle-Ottawa Scale to Assess Quality of Studies involved in Systematic Review
|
selection
|
|
Outcome
|
|
|
representativeness of expressed cohort
|
selection of non expressed cohort
|
ascertainment of exposure
|
outcome not present at the start of the study
|
Comparability
|
Assessment of outcomes
|
Length of follow-up
|
Adequacy of follow-up
|
Total score
|
Maiti et al. [25]
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Torre-Fuentes et al. [6]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Gomez et al. [7]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Zhang et al. [8]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Hussain et al. [11]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Gomez et al. [9]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Wang et al. [12]
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Fujikura et al. [15]
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Yamamoto et al. [16]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Sienko et al. [17]
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Paniri et al. [18]
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Nguyen et al. [10]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Senapati et al. [19]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Novelli et al. [20]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Vargas-Alarcón et al. [21]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Benetti et al. [22]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Strafella et al. [13]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Shikov et al. [23]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Srivastava et al. [14]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Yang et al. [26]
|
b
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Kim et al. [24]
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
a
|
8
|
Genes and SNPs related
In the 21 included papers, ACE2 was mentioned most frequently, and TMPRSS2 and IFITM3 were also mentioned in some papers. Overall, there were some SNPs reported in multiple studies as being related to infection with SARS-CoV-2 and the severity of COVID-19.
As the study was performed with limited data sources and the diversity of the study populations varied, it was difficult to identify common SNPs. However, several common SNPs were found in the studies, namely rs12252-C [7, 8], rs143936283 [11, 12], rs2285666 [13, 14], rs41303171 [6, 13], and rs35803318 [6, 13]. (Table 3) Two studies mentioned rs12252-C. These studies investigated IFITM3 (transmembrane protein 3), which is known to be associated with the severity of influenza and other viral infections. Gomez et al. 2021 [7] database was on Spanish population and Zhang et al. 2020 [8] study was based on Chinese population. The rs12252 C variant is known to be highly associated with Chinese population’s influenza infection. However, as it is commonly found as a risk factor in Spanish database study suggests that rs12252 C affects all population’s SARS-CoV-2 infection including European population. The other SNPs that were investigated in multiple studies, namely, rs143936283, rs2285666, rs41303171, and rs35803318 are in ACE2. The papers these SNPs were measured based their study on general databases such as Ensembl, 1000 Genomes, and GnomAD. Therefore, these SNPs can’t be specified or analyzed in affecting a certain ethnicity group. Moreover, when looking at the genes and corresponding related SNPs mentioned, ACE2 and TMPRSS2 are often indicated together. Some studies suggest that ACE2 and TMPRSS2 have synergistic effects together, activating the ACE2 as an entry receptor. (Table 1) In detail, TMPRSS2 cleaves the viral spike glycoprotein (S) and leads to viral activation facilitation. [3] Adding on to the above-mentioned SNPs, rs75603675, rs2285666, rs879922, rs73635825, rs143936283, rs143936283 rs267606406 rs4646116, rs149039346, rs147311723, rs714205, rs1514283, rs4646175, rs3746444, rs113808830, rs3751304, rs112657409, rs11910678, rs77675406, rs713400, rs13015258, rs12329760, rs775181355, rs762890235, rs35803318, rs41303171, rs774469453, rs773676270, rs2285666, rs146598386, rs73195521, rs755766792, rs2285666, and rs6598045, in total 34 SNPs, showed relation with ACE2 gene action. 9 SNPs, rs61735794, rs61735792, rs75603675, rs112657409, rs11910678, rs77675406, rs713400, rs13015258, and rs12329760, were the SNPs all showed to have linkage with TMPRSS2. IFITM3 had 2 associated SNPs mentioned out of the studies reviewed, which were rs12252-C and rs6598045. (Table 4) ACE2 had the greatest number of related SNPs and IFITM3, then TMPRSS2.
Table 3
SNPs mentioned twice or more in the reviewed studies
SNP
|
Gene
|
Mentioned Paper
|
rs12252-C
|
IFITM3
|
Gomez et al. 2021 [7]
|
Zhang et al. 2020 [8]
|
rs143936283
|
ACE2
|
Hussain et al. 2020 [11]
|
Wang et al. 2020 [12]
|
rs2285666
|
ACE2
|
Strafella et al. 2020 [13]
|
Srivastava et al. 2020 [14]
|
rs41303171
|
ACE2
|
Torre-Fuentes et al. 2020 [6]
|
Strafella et al. 2020 [13]
|
rs35803318
|
ACE2
|
Torre-Fuentes et al. 2020 [6]
|
Strafella et al. 2020 [13]
|
Table 4
Genes mentioned twice or more in the reviewed studies and the according related SNPs mentioned
Gene
|
Related SNPs
|
Role of the gene
|
ACE2 (angiotensin I converting enzyme 2)
|
rs75603675, rs2285666, rs879922, rs73635825, rs143936283, rs143936283 rs267606406 rs4646116, rs149039346, rs147311723, rs714205, rs1514283, rs4646175, rs3746444, rs113808830, rs3751304, rs112657409, rs11910678,
rs77675406, rs713400, rs13015258, rs12329760, rs775181355, rs762890235, rs35803318, rs41303171, rs774469453,
rs773676270, rs2285666, rs146598386, rs73195521, rs755766792, rs2285666, rs6598045
|
SARS-CoV-2 spike protein entry receptor [15]
|
IFITM3 (interferon-induced transmembrane protein 3)
|
rs12252-C, rs6598045
|
Gene variants of IFITM3 are related to the infection of influenza and viruses. IFITM3 is significant in taking antiviral actions. It prevents cellular lipid bilayer getting bisected by viruses. [7] Immune effector protein that is significant to restriction of virus is encoded by IFITM3. Also, membrane restriction is done by IFITM3. [8]
|
TMPRSS2 (transmembrane protease, serine 2)
|
rs61735794, rs61735792, rs75603675, rs112657409, rs11910678, rs77675406, rs713400, rs13015258, rs12329760
|
Cleavage of TMPRSS2 activates influenza virus hemagglutinin and the human metapneumovirus F protein [3]
|