Introduction
The coronavirus disease-2019 (COVID-19) pandemic,
caused by severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2), was first detected in Wuhan, China, in 2019, followed
by a widespread trajectory throughout the world (1), making COVID-19 one of the 21st-century
pandemics owing to its high morbidity and mortality rates worldwide
(2). Historically, several viruses
from the same coronavirus family have emerged, including the famous
SARS-CoV and MERS-CoV. However, the characteristics of the strain
that caused the COVID-19 pandemic made the resulting infection more
severe and the spread of the disease faster (1). SARS-CoV-2 has affected over 759
million individuals worldwide and caused over 6.8 million
mortalities.
The severity and clinical outcomes of COVID-19 vary
markedly among individuals, with some remaining asymptomatic,
whereas others develop severe respiratory illnesses, organ damage,
or even death. Identifying the factors that predispose individuals
to COVID-19 susceptibility and poor outcomes has been the subject
of intense research. One such area of investigation is the role of
genetics in COVID-19 susceptibility and its exacerbation. Genetic
factors have been proposed to contribute to the susceptibility to
and severity of COVID-19.
Genome-wide association studies (GWAS) have been
conducted to investigate the genetic factors that may contribute to
the susceptibility and exacerbation of COVID-19 (3,4). These
studies identified genetic variations associated with an increased
risk of developing COVID-19 and severe clinical outcomes. These
genetic variations affect various aspects of the immune response,
including cytokine regulation, immune cell responses, and
susceptibility to viral infections (4). One GWAS investigated different genetic
mechanisms associated with COVID-19 in a large cohort of
individuals. The study highlighted several single-nucleotide
polymorphisms (SNPs) in several genes, such as interleukin-6 (IL6),
tumor necrosis factor (TNF) and genes involved in
angiotensin-converting enzyme 2 (ACE2) regulation, which have been
proven to be involved in the immune response. Other significant
SNPs were identified in the NOTCH4 (rs3131294) and
TYK2 (rs74956615) genes, which have been proven to be
associated with critical illness in COVID-19 patients and an
exacerbating effect in these patients (3).
TYK2 encodes a tyrosine kinase involved in
cytokine signaling pathways, including those of type I and III
interferons (IFN) (5). TYK2
is also associated with the IFNAR1 subunit of type 1 IFN receptors
(6). Interferons play a critical
role in the antiviral immune response by inducing the expression of
genes that inhibit viral replication and promote the destruction of
infected cells. Several studies have identified TYK2
variants that are associated with an increased risk of severe
COVID-19 symptoms. For example, Pairo-Castineira et al
(3,4) reported that a missense variant in
TYK2 (p.Arg203His) is associated with a higher risk of
respiratory failure in COVID-19 patients (3,4).
Another study reported that TYK2 haploinsufficiency is
associated with an increased risk of severe COVID-19 in a cohort of
patients with innate immunity defects. A GWAS conducted by the
COVID-19 Host Genetics Initiative found that genetic variations in
TYK2 were associated with an increased risk of severe
COVID-19. Specifically, a study found that a rare variant of
TYK2 is associated with a 3.5-fold increased risk of severe
COVID-19(7). Another study
identified that the rs2304257C>T polymorphism in TYK2 is
associated with increased susceptibility to severe COVID-19 in the
Chinese population (8). These
findings highlight the importance of TYK2 in the immune
response to SARS-CoV-2 infection, and suggest that TYK2
variants may serve as biomarkers for predicting COVID-19
outcomes.
Another genetic factor implicated in COVID-19
susceptibility and exacerbation is NOTCH4, which encodes a
transmembrane receptor involved in immune system regulation. NOTCH
signaling plays a critical role in the differentiation and function
of immune cells, including T, B, and myeloid cells. NOTCH4
gene expression is increased in patients with COVID-19(9). This increased expression results in
the disruption of the repair mechanism in Regulatory T cells,
leading to the exacerbation of respiratory infections in COVID-19
patients (10). This also indicates
that NOTCH4 signaling is an obstacle to tissue repair and a
promoter of more severe respiratory infections (10,11).
Several studies have reported an association between NOTCH4
variants and the risk of developing severe COVID-19 (3,10,11).
These findings suggest that NOTCH4 plays a critical role in
the immune response to SARS-CoV-2 infection and may serve as a
potential therapeutic target for COVID-19.
The present study aimed to investigate the
association between genetic variants of TYK2 (rs74956615)
and NOTCH4 (rs3131294) and the severity and mortality of
COVID-19 in a Jordanian cohort.
Materials and methods
Study design and population
The present study included individuals diagnosed
with COVID-19 between March 2020 and February 2021 who were
hospitalized at the Prince Hamzah Hospital in Amman, Jordan. Cases
were defined as individuals with laboratory-confirmed COVID-19
infection as determined by a positive PCR test. All participants in
the COVID-19 study were Jordanian. The clinical characteristics of
the included patients are given in Table I.
 | Table IClinical characteristics of the
included patients. |
Table I
Clinical characteristics of the
included patients.
| Parameter | Frequency | Percentage | Mean ± standard
deviation |
|---|
| Total patients | 365 | 100.0 | |
| Sex | | | |
|
Male | 206 | 56.6 | |
|
Female | 157 | 43.4 | |
| Age | | | 54.28
(±16.000) |
| COVID-19 | | | |
|
Non-severe | 141 | 39.0 | |
|
Severe | 117 | 32.3 | |
|
Critical | 104 | 28.7 | |
| Clinical
outcome | | | |
|
Non-survivor | 82 | 22.7 | |
|
Survivor | 280 | 77.3 | |
|
ICAM5-TYK2_variant | | | |
|
TT
(reference genotype) | 104 | 28.7 | |
|
TA
(heterozygous genotype) | 13 | 3.6 | |
|
AA
(homozygous variant genotype) | 2 | 0.6 | |
|
Missing | 243 | 67.1 | |
|
NOTCH4_variant | | | |
|
CC
(reference genotype) | 252 | 69.6 | |
|
CT
(heterozygous genotype) | 28 | 7.7 | |
|
TT
(homozygous variant genotype) | 3 | 0.8 | |
|
Missing | 79 | 21.8 | |
The present study conducted a post-hoc power
analysis to assess the adequacy of the sample size and robustness
of the observed associations. Using the effect sizes derived from
the logistic regression models and the proportion of variant
carriers in the cohort, the analysis demonstrated that the study
had sufficient statistical power (>80% for the primary outcomes)
to detect moderate-to-large effect sizes for both TYK2 and
NOTCH4 variants in the cohort.
The power calculation was based on the number of
cases with complete genotype and outcome data included in the
regression models (complete-case analysis), excluding individuals
with missing genotypic data.
Sample collection
A total of 410 peripheral blood samples were
collected in ethylenediaminetetraacetic acid (EDTA)-treated tubes
and labeled with the same medical record ID number for each patient
to facilitate access to the patient's medical file. The medical
records and samples were accessed during the period February 2021
to December 2022. The collected blood samples were transferred on
ice to the -80˚C freezers at Applied Science University, Amman,
Jordan. DNA extraction and PCR amplification were conducted in the
Laboratory for Molecular and Microbial Ecology (LaMME) at the
University of Jordan, Amman, Jordan. Sanger sequencing was
conducted through the service provider Macrogen, Inc. Biometric
data were collected for each patient, such as the severity index of
the case, concentrations of O2 and CO2, the
need to be placed on oxygen ventilation, and other disease
variables that would help establish a correlation between genomic
information and disease status. All these data were collected
during the study period (between March 2020 and February 2021).
Ethical approval was obtained from the ethics committee of Prince
Hamza Hospital (approval no. 6-11-2021-129), and the samples were
collected respectively. The requirement for written informed
consent was formally waived by the Institutional Review Board. All
procedures were conducted in accordance with institutional ethical
standards and the Declaration of Helsinki.
DNA extraction, quantification,
amplification and DNA sequencing
Genomic DNA was extracted from blood samples using
the Wizard Genomic DNA Purification Kit (Promega Corporation),
following the manufacturer's protocol. DNA yield and quality were
measured using NanoDrop to ensure that the amount of DNA in each
sample was sufficient for PCR and to ensure the efficiency of the
extraction kit and protocol.
PCR amplification of NOTCH4 and TYK2
was performed using the following primers: NOTCH4 Forward
5'-CCATCTCTGGGCTGAGAATC-3', NOTCH4 Reverse
5'-GCCTCAAGTGAGGACAAGTG-3', TYK2 Forward
5'-GGGCCTTGAAGGAATCAGAG-3' and TYK2 Reverse
5'-CCCTGCAGCCTTAAGAGAGA-3'. Thermocycling was initiated with a
denaturation step at 95˚C for 3-5 min, followed by 30 cycles
consisting of denaturation at 95˚C for 30 sec, annealing at 56˚C
for 30 sec and elongation at 72˚C for 30-45 sec, with a final
extension completed at 72˚C for 10 min. Subsequently, the amplified
products were resolved on a 1.5% agarose gel and visualized using
ethidium bromide. The amplified DNA was subjected to Sanger
sequencing (Macrogen, Inc.) (12,13).
Bioinformatics analysis
After obtaining the sequenced products,
full-sequence analysis of the genes was performed for each sample.
The resulting genomic data were then compared with different
patient variables collected previously to assess the significance
and correlation of polymorphisms in the genes with the disease
severity.
Data collection occurred during early 2020-2021;
therefore, the dominant variant circulating in Jordan was the
ancestral strain and before the widespread emergence of variants of
concern (VOCs). During this period, the dominant circulating strain
in Jordan corresponded to early SARS-CoV-2 lineages closely related
to the Wuhan-Hu-1 reference strain (lineage B).
Methodology for stratifying the
severity and clinical outcomes of COVID-19
The present study extracted all patient data from
the medical records. The data included age, sex, length of hospital
stay, comorbidities, treatment, laboratory results, and clinical
outcomes.
Patients were divided into two classes. The first
was discharge status: Survivor or non-survivor. Disease severity
(non-severe, severe, or critical) was used to determine the second
classification. The WHO established this classification using the
same cohort as that in a recent study (14).
Non-severe cases were defined as individuals with
peripheral capillary oxygen saturation (SpO2) ≥94% on
room air, no signs of respiratory distress, and normal or mild
radiographic findings. Severe disease was classified by the
presence of SpO2 <94%, respiratory rate ≥30
breaths/min, or radiological evidence of pneumonia involving
>50% of the lung fields within 24-48 h. Critical cases were
defined as patients presenting with respiratory failure requiring
mechanical ventilation, shock, or multi-organ dysfunction.
Statistical analysis
Data analysis was conducted using SPSS version 26
(IBM Corp.). When applicable, continuous variables were reported as
the mean with standard deviation or the median with interquartile
range. Categorical variables are presented as frequencies and
percentages. The χ2 test and Fisher's exact test were
used to compare various categorical variables, specifically between
survivor and non-survivor categories, as well as between different
categorical variables and the severity classification of COVID-19
patients. Logistic regression models were used to assess the
association between genetic variations in TYK2 and
NOTCH4 and COVID-19 severity.
Binomial logistic regression models were constructed
to evaluate the association between TYK2 and NOTCH4
variants and COVID-19 severity and mortality. All models were
modified for significant clinical covariates, including age and
sex, due to their recognized influence as predictors of COVID-19
outcomes. The adjusted odds ratios (ORs) with 95% confidence
intervals (CIs) were reported.
Bonferroni correction was used on the set of
statistical tests that had already been chosen to take into account
multiple comparisons. In particular, there were eight comparisons,
and the level of statistical significance was changed to match
(α=0.05/8 = 0.00625). All reported P-values were analyzed in
relation to this adjusted significance level. P<0.05 was
considered to indicate a statistically significant difference.
Results
A total of 362 individuals diagnosed with COVID-19,
comprising diverse populations from different geographic regions,
were included in the analysis. Participants were categorized into
three groups based on disease severity: Non-severe, severe and
critical, and into two groups based on clinical outcomes:
non-survivors and survivors. The clinical characteristics of the
study participants were presented in Table I.
Genotypic and allelic distributions of TYK2
(rs74956615) and NOTCH4 (rs3131294), along with
Hardy-Weinberg equilibrium testing and association measures, were
summarized in Table II. The
distribution of genotypes showed a predominance of the wild-type
alleles for both SNPs, with relatively low frequencies of
homozygous variant genotypes. HWE analysis based on complete-case
data indicated no major deviation for TYK2 (P>0.05),
whereas a slight deviation was observed for NOTCH4, which
may reflect small genotype counts or missing data rather than
genotyping error.
| Table IIGenotypic distribution, allelic
frequencies, Hardy-Weinberg equilibrium and association analysis of
TYK2 and NOTCH4 variants. |
Table II
Genotypic distribution, allelic
frequencies, Hardy-Weinberg equilibrium and association analysis of
TYK2 and NOTCH4 variants.
| A, TYK2
(rs74956615) |
|---|
| Allele
Frequencies | | Allele Count
(n) | | Frequency (%) |
|---|
| T | | 221 | | 92.9% |
| A | | 17 | | 7.1% |
| Model | Outcome | Adjusted OR (95%
CI) | P-value |
| Dominant (TA+AA vs.
TT) | Severity | 2.55
(0.83-7.84) | 0.104 |
| Dominant (TA+AA vs.
TT) | Mortality | 8.29
(2.53-27.13) | <0.001 |
| B, NOTCH4
(rs3131294) |
| Category | CC (Ref) | CT | TT | Total (n) | HWE χ²
(P-value) |
| Genotype n (%) | 252 (89.0%) | 28 (9.9%) | 3 (1.1%) | 283 | 4.33 (0.037) |
| Allele
Frequencies | | Allele Count
(n) | | Frequency (%) |
| C | | 532 | | 94.0% |
| T | | 34 | | 6.0% |
| Model | Outcome | Adjusted OR (95%
CI) | P-value |
| Dominant (CT+TT vs.
CC) | Severity | 15.29
(1.95-120.02) | 0.010 |
| Dominant (CT+TT vs.
CC) | Mortality | 8.14
(2.35-28.25) | 0.001 |
TYK2 gene variants and COVID-19
severity
Analysis of TYK2 variants revealed that 94.6%
of the participants in the non-severe category had the homozygous
reference genotype, 3.6% had the heterozygous genotype, and 1.8%
had the homozygous variant genotype. The severe category showed
94.6% homozygous reference and 5.4% heterozygous genotypes. The
critical group comprised 61.5, 34.6 and 3.8% of patients with
homozygous reference, heterozygous, and homozygous variant
genotypes, respectively. These findings indicate that the critical
group included the highest heterozygous genotype compared to the
other genotypes, followed by the homozygous variant genotype
(Pearson's chi-square test, P<0.001; Table III).
| Table IIIDistribution of the NOTCH4 and
TYK2 gene variants according to COVID-19 severity and
mortality. |
Table III
Distribution of the NOTCH4 and
TYK2 gene variants according to COVID-19 severity and
mortality.
| Gene variant | Frequency | Percentage | Frequency | Percentage | Frequency | Percentage |
|---|
|
ICAM5-TYK2_variant | TT (reference
genotype) | TA (heterozygous
genotype) | AA (homozygous
variant genotype) |
|
non-severe | 53 | 95% | 2 | 4% | 1 | 1% |
|
severe | 35 | 95% | 2 | 5% | 0 | 0 |
|
critical | 16 | 62% | 9 | 34% | 1 | 4% |
|
NOTCH4_variant | CC (reference
genotype) | CT (heterozygous
genotype) | TT (homozygous
variant genotype) |
|
non-severe | 129 | 99% | 1 | 1% | 0 | 0 |
|
severe | 79 | 94% | 5 | 6% | 0 | 0 |
|
critical | 44 | 64% | 22 | 32% | 3 | 4% |
|
ICAM5-TYK2_variant | TT | | TA | | AA | |
|
Non-survivor | 10 | 56% | 7 | 39% | 1 | 5% |
|
Survivor | 94 | 93% | 6 | 6% | 1 | 1% |
|
NOTCH4_variant | CC | | CT | | TT | |
|
Non-survivor | 38 | 67% | 16 | 28% | 3 | 5% |
|
Survivor | 214 | 95% | 12 | 5% | 0 | 0 |
NOTCH4 variants and COVID-19
severity
Analysis of NOTCH4 gene variants revealed
that 99% of the participants in the non-severe category were
homozygous for the reference genotype and 0.8% were heterozygous.
The severe category showed 94.0% homozygous reference and 6.0%
heterozygous genotypes. The critical group was distributed at 64,
32 and 34% for the homozygous reference, heterozygous, and
homozygous variant genotypes, respectively. These findings
indicated that the critical group included the highest proportion
of heterozygous genotypes compared to other genotypes, followed by
the homozygous reference genotype (Pearson's χ2 test,
P<0.001; Table III).
TYK2 gene variants and clinical outcome. In
terms of clinical outcomes, the presence of certain TYK2
variants was markedly associated with COVID-19 mortality. The TT
variant was found in 93% of survivors and 56% of non-survivors. The
TA variant was detected in 39 and 6% of the non-survivors and
survivors, respectively. Moreover, the AA homozygous variant
genotype was detected in 5.6% of the non-survivor group and 1.0% of
the survivor group (P<0.001; Table
III).
NOTCH4 gene variants and clinical
outcome
In terms of clinical outcomes, the presence of
specific NOTCH4 variants was markedly associated with
COVID-19 mortality. The TT variant was found in 94.7% of survivors
and 66.7% of non-survivors. The TA variant was detected in 28.1 and
5.3% of the non-survivors and survivors, respectively. Moreover,
the AA homozygous variant genotype was detected in 5.3% of the
non-survivor group and 0.0% of the survivor group (P<0.001;
Table III).
Binomial logistic regression was performed to
ascertain the effects of the variants of the two tested genes on
the likelihood of severe or critical status and mortality. The
linearity of continuous variables with respect to the logit of the
dependent variable was assessed using Tidwell's method (15). The Bonferroni correction was applied
using all eight terms in the model, resulting in statistical
significance being accepted when P<00625(16). Based on this assessment, all
continuous independent variables are linearly related to the logit
of the dependent variable. The logistic regression model was
statistically significant, χ2(2)=17.188, P<0.001 and
χ2(2)=24.476, P<0.001
for the severity and mortality statistics, respectively.
The heterozygous or variant NOTCH4 genotypes
were 15. The odds of exhibiting a severe or critical state of
COVID-19 were 285 times higher than those of the reference genotype
(P=0.010). Furthermore, heterozygous and variant genotypes of both
genes (NOTCH4 and ICAM5-TYK2) had approximately
eight-fold higher odds of mortality than the reference genotype
(P=0.001 and <0.001, respectively; Table IV).
| Table IVBinomial logistic regression
results. |
Table IV
Binomial logistic regression
results.
| Outcome | Gene | B | SE | Wald | df | Sig. | OR | 95% CI EXP(B) |
|---|
| Severity
(non-severe vs. severe/critical | | | | | | | | Lower | Upper |
| |
NOTCH4_variant (heterozygous or
homozygous variant genotype) | 2.727 | 1.051 | 6.726 | 1 | 0.010 | 15.285 | 1.947 | 120.016 |
| |
ICAM5-TYK2_variant (heterozygous or
homozygous variant genotype) | 0.934 | 0.574 | 2.648 | 1 | 0.104 | 2.545 | 0.826 | 7.842 |
| Mortality (survivor
vs. non-survivor) | | | | | | | | | |
| |
NOTCH4_variant (heterozygous or
homozygous variant genotype) | 2.097 | .635 | 10.921 | 1 | 0.001 | 8.143 | 2.348 | 28.247 |
| |
ICAM5-TYK2_variant (heterozygous or
homozygous variant genotype) | 2.114 | .605 | 12.205 | 1 | <0.001 | 8.285 | 2.530 | 27.132 |
Discussion
The present study addressed the association between
genetic variants of TYK2 and NOTCH4 and the severity
and clinical outcomes of COVID-19. The analysis encompassed a
diverse population of 362 individuals from different geographic
regions, who were categorized based on disease severity and
clinical outcomes. The findings revealed significant associations
between specific gene variants and the severity and mortality of
COVID-19. The present study found that variant alleles of
TYK2 and NOTCH4 were increased in individuals with
COVID-19 as a function of disease severity and were associated with
heightened mortality, demonstrating that this mechanism is
effective in SARS-CoV-2 infection and may be critically involved in
the pathogenesis of acute respiratory failure.
TYK2 produces tyrosine kinase, which is
essential for cytokine signaling, particularly in the type I and
III interferon pathways (5).
TYK2 plays an essential role in the stable expression of
IFNAR1 on the cell surface (17,18).
Thus, appropriate TYK2 activity is a key step in the
initiation of the type I IFN response (17,18).
IFNs are important antiviral cytokines that induce the expression
of genes that inhibit viral replication during the early stages of
viral infection (6,17,18).
Studies have shown that COVID-19 infection does not elicit a
competent IFN response, leading to decreased disease severity
(19,20). The analysis of TYK2 variants
revealed a strong association between them and COVID-19 severity.
This association is congruent with that reported by
Pairo-Castineira et al (3),
who reported that rs74956615 increases the severity of COVID-19 by
1.6 and has a very strong P-value (OR=1.6, discovery
P=2.3x10-8). Located within the chromosome 19 genomic
sequence (NCBI Accession: NC_000019.10), this regulatory eQTL locus
(at position 10,317,045) is highly critical because its risk allele
correlates with upregulated TYK2 expression, functioning as
an inflammatory driver that promotes the hyper-activation of
downstream JAK-STAT cytokine signaling during severe infection
(3). The genotype distribution
varied markedly among the non-severe, severe and critical groups,
with the critical group exhibiting a higher proportion of
heterozygous genotypes than those in the other groups. This finding
suggests a potential role for TYK2 variants in influencing
the progression of critical illness (7). The observed association aligns with
the existing literature implicating TYK2 in immune response
regulation, supporting the idea that genetic variations in this
gene may contribute to diverse clinical outcomes in COVID-19
patients (7). These findings may
contribute to an improved understanding of the mechanisms
underlying the disease progression.
Several studies have identified TYK2 variants
that are associated with an increased risk of severe COVID-19
symptoms. For example, Pairo-Castineira et al (3) reported that a missense variant in
TYK2 (p.Arg203His) is associated with a higher risk of
respiratory failure in COVID-19 patients (4,17).
Another study reported that TYK2 haploinsufficiency is
associated with an increased risk of severe COVID-19 in patients
with inborn errors of immunity (5).
Notably, a study found that a rare variant of TYK2 is
associated with a 3.5-fold increased risk of severe COVID-19
disease (7). Another study
identified that the rs2304257C>T polymorphism in TYK2 is
associated with increased susceptibility to severe COVID-19 in the
Chinese population (8). These
findings highlight the importance of TYK2 in the immune
response to SARS-CoV-2 infection, and suggest that TYK2
variants may serve as biomarkers for predicting COVID-19 outcomes.
As aforementioned, TYK2 prevents the accumulation of IFNAR1
in the intracellular compartment and increases its stabilization at
the cell surface (17,18). However, another route of IFNAR1
stabilization at the cell surface is TYK2 independent. For
example, the RNA-binding protein RBM47 stabilizes IFNAR1
transcripts (21).
NOTCH4 is a critical regulator of disease
severity in various respiratory viral infections. NOTCH4
encodes a transmembrane receptor crucial for immune system
regulation. NOTCH signaling is vital for the differentiation and
function of immune cells, such as T, B and myeloid cells. Patients
with COVID-19 exhibit elevated NOTCH4 gene expression
levels, indicating its potential involvement in the disease
(9).
The findings of the current study revealed that
NOTCH4 variants were substantially associated with the
severity of COVID-19. The critical group displayed a higher
prevalence of heterozygous genotypes, indicating a potential
correlation between NOTCH4 variants and critical illness
development. This finding was consistent with the role of
NOTCH4 in immune regulation and suggested that genetic
variations in this gene may contribute to the variability in
COVID-19 severity. Studies have reported that NOTCH4
expression increases in tissue Treg cells early in lung
inflammation and promotes the innate immune response when adaptive
immunity has not yet been effectively mobilized (10,11).
In the context of severe virus-mediated damage, NOTCH4
promotes the development of an intrinsic protective response driven
by lung epithelium-derived danger signals in the host. This
physiological function of NOTCH4 is lost in severe
respiratory viral diseases, leading to uncontrolled and excessive
activation of innate immunity, which harms lung tissue (10). Moreover, several studies have
reported an association between NOTCH4 variants and the risk
of severe COVID-19 (3,22,23).
These findings suggest that NOTCH4 plays a critical role in
the immune response to SARS-CoV-2 infection and may serve as a
potential therapeutic target for COVID-19. These findings could aid
in the development of targeted therapeutic approaches for managing
COVID-19 based on genetic profiles.
Examining the association between gene variants and
clinical outcomes, the present study found that specific
TYK2 variants were markedly associated with COVID-19
mortality. TA and AA variants were more prevalent in the
non-survivor group, whereas the TT variant was predominant in the
survivor group. This emphasized the potential prognostic value of
TYK2 variants in predicting mortality outcomes in COVID-19
patients. Similarly, specific NOTCH4 variants were found to
be markedly associated with COVID-19 mortality. The non-survivor
group exhibited a higher prevalence of TA and AA variants,
indicating the potential role of NOTCH4 gene variants as
predictors of adverse clinical outcomes in COVID-19 patients.
GWAS examining genetic factors in COVID-19
susceptibility and severity have identified variations associated
with an increased risk of contracting the virus and experiencing
severe clinical outcomes (3,4,8).
These genetic variations affect diverse aspects of the immune
response, including cytokine regulation, immune cell responses, and
susceptibility to viral infections (4). Notably, specific SNPs in genes such as
NOTCH4 (rs3131294) and TYK2 (rs74956615) have been
linked to critical illnesses in COVID-19 patients, underscoring the
genetic basis of disease exacerbation (3,7).
The methodology for accurately stratifying the
exacerbation level of COVID-19 patients can be based on a
combination of clinical prediction scores, radiographic imaging and
biomarkers. Studies have shown that combining these methodologies
can lead to a more accurate and holistic assessment of a patient's
condition (24-26). Binomial
logistic regression analysis confirmed the significant effects of
TYK2 and NOTCH4 variants on COVID-19 severity and
mortality. The model explained a considerable proportion of the
variance in both the severity and mortality, highlighting the
potential predictive power of these genetic variants. Notably,
individuals with heterozygous or variant alleles of NOTCH4
have markedly higher odds of exhibiting severe or critical disease,
emphasizing the clinical relevance of this gene in determining
disease severity. Additionally, heterozygous or variant alleles of
both NOTCH4 and TYK2 were associated with increased
odds of mortality, highlighting the potential utility of genetic
information in identifying patients at a higher risk of adverse
outcomes.
It is important to note that the methods mentioned
in the present study have been utilized in several studies;
however, it is recommended that further validation studies be
conducted on diverse populations worldwide, including the
population in which the present study was conducted. Additionally,
these methods should not be used in isolation, but as part of an
integrated approach that considers other risk factors, such as
environmental and lifestyle factors.
Collapsing the heterozygous and mutant genotypes
into a single ‘variant-carrier’ category is justified and
consistent with the biological rationale and statistical
characteristics of the present study dataset. First, the homozygous
mutant genotype was extremely rare in the cohort, accounting for
<1% of patients. Separate estimation of the effects of the
mutant genotype would yield unstable odds ratios with wide
confidence intervals and limited interpretability. Combining
heterozygous and mutant individuals into a single ‘variant-present’
category is an accepted approach for rare alleles in genetic
epidemiology and preserves the statistical power without
introducing artificial comparisons. Second, previous GWAS and
functional studies have demonstrated that both heterozygous and
homozygous non-reference forms of TYK2 and NOTCH4
produce a similar directional biological effect on interferon
signaling and immune dysregulation. Therefore, expanding the
analysis to include additive, recessive and codominant models is
unlikely to provide additional meaningful insights and may instead
introduce unstable estimates due to sparse cell counts. Thus, the
current modelling strategy remains justified.
Furthermore, the multiple-testing strategy employed
in the present study must be understood within the framework of a
hypothesis-driven design. The analysis concentrated on two
predetermined SNPs (TYK2 rs74956615 and NOTCH4
rs3131294) derived from previous GWAS findings, rather than
employing an exploratory genome-wide or multi-locus screening
methodology. The Bonferroni correction for the small number of
planned comparisons (eight tests) is therefore considered
appropriate and sufficiently conservative. Extending the correction
to encompass all potential genetic inheritance models (additive,
recessive, and codominant) or implementing false discovery rate
(FDR) adjustments would not conform to the established analytical
framework and could introduce superfluous statistical penalties,
especially in the context of sparse genotype frequencies.
The present study had some limitations. The sample
size of the present study was relatively small. Future studies with
larger sample sizes could provide more definitive insights into the
roles of TYK2 and NOTCH4 in COVID-19 susceptibility
and exacerbation. Additionally, the genetic factors examined
accounted for only a small proportion of the COVID-19
susceptibility and severity. Other genes, environmental and
clinical factors (such as treatment strategies) may also serve a
role in COVID-19 severity and mortality; however, these factors
were not analyzed in the present study, which is a limitation.
Finally, data were collected during early 2020-2021; therefore, the
dominant variant circulating in Jordan was the ancestral strain.
Another limitation of the present study is the use of a dominant
genetic model, whereby heterozygous and homozygous variant
genotypes were combined due to the very low frequency of the mutant
genotype; although this approach preserved statistical power, it
precluded evaluation of additive or recessive effects, which could
be explored in larger cohorts with sufficient genotype
representation.
Another limitation pertains to the multiple-testing
correction strategy. The present study applied the Bonferroni
correction to the predefined comparisons, but did not apply it to
all possible genetic models (additive, recessive and codominant) or
adjust for FDR. This approach aligns with the study's
hypothesis-driven design; however, it may increase the risk of
residual type I error when interpreting the reported
associations.
A further constraint is the absence of functional
validation for the identified genetic associations within the study
cohort. While rs74956615 has established eQTL regulatory roles
(3), no independent mechanistic
analyses (such as eQTL or sQTL evaluations) were conducted here to
ascertain their specific biological impacts in the sample
population. As a result, while these associations align with known
expression pathways, direct causality within the cohort has yet to
be determined.
In conclusion, the present study provided valuable
insights into the roles of TYK2 and NOTCH4 variants
in the severity and clinical outcomes of COVID-19. Understanding
the genetic determinants of disease progression may contribute to
the development of personalized and effective management strategies
for COVID-19 patients. Further research and validation studies are
warranted to confirm these associations and to explore the
mechanisms underlying the interplay between host genetics and
COVID-19 outcomes. Accurately stratifying the exacerbation level of
COVID-19 patients is crucial for identifying high-risk patients who
require intensive care and for guiding treatment and management
strategies. A scientific methodology based on clinical data,
including clinical prediction scores, radiographic imaging and
biomarkers, combined with a multidisciplinary approach, can be used
to accurately stratify COVID-19 patients based on the severity of
their illness. Further research is needed to validate these methods
and determine the most effective approach for stratifying COVID-19
patients based on disease severity. The present study provided
evidence that genetic factors, specifically TYK2 and
NOTCH4 variants, may contribute to COVID-19 susceptibility
and severity. These findings have important implications for
understanding the underlying mechanisms of COVID-19 and developing
more targeted prevention and treatment strategies.
Acknowledgements
The present study benefited from the Prince Hamzah
Hospital, which provided support in collecting COVID-19 samples for
this research. The authors also relied on the data and assistance
provided by the hospital staff, which were essential to the
completion of the present study.
Funding
Funding: The present study was supported by the Deanship of
Scientific Research at the University of Jordan through a
sabbatical leave for Dr. Mamoon Al-Rshaidat during the academic
year 2024-2025.
Availability of data and materials
The data generated in the present study are included
in the figures and/or tables of this article.
Authors' contributions
Conceptualization was by MA, AA, HA and MZ. Formal
analysis was by MA, AA, OA, AI and MZ. Project administration was
by MA, AA and MZ. Writing the original draft was by MA, AA, OA and
MZ. Writing, review and editing was by MA, AA, OA, AI, HA and MZ.
MA, AA and MZ confirm the authenticity of all the raw data. All
authors reviewed and approved the final manuscript.
Ethics approval and consent to
participate
Ethical approval was obtained from the ethics
committee of Prince Hamza Hospital (approval no. 6-11-2021-129) and
the samples were collected respectively. The requirement for
written informed consent was formally waived by the Institutional
Review Board. All procedures were conducted in accordance with
institutional ethical standards and the Declaration of
Helsinki.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing
interests.
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